Merge branch 'main' into adil/agents_framework

.github/workflows/e2e_archgw.yml

@@ -30,7 +30,7 @@ jobs:
 
       - name: build arch docker image
         run: |
-          cd ../../ && docker build -f arch/Dockerfile . -t katanemo/archgw -t katanemo/archgw:0.3.20 -t katanemo/archgw:latest
+          cd ../../ && docker build -f arch/Dockerfile . -t katanemo/archgw -t katanemo/archgw:0.3.21 -t katanemo/archgw:latest
 
       - name: start archgw
         env:

.github/workflows/e2e_test_currency_convert.yml

@@ -24,7 +24,7 @@ jobs:
 
       - name: build arch docker image
         run: |
-          docker build  -f arch/Dockerfile . -t katanemo/archgw -t katanemo/archgw:0.3.20
+          docker build  -f arch/Dockerfile . -t katanemo/archgw -t katanemo/archgw:0.3.21
 
       - name: install poetry
         run: |

.github/workflows/e2e_test_preference_based_routing.yml

@@ -24,7 +24,7 @@ jobs:
 
       - name: build arch docker image
         run: |
-          docker build  -f arch/Dockerfile . -t katanemo/archgw -t katanemo/archgw:0.3.20
+          docker build  -f arch/Dockerfile . -t katanemo/archgw -t katanemo/archgw:0.3.21
 
       - name: install poetry
         run: |

.github/workflows/e2e_tests.yml

@@ -59,6 +59,7 @@ jobs:
           ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
           AZURE_API_KEY: ${{ secrets.AZURE_API_KEY }}
           AWS_BEARER_TOKEN_BEDROCK: ${{ secrets.AWS_BEARER_TOKEN_BEDROCK }}
+          GROK_API_KEY : ${{ secrets.GROK_API_KEY }}
         run: |
           python -mvenv venv
           source venv/bin/activate && cd tests/e2e && bash run_e2e_tests.sh

.github/workflows/validate_arch_config.yml

@@ -24,7 +24,7 @@ jobs:
 
       - name: build arch docker image
         run: |
-          docker build  -f arch/Dockerfile . -t katanemo/archgw -t katanemo/archgw:0.3.20
+          docker build  -f arch/Dockerfile . -t katanemo/archgw -t katanemo/archgw:0.3.21
 
       - name: validate arch config
         run: |

README.md

@@ -87,7 +87,7 @@ Arch's CLI allows you to manage and interact with the Arch gateway efficiently.
 ```console
 $ python3.12 -m venv venv
 $ source venv/bin/activate   # On Windows, use: venv\Scripts\activate
-$ pip install archgw==0.3.20
+$ pip install archgw==0.3.21
 ```
 
 ### Use Arch as a LLM Router
@@ -276,7 +276,7 @@ endpoints:
 ```sh
 
 $ archgw up arch_config.yaml
-2024-12-05 16:56:27,979 - cli.main - INFO - Starting archgw cli version: 0.3.20
+2024-12-05 16:56:27,979 - cli.main - INFO - Starting archgw cli version: 0.3.21
 2024-12-05 16:56:28,485 - cli.utils - INFO - Schema validation successful!
 2024-12-05 16:56:28,485 - cli.main - INFO - Starting arch model server and arch gateway
 2024-12-05 16:56:51,647 - cli.core - INFO - Container is healthy!

arch/tools/README.md

@@ -19,7 +19,7 @@ source venv/bin/activate
 
 ### Step 3: Run the build script
 ```bash
-pip install archgw==0.3.20
+pip install archgw==0.3.21
 ```
 
 ## Uninstall Instructions: archgw CLI

arch/tools/cli/consts.py

@@ -2,4 +2,4 @@ import os
 
 SERVICE_NAME_ARCHGW = "archgw"
 ARCHGW_DOCKER_NAME = "archgw"
-ARCHGW_DOCKER_IMAGE = os.getenv("ARCHGW_DOCKER_IMAGE", "katanemo/archgw:0.3.20")
+ARCHGW_DOCKER_IMAGE = os.getenv("ARCHGW_DOCKER_IMAGE", "katanemo/archgw:0.3.21")

arch/tools/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "archgw"
-version = "0.3.20"
+version = "0.3.21"
 description = "Python-based CLI tool to manage Arch Gateway."
 authors = ["Katanemo Labs, Inc."]
 readme = "README.md"

crates/Cargo.lock

@@ -912,10 +912,12 @@ version = "0.1.0"
 dependencies = [
  "aws-smithy-eventstream",
  "bytes",
+ "log",
  "serde",
  "serde_json",
  "serde_with",
  "thiserror 2.0.12",
+ "uuid",
 ]
 
 [[package]]

crates/brightstaff/src/handlers/router.rs

@@ -3,7 +3,7 @@ use common::configuration::{ModelAlias, ModelUsagePreference};
 use common::consts::{ARCH_IS_STREAMING_HEADER, ARCH_PROVIDER_HINT_HEADER};
 use hermesllm::apis::openai::ChatCompletionsRequest;
 use hermesllm::clients::endpoints::SupportedUpstreamAPIs;
-use hermesllm::clients::SupportedAPIs;
+use hermesllm::clients::SupportedAPIsFromClient;
 use hermesllm::{ProviderRequest, ProviderRequestType};
 use http_body_util::combinators::BoxBody;
 use http_body_util::{BodyExt, Full};
@@ -39,7 +39,7 @@ pub async fn router_chat(
 
     let mut client_request = match ProviderRequestType::try_from((
         &chat_request_bytes[..],
-        &SupportedAPIs::from_endpoint(request_path.as_str()).unwrap(),
+        &SupportedAPIsFromClient::from_endpoint(request_path.as_str()).unwrap(),
     )) {
         Ok(request) => request,
         Err(err) => {
@@ -58,7 +58,7 @@ pub async fn router_chat(
     let resolved_model = if let Some(model_aliases) = model_aliases.as_ref() {
         if let Some(model_alias) = model_aliases.get(&model_from_request) {
             debug!(
-                "Model Alias: 'From {}' -> 'To{}'",
+                "Model Alias: 'From {}' -> 'To {}'",
                 model_from_request, model_alias.target
             );
             model_alias.target.clone()
@@ -91,10 +91,11 @@ pub async fn router_chat(
             Ok(
                 ProviderRequestType::MessagesRequest(_)
                 | ProviderRequestType::BedrockConverse(_)
-                | ProviderRequestType::BedrockConverseStream(_),
+                | ProviderRequestType::BedrockConverseStream(_)
+                | ProviderRequestType::ResponsesAPIRequest(_),
             ) => {
                 // This should not happen after conversion to OpenAI format
-                warn!("Unexpected: got MessagesRequest after converting to OpenAI format");
+                warn!("Unexpected: got non-ChatCompletions request after converting to OpenAI format");
                 let err_msg = "Request conversion failed".to_string();
                 let mut bad_request = Response::new(full(err_msg));
                 *bad_request.status_mut() = StatusCode::BAD_REQUEST;

crates/brightstaff/src/main.rs

@@ -6,7 +6,7 @@ use brightstaff::router::llm_router::RouterService;
 use brightstaff::utils::tracing::init_tracer;
 use bytes::Bytes;
 use common::configuration::Configuration;
-use common::consts::{CHAT_COMPLETIONS_PATH, MESSAGES_PATH};
+use common::consts::{CHAT_COMPLETIONS_PATH, MESSAGES_PATH, OPENAI_RESPONSES_API_PATH};
 use http_body_util::{combinators::BoxBody, BodyExt, Empty};
 use hyper::body::Incoming;
 use hyper::server::conn::http1;
@@ -126,7 +126,7 @@ async fn main() -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
 
             async move {
                 match (req.method(), req.uri().path()) {
-                    (&Method::POST, CHAT_COMPLETIONS_PATH | MESSAGES_PATH) => {
+                    (&Method::POST, CHAT_COMPLETIONS_PATH | MESSAGES_PATH | OPENAI_RESPONSES_API_PATH) => {
                         let fully_qualified_url =
                             format!("{}{}", llm_provider_url, req.uri().path());
                         router_chat(req, router_service, fully_qualified_url, model_aliases)

crates/common/src/consts.rs

@@ -13,6 +13,7 @@ pub const MESSAGES_KEY: &str = "messages";
 pub const ARCH_PROVIDER_HINT_HEADER: &str = "x-arch-llm-provider-hint";
 pub const ARCH_IS_STREAMING_HEADER: &str = "x-arch-streaming-request";
 pub const CHAT_COMPLETIONS_PATH: &str = "/v1/chat/completions";
+pub const OPENAI_RESPONSES_API_PATH: &str = "/v1/responses";
 pub const MESSAGES_PATH: &str = "/v1/messages";
 pub const HEALTHZ_PATH: &str = "/healthz";
 pub const X_ARCH_STATE_HEADER: &str = "x-arch-state";

crates/hermesllm/Cargo.toml

@@ -10,3 +10,5 @@ serde_with = {version = "3.12.0", features = ["base64"]}
 thiserror = "2.0.12"
 aws-smithy-eventstream = "0.60"
 bytes = "1.10"
+uuid = { version = "1.11", features = ["v4"] }
+log = "0.4"

crates/hermesllm/src/apis/amazon_bedrock.rs

@@ -7,7 +7,7 @@ use thiserror::Error;
 
 use super::ApiDefinition;
 use crate::providers::request::{ProviderRequest, ProviderRequestError};
-use crate::providers::response::ProviderStreamResponse;
+use crate::providers::streaming_response::ProviderStreamResponse;
 
 // ============================================================================
 // AMAZON BEDROCK CONVERSE API ENUMERATION

crates/hermesllm/src/apis/anthropic.rs

@@ -6,7 +6,8 @@ use std::collections::HashMap;
 
 use super::ApiDefinition;
 use crate::providers::request::{ProviderRequest, ProviderRequestError};
-use crate::providers::response::{ProviderResponse, ProviderStreamResponse};
+use crate::providers::response::ProviderResponse;
+use crate::providers::streaming_response::ProviderStreamResponse;
 use crate::transforms::lib::ExtractText;
 use crate::MESSAGES_PATH;
 

crates/hermesllm/src/apis/mod.rs

@@ -1,8 +1,8 @@
 pub mod amazon_bedrock;
-pub mod amazon_bedrock_binary_frame;
 pub mod anthropic;
 pub mod openai;
-pub mod sse;
+pub mod openai_responses;
+pub mod streaming_shapes;
 
 // Explicit exports to avoid naming conflicts
 pub use amazon_bedrock::{AmazonBedrockApi, ConverseRequest, ConverseStreamRequest};
@@ -88,8 +88,9 @@ mod tests {
     fn test_all_variants_method() {
         // Test that all_variants returns the expected variants
         let openai_variants = OpenAIApi::all_variants();
-        assert_eq!(openai_variants.len(), 1);
+        assert_eq!(openai_variants.len(), 2);
         assert!(openai_variants.contains(&OpenAIApi::ChatCompletions));
+        assert!(openai_variants.contains(&OpenAIApi::Responses));
 
         let anthropic_variants = AnthropicApi::all_variants();
         assert_eq!(anthropic_variants.len(), 1);

crates/hermesllm/src/apis/openai.rs

@@ -7,9 +7,10 @@ use thiserror::Error;
 
 use super::ApiDefinition;
 use crate::providers::request::{ProviderRequest, ProviderRequestError};
-use crate::providers::response::{ProviderResponse, ProviderStreamResponse, TokenUsage};
+use crate::providers::response::{ProviderResponse, TokenUsage};
+use crate::providers::streaming_response::ProviderStreamResponse;
 use crate::transforms::lib::ExtractText;
-use crate::CHAT_COMPLETIONS_PATH;
+use crate::{CHAT_COMPLETIONS_PATH, OPENAI_RESPONSES_API_PATH};
 
 // ============================================================================
 // OPENAI API ENUMERATION
@@ -19,6 +20,7 @@ use crate::CHAT_COMPLETIONS_PATH;
 #[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
 pub enum OpenAIApi {
     ChatCompletions,
+    Responses,
     // Future APIs can be added here:
     // Embeddings,
     // FineTuning,
@@ -29,12 +31,14 @@ impl ApiDefinition for OpenAIApi {
     fn endpoint(&self) -> &'static str {
         match self {
             OpenAIApi::ChatCompletions => CHAT_COMPLETIONS_PATH,
+            OpenAIApi::Responses => OPENAI_RESPONSES_API_PATH,
         }
     }
 
     fn from_endpoint(endpoint: &str) -> Option<Self> {
         match endpoint {
             CHAT_COMPLETIONS_PATH => Some(OpenAIApi::ChatCompletions),
+            OPENAI_RESPONSES_API_PATH => Some(OpenAIApi::Responses),
             _ => None,
         }
     }
@@ -42,23 +46,26 @@ impl ApiDefinition for OpenAIApi {
     fn supports_streaming(&self) -> bool {
         match self {
             OpenAIApi::ChatCompletions => true,
+            OpenAIApi::Responses => true,
         }
     }
 
     fn supports_tools(&self) -> bool {
         match self {
             OpenAIApi::ChatCompletions => true,
+            OpenAIApi::Responses => true,
         }
     }
 
     fn supports_vision(&self) -> bool {
         match self {
             OpenAIApi::ChatCompletions => true,
+            OpenAIApi::Responses => true,
         }
     }
 
     fn all_variants() -> Vec<Self> {
-        vec![OpenAIApi::ChatCompletions]
+        vec![OpenAIApi::ChatCompletions, OpenAIApi::Responses]
     }
 }
 
@@ -1077,8 +1084,9 @@ mod tests {
 
         // Test all_variants
         let all_variants = OpenAIApi::all_variants();
-        assert_eq!(all_variants.len(), 1);
+        assert_eq!(all_variants.len(), 2);
-        assert_eq!(all_variants[0], OpenAIApi::ChatCompletions);
+        assert!(all_variants.contains(&OpenAIApi::ChatCompletions));
+        assert!(all_variants.contains(&OpenAIApi::Responses));
     }
 
     #[test]

crates/hermesllm/src/apis/streaming_shapes/amazon_bedrock_binary_frame.rs

@@ -1,7 +1,6 @@
 use aws_smithy_eventstream::frame::DecodedFrame;
 use aws_smithy_eventstream::frame::MessageFrameDecoder;
 use bytes::Buf;
-use std::collections::HashSet;
 
 /// AWS Event Stream frame decoder wrapper
 pub struct BedrockBinaryFrameDecoder<B>
@@ -10,7 +9,6 @@ where
 {
     decoder: MessageFrameDecoder,
     buffer: B,
-    content_block_start_indices: HashSet<i32>,
 }
 
 impl BedrockBinaryFrameDecoder<bytes::BytesMut> {
@@ -20,7 +18,6 @@ impl BedrockBinaryFrameDecoder<bytes::BytesMut> {
         Self {
             decoder: MessageFrameDecoder::new(),
             buffer,
-            content_block_start_indices: std::collections::HashSet::new(),
         }
     }
 }
@@ -33,7 +30,6 @@ where
         Self {
             decoder: MessageFrameDecoder::new(),
             buffer,
-            content_block_start_indices: HashSet::new(),
         }
     }
 
@@ -52,14 +48,4 @@ where
     pub fn has_remaining(&self) -> bool {
         self.buffer.has_remaining()
     }
-
-    /// Check if a content_block_start event has been sent for the given index
-    pub fn has_content_block_start_been_sent(&self, index: i32) -> bool {
-        self.content_block_start_indices.contains(&index)
-    }
-
-    /// Mark that a content_block_start event has been sent for the given index
-    pub fn set_content_block_start_sent(&mut self, index: i32) {
-        self.content_block_start_indices.insert(index);
-    }
 }

crates/hermesllm/src/apis/streaming_shapes/anthropic_streaming_buffer.rs

@@ -0,0 +1,507 @@
+use crate::apis::streaming_shapes::sse::{SseEvent, SseStreamBufferTrait};
+use crate::apis::anthropic::MessagesStreamEvent;
+use crate::providers::streaming_response::ProviderStreamResponseType;
+use std::collections::HashSet;
+
+/// SSE Stream Buffer for Anthropic Messages API streaming.
+///
+/// This buffer manages the wire format for Anthropic Messages API streaming,
+/// handling the specific event sequencing requirements:
+/// - MessageStart → ContentBlockStart → ContentBlockDelta(s) → ContentBlockStop → MessageDelta → MessageStop
+///
+/// When converting from OpenAI to Anthropic format, this buffer injects the required
+/// ContentBlockStart and ContentBlockStop events to maintain proper Anthropic protocol.
+pub struct AnthropicMessagesStreamBuffer {
+    /// Buffered SSE events ready to be written to wire
+    buffered_events: Vec<SseEvent>,
+
+    /// Track if we've seen a message_start event
+    message_started: bool,
+
+    /// Track content block indices that have received ContentBlockStart events
+    content_block_start_indices: HashSet<i32>,
+
+    /// Track if we need to inject ContentBlockStop before message_delta
+    needs_content_block_stop: bool,
+
+    /// Track if we've seen a MessageDelta (so we need to send MessageStop at the end)
+    seen_message_delta: bool,
+
+    /// Model name to use when generating message_start events
+    model: Option<String>,
+}
+
+impl AnthropicMessagesStreamBuffer {
+    pub fn new() -> Self {
+        Self {
+            buffered_events: Vec::new(),
+            message_started: false,
+            content_block_start_indices: HashSet::new(),
+            needs_content_block_stop: false,
+            seen_message_delta: false,
+            model: None,
+        }
+    }
+
+    /// Check if a content_block_start event has been sent for the given index
+    fn has_content_block_start_been_sent(&self, index: i32) -> bool {
+        self.content_block_start_indices.contains(&index)
+    }
+
+    /// Mark that a content_block_start event has been sent for the given index
+    fn set_content_block_start_sent(&mut self, index: i32) {
+        self.content_block_start_indices.insert(index);
+    }
+
+    /// Helper to create and format a ContentBlockStart SSE event
+    fn create_content_block_start_event() -> SseEvent {
+        let content_block_start = MessagesStreamEvent::ContentBlockStart {
+            index: 0,
+            content_block: crate::apis::anthropic::MessagesContentBlock::Text {
+                text: String::new(),
+                cache_control: None,
+            },
+        };
+        let sse_string: String = content_block_start.into();
+
+        SseEvent {
+            data: None,
+            event: Some("content_block_start".to_string()),
+            raw_line: sse_string.clone(),
+            sse_transformed_lines: sse_string,
+            provider_stream_response: None,
+        }
+    }
+
+    /// Helper to create and format a MessageStart SSE event
+    fn create_message_start_event(model: &str) -> SseEvent {
+        let message_start = MessagesStreamEvent::MessageStart {
+            message: crate::apis::anthropic::MessagesStreamMessage {
+                id: format!("msg_{}", uuid::Uuid::new_v4().to_string().replace("-", "")),
+                obj_type: "message".to_string(),
+                role: crate::apis::anthropic::MessagesRole::Assistant,
+                content: vec![],
+                model: model.to_string(),
+                stop_reason: None,
+                stop_sequence: None,
+                usage: crate::apis::anthropic::MessagesUsage {
+                    input_tokens: 0,
+                    output_tokens: 0,
+                    cache_creation_input_tokens: None,
+                    cache_read_input_tokens: None,
+                },
+            },
+        };
+        let sse_string: String = message_start.into();
+
+        SseEvent {
+            data: None,
+            event: Some("message_start".to_string()),
+            raw_line: sse_string.clone(),
+            sse_transformed_lines: sse_string,
+            provider_stream_response: None,
+        }
+    }
+
+    /// Helper to create and format a ContentBlockStop SSE event
+    fn create_content_block_stop_event() -> SseEvent {
+        let content_block_stop = MessagesStreamEvent::ContentBlockStop { index: 0 };
+        let sse_string: String = content_block_stop.into();
+
+        SseEvent {
+            data: None,
+            event: Some("content_block_stop".to_string()),
+            raw_line: sse_string.clone(),
+            sse_transformed_lines: sse_string,
+            provider_stream_response: None,
+        }
+    }
+}
+
+impl SseStreamBufferTrait for AnthropicMessagesStreamBuffer {
+    fn add_transformed_event(&mut self, event: SseEvent) {
+        // Skip ping messages
+        if event.should_skip() {
+            return;
+        }
+
+        // FIRST: Try to extract model name from the raw event data before transformation
+        // The provider_stream_response has already been transformed to Anthropic format,
+        // so we need to extract the model from the original raw data if available
+        if self.model.is_none() {
+            if let Some(data) = &event.data {
+                // Try to parse as JSON and extract model field
+                if let Ok(json) = serde_json::from_str::<serde_json::Value>(data) {
+                    if let Some(model) = json.get("model").and_then(|m| m.as_str()) {
+                        self.model = Some(model.to_string());
+                    }
+                }
+            }
+        }
+
+        // Match directly on the provider response type to handle event processing
+        // We match on a reference first to determine the type, then move the event
+        match &event.provider_stream_response {
+            Some(ProviderStreamResponseType::MessagesStreamEvent(evt)) => {
+                match evt {
+                    MessagesStreamEvent::MessageStart { .. } => {
+                        // Add the message_start event
+                        self.buffered_events.push(event);
+                        self.message_started = true;
+                    }
+                    MessagesStreamEvent::ContentBlockStart { index, .. } => {
+                        let index = *index as i32;
+                        // Inject message_start if needed
+                        if !self.message_started {
+                            let model = self.model.as_deref().unwrap_or("unknown");
+                            let message_start = AnthropicMessagesStreamBuffer::create_message_start_event(model);
+                            self.buffered_events.push(message_start);
+                            self.message_started = true;
+                        }
+
+                        // Add the content_block_start event (from tool calls or other sources)
+                        self.buffered_events.push(event);
+                        self.set_content_block_start_sent(index);
+                        self.needs_content_block_stop = true;
+                    }
+                    MessagesStreamEvent::ContentBlockDelta { index, .. } => {
+                        let index = *index as i32;
+                        // Inject message_start if needed
+                        if !self.message_started {
+                            let model = self.model.as_deref().unwrap_or("unknown");
+                            let message_start = AnthropicMessagesStreamBuffer::create_message_start_event(model);
+                            self.buffered_events.push(message_start);
+                            self.message_started = true;
+                        }
+
+                        // Check if ContentBlockStart was sent for this index
+                        if !self.has_content_block_start_been_sent(index) {
+                            // Inject ContentBlockStart before delta
+                            let content_block_start = AnthropicMessagesStreamBuffer::create_content_block_start_event();
+                            self.buffered_events.push(content_block_start);
+                            self.set_content_block_start_sent(index);
+                            self.needs_content_block_stop = true;
+                        }
+
+                        // Content deltas are between ContentBlockStart and ContentBlockStop
+                        self.buffered_events.push(event);
+                    }
+                    MessagesStreamEvent::MessageDelta { usage, .. } => {
+                        // Inject ContentBlockStop before message_delta
+                        if self.needs_content_block_stop {
+                            let content_block_stop = AnthropicMessagesStreamBuffer::create_content_block_stop_event();
+                            self.buffered_events.push(content_block_stop);
+                            self.needs_content_block_stop = false;
+                        }
+
+                        // Check if the last event was also a MessageDelta - if so, merge them
+                        // This handles Bedrock's split of stop_reason (MessageStop) and usage (Metadata)
+                        if let Some(last_event) = self.buffered_events.last_mut() {
+                            if let Some(ProviderStreamResponseType::MessagesStreamEvent(
+                                MessagesStreamEvent::MessageDelta {
+                                    usage: last_usage,
+                                    ..
+                                }
+                            )) = &mut last_event.provider_stream_response {
+                                // Merge: take stop_reason from first, usage from second (if non-zero)
+                                if usage.input_tokens > 0 || usage.output_tokens > 0 {
+                                    *last_usage = usage.clone();
+                                }
+                                // Mark that we've seen MessageDelta (need to send MessageStop later)
+                                self.seen_message_delta = true;
+                                // Don't push the new event, we've merged it
+                                return;
+                            }
+                        }
+
+                        // No previous MessageDelta to merge with, add this one
+                        self.buffered_events.push(event);
+                        self.seen_message_delta = true;
+                    }
+                    MessagesStreamEvent::ContentBlockStop { .. } => {
+                        // ContentBlockStop received from upstream (e.g., Bedrock)
+                        // Clear the flag so we don't inject another one
+                        self.needs_content_block_stop = false;
+                        self.buffered_events.push(event);
+                    }
+                    MessagesStreamEvent::MessageStop => {
+                        // MessageStop received from upstream (e.g., OpenAI via [DONE])
+                        // Clear the flag so we don't inject another one
+                        self.seen_message_delta = false;
+                        self.buffered_events.push(event);
+                    }
+                    _ => {
+                        // Other Anthropic event types (Ping, etc.), just accumulate
+                        self.buffered_events.push(event);
+                    }
+                }
+            }
+            _ => {
+                // Non-Anthropic events or events without provider_stream_response, just accumulate
+                self.buffered_events.push(event);
+            }
+        }
+    }
+
+    fn into_bytes(&mut self) -> Vec<u8> {
+        // Convert all accumulated events to bytes and clear buffer
+        // NOTE: We do NOT inject ContentBlockStop here because it's injected when we see MessageDelta
+        // or MessageStop. Injecting it here causes premature ContentBlockStop in the middle of streaming.
+
+        // Inject MessageStop after MessageDelta if we've seen one
+        // This completes the Anthropic Messages API event sequence
+        if self.seen_message_delta {
+            let message_stop = MessagesStreamEvent::MessageStop;
+            let sse_string: String = message_stop.into();
+            let message_stop_event = SseEvent {
+                data: None,
+                event: Some("message_stop".to_string()),
+                raw_line: sse_string.clone(),
+                sse_transformed_lines: sse_string,
+                provider_stream_response: None,
+            };
+            self.buffered_events.push(message_stop_event);
+            self.seen_message_delta = false;
+        }
+
+        let mut buffer = Vec::new();
+        for event in self.buffered_events.drain(..) {
+            let event_bytes: Vec<u8> = event.into();
+            buffer.extend_from_slice(&event_bytes);
+        }
+        buffer
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::clients::{SupportedAPIsFromClient, SupportedUpstreamAPIs};
+    use crate::apis::anthropic::AnthropicApi;
+    use crate::apis::openai::OpenAIApi;
+    use crate::apis::streaming_shapes::sse::SseStreamIter;
+
+    #[test]
+    fn test_openai_to_anthropic_complete_transformation() {
+        // OpenAI ChatCompletions input that will be transformed to Anthropic Messages API
+        let raw_input = r#"data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1234567890,"model":"gpt-4o","choices":[{"index":0,"delta":{"role":"assistant","content":"Hello"},"finish_reason":null}]}
+
+data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1234567890,"model":"gpt-4o","choices":[{"index":0,"delta":{"content":" world"},"finish_reason":null}]}
+
+data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1234567890,"model":"gpt-4o","choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}
+
+data: [DONE]"#;
+
+        println!("\n{}", "=".repeat(80));
+        println!("TEST 1: OpenAI → Anthropic Messages API Complete Transformation");
+        println!("{}", "=".repeat(80));
+        println!("\nRAW INPUT (OpenAI ChatCompletions):");
+        println!("{}", "-".repeat(80));
+        println!("{}", raw_input);
+
+        // Setup API configuration for transformation (client wants Anthropic, upstream is OpenAI)
+        let client_api = SupportedAPIsFromClient::AnthropicMessagesAPI(AnthropicApi::Messages);
+        let upstream_api = SupportedUpstreamAPIs::OpenAIChatCompletions(OpenAIApi::ChatCompletions);
+
+        // Parse events and apply transformation
+        let stream_iter = SseStreamIter::try_from(raw_input.as_bytes()).unwrap();
+        let mut buffer = AnthropicMessagesStreamBuffer::new();
+
+        for raw_event in stream_iter {
+            let transformed_event = SseEvent::try_from((raw_event, &client_api, &upstream_api)).unwrap();
+            buffer.add_transformed_event(transformed_event);
+        }
+
+        let output_bytes = buffer.into_bytes();
+        let output = String::from_utf8_lossy(&output_bytes);
+
+        println!("\nTRANSFORMED OUTPUT (Anthropic Messages API):");
+        println!("{}", "-".repeat(80));
+        println!("{}", output);
+
+        // Assertions
+        assert!(!output_bytes.is_empty(), "Should have output");
+        assert!(output.contains("event: message_start"), "Should have message_start");
+        assert!(output.contains("event: content_block_start"), "Should have content_block_start (injected)");
+
+        let delta_count = output.matches("event: content_block_delta").count();
+        assert_eq!(delta_count, 2, "Should have exactly 2 content_block_delta events");
+
+        // Verify both pieces of content are present
+        assert!(output.contains("\"text\":\"Hello\""), "Should have first content delta 'Hello'");
+        assert!(output.contains("\"text\":\" world\""), "Should have second content delta ' world'");
+
+        assert!(output.contains("event: content_block_stop"), "Should have content_block_stop (injected)");
+        assert!(output.contains("event: message_delta"), "Should have message_delta");
+        assert!(output.contains("event: message_stop"), "Should have message_stop");
+
+        println!("\nVALIDATION SUMMARY:");
+        println!("{}", "-".repeat(80));
+        println!("✓ Complete transformation: OpenAI ChatCompletions → Anthropic Messages API");
+        println!("✓ Injected lifecycle events: message_start, content_block_start, content_block_stop");
+        println!("✓ Content deltas: {} events (BOTH 'Hello' and ' world' preserved!)", delta_count);
+        println!("✓ Complete stream with message_stop");
+        println!("✓ Proper Anthropic protocol sequencing\n");
+    }
+
+    #[test]
+    fn test_openai_to_anthropic_partial_transformation() {
+        // Partial OpenAI ChatCompletions stream - no [DONE]
+        let raw_input = r#"data: {"id":"chatcmpl-456","object":"chat.completion.chunk","created":1234567890,"model":"gpt-4o","choices":[{"index":0,"delta":{"role":"assistant","content":"The weather"},"finish_reason":null}]}
+
+data: {"id":"chatcmpl-456","object":"chat.completion.chunk","created":1234567890,"model":"gpt-4o","choices":[{"index":0,"delta":{"content":" in San Francisco"},"finish_reason":null}]}
+
+data: {"id":"chatcmpl-456","object":"chat.completion.chunk","created":1234567890,"model":"gpt-4o","choices":[{"index":0,"delta":{"content":" is"},"finish_reason":null}]}"#;
+
+        println!("\n{}", "=".repeat(80));
+        println!("TEST 2: OpenAI → Anthropic Partial Transformation (NO [DONE])");
+        println!("{}", "=".repeat(80));
+        println!("\nRAW INPUT (OpenAI ChatCompletions - NO [DONE]):");
+        println!("{}", "-".repeat(80));
+        println!("{}", raw_input);
+
+        // Setup API configuration for transformation
+        let client_api = SupportedAPIsFromClient::AnthropicMessagesAPI(AnthropicApi::Messages);
+        let upstream_api = SupportedUpstreamAPIs::OpenAIChatCompletions(OpenAIApi::ChatCompletions);
+
+        // Parse and transform events
+        let stream_iter = SseStreamIter::try_from(raw_input.as_bytes()).unwrap();
+        let mut buffer = AnthropicMessagesStreamBuffer::new();
+
+        for raw_event in stream_iter {
+            let transformed_event = SseEvent::try_from((raw_event, &client_api, &upstream_api)).unwrap();
+            buffer.add_transformed_event(transformed_event);
+        }
+
+        let output_bytes = buffer.into_bytes();
+        let output = String::from_utf8_lossy(&output_bytes);
+
+        println!("\nTRANSFORMED OUTPUT (Anthropic Messages API):");
+        println!("{}", "-".repeat(80));
+        println!("{}", output);
+
+        // Assertions
+        assert!(!output_bytes.is_empty(), "Should have output");
+        assert!(output.contains("event: message_start"), "Should have message_start");
+        assert!(output.contains("event: content_block_start"), "Should have content_block_start (injected)");
+
+        let delta_count = output.matches("event: content_block_delta").count();
+        assert_eq!(delta_count, 3, "Should have exactly 3 content_block_delta events");
+
+        // Verify all three pieces of content are present
+        assert!(output.contains("\"text\":\"The weather\""), "Should have first content delta");
+        assert!(output.contains("\"text\":\" in San Francisco\""), "Should have second content delta");
+        assert!(output.contains("\"text\":\" is\""), "Should have third content delta");
+
+        // For partial streams (no finish_reason, no [DONE]), we do NOT inject content_block_stop
+        // because the stream may continue. This is correct behavior - only inject lifecycle events
+        // when we have explicit signals from upstream (finish_reason, [DONE], etc.)
+        assert!(!output.contains("event: content_block_stop"), "Should NOT have content_block_stop for partial stream");
+
+        // Should NOT have completion events
+        assert!(!output.contains("event: message_delta"), "Should NOT have message_delta");
+        assert!(!output.contains("event: message_stop"), "Should NOT have message_stop");
+
+        println!("\nVALIDATION SUMMARY:");
+        println!("{}", "-".repeat(80));
+        println!("✓ Partial transformation: OpenAI → Anthropic (stream interrupted)");
+        println!("✓ Injected: message_start, content_block_start at beginning");
+        println!("✓ Incremental deltas: {} events (ALL content preserved!)", delta_count);
+        println!("✓ NO completion events (partial stream, no [DONE])");
+        println!("✓ Buffer maintains Anthropic protocol for active streams\n");
+    }
+
+    #[test]
+    fn test_openai_tool_calling_to_anthropic_transformation() {
+        // OpenAI ChatCompletions tool calling stream
+        let raw_input = r#"data: {"id":"chatcmpl-Cgx6pZPBgfLcMqfT0ILIH2mID2zWQ","object":"chat.completion.chunk","created":1764353027,"model":"gpt-4o-2024-08-06","service_tier":"default","system_fingerprint":"fp_7eeb46f068","choices":[{"index":0,"delta":{"role":"assistant","content":null,"tool_calls":[{"index":0,"id":"call_2Uzw0AEZQeOex2CP2TKjcLKc","type":"function","function":{"name":"get_weather","arguments":""}}],"refusal":null},"logprobs":null,"finish_reason":null}],"obfuscation":"uSpCcO"}
+
+data: {"id":"chatcmpl-Cgx6pZPBgfLcMqfT0ILIH2mID2zWQ","object":"chat.completion.chunk","created":1764353027,"model":"gpt-4o-2024-08-06","service_tier":"default","system_fingerprint":"fp_7eeb46f068","choices":[{"index":0,"delta":{"tool_calls":[{"index":0,"function":{"arguments":"{\""}}]},"logprobs":null,"finish_reason":null}],"obfuscation":""}
+
+data: {"id":"chatcmpl-Cgx6pZPBgfLcMqfT0ILIH2mID2zWQ","object":"chat.completion.chunk","created":1764353027,"model":"gpt-4o-2024-08-06","service_tier":"default","system_fingerprint":"fp_7eeb46f068","choices":[{"index":0,"delta":{"tool_calls":[{"index":0,"function":{"arguments":"location"}}]},"logprobs":null,"finish_reason":null}],"obfuscation":"24WSqt08jtf"}
+
+data: {"id":"chatcmpl-Cgx6pZPBgfLcMqfT0ILIH2mID2zWQ","object":"chat.completion.chunk","created":1764353027,"model":"gpt-4o-2024-08-06","service_tier":"default","system_fingerprint":"fp_7eeb46f068","choices":[{"index":0,"delta":{"tool_calls":[{"index":0,"function":{"arguments":"\":\""}}]},"logprobs":null,"finish_reason":null}],"obfuscation":"6CleV8twTxkKYg"}
+
+data: {"id":"chatcmpl-Cgx6pZPBgfLcMqfT0ILIH2mID2zWQ","object":"chat.completion.chunk","created":1764353027,"model":"gpt-4o-2024-08-06","service_tier":"default","system_fingerprint":"fp_7eeb46f068","choices":[{"index":0,"delta":{"tool_calls":[{"index":0,"function":{"arguments":"San"}}]},"logprobs":null,"finish_reason":null}],"obfuscation":""}
+
+data: {"id":"chatcmpl-Cgx6pZPBgfLcMqfT0ILIH2mID2zWQ","object":"chat.completion.chunk","created":1764353027,"model":"gpt-4o-2024-08-06","service_tier":"default","system_fingerprint":"fp_7eeb46f068","choices":[{"index":0,"delta":{"tool_calls":[{"index":0,"function":{"arguments":" Francisco"}}]},"logprobs":null,"finish_reason":null}],"obfuscation":"1XLz89l3v"}
+
+data: {"id":"chatcmpl-Cgx6pZPBgfLcMqfT0ILIH2mID2zWQ","object":"chat.completion.chunk","created":1764353027,"model":"gpt-4o-2024-08-06","service_tier":"default","system_fingerprint":"fp_7eeb46f068","choices":[{"index":0,"delta":{"tool_calls":[{"index":0,"function":{"arguments":","}}]},"logprobs":null,"finish_reason":null}],"obfuscation":"sh"}
+
+data: {"id":"chatcmpl-Cgx6pZPBgfLcMqfT0ILIH2mID2zWQ","object":"chat.completion.chunk","created":1764353027,"model":"gpt-4o-2024-08-06","service_tier":"default","system_fingerprint":"fp_7eeb46f068","choices":[{"index":0,"delta":{"tool_calls":[{"index":0,"function":{"arguments":" CA"}}]},"logprobs":null,"finish_reason":null}],"obfuscation":""}
+
+data: {"id":"chatcmpl-Cgx6pZPBgfLcMqfT0ILIH2mID2zWQ","object":"chat.completion.chunk","created":1764353027,"model":"gpt-4o-2024-08-06","service_tier":"default","system_fingerprint":"fp_7eeb46f068","choices":[{"index":0,"delta":{"tool_calls":[{"index":0,"function":{"arguments":"\"}"}}]},"logprobs":null,"finish_reason":null}],"obfuscation":""}
+
+data: {"id":"chatcmpl-Cgx6pZPBgfLcMqfT0ILIH2mID2zWQ","object":"chat.completion.chunk","created":1764353027,"model":"gpt-4o-2024-08-06","service_tier":"default","system_fingerprint":"fp_7eeb46f068","choices":[{"index":0,"delta":{},"logprobs":null,"finish_reason":"tool_calls"}],"obfuscation":"I"}
+
+data: [DONE]"#;
+
+        println!("\n{}", "=".repeat(80));
+        println!("TEST 3: OpenAI Tool Calling → Anthropic Messages API Transformation");
+        println!("{}", "=".repeat(80));
+        println!("\nRAW INPUT (OpenAI ChatCompletions with Tool Calls):");
+        println!("{}", "-".repeat(80));
+        println!("{}", raw_input);
+
+        // Setup API configuration for transformation
+        let client_api = SupportedAPIsFromClient::AnthropicMessagesAPI(AnthropicApi::Messages);
+        let upstream_api = SupportedUpstreamAPIs::OpenAIChatCompletions(OpenAIApi::ChatCompletions);
+
+        // Parse and transform events
+        let stream_iter = SseStreamIter::try_from(raw_input.as_bytes()).unwrap();
+        let mut buffer = AnthropicMessagesStreamBuffer::new();
+
+        for raw_event in stream_iter {
+            let transformed_event = SseEvent::try_from((raw_event, &client_api, &upstream_api)).unwrap();
+            buffer.add_transformed_event(transformed_event);
+        }
+
+        let output_bytes = buffer.into_bytes();
+        let output = String::from_utf8_lossy(&output_bytes);
+
+        println!("\nTRANSFORMED OUTPUT (Anthropic Messages API):");
+        println!("{}", "-".repeat(80));
+        println!("{}", output);
+
+        // Assertions for tool calling transformation
+        assert!(!output_bytes.is_empty(), "Should have output");
+
+        // Should have lifecycle events (injected by buffer)
+        assert!(output.contains("event: message_start"), "Should have message_start (injected)");
+        assert!(output.contains("event: content_block_start"), "Should have content_block_start");
+        assert!(output.contains("event: content_block_stop"), "Should have content_block_stop (injected)");
+        assert!(output.contains("event: message_delta"), "Should have message_delta");
+        assert!(output.contains("event: message_stop"), "Should have message_stop");
+
+        // Should have tool_use content block
+        assert!(output.contains("\"type\":\"tool_use\""), "Should have tool_use type");
+        assert!(output.contains("\"name\":\"get_weather\""), "Should have correct function name");
+        assert!(output.contains("\"id\":\"call_2Uzw0AEZQeOex2CP2TKjcLKc\""), "Should have correct tool call ID");
+
+        // Count input_json_delta events - should match the number of argument chunks
+        let delta_count = output.matches("event: content_block_delta").count();
+        assert!(delta_count >= 8, "Should have at least 8 input_json_delta events");
+
+        // Verify argument deltas are present
+        assert!(output.contains("\"type\":\"input_json_delta\""), "Should have input_json_delta type");
+        assert!(output.contains("\"partial_json\":"), "Should have partial_json field");
+
+        // Verify the accumulated arguments contain the location
+        assert!(output.contains("San"), "Arguments should contain 'San'");
+        assert!(output.contains("Francisco"), "Arguments should contain 'Francisco'");
+        assert!(output.contains("CA"), "Arguments should contain 'CA'");
+
+        // Verify stop reason is tool_use
+        assert!(output.contains("\"stop_reason\":\"tool_use\""), "Should have stop_reason as tool_use");
+
+        println!("\nVALIDATION SUMMARY:");
+        println!("{}", "-".repeat(80));
+        println!("✓ Complete tool calling transformation: OpenAI → Anthropic Messages API");
+        println!("✓ Injected lifecycle: message_start, content_block_stop");
+        println!("✓ Tool metadata: name='get_weather', id='call_2Uzw0AEZQeOex2CP2TKjcLKc'");
+        println!("✓ Argument deltas: {} events", delta_count);
+        println!("✓ Complete JSON arguments: '{{\"location\":\"San Francisco, CA\"}}'");
+        println!("✓ Stop reason: tool_use");
+        println!("✓ Proper Anthropic tool_use protocol\n");
+    }
+}

crates/hermesllm/src/apis/streaming_shapes/chat_completions_streaming_buffer.rs

@@ -0,0 +1,39 @@
+use crate::apis::streaming_shapes::sse::{SseEvent, SseStreamBufferTrait};
+
+///  OpenAI Chat Completions SSE Stream Buffer for when client and upstream APIs match.
+pub struct OpenAIChatCompletionsStreamBuffer {
+    /// Buffered SSE events ready to be written to wire
+    buffered_events: Vec<SseEvent>,
+}
+
+impl OpenAIChatCompletionsStreamBuffer {
+    pub fn new() -> Self {
+        Self {
+            buffered_events: Vec::new(),
+        }
+    }
+}
+
+impl SseStreamBufferTrait for OpenAIChatCompletionsStreamBuffer {
+    fn add_transformed_event(&mut self, event: SseEvent) {
+        // Skip ping messages
+        if event.should_skip() {
+            return;
+        }
+
+        // For OpenAI Chat Completions, events are already properly transformed
+        // Just accumulate them for later wire transmission
+        self.buffered_events.push(event);
+    }
+
+    fn into_bytes(&mut self) -> Vec<u8> {
+        // No finalization needed for OpenAI Chat Completions
+        // The [DONE] marker is already handled by the transformation layer
+        let mut buffer = Vec::new();
+        for event in self.buffered_events.drain(..) {
+            let event_bytes: Vec<u8> = event.into();
+            buffer.extend_from_slice(&event_bytes);
+        }
+        buffer
+    }
+}

crates/hermesllm/src/apis/streaming_shapes/mod.rs

@@ -0,0 +1,6 @@
+pub mod sse;
+pub mod amazon_bedrock_binary_frame;
+pub mod anthropic_streaming_buffer;
+pub mod chat_completions_streaming_buffer;
+pub mod passthrough_streaming_buffer;
+pub mod responses_api_streaming_buffer;

crates/hermesllm/src/apis/streaming_shapes/passthrough_streaming_buffer.rs

@@ -0,0 +1,95 @@
+use crate::apis::streaming_shapes::sse::{SseEvent, SseStreamBufferTrait};
+
+/// Passthrough SSE Stream Buffer for when client and upstream APIs match.
+pub struct PassthroughStreamBuffer {
+    /// Buffered SSE events ready to be written to wire
+    buffered_events: Vec<SseEvent>,
+}
+
+impl PassthroughStreamBuffer {
+    pub fn new() -> Self {
+        Self {
+            buffered_events: Vec::new(),
+        }
+    }
+}
+
+impl SseStreamBufferTrait for PassthroughStreamBuffer {
+    fn add_transformed_event(&mut self, event: SseEvent) {
+        // Skip ping messages
+        if event.should_skip() {
+            return;
+        }
+
+        // Skip events with empty transformed lines (e.g., suppressed event-only lines)
+        if event.sse_transformed_lines.is_empty() {
+            return;
+        }
+
+        // Just accumulate events as-is
+        self.buffered_events.push(event);
+    }
+
+    fn into_bytes(&mut self) -> Vec<u8> {
+        // No finalization needed for passthrough - just convert accumulated events to bytes
+        let mut buffer = Vec::new();
+        for event in self.buffered_events.drain(..) {
+            let event_bytes: Vec<u8> = event.into();
+            buffer.extend_from_slice(&event_bytes);
+        }
+        buffer
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use crate::apis::streaming_shapes::passthrough_streaming_buffer::PassthroughStreamBuffer;
+    use crate::apis::streaming_shapes::sse::{SseStreamIter, SseStreamBufferTrait};
+
+    #[test]
+    fn test_chat_completions_passthrough_buffer() {
+        let raw_input = r#"data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1234567890,"model":"gpt-4o","choices":[{"index":0,"delta":{"role":"assistant","content":null,"tool_calls":[{"index":0,"id":"call_abc","type":"function","function":{"name":"get_weather","arguments":""}}],"refusal":null},"logprobs":null,"finish_reason":null}]}
+
+    data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1234567890,"model":"gpt-4o","choices":[{"index":0,"delta":{"tool_calls":[{"index":0,"function":{"arguments":"{\""}}]},"logprobs":null,"finish_reason":null}]}
+
+    data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1234567890,"model":"gpt-4o","choices":[{"index":0,"delta":{"tool_calls":[{"index":0,"function":{"arguments":"location"}}]},"logprobs":null,"finish_reason":null}]}
+
+    data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1234567890,"model":"gpt-4o","choices":[{"index":0,"delta":{},"logprobs":null,"finish_reason":"tool_calls"}]}
+
+    data: [DONE]"#;
+
+        println!("\n{}", "=".repeat(80));
+        println!("TEST 1: ChatCompletions Passthrough Buffer");
+        println!("{}", "=".repeat(80));
+        println!("\nRAW INPUT (ChatCompletions):");
+        println!("{}", "-".repeat(80));
+        println!("{}", raw_input);
+
+        // Parse and process through buffer
+        let stream_iter = SseStreamIter::try_from(raw_input.as_bytes()).unwrap();
+        let mut buffer = PassthroughStreamBuffer::new();
+
+        for event in stream_iter {
+            buffer.add_transformed_event(event);
+        }
+
+        let output_bytes = buffer.into_bytes();
+        let output = String::from_utf8_lossy(&output_bytes);
+
+        println!("\nTRANSFORMED OUTPUT (ChatCompletions - Passthrough):");
+        println!("{}", "-".repeat(80));
+        println!("{}", output);
+
+        // Assertions
+        assert!(!output_bytes.is_empty());
+        assert!(output.contains("chatcmpl-123"));
+        assert!(output.contains("[DONE]"));
+        assert_eq!(raw_input.trim(), output.trim(), "Passthrough should preserve input");
+
+        println!("\nVALIDATION SUMMARY:");
+        println!("{}", "-".repeat(80));
+        println!("✓ Passthrough buffer: input = output (no transformation)");
+        println!("✓ All events preserved including [DONE]");
+        println!("✓ Function calling events preserved\n");
+    }
+}

crates/hermesllm/src/apis/streaming_shapes/responses_api_streaming_buffer.rs

@@ -0,0 +1,600 @@
+use std::collections::HashMap;
+use log::debug;
+use crate::apis::openai_responses::{
+    ResponsesAPIStreamEvent, ResponsesAPIResponse, OutputItem, OutputItemStatus,
+    ResponseStatus, TextConfig, TextFormat, Reasoning,
+};
+use crate::apis::streaming_shapes::sse::{SseEvent, SseStreamBufferTrait};
+
+/// Helper to convert ResponseAPIStreamEvent to SseEvent
+fn event_to_sse(event: ResponsesAPIStreamEvent) -> SseEvent {
+    let event_type = match &event {
+        ResponsesAPIStreamEvent::ResponseCreated { .. } => "response.created",
+        ResponsesAPIStreamEvent::ResponseInProgress { .. } => "response.in_progress",
+        ResponsesAPIStreamEvent::ResponseCompleted { .. } => "response.completed",
+        ResponsesAPIStreamEvent::ResponseOutputItemAdded { .. } => "response.output_item.added",
+        ResponsesAPIStreamEvent::ResponseOutputItemDone { .. } => "response.output_item.done",
+        ResponsesAPIStreamEvent::ResponseOutputTextDelta { .. } => "response.output_text.delta",
+        ResponsesAPIStreamEvent::ResponseOutputTextDone { .. } => "response.output_text.done",
+        ResponsesAPIStreamEvent::ResponseFunctionCallArgumentsDelta { .. } => "response.function_call_arguments.delta",
+        ResponsesAPIStreamEvent::ResponseFunctionCallArgumentsDone { .. } => "response.function_call_arguments.done",
+        unknown => {
+            debug!("Unknown ResponsesAPIStreamEvent type encountered: {:?}", unknown);
+            "unknown"
+        }
+    };
+
+    let json_data = match serde_json::to_string(&event) {
+        Ok(data) => data,
+        Err(e) => {
+            debug!("Error serializing ResponsesAPIStreamEvent to JSON: {}", e);
+            String::new()
+        }
+    };
+    let wire_format: String = event.into();
+
+    SseEvent {
+        data: Some(json_data),
+        event: Some(event_type.to_string()),
+        raw_line: wire_format.clone(),
+        sse_transformed_lines: wire_format,
+        provider_stream_response: None,
+    }
+}
+
+/// SSE Stream Buffer for ResponsesAPIStreamEvent with full lifecycle management.
+///
+/// This buffer manages the wire format for v1/responses streaming, handling
+/// delta events and emitting complete lifecycle events.
+///
+pub struct ResponsesAPIStreamBuffer {
+    /// Sequence number for events
+    sequence_number: i32,
+
+    /// Track item IDs by output index
+    item_ids: HashMap<i32, String>,
+
+    /// Response metadata
+    response_id: Option<String>,
+    model: Option<String>,
+    created_at: Option<i64>,
+
+    /// Lifecycle state flags
+    created_emitted: bool,
+    in_progress_emitted: bool,
+
+    /// Track which output items we've added
+    output_items_added: HashMap<i32, String>, // output_index -> item_id
+
+    /// Accumulated content by item_id
+    text_content: HashMap<String, String>,
+    function_arguments: HashMap<String, String>,
+
+    /// Tool call metadata by output_index
+    tool_call_metadata: HashMap<i32, (String, String)>, // output_index -> (call_id, name)
+
+    /// Final completed response (for logging/tracing/persistence)
+    completed_response: Option<ResponsesAPIResponse>,
+
+    /// Buffered SSE events ready to be written to wire
+    buffered_events: Vec<SseEvent>,
+}
+
+impl ResponsesAPIStreamBuffer {
+    pub fn new() -> Self {
+        Self {
+            sequence_number: 0,
+            item_ids: HashMap::new(),
+            response_id: None,
+            model: None,
+            created_at: None,
+            created_emitted: false,
+            in_progress_emitted: false,
+            output_items_added: HashMap::new(),
+            text_content: HashMap::new(),
+            function_arguments: HashMap::new(),
+            tool_call_metadata: HashMap::new(),
+            completed_response: None,
+            buffered_events: Vec::new(),
+        }
+    }
+
+    fn next_sequence_number(&mut self) -> i32 {
+        let seq = self.sequence_number;
+        self.sequence_number += 1;
+        seq
+    }
+
+    fn generate_item_id(prefix: &str) -> String {
+        format!("{}_{}", prefix, uuid::Uuid::new_v4().to_string().replace("-", ""))
+    }
+
+    fn get_or_create_item_id(&mut self, output_index: i32, prefix: &str) -> String {
+        if let Some(id) = self.item_ids.get(&output_index) {
+            return id.clone();
+        }
+        let id = ResponsesAPIStreamBuffer::generate_item_id(prefix);
+        self.item_ids.insert(output_index, id.clone());
+        id
+    }
+
+    /// Create response.created event
+    fn create_response_created_event(&mut self) -> SseEvent {
+        let response = self.build_response(ResponseStatus::InProgress);
+        let event = ResponsesAPIStreamEvent::ResponseCreated {
+            response,
+            sequence_number: self.next_sequence_number(),
+        };
+        event_to_sse(event)
+    }
+
+    /// Create response.in_progress event
+    fn create_response_in_progress_event(&mut self) -> SseEvent {
+        let response = self.build_response(ResponseStatus::InProgress);
+        let event = ResponsesAPIStreamEvent::ResponseInProgress {
+            response,
+            sequence_number: self.next_sequence_number(),
+        };
+        event_to_sse(event)
+    }
+
+    /// Create output_item.added event for text
+    fn create_output_item_added_event(&mut self, output_index: i32, item_id: &str) -> SseEvent {
+        let event = ResponsesAPIStreamEvent::ResponseOutputItemAdded {
+            output_index,
+            item: OutputItem::Message {
+                id: item_id.to_string(),
+                status: OutputItemStatus::InProgress,
+                role: "assistant".to_string(),
+                content: vec![],
+            },
+            sequence_number: self.next_sequence_number(),
+        };
+        event_to_sse(event)
+    }
+
+    /// Create output_item.added event for tool call
+    fn create_tool_call_added_event(&mut self, output_index: i32, item_id: &str, call_id: &str, name: &str) -> SseEvent {
+        let event = ResponsesAPIStreamEvent::ResponseOutputItemAdded {
+            output_index,
+            item: OutputItem::FunctionCall {
+                id: item_id.to_string(),
+                status: OutputItemStatus::InProgress,
+                call_id: call_id.to_string(),
+                name: Some(name.to_string()),
+                arguments: Some(String::new()),
+            },
+            sequence_number: self.next_sequence_number(),
+        };
+        event_to_sse(event)
+    }
+
+    /// Build the base response object with current state
+    fn build_response(&self, status: ResponseStatus) -> ResponsesAPIResponse {
+        ResponsesAPIResponse {
+            id: self.response_id.clone().unwrap_or_default(),
+            object: "response".to_string(),
+            created_at: self.created_at.unwrap_or(0),
+            status,
+            error: None,
+            incomplete_details: None,
+            instructions: None,
+            model: self.model.clone().unwrap_or_else(|| "unknown".to_string()),
+            output: vec![],
+            usage: None,
+            parallel_tool_calls: true,
+            conversation: None,
+            previous_response_id: None,
+            tools: vec![],
+            tool_choice: "auto".to_string(),
+            temperature: 1.0,
+            top_p: 1.0,
+            metadata: HashMap::new(),
+            truncation: Some("disabled".to_string()),
+            max_output_tokens: None,
+            reasoning: Some(Reasoning {
+                effort: None,
+                summary: None,
+            }),
+            store: Some(true),
+            text: Some(TextConfig {
+                format: TextFormat::Text,
+            }),
+            audio: None,
+            modalities: None,
+            service_tier: Some("auto".to_string()),
+            background: Some(false),
+            top_logprobs: Some(0),
+            max_tool_calls: None,
+        }
+    }
+
+    /// Get the completed response after finalization (for logging/tracing/persistence)
+    pub fn get_completed_response(&self) -> Option<&ResponsesAPIResponse> {
+        self.completed_response.as_ref()
+    }
+
+    /// Finalize the response by emitting all *.done events and response.completed.
+    /// Call this when the stream is complete (after seeing [DONE] or end_of_stream).
+    pub fn finalize(&mut self) {
+        let mut events = Vec::new();
+
+        // Emit done events for all accumulated content
+
+        // Text content done events
+        let text_items: Vec<_> = self.text_content.iter().map(|(id, content)| (id.clone(), content.clone())).collect();
+        for (item_id, content) in text_items {
+            let output_index = self.output_items_added.iter()
+                .find(|(_, id)| **id == item_id)
+                .map(|(idx, _)| *idx)
+                .unwrap_or(0);
+
+            let seq1 = self.next_sequence_number();
+            let text_done_event = ResponsesAPIStreamEvent::ResponseOutputTextDone {
+                item_id: item_id.clone(),
+                output_index,
+                content_index: 0,
+                text: content.clone(),
+                logprobs: vec![],
+                sequence_number: seq1,
+            };
+            events.push(event_to_sse(text_done_event));
+
+            let seq2 = self.next_sequence_number();
+            let item_done_event = ResponsesAPIStreamEvent::ResponseOutputItemDone {
+                output_index,
+                item: OutputItem::Message {
+                    id: item_id.clone(),
+                    status: OutputItemStatus::Completed,
+                    role: "assistant".to_string(),
+                    content: vec![],
+                },
+                sequence_number: seq2,
+            };
+            events.push(event_to_sse(item_done_event));
+        }
+
+        // Function call done events
+        let func_items: Vec<_> = self.function_arguments.iter().map(|(id, args)| (id.clone(), args.clone())).collect();
+        for (item_id, arguments) in func_items {
+            let output_index = self.output_items_added.iter()
+                .find(|(_, id)| **id == item_id)
+                .map(|(idx, _)| *idx)
+                .unwrap_or(0);
+
+            let seq1 = self.next_sequence_number();
+            let args_done_event = ResponsesAPIStreamEvent::ResponseFunctionCallArgumentsDone {
+                output_index,
+                item_id: item_id.clone(),
+                arguments: arguments.clone(),
+                sequence_number: seq1,
+            };
+            events.push(event_to_sse(args_done_event));
+
+            let (call_id, name) = self.tool_call_metadata.get(&output_index)
+                .cloned()
+                .unwrap_or_else(|| (format!("call_{}", uuid::Uuid::new_v4()), "unknown".to_string()));
+
+            let seq2 = self.next_sequence_number();
+            let item_done_event = ResponsesAPIStreamEvent::ResponseOutputItemDone {
+                output_index,
+                item: OutputItem::FunctionCall {
+                    id: item_id.clone(),
+                    status: OutputItemStatus::Completed,
+                    call_id,
+                    name: Some(name),
+                    arguments: Some(arguments.clone()),
+                },
+                sequence_number: seq2,
+            };
+            events.push(event_to_sse(item_done_event));
+        }
+
+        // Build final response
+        let mut output_items = Vec::new();
+
+        // Add tool calls to output
+        for (item_id, arguments) in &self.function_arguments {
+            let output_index = self.output_items_added.iter()
+                .find(|(_, id)| *id == item_id)
+                .map(|(idx, _)| *idx)
+                .unwrap_or(0);
+
+            let (call_id, name) = self.tool_call_metadata.get(&output_index)
+                .cloned()
+                .unwrap_or_else(|| (format!("call_{}", uuid::Uuid::new_v4()), "unknown".to_string()));
+
+            output_items.push(OutputItem::FunctionCall {
+                id: item_id.clone(),
+                status: OutputItemStatus::Completed,
+                call_id,
+                name: Some(name),
+                arguments: Some(arguments.clone()),
+            });
+        }
+
+        let mut final_response = self.build_response(ResponseStatus::Completed);
+        final_response.output = output_items;
+
+        // Store completed response
+        self.completed_response = Some(final_response.clone());
+
+        // Emit response.completed
+        let seq_final = self.next_sequence_number();
+        let completed_event = ResponsesAPIStreamEvent::ResponseCompleted {
+            response: final_response,
+            sequence_number: seq_final,
+        };
+        events.push(event_to_sse(completed_event));
+
+        // Add all finalization events to the buffer
+        self.buffered_events.extend(events);
+    }
+}
+
+impl SseStreamBufferTrait for ResponsesAPIStreamBuffer {
+    fn add_transformed_event(&mut self, event: SseEvent) {
+        // Skip ping messages
+        if event.should_skip() {
+            return;
+        }
+
+        // Handle [DONE] marker - trigger finalization
+        if event.is_done() {
+            self.finalize();
+            return;
+        }
+
+        // Extract the ResponseAPIStreamEvent from the SseEvent's provider_stream_response
+        let provider_response = match event.provider_stream_response.as_ref() {
+            Some(response) => response,
+            None => {
+                eprintln!("Warning: Event missing provider_stream_response");
+                return;
+            }
+        };
+
+        // Extract ResponseAPIStreamEvent from the enum
+        let stream_event = match provider_response {
+            crate::providers::streaming_response::ProviderStreamResponseType::ResponseAPIStreamEvent(evt) => evt,
+            _ => {
+                eprintln!("Warning: Expected ResponseAPIStreamEvent in provider_stream_response");
+                return;
+            }
+        };
+
+        let mut events = Vec::new();
+
+        // Emit lifecycle events if not yet emitted
+        if !self.created_emitted {
+            // Initialize metadata from first event if needed
+            if self.response_id.is_none() {
+                self.response_id = Some(format!("resp_{}", uuid::Uuid::new_v4().to_string().replace("-", "")));
+                self.created_at = Some(std::time::SystemTime::now()
+                    .duration_since(std::time::UNIX_EPOCH)
+                    .unwrap()
+                    .as_secs() as i64);
+                self.model = Some("unknown".to_string()); // Will be set by caller if available
+            }
+
+            events.push(self.create_response_created_event());
+            self.created_emitted = true;
+        }
+
+        if !self.in_progress_emitted {
+            events.push(self.create_response_in_progress_event());
+            self.in_progress_emitted = true;
+        }
+
+        // Process the delta event
+        match stream_event {
+            ResponsesAPIStreamEvent::ResponseOutputTextDelta { output_index, delta, .. } => {
+                let item_id = self.get_or_create_item_id(*output_index, "msg");
+
+                // Emit output_item.added if this is the first time we see this output index
+                if !self.output_items_added.contains_key(output_index) {
+                    self.output_items_added.insert(*output_index, item_id.clone());
+                    events.push(self.create_output_item_added_event(*output_index, &item_id));
+                }
+
+                // Accumulate text content
+                self.text_content.entry(item_id.clone())
+                    .and_modify(|content| content.push_str(delta))
+                    .or_insert_with(|| delta.clone());
+
+                // Emit text delta with filled-in item_id and sequence_number
+                let mut delta_event = stream_event.clone();
+                if let ResponsesAPIStreamEvent::ResponseOutputTextDelta { item_id: ref mut id, sequence_number: ref mut seq, .. } = delta_event {
+                    *id = item_id;
+                    *seq = self.next_sequence_number();
+                }
+                events.push(event_to_sse(delta_event));
+            }
+            ResponsesAPIStreamEvent::ResponseFunctionCallArgumentsDelta { output_index, delta, call_id, name, .. } => {
+                let item_id = self.get_or_create_item_id(*output_index, "fc");
+
+                // Store metadata if provided (from initial tool call event)
+                if let (Some(cid), Some(n)) = (call_id, name) {
+                    self.tool_call_metadata.insert(*output_index, (cid.clone(), n.clone()));
+                }
+
+                // Emit output_item.added if this is the first time we see this tool call
+                if !self.output_items_added.contains_key(output_index) {
+                    self.output_items_added.insert(*output_index, item_id.clone());
+
+                    // For tool calls, we need call_id and name from metadata
+                    // These should now be populated from the event itself
+                    let (call_id, name) = self.tool_call_metadata.get(output_index)
+                        .cloned()
+                        .unwrap_or_else(|| (format!("call_{}", uuid::Uuid::new_v4()), "unknown".to_string()));
+
+                    events.push(self.create_tool_call_added_event(*output_index, &item_id, &call_id, &name));
+                }
+
+                // Accumulate function arguments
+                self.function_arguments.entry(item_id.clone())
+                    .and_modify(|args| args.push_str(delta))
+                    .or_insert_with(|| delta.clone());
+
+                // Emit function call arguments delta with filled-in item_id and sequence_number
+                let mut delta_event = stream_event.clone();
+                if let ResponsesAPIStreamEvent::ResponseFunctionCallArgumentsDelta { item_id: ref mut id, sequence_number: ref mut seq, .. } = delta_event {
+                    *id = item_id;
+                    *seq = self.next_sequence_number();
+                }
+                events.push(event_to_sse(delta_event));
+            }
+            _ => {
+                // For other event types, just pass through with sequence number
+                let other_event = stream_event.clone();
+                // TODO: Add sequence number to other event types if needed
+                events.push(event_to_sse(other_event));
+            }
+        }
+
+        // Store all generated events in the buffer
+        self.buffered_events.extend(events);
+    }
+
+
+    fn into_bytes(&mut self) -> Vec<u8> {
+        // For Responses API, we need special handling:
+        // - Most events are already in buffered_events from add_transformed_event
+        // - We should NOT finalize here - finalization happens when we detect [DONE] or end of stream
+        // - Just flush the accumulated events and clear the buffer
+
+        // Convert all accumulated events to bytes and clear buffer
+        let mut buffer = Vec::new();
+        for event in self.buffered_events.drain(..) {
+            let event_bytes: Vec<u8> = event.into();
+            buffer.extend_from_slice(&event_bytes);
+        }
+        buffer
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::clients::{SupportedAPIsFromClient, SupportedUpstreamAPIs};
+    use crate::apis::openai::OpenAIApi;
+    use crate::apis::streaming_shapes::sse::SseStreamIter;
+
+    #[test]
+    fn test_chat_completions_to_responses_api_transformation() {
+        // ChatCompletions input that will be transformed to ResponsesAPI
+        let raw_input = r#"data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1234567890,"model":"gpt-4o","choices":[{"index":0,"delta":{"role":"assistant","content":"Hello"},"finish_reason":null}]}
+
+    data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1234567890,"model":"gpt-4o","choices":[{"index":0,"delta":{"content":" world"},"finish_reason":null}]}
+
+    data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1234567890,"model":"gpt-4o","choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}
+
+    data: [DONE]"#;
+
+        println!("\n{}", "=".repeat(80));
+        println!("TEST 2: ChatCompletions → ResponsesAPI Transformation (with [DONE])");
+        println!("{}", "=".repeat(80));
+        println!("\nRAW INPUT (ChatCompletions):");
+        println!("{}", "-".repeat(80));
+        println!("{}", raw_input);
+
+        // Setup API configuration for transformation
+        let client_api = SupportedAPIsFromClient::OpenAIResponsesAPI(OpenAIApi::Responses);
+        let upstream_api = SupportedUpstreamAPIs::OpenAIChatCompletions(OpenAIApi::ChatCompletions);
+
+        // Parse events and apply transformation
+        let stream_iter = SseStreamIter::try_from(raw_input.as_bytes()).unwrap();
+        let mut buffer = ResponsesAPIStreamBuffer::new();
+
+        for raw_event in stream_iter {
+            // Transform the event using the client/upstream APIs
+            let transformed_event = SseEvent::try_from((raw_event, &client_api, &upstream_api)).unwrap();
+            buffer.add_transformed_event(transformed_event);
+        }
+
+        let output_bytes = buffer.into_bytes();
+        let output = String::from_utf8_lossy(&output_bytes);
+
+        println!("\nTRANSFORMED OUTPUT (ResponsesAPI):");
+        println!("{}", "-".repeat(80));
+        println!("{}", output);
+
+        // Assertions
+        assert!(!output_bytes.is_empty(), "Should have output");
+        assert!(output.contains("response.created"), "Should have response.created");
+        assert!(output.contains("response.in_progress"), "Should have response.in_progress");
+        assert!(output.contains("response.output_item.added"), "Should have output_item.added");
+        assert!(output.contains("response.output_text.delta"), "Should have text deltas");
+        assert!(output.contains("response.output_text.done"), "Should have text.done");
+        assert!(output.contains("response.output_item.done"), "Should have output_item.done");
+        assert!(output.contains("response.completed"), "Should have response.completed");
+
+        println!("\nVALIDATION SUMMARY:");
+        println!("{}", "-".repeat(80));
+        println!("✓ Lifecycle events: response.created, response.in_progress, response.completed");
+        println!("✓ Output item lifecycle: output_item.added, output_item.done");
+        println!("✓ Text streaming: output_text.delta (2 deltas), output_text.done");
+        println!("✓ Complete transformation with finalization ([DONE] processed)\n");
+    }
+
+    #[test]
+    fn test_partial_streaming_incremental_output() {
+        let raw_input = r#"data: {"id":"chatcmpl-CfpqklihniLRuuQfP7inMb2ghtGmT","object":"chat.completion.chunk","created":1764086794,"model":"gpt-4o-2024-08-06","service_tier":"default","system_fingerprint":"fp_7eeb46f068","choices":[{"index":0,"delta":{"role":"assistant","content":null,"tool_calls":[{"index":0,"id":"call_mD5ggLKk3SMKGPFqFdcpKg6q","type":"function","function":{"name":"get_weather","arguments":""}}],"refusal":null},"logprobs":null,"finish_reason":null}],"obfuscation":"PCFrpy"}
+
+    data: {"id":"chatcmpl-CfpqklihniLRuuQfP7inMb2ghtGmT","object":"chat.completion.chunk","created":1764086794,"model":"gpt-4o-2024-08-06","service_tier":"default","system_fingerprint":"fp_7eeb46f068","choices":[{"index":0,"delta":{"tool_calls":[{"index":0,"function":{"arguments":"{\""}}]},"logprobs":null,"finish_reason":null}],"obfuscation":""}
+
+    data: {"id":"chatcmpl-CfpqklihniLRuuQfP7inMb2ghtGmT","object":"chat.completion.chunk","created":1764086794,"model":"gpt-4o-2024-08-06","service_tier":"default","system_fingerprint":"fp_7eeb46f068","choices":[{"index":0,"delta":{"tool_calls":[{"index":0,"function":{"arguments":"location"}}]},"logprobs":null,"finish_reason":null}],"obfuscation":"TC58A3QEIx8"}
+
+    data: {"id":"chatcmpl-CfpqklihniLRuuQfP7inMb2ghtGmT","object":"chat.completion.chunk","created":1764086794,"model":"gpt-4o-2024-08-06","service_tier":"default","system_fingerprint":"fp_7eeb46f068","choices":[{"index":0,"delta":{"tool_calls":[{"index":0,"function":{"arguments":"\":\""}}]},"logprobs":null,"finish_reason":null}],"obfuscation":"PK4oFzlVlGTUP5"}"#;
+
+        println!("\n{}", "=".repeat(80));
+        println!("TEST 3: Partial Streaming - Function Calling (NO [DONE])");
+        println!("{}", "=".repeat(80));
+        println!("\nRAW INPUT (ChatCompletions - NO [DONE]):");
+        println!("{}", "-".repeat(80));
+        println!("{}", raw_input);
+
+        // Setup API configuration for transformation
+        let client_api = SupportedAPIsFromClient::OpenAIResponsesAPI(OpenAIApi::Responses);
+        let upstream_api = SupportedUpstreamAPIs::OpenAIChatCompletions(OpenAIApi::ChatCompletions);
+
+        // Transform all events
+        let stream_iter = SseStreamIter::try_from(raw_input.as_bytes()).unwrap();
+        let mut buffer = ResponsesAPIStreamBuffer::new();
+
+        for raw_event in stream_iter {
+            let transformed = SseEvent::try_from((raw_event, &client_api, &upstream_api)).unwrap();
+            buffer.add_transformed_event(transformed);
+        }
+
+        let output_bytes = buffer.into_bytes();
+        let output = String::from_utf8_lossy(&output_bytes);
+
+        println!("\nTRANSFORMED OUTPUT (ResponsesAPI):");
+        println!("{}", "-".repeat(80));
+        println!("{}", output);
+
+        // Assertions
+        assert!(output.contains("response.created"), "Should have response.created");
+        assert!(output.contains("response.in_progress"), "Should have response.in_progress");
+        assert!(output.contains("response.output_item.added"), "Should have output_item.added");
+        assert!(output.contains("\"type\":\"function_call\""), "Should be function_call type");
+        assert!(output.contains("\"name\":\"get_weather\""), "Should have function name");
+        assert!(output.contains("\"call_id\":\"call_mD5ggLKk3SMKGPFqFdcpKg6q\""), "Should have correct call_id");
+
+        let delta_count = output.matches("event: response.function_call_arguments.delta").count();
+        assert_eq!(delta_count, 4, "Should have 4 delta events");
+
+        assert!(!output.contains("response.function_call_arguments.done"), "Should NOT have arguments.done");
+        assert!(!output.contains("response.output_item.done"), "Should NOT have output_item.done");
+        assert!(!output.contains("response.completed"), "Should NOT have response.completed");
+
+        println!("\nVALIDATION SUMMARY:");
+        println!("{}", "-".repeat(80));
+        println!("✓ Lifecycle events: response.created, response.in_progress");
+        println!("✓ Function call metadata: name='get_weather', call_id='call_mD5ggLKk3SMKGPFqFdcpKg6q'");
+        println!("✓ Incremental deltas: 4 events (1 initial + 3 argument chunks)");
+        println!("✓ NO completion events (partial stream, no [DONE])");
+        println!("✓ Arguments accumulated: '{{\"location\":\"'\n");
+    }
+}

crates/hermesllm/src/apis/streaming_shapes/sse.rs

@@ -1,10 +1,73 @@
-use crate::providers::response::ProviderStreamResponse;
+use crate::providers::streaming_response::ProviderStreamResponse;
-use crate::providers::response::ProviderStreamResponseType;
+use crate::providers::streaming_response::ProviderStreamResponseType;
+use crate::apis::streaming_shapes::chat_completions_streaming_buffer::OpenAIChatCompletionsStreamBuffer;
+use crate::apis::streaming_shapes::anthropic_streaming_buffer::AnthropicMessagesStreamBuffer;
+use crate::apis::streaming_shapes::passthrough_streaming_buffer::PassthroughStreamBuffer;
+use crate::apis::streaming_shapes::responses_api_streaming_buffer::ResponsesAPIStreamBuffer;
 use serde::{Deserialize, Serialize};
 use std::error::Error;
 use std::fmt;
 use std::str::FromStr;
 
+/// Trait defining the interface for SSE stream buffers.
+///
+/// This trait is implemented by both the enum `SseStreamBuffer` (for zero-cost dispatch)
+/// and individual buffer implementations (for direct use).
+///
+pub trait SseStreamBufferTrait: Send + Sync {
+    /// Add a transformed SSE event to the buffer.
+    ///
+    /// The buffer may inject additional events as needed based on internal state.
+    /// For example, Anthropic buffers inject ContentBlockStart before the first ContentBlockDelta.
+    ///
+    /// All events (original + injected) are accumulated internally for the next `into_bytes()` call.
+    ///
+    /// # Arguments
+    /// * `event` - A transformed SSE event to accumulate
+    fn add_transformed_event(&mut self, event: SseEvent);
+
+    /// Get bytes for all accumulated events since the last call.
+    ///
+    /// This method:
+    /// - Converts all buffered events to wire format bytes
+    /// - Clears the internal event buffer
+    /// - Preserves state for subsequent `add_transformed_event()` calls
+    ///
+    /// Call this after processing each chunk of upstream events to get bytes for immediate transmission.
+    ///
+    /// # Returns
+    /// Bytes ready for wire transmission (may be empty if no events were accumulated)
+    fn into_bytes(&mut self) -> Vec<u8>;
+}
+
+/// Unified SSE Stream Buffer enum that provides a zero-cost abstraction
+pub enum SseStreamBuffer {
+    Passthrough(PassthroughStreamBuffer),
+    OpenAIChatCompletions(OpenAIChatCompletionsStreamBuffer),
+    AnthropicMessages(AnthropicMessagesStreamBuffer),
+    OpenAIResponses(ResponsesAPIStreamBuffer),
+}
+
+impl SseStreamBufferTrait for SseStreamBuffer {
+    fn add_transformed_event(&mut self, event: SseEvent) {
+        match self {
+            Self::Passthrough(buffer) => buffer.add_transformed_event(event),
+            Self::OpenAIChatCompletions(buffer) => buffer.add_transformed_event(event),
+            Self::AnthropicMessages(buffer) => buffer.add_transformed_event(event),
+            Self::OpenAIResponses(buffer) => buffer.add_transformed_event(event),
+        }
+    }
+
+    fn into_bytes(&mut self) -> Vec<u8> {
+        match self {
+            Self::Passthrough(buffer) => buffer.into_bytes(),
+            Self::OpenAIChatCompletions(buffer) => buffer.into_bytes(),
+            Self::AnthropicMessages(buffer) => buffer.into_bytes(),
+            Self::OpenAIResponses(buffer) => buffer.into_bytes(),
+        }
+    }
+}
+
 // ============================================================================
 // SSE EVENT CONTAINER
 // ============================================================================
@@ -22,16 +85,31 @@ pub struct SseEvent {
     pub raw_line: String, // The complete line as received including "data: " prefix and "\n\n"
 
     #[serde(skip_serializing, skip_deserializing)]
-    pub sse_transform_buffer: String, // The complete line as received including "data: " prefix and "\n\n"
+    pub sse_transformed_lines: String, // The complete line as received including "data: " prefix and "\n\n"
 
     #[serde(skip_serializing, skip_deserializing)]
     pub provider_stream_response: Option<ProviderStreamResponseType>, // Parsed provider stream response object
 }
 
 impl SseEvent {
+    /// Create an SseEvent from a ProviderStreamResponseType
+    /// This is useful for binary frame formats (like Bedrock) that need to be converted to SSE
+    pub fn from_provider_response(response: ProviderStreamResponseType) -> Self {
+        // Convert the provider response to SSE format string
+        let sse_string: String = response.clone().into();
+
+        SseEvent {
+            data: None, // Data is embedded in sse_transformed_lines
+            event: None, // Event type is embedded in sse_transformed_lines
+            raw_line: sse_string.clone(),
+            sse_transformed_lines: sse_string,
+            provider_stream_response: Some(response),
+        }
+    }
+
     /// Check if this event represents the end of the stream
     pub fn is_done(&self) -> bool {
-        self.data == Some("[DONE]".into())
+        self.data == Some("[DONE]".into()) || self.event == Some("message_stop".into())
     }
 
     /// Check if this event should be skipped during processing
@@ -61,23 +139,35 @@ impl FromStr for SseEvent {
     type Err = SseParseError;
 
     fn from_str(line: &str) -> Result<Self, Self::Err> {
-        if line.starts_with("data: ") {
+        // Trim leading/trailing whitespace for parsing
-            let data: String = line[6..].to_string(); // Remove "data: " prefix
+        let trimmed_line = line.trim();
-            if data.is_empty() {
+
+        // Skip empty or whitespace-only lines (SSE event separators)
+        if trimmed_line.is_empty() {
+            return Err(SseParseError {
+                message: "Empty line (SSE event separator)".to_string(),
+            });
+        }
+
+        if trimmed_line.starts_with("data: ") {
+            let data: String = trimmed_line[6..].to_string(); // Remove "data: " prefix
+            // Allow empty data content after "data: " prefix
+            // This handles cases like "data: " followed by newline
+            if data.trim().is_empty() {
                 return Err(SseParseError {
-                    message: "Empty data field is not a valid SSE event".to_string(),
+                    message: "Empty data field after 'data: ' prefix".to_string(),
                 });
             }
             Ok(SseEvent {
                 data: Some(data),
                 event: None,
                 raw_line: line.to_string(),
-                sse_transform_buffer: line.to_string(),
+                // Preserve original line format for passthrough, use trimmed for transformations
+                sse_transformed_lines: line.to_string(),
                 provider_stream_response: None,
             })
-        } else if line.starts_with("event: ") {
+        } else if trimmed_line.starts_with("event: ") {
-            //used by Anthropic
+            let event_type = trimmed_line[7..].to_string();
-            let event_type = line[7..].to_string();
             if event_type.is_empty() {
                 return Err(SseParseError {
                     message: "Empty event field is not a valid SSE event".to_string(),
@@ -87,12 +177,13 @@ impl FromStr for SseEvent {
                 data: None,
                 event: Some(event_type),
                 raw_line: line.to_string(),
-                sse_transform_buffer: line.to_string(),
+                // Preserve original line format for passthrough, use trimmed for transformations
+                sse_transformed_lines: line.to_string(),
                 provider_stream_response: None,
             })
         } else {
             Err(SseParseError {
-                message: format!("Line does not start with 'data: ' or 'event: ': {}", line),
+                message: format!("Line does not start with 'data: ' or 'event: ': {}", trimmed_line),
             })
         }
     }
@@ -100,14 +191,14 @@ impl FromStr for SseEvent {
 
 impl fmt::Display for SseEvent {
     fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
-        write!(f, "{}", self.sse_transform_buffer)
+        write!(f, "{}", self.sse_transformed_lines)
     }
 }
 
 // Into implementation to convert SseEvent to bytes for response buffer
 impl Into<Vec<u8>> for SseEvent {
     fn into(self) -> Vec<u8> {
-        format!("{}\n\n", self.sse_transform_buffer).into_bytes()
+        format!("{}\n\n", self.sse_transformed_lines).into_bytes()
     }
 }
 

crates/hermesllm/src/clients/endpoints.rs

@@ -4,9 +4,10 @@ use std::fmt;
 
 /// Unified enum representing all supported API endpoints across providers
 #[derive(Debug, Clone, PartialEq)]
-pub enum SupportedAPIs {
+pub enum SupportedAPIsFromClient {
     OpenAIChatCompletions(OpenAIApi),
     AnthropicMessagesAPI(AnthropicApi),
+    OpenAIResponsesAPI(OpenAIApi),
 }
 
 #[derive(Debug, Clone, PartialEq)]
@@ -15,17 +16,21 @@ pub enum SupportedUpstreamAPIs {
     AnthropicMessagesAPI(AnthropicApi),
     AmazonBedrockConverse(AmazonBedrockApi),
     AmazonBedrockConverseStream(AmazonBedrockApi),
+    OpenAIResponsesAPI(OpenAIApi),
 }
 
-impl fmt::Display for SupportedAPIs {
+impl fmt::Display for SupportedAPIsFromClient {
     fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
         match self {
-            SupportedAPIs::OpenAIChatCompletions(api) => {
+            SupportedAPIsFromClient::OpenAIChatCompletions(api) => {
                 write!(f, "OpenAI ({})", api.endpoint())
             }
-            SupportedAPIs::AnthropicMessagesAPI(api) => {
+            SupportedAPIsFromClient::AnthropicMessagesAPI(api) => {
                 write!(f, "Anthropic AI ({})", api.endpoint())
             }
+            SupportedAPIsFromClient::OpenAIResponsesAPI(api) => {
+                write!(f, "OpenAI Responses ({})", api.endpoint())
+            }
         }
     }
 }
@@ -45,19 +50,27 @@ impl fmt::Display for SupportedUpstreamAPIs {
             SupportedUpstreamAPIs::AmazonBedrockConverseStream(api) => {
                 write!(f, "Amazon Bedrock ({})", api.endpoint())
             }
+            SupportedUpstreamAPIs::OpenAIResponsesAPI(api) => {
+                write!(f, "OpenAI Responses ({})", api.endpoint())
+            }
         }
     }
 }
 
-impl SupportedAPIs {
+impl SupportedAPIsFromClient {
     /// Create a SupportedApi from an endpoint path
     pub fn from_endpoint(endpoint: &str) -> Option<Self> {
         if let Some(openai_api) = OpenAIApi::from_endpoint(endpoint) {
-            return Some(SupportedAPIs::OpenAIChatCompletions(openai_api));
+            // Check if this is the Responses API endpoint
+            if openai_api == OpenAIApi::Responses {
+                return Some(SupportedAPIsFromClient::OpenAIResponsesAPI(openai_api));
+            }
+            // Otherwise it's ChatCompletions
+            return Some(SupportedAPIsFromClient::OpenAIChatCompletions(openai_api));
         }
 
         if let Some(anthropic_api) = AnthropicApi::from_endpoint(endpoint) {
-            return Some(SupportedAPIs::AnthropicMessagesAPI(anthropic_api));
+            return Some(SupportedAPIsFromClient::AnthropicMessagesAPI(anthropic_api));
         }
 
         None
@@ -66,8 +79,9 @@ impl SupportedAPIs {
     /// Get the endpoint path for this API
     pub fn endpoint(&self) -> &'static str {
         match self {
-            SupportedAPIs::OpenAIChatCompletions(api) => api.endpoint(),
+            SupportedAPIsFromClient::OpenAIChatCompletions(api) => api.endpoint(),
-            SupportedAPIs::AnthropicMessagesAPI(api) => api.endpoint(),
+            SupportedAPIsFromClient::AnthropicMessagesAPI(api) => api.endpoint(),
+            SupportedAPIsFromClient::OpenAIResponsesAPI(api) => api.endpoint(),
         }
     }
 
@@ -94,8 +108,62 @@ impl SupportedAPIs {
             }
         };
 
+        // Helper function to route based on provider with a specific endpoint suffix
+        let route_by_provider = |endpoint_suffix: &str| -> String {
+            match provider_id {
+                ProviderId::Groq => {
+                    if request_path.starts_with("/v1/") {
+                        build_endpoint("/openai", request_path)
+                    } else {
+                        build_endpoint("/v1", endpoint_suffix)
+                    }
+                }
+                ProviderId::Zhipu => {
+                    if request_path.starts_with("/v1/") {
+                        build_endpoint("/api/paas/v4", endpoint_suffix)
+                    } else {
+                        build_endpoint("/v1", endpoint_suffix)
+                    }
+                }
+                ProviderId::Qwen => {
+                    if request_path.starts_with("/v1/") {
+                        build_endpoint("/compatible-mode/v1", endpoint_suffix)
+                    } else {
+                        build_endpoint("/v1", endpoint_suffix)
+                    }
+                }
+                ProviderId::AzureOpenAI => {
+                    if request_path.starts_with("/v1/") {
+                        let suffix = endpoint_suffix.trim_start_matches('/');
+                        build_endpoint("/openai/deployments", &format!("/{}/{}?api-version=2025-01-01-preview", model_id, suffix))
+                    } else {
+                        build_endpoint("/v1", endpoint_suffix)
+                    }
+                }
+                ProviderId::Gemini => {
+                    if request_path.starts_with("/v1/") {
+                        build_endpoint("/v1beta/openai", endpoint_suffix)
+                    } else {
+                        build_endpoint("/v1", endpoint_suffix)
+                    }
+                }
+                ProviderId::AmazonBedrock => {
+                    if request_path.starts_with("/v1/") {
+                        if !is_streaming {
+                            build_endpoint("", &format!("/model/{}/converse", model_id))
+                        } else {
+                            build_endpoint("", &format!("/model/{}/converse-stream", model_id))
+                        }
+                    } else {
+                        build_endpoint("/v1", endpoint_suffix)
+                    }
+                }
+                _ => build_endpoint("/v1", endpoint_suffix),
+            }
+        };
+
         match self {
-            SupportedAPIs::AnthropicMessagesAPI(AnthropicApi::Messages) => match provider_id {
+            SupportedAPIsFromClient::AnthropicMessagesAPI(AnthropicApi::Messages) => match provider_id {
                 ProviderId::Anthropic => build_endpoint("/v1", "/messages"),
                 ProviderId::AmazonBedrock => {
                     if request_path.starts_with("/v1/") && !is_streaming {
@@ -108,55 +176,19 @@ impl SupportedAPIs {
                 }
                 _ => build_endpoint("/v1", "/chat/completions"),
             },
-            _ => match provider_id {
+            SupportedAPIsFromClient::OpenAIResponsesAPI(_) => {
-                ProviderId::Groq => {
+                // For Responses API, check if provider supports it, otherwise translate to chat/completions
-                    if request_path.starts_with("/v1/") {
+                match provider_id {
-                        build_endpoint("/openai", request_path)
+                    // OpenAI and compatible providers that support /v1/responses
-                    } else {
+                    ProviderId::OpenAI => route_by_provider("/responses"),
-                        build_endpoint("/v1", "/chat/completions")
+                    // All other providers: translate to /chat/completions
-                    }
+                    _ => route_by_provider("/chat/completions"),
                 }
-                ProviderId::Zhipu => {
+            }
-                    if request_path.starts_with("/v1/") {
+            SupportedAPIsFromClient::OpenAIChatCompletions(_) => {
-                        build_endpoint("/api/paas/v4", "/chat/completions")
+                // For Chat Completions API, use the standard chat/completions path
-                    } else {
+                route_by_provider("/chat/completions")
-                        build_endpoint("/v1", "/chat/completions")
+            }
-                    }
-                }
-                ProviderId::Qwen => {
-                    if request_path.starts_with("/v1/") {
-                        build_endpoint("/compatible-mode/v1", "/chat/completions")
-                    } else {
-                        build_endpoint("/v1", "/chat/completions")
-                    }
-                }
-                ProviderId::AzureOpenAI => {
-                    if request_path.starts_with("/v1/") {
-                        build_endpoint("/openai/deployments", &format!("/{}/chat/completions?api-version=2025-01-01-preview", model_id))
-                    } else {
-                        build_endpoint("/v1", "/chat/completions")
-                    }
-                }
-                ProviderId::Gemini => {
-                    if request_path.starts_with("/v1/") {
-                        build_endpoint("/v1beta/openai", "/chat/completions")
-                    } else {
-                        build_endpoint("/v1", "/chat/completions")
-                    }
-                }
-                ProviderId::AmazonBedrock => {
-                    if request_path.starts_with("/v1/") {
-                        if !is_streaming {
-                            build_endpoint("", &format!("/model/{}/converse", model_id))
-                        } else {
-                            build_endpoint("", &format!("/model/{}/converse-stream", model_id))
-                        }
-                    } else {
-                        build_endpoint("/v1", "/chat/completions")
-                    }
-                }
-                _ => build_endpoint("/v1", "/chat/completions"),
-            },
         }
     }
 }
@@ -198,22 +230,23 @@ mod tests {
     #[test]
     fn test_is_supported_endpoint() {
         // OpenAI endpoints
-        assert!(SupportedAPIs::from_endpoint("/v1/chat/completions").is_some());
+        assert!(SupportedAPIsFromClient::from_endpoint("/v1/chat/completions").is_some());
         // Anthropic endpoints
-        assert!(SupportedAPIs::from_endpoint("/v1/messages").is_some());
+        assert!(SupportedAPIsFromClient::from_endpoint("/v1/messages").is_some());
 
         // Unsupported endpoints
-        assert!(!SupportedAPIs::from_endpoint("/v1/unknown").is_some());
+        assert!(!SupportedAPIsFromClient::from_endpoint("/v1/unknown").is_some());
-        assert!(!SupportedAPIs::from_endpoint("/v2/chat").is_some());
+        assert!(!SupportedAPIsFromClient::from_endpoint("/v2/chat").is_some());
-        assert!(!SupportedAPIs::from_endpoint("").is_some());
+        assert!(!SupportedAPIsFromClient::from_endpoint("").is_some());
     }
 
     #[test]
     fn test_supported_endpoints() {
         let endpoints = supported_endpoints();
-        assert_eq!(endpoints.len(), 2); // We have 2 APIs defined
+        assert_eq!(endpoints.len(), 3); // We have 3 APIs defined
         assert!(endpoints.contains(&"/v1/chat/completions"));
         assert!(endpoints.contains(&"/v1/messages"));
+        assert!(endpoints.contains(&"/v1/responses"));
     }
 
     #[test]
@@ -263,7 +296,7 @@ mod tests {
 
     #[test]
     fn test_target_endpoint_without_base_url_prefix() {
-        let api = SupportedAPIs::OpenAIChatCompletions(OpenAIApi::ChatCompletions);
+        let api = SupportedAPIsFromClient::OpenAIChatCompletions(OpenAIApi::ChatCompletions);
 
         // Test default OpenAI provider
         assert_eq!(
@@ -340,7 +373,7 @@ mod tests {
 
     #[test]
     fn test_target_endpoint_with_base_url_prefix() {
-        let api = SupportedAPIs::OpenAIChatCompletions(OpenAIApi::ChatCompletions);
+        let api = SupportedAPIsFromClient::OpenAIChatCompletions(OpenAIApi::ChatCompletions);
 
         // Test Zhipu with custom base_url_path_prefix
         assert_eq!(
@@ -405,7 +438,7 @@ mod tests {
 
     #[test]
     fn test_target_endpoint_with_empty_base_url_prefix() {
-        let api = SupportedAPIs::OpenAIChatCompletions(OpenAIApi::ChatCompletions);
+        let api = SupportedAPIsFromClient::OpenAIChatCompletions(OpenAIApi::ChatCompletions);
 
         // Test with just slashes - trims to empty, uses provider default
         assert_eq!(
@@ -434,7 +467,7 @@ mod tests {
 
     #[test]
     fn test_amazon_bedrock_endpoints() {
-        let api = SupportedAPIs::AnthropicMessagesAPI(AnthropicApi::Messages);
+        let api = SupportedAPIsFromClient::AnthropicMessagesAPI(AnthropicApi::Messages);
 
         // Test Bedrock non-streaming without prefix
         assert_eq!(
@@ -487,7 +520,7 @@ mod tests {
 
     #[test]
     fn test_anthropic_messages_endpoint() {
-        let api = SupportedAPIs::AnthropicMessagesAPI(AnthropicApi::Messages);
+        let api = SupportedAPIsFromClient::AnthropicMessagesAPI(AnthropicApi::Messages);
 
         // Test Anthropic without prefix
         assert_eq!(
@@ -516,7 +549,7 @@ mod tests {
 
     #[test]
     fn test_non_v1_request_paths() {
-        let api = SupportedAPIs::OpenAIChatCompletions(OpenAIApi::ChatCompletions);
+        let api = SupportedAPIsFromClient::OpenAIChatCompletions(OpenAIApi::ChatCompletions);
 
         // Test Groq with non-v1 path (should use default)
         assert_eq!(
@@ -557,7 +590,7 @@ mod tests {
 
     #[test]
     fn test_azure_openai_with_query_params() {
-        let api = SupportedAPIs::OpenAIChatCompletions(OpenAIApi::ChatCompletions);
+        let api = SupportedAPIsFromClient::OpenAIChatCompletions(OpenAIApi::ChatCompletions);
 
         // Test Azure without prefix - should include query params
         assert_eq!(

crates/hermesllm/src/clients/mod.rs

@@ -1,9 +1,8 @@
 pub mod endpoints;
 pub mod lib;
-pub mod transformer;
 
 // Re-export the main items for easier access
-pub use endpoints::{identify_provider, SupportedAPIs};
+pub use endpoints::*;
 pub use lib::*;
 
 // Note: transformer module contains TryFrom trait implementations that are automatically available

crates/hermesllm/src/clients/transformer.rs

@@ -1,694 +0,0 @@
-// Re-export new transformation modules for backward compatibility
-
-//KEEPING THE TESTS TO MAKE SURE ALL THE REFACTORING DIDN'T BREAK ANYTHING
-
-// ============================================================================
-// TESTS
-// ============================================================================
-
-#[cfg(test)]
-mod tests {
-    use crate::apis::anthropic::*;
-    use crate::apis::openai::*;
-    use crate::transforms::*;
-    use serde_json::json;
-    type AnthropicMessagesRequest = MessagesRequest;
-
-    #[test]
-    fn test_anthropic_to_openai_basic_request() {
-        let anthropic_req = AnthropicMessagesRequest {
-            model: "claude-3-sonnet-20240229".to_string(),
-            system: Some(MessagesSystemPrompt::Single("You are helpful".to_string())),
-            messages: vec![MessagesMessage {
-                role: MessagesRole::User,
-                content: MessagesMessageContent::Single("Hello, world!".to_string()),
-            }],
-            max_tokens: 1024,
-            container: None,
-            mcp_servers: None,
-            service_tier: None,
-            thinking: None,
-            temperature: Some(0.7),
-            top_p: Some(0.9),
-            top_k: Some(50),
-            stream: Some(false),
-            stop_sequences: Some(vec!["STOP".to_string()]),
-            tools: None,
-            tool_choice: None,
-            metadata: None,
-        };
-
-        let openai_req: ChatCompletionsRequest = anthropic_req.try_into().unwrap();
-
-        assert_eq!(openai_req.model, "claude-3-sonnet-20240229");
-        assert_eq!(openai_req.messages.len(), 2); // system + user message
-        assert_eq!(openai_req.max_completion_tokens, Some(1024));
-        assert_eq!(openai_req.temperature, Some(0.7));
-        assert_eq!(openai_req.top_p, Some(0.9));
-        assert_eq!(openai_req.stream, Some(false));
-        assert_eq!(openai_req.stop, Some(vec!["STOP".to_string()]));
-    }
-
-    #[test]
-    fn test_roundtrip_consistency() {
-        // Test that converting back and forth maintains consistency
-        let original_anthropic = AnthropicMessagesRequest {
-            model: "claude-3-sonnet".to_string(),
-            system: Some(MessagesSystemPrompt::Single("System prompt".to_string())),
-            messages: vec![MessagesMessage {
-                role: MessagesRole::User,
-                content: MessagesMessageContent::Single("User message".to_string()),
-            }],
-            max_tokens: 1000,
-            container: None,
-            mcp_servers: None,
-            service_tier: None,
-            thinking: None,
-            temperature: Some(0.5),
-            top_p: Some(1.0),
-            top_k: None,
-            stream: Some(false),
-            stop_sequences: None,
-            tools: None,
-            tool_choice: None,
-            metadata: None,
-        };
-
-        // Convert to OpenAI and back
-        let openai_req: ChatCompletionsRequest = original_anthropic.clone().try_into().unwrap();
-        let roundtrip_anthropic: AnthropicMessagesRequest = openai_req.try_into().unwrap();
-
-        // Check key fields are preserved
-        assert_eq!(original_anthropic.model, roundtrip_anthropic.model);
-        assert_eq!(
-            original_anthropic.max_tokens,
-            roundtrip_anthropic.max_tokens
-        );
-        assert_eq!(
-            original_anthropic.temperature,
-            roundtrip_anthropic.temperature
-        );
-        assert_eq!(original_anthropic.top_p, roundtrip_anthropic.top_p);
-        assert_eq!(original_anthropic.stream, roundtrip_anthropic.stream);
-        assert_eq!(
-            original_anthropic.messages.len(),
-            roundtrip_anthropic.messages.len()
-        );
-    }
-
-    #[test]
-    fn test_tool_choice_auto() {
-        let anthropic_req = AnthropicMessagesRequest {
-            model: "claude-3".to_string(),
-            system: None,
-            messages: vec![],
-            max_tokens: 100,
-            container: None,
-            mcp_servers: None,
-            service_tier: None,
-            thinking: None,
-            temperature: None,
-            top_p: None,
-            top_k: None,
-            stream: None,
-            stop_sequences: None,
-            tools: Some(vec![MessagesTool {
-                name: "test_tool".to_string(),
-                description: Some("A test tool".to_string()),
-                input_schema: json!({"type": "object"}),
-            }]),
-            tool_choice: Some(MessagesToolChoice {
-                kind: MessagesToolChoiceType::Auto,
-                name: None,
-                disable_parallel_tool_use: Some(true),
-            }),
-            metadata: None,
-        };
-
-        let openai_req: ChatCompletionsRequest = anthropic_req.try_into().unwrap();
-
-        assert!(openai_req.tools.is_some());
-        assert_eq!(openai_req.tools.as_ref().unwrap().len(), 1);
-
-        if let Some(ToolChoice::Type(choice)) = openai_req.tool_choice {
-            assert_eq!(choice, ToolChoiceType::Auto);
-        } else {
-            panic!("Expected auto tool choice");
-        }
-
-        assert_eq!(openai_req.parallel_tool_calls, Some(false));
-    }
-
-    #[test]
-    fn test_default_max_tokens_used_when_openai_has_none() {
-        // Test that DEFAULT_MAX_TOKENS is used when OpenAI request has no max_tokens
-        let openai_req = ChatCompletionsRequest {
-            model: "gpt-4".to_string(),
-            messages: vec![Message {
-                role: Role::User,
-                content: MessageContent::Text("Hello".to_string()),
-                name: None,
-                tool_calls: None,
-                tool_call_id: None,
-            }],
-            max_tokens: None, // No max_tokens specified
-            ..Default::default()
-        };
-
-        let anthropic_req: AnthropicMessagesRequest = openai_req.try_into().unwrap();
-
-        assert_eq!(anthropic_req.max_tokens, DEFAULT_MAX_TOKENS);
-    }
-
-    #[test]
-    fn test_anthropic_message_start_streaming() {
-        let event = MessagesStreamEvent::MessageStart {
-            message: MessagesStreamMessage {
-                id: "msg_stream_123".to_string(),
-                obj_type: "message".to_string(),
-                role: MessagesRole::Assistant,
-                content: vec![],
-                model: "claude-3".to_string(),
-                stop_reason: None,
-                stop_sequence: None,
-                usage: MessagesUsage {
-                    input_tokens: 5,
-                    output_tokens: 0,
-                    cache_creation_input_tokens: None,
-                    cache_read_input_tokens: None,
-                },
-            },
-        };
-
-        let openai_resp: ChatCompletionsStreamResponse = event.try_into().unwrap();
-
-        assert_eq!(openai_resp.id, "msg_stream_123");
-        assert_eq!(openai_resp.object.as_deref(), Some("chat.completion.chunk"));
-        assert_eq!(openai_resp.model, "claude-3");
-        assert_eq!(openai_resp.choices.len(), 1);
-
-        let choice = &openai_resp.choices[0];
-        assert_eq!(choice.index, 0);
-        assert_eq!(choice.delta.role, Some(Role::Assistant));
-        assert_eq!(choice.delta.content, None);
-        assert_eq!(choice.finish_reason, None);
-    }
-
-    #[test]
-    fn test_anthropic_content_block_delta_streaming() {
-        let event = MessagesStreamEvent::ContentBlockDelta {
-            index: 0,
-            delta: MessagesContentDelta::TextDelta {
-                text: "Hello, world!".to_string(),
-            },
-        };
-
-        let openai_resp: ChatCompletionsStreamResponse = event.try_into().unwrap();
-
-        assert_eq!(openai_resp.object.as_deref(), Some("chat.completion.chunk"));
-        assert_eq!(openai_resp.choices.len(), 1);
-
-        let choice = &openai_resp.choices[0];
-        assert_eq!(choice.index, 0);
-        assert_eq!(choice.delta.content, Some("Hello, world!".to_string()));
-        assert_eq!(choice.delta.role, None);
-        assert_eq!(choice.finish_reason, None);
-    }
-
-    #[test]
-    fn test_anthropic_tool_use_streaming() {
-        // Test tool use start
-        let tool_start = MessagesStreamEvent::ContentBlockStart {
-            index: 0,
-            content_block: MessagesContentBlock::ToolUse {
-                id: "call_123".to_string(),
-                name: "get_weather".to_string(),
-                input: json!({}),
-                cache_control: None,
-            },
-        };
-
-        let openai_resp: ChatCompletionsStreamResponse = tool_start.try_into().unwrap();
-
-        assert_eq!(openai_resp.choices.len(), 1);
-        let choice = &openai_resp.choices[0];
-        assert!(choice.delta.tool_calls.is_some());
-
-        let tool_calls = choice.delta.tool_calls.as_ref().unwrap();
-        assert_eq!(tool_calls.len(), 1);
-        assert_eq!(tool_calls[0].id, Some("call_123".to_string()));
-        assert_eq!(
-            tool_calls[0].function.as_ref().unwrap().name,
-            Some("get_weather".to_string())
-        );
-    }
-
-    #[test]
-    fn test_anthropic_tool_input_delta_streaming() {
-        let event = MessagesStreamEvent::ContentBlockDelta {
-            index: 0,
-            delta: MessagesContentDelta::InputJsonDelta {
-                partial_json: r#"{"location": "San Francisco"#.to_string(),
-            },
-        };
-
-        let openai_resp: ChatCompletionsStreamResponse = event.try_into().unwrap();
-
-        assert_eq!(openai_resp.choices.len(), 1);
-        let choice = &openai_resp.choices[0];
-        assert!(choice.delta.tool_calls.is_some());
-
-        let tool_calls = choice.delta.tool_calls.as_ref().unwrap();
-        assert_eq!(tool_calls.len(), 1);
-        assert_eq!(
-            tool_calls[0].function.as_ref().unwrap().arguments,
-            Some(r#"{"location": "San Francisco"#.to_string())
-        );
-    }
-
-    #[test]
-    fn test_anthropic_message_delta_with_usage() {
-        let event = MessagesStreamEvent::MessageDelta {
-            delta: MessagesMessageDelta {
-                stop_reason: MessagesStopReason::EndTurn,
-                stop_sequence: None,
-            },
-            usage: MessagesUsage {
-                input_tokens: 10,
-                output_tokens: 25,
-                cache_creation_input_tokens: None,
-                cache_read_input_tokens: None,
-            },
-        };
-
-        let openai_resp: ChatCompletionsStreamResponse = event.try_into().unwrap();
-
-        assert_eq!(openai_resp.choices.len(), 1);
-        let choice = &openai_resp.choices[0];
-        assert_eq!(choice.finish_reason, Some(FinishReason::Stop));
-
-        assert!(openai_resp.usage.is_some());
-        let usage = openai_resp.usage.unwrap();
-        assert_eq!(usage.prompt_tokens, 10);
-        assert_eq!(usage.completion_tokens, 25);
-        assert_eq!(usage.total_tokens, 35);
-    }
-
-    #[test]
-    fn test_anthropic_message_stop_streaming() {
-        let event = MessagesStreamEvent::MessageStop;
-
-        let openai_resp: ChatCompletionsStreamResponse = event.try_into().unwrap();
-
-        assert_eq!(openai_resp.choices.len(), 1);
-        let choice = &openai_resp.choices[0];
-        assert_eq!(choice.finish_reason, Some(FinishReason::Stop));
-    }
-
-    #[test]
-    fn test_anthropic_ping_streaming() {
-        let event = MessagesStreamEvent::Ping;
-
-        let openai_resp: ChatCompletionsStreamResponse = event.try_into().unwrap();
-
-        assert_eq!(openai_resp.object.as_deref(), Some("chat.completion.chunk"));
-        assert_eq!(openai_resp.choices.len(), 0); // Ping has no choices
-    }
-
-    #[test]
-    fn test_openai_to_anthropic_streaming_role_start() {
-        let openai_resp = ChatCompletionsStreamResponse {
-            id: "chatcmpl-123".to_string(),
-            object: Some("chat.completion.chunk".to_string()),
-            created: 1234567890,
-            model: "gpt-4".to_string(),
-            choices: vec![StreamChoice {
-                index: 0,
-                delta: MessageDelta {
-                    role: Some(Role::Assistant),
-                    content: None,
-                    refusal: None,
-                    function_call: None,
-                    tool_calls: None,
-                },
-                finish_reason: None,
-                logprobs: None,
-            }],
-            usage: None,
-            system_fingerprint: None,
-            service_tier: None,
-        };
-
-        let anthropic_event: MessagesStreamEvent = openai_resp.try_into().unwrap();
-
-        match anthropic_event {
-            MessagesStreamEvent::MessageStart { message } => {
-                assert_eq!(message.id, "chatcmpl-123");
-                assert_eq!(message.role, MessagesRole::Assistant);
-                assert_eq!(message.model, "gpt-4");
-            }
-            _ => panic!("Expected MessageStart event"),
-        }
-    }
-
-    #[test]
-    fn test_openai_to_anthropic_streaming_content_delta() {
-        let openai_resp = ChatCompletionsStreamResponse {
-            id: "chatcmpl-123".to_string(),
-            object: Some("chat.completion.chunk".to_string()),
-            created: 1234567890,
-            model: "gpt-4".to_string(),
-            choices: vec![StreamChoice {
-                index: 0,
-                delta: MessageDelta {
-                    role: None,
-                    content: Some("Hello there!".to_string()),
-                    refusal: None,
-                    function_call: None,
-                    tool_calls: None,
-                },
-                finish_reason: None,
-                logprobs: None,
-            }],
-            usage: None,
-            system_fingerprint: None,
-            service_tier: None,
-        };
-
-        let anthropic_event: MessagesStreamEvent = openai_resp.try_into().unwrap();
-
-        match anthropic_event {
-            MessagesStreamEvent::ContentBlockDelta { index, delta } => {
-                assert_eq!(index, 0);
-                match delta {
-                    MessagesContentDelta::TextDelta { text } => {
-                        assert_eq!(text, "Hello there!");
-                    }
-                    _ => panic!("Expected TextDelta"),
-                }
-            }
-            _ => panic!("Expected ContentBlockDelta event"),
-        }
-    }
-
-    #[test]
-    fn test_openai_to_anthropic_streaming_tool_calls() {
-        let openai_resp = ChatCompletionsStreamResponse {
-            id: "chatcmpl-123".to_string(),
-            object: Some("chat.completion.chunk".to_string()),
-            created: 1234567890,
-            model: "gpt-4".to_string(),
-            choices: vec![StreamChoice {
-                index: 0,
-                delta: MessageDelta {
-                    role: None,
-                    content: None,
-                    refusal: None,
-                    function_call: None,
-                    tool_calls: Some(vec![ToolCallDelta {
-                        index: 0,
-                        id: Some("call_abc123".to_string()),
-                        call_type: Some("function".to_string()),
-                        function: Some(FunctionCallDelta {
-                            name: Some("get_current_weather".to_string()),
-                            arguments: Some("".to_string()),
-                        }),
-                    }]),
-                },
-                finish_reason: None,
-                logprobs: None,
-            }],
-            usage: None,
-            system_fingerprint: None,
-            service_tier: None,
-        };
-
-        let anthropic_event: MessagesStreamEvent = openai_resp.try_into().unwrap();
-
-        match anthropic_event {
-            MessagesStreamEvent::ContentBlockStart {
-                index,
-                content_block,
-            } => {
-                assert_eq!(index, 0);
-                match content_block {
-                    MessagesContentBlock::ToolUse { id, name, .. } => {
-                        assert_eq!(id, "call_abc123");
-                        assert_eq!(name, "get_current_weather");
-                    }
-                    _ => panic!("Expected ToolUse content block"),
-                }
-            }
-            _ => panic!("Expected ContentBlockStart event"),
-        }
-    }
-
-    #[test]
-    fn test_openai_to_anthropic_streaming_final_usage() {
-        let openai_resp = ChatCompletionsStreamResponse {
-            id: "chatcmpl-123".to_string(),
-            object: Some("chat.completion.chunk".to_string()),
-            created: 1234567890,
-            model: "gpt-4".to_string(),
-            choices: vec![StreamChoice {
-                index: 0,
-                delta: MessageDelta {
-                    role: None,
-                    content: None,
-                    refusal: None,
-                    function_call: None,
-                    tool_calls: None,
-                },
-                finish_reason: Some(FinishReason::Stop),
-                logprobs: None,
-            }],
-            usage: Some(Usage {
-                prompt_tokens: 15,
-                completion_tokens: 30,
-                total_tokens: 45,
-                prompt_tokens_details: None,
-                completion_tokens_details: None,
-            }),
-            system_fingerprint: None,
-            service_tier: None,
-        };
-
-        let anthropic_event: MessagesStreamEvent = openai_resp.try_into().unwrap();
-
-        match anthropic_event {
-            MessagesStreamEvent::MessageDelta { delta, usage } => {
-                assert_eq!(delta.stop_reason, MessagesStopReason::EndTurn);
-                assert_eq!(usage.input_tokens, 15);
-                assert_eq!(usage.output_tokens, 30);
-            }
-            _ => panic!("Expected MessageDelta event"),
-        }
-    }
-
-    #[test]
-    fn test_openai_empty_choices_to_anthropic_ping() {
-        let openai_resp = ChatCompletionsStreamResponse {
-            id: "chatcmpl-123".to_string(),
-            object: Some("chat.completion.chunk".to_string()),
-            created: 1234567890,
-            model: "gpt-4".to_string(),
-            choices: vec![], // Empty choices
-            usage: None,
-            system_fingerprint: None,
-            service_tier: None,
-        };
-
-        let anthropic_event: MessagesStreamEvent = openai_resp.try_into().unwrap();
-
-        match anthropic_event {
-            MessagesStreamEvent::Ping => {
-                // Expected behavior
-            }
-            _ => panic!("Expected Ping event for empty choices"),
-        }
-    }
-
-    #[test]
-    fn test_streaming_roundtrip_consistency() {
-        // Test that streaming events can roundtrip through conversions
-        let original_event = MessagesStreamEvent::ContentBlockDelta {
-            index: 0,
-            delta: MessagesContentDelta::TextDelta {
-                text: "Test message".to_string(),
-            },
-        };
-
-        // Convert to OpenAI and back
-        let openai_resp: ChatCompletionsStreamResponse = original_event.try_into().unwrap();
-        let roundtrip_event: MessagesStreamEvent = openai_resp.try_into().unwrap();
-
-        // Verify the roundtrip maintains the essential information
-        match roundtrip_event {
-            MessagesStreamEvent::ContentBlockDelta { index, delta } => {
-                assert_eq!(index, 0);
-                match delta {
-                    MessagesContentDelta::TextDelta { text } => {
-                        assert_eq!(text, "Test message");
-                    }
-                    _ => panic!("Expected TextDelta after roundtrip"),
-                }
-            }
-            _ => panic!("Expected ContentBlockDelta after roundtrip"),
-        }
-    }
-
-    #[test]
-    fn test_streaming_tool_argument_accumulation() {
-        // Test multiple tool argument deltas that should accumulate
-        let tool_start = MessagesStreamEvent::ContentBlockStart {
-            index: 0,
-            content_block: MessagesContentBlock::ToolUse {
-                id: "call_weather".to_string(),
-                name: "get_weather".to_string(),
-                input: json!({}),
-                cache_control: None,
-            },
-        };
-
-        let arg_delta1 = MessagesStreamEvent::ContentBlockDelta {
-            index: 0,
-            delta: MessagesContentDelta::InputJsonDelta {
-                partial_json: r#"{"location": "#.to_string(),
-            },
-        };
-
-        let arg_delta2 = MessagesStreamEvent::ContentBlockDelta {
-            index: 0,
-            delta: MessagesContentDelta::InputJsonDelta {
-                partial_json: r#"San Francisco", "unit": "fahrenheit"}"#.to_string(),
-            },
-        };
-
-        // Test that each delta converts properly to OpenAI format
-        let openai_start: ChatCompletionsStreamResponse = tool_start.try_into().unwrap();
-        let openai_delta1: ChatCompletionsStreamResponse = arg_delta1.try_into().unwrap();
-        let openai_delta2: ChatCompletionsStreamResponse = arg_delta2.try_into().unwrap();
-
-        // Verify tool start
-        let tool_calls = &openai_start.choices[0].delta.tool_calls.as_ref().unwrap();
-        assert_eq!(tool_calls[0].id, Some("call_weather".to_string()));
-        assert_eq!(
-            tool_calls[0].function.as_ref().unwrap().name,
-            Some("get_weather".to_string())
-        );
-
-        // Verify argument deltas
-        let args1 = &openai_delta1.choices[0].delta.tool_calls.as_ref().unwrap()[0]
-            .function
-            .as_ref()
-            .unwrap()
-            .arguments;
-        assert_eq!(args1, &Some(r#"{"location": "#.to_string()));
-
-        let args2 = &openai_delta2.choices[0].delta.tool_calls.as_ref().unwrap()[0]
-            .function
-            .as_ref()
-            .unwrap()
-            .arguments;
-        assert_eq!(
-            args2,
-            &Some(r#"San Francisco", "unit": "fahrenheit"}"#.to_string())
-        );
-    }
-
-    #[test]
-    fn test_streaming_multiple_finish_reasons() {
-        // Test different finish reasons in streaming
-        let test_cases = vec![
-            (MessagesStopReason::EndTurn, FinishReason::Stop),
-            (MessagesStopReason::MaxTokens, FinishReason::Length),
-            (MessagesStopReason::ToolUse, FinishReason::ToolCalls),
-            (MessagesStopReason::StopSequence, FinishReason::Stop),
-        ];
-
-        for (anthropic_reason, expected_openai_reason) in test_cases {
-            let event = MessagesStreamEvent::MessageDelta {
-                delta: MessagesMessageDelta {
-                    stop_reason: anthropic_reason.clone(),
-                    stop_sequence: None,
-                },
-                usage: MessagesUsage {
-                    input_tokens: 10,
-                    output_tokens: 20,
-                    cache_creation_input_tokens: None,
-                    cache_read_input_tokens: None,
-                },
-            };
-
-            let openai_resp: ChatCompletionsStreamResponse = event.try_into().unwrap();
-            assert_eq!(
-                openai_resp.choices[0].finish_reason,
-                Some(expected_openai_reason)
-            );
-
-            // Test reverse conversion
-            let roundtrip_event: MessagesStreamEvent = openai_resp.try_into().unwrap();
-            match roundtrip_event {
-                MessagesStreamEvent::MessageDelta { delta, .. } => {
-                    // Note: Some precision may be lost in roundtrip due to mapping differences
-                    assert!(matches!(
-                        delta.stop_reason,
-                        MessagesStopReason::EndTurn
-                            | MessagesStopReason::MaxTokens
-                            | MessagesStopReason::ToolUse
-                            | MessagesStopReason::StopSequence
-                    ));
-                }
-                _ => panic!("Expected MessageDelta after roundtrip"),
-            }
-        }
-    }
-
-    #[test]
-    fn test_streaming_error_handling() {
-        // Test that malformed streaming events are handled gracefully
-        let openai_resp_with_missing_data = ChatCompletionsStreamResponse {
-            id: "test".to_string(),
-            object: Some("chat.completion.chunk".to_string()),
-            created: 1234567890,
-            model: "test".to_string(),
-            choices: vec![StreamChoice {
-                index: 0,
-                delta: MessageDelta {
-                    role: None,
-                    content: None,
-                    refusal: None,
-                    function_call: None,
-                    tool_calls: None,
-                },
-                finish_reason: None,
-                logprobs: None,
-            }],
-            usage: None,
-            system_fingerprint: None,
-            service_tier: None,
-        };
-
-        // Should convert to Ping when no meaningful content
-        let anthropic_event: MessagesStreamEvent =
-            openai_resp_with_missing_data.try_into().unwrap();
-        assert!(matches!(anthropic_event, MessagesStreamEvent::Ping));
-    }
-
-    #[test]
-    fn test_streaming_content_block_stop() {
-        let event = MessagesStreamEvent::ContentBlockStop { index: 0 };
-
-        let openai_resp: ChatCompletionsStreamResponse = event.try_into().unwrap();
-
-        // ContentBlockStop should produce an empty chunk
-        assert_eq!(openai_resp.object.as_deref(), Some("chat.completion.chunk"));
-        assert_eq!(openai_resp.choices.len(), 1);
-
-        let choice = &openai_resp.choices[0];
-        assert_eq!(choice.delta.role, None);
-        assert_eq!(choice.delta.content, None);
-        assert_eq!(choice.delta.tool_calls, None);
-        assert_eq!(choice.finish_reason, None);
-    }
-}

crates/hermesllm/src/lib.rs

@@ -6,18 +6,21 @@ pub mod clients;
 pub mod providers;
 pub mod transforms;
 // Re-export important types and traits
-pub use apis::amazon_bedrock_binary_frame::BedrockBinaryFrameDecoder;
+pub use apis::streaming_shapes::amazon_bedrock_binary_frame::BedrockBinaryFrameDecoder;
-pub use apis::sse::{SseEvent, SseStreamIter};
+pub use apis::streaming_shapes::sse::{SseEvent, SseStreamIter};
 pub use aws_smithy_eventstream::frame::DecodedFrame;
 pub use providers::id::ProviderId;
 pub use providers::request::{ProviderRequest, ProviderRequestError, ProviderRequestType};
 pub use providers::response::{
-    ProviderResponse, ProviderResponseError, ProviderResponseType, ProviderStreamResponse,
+    ProviderResponse, ProviderResponseType, TokenUsage, ProviderResponseError
-    ProviderStreamResponseType, TokenUsage,
+};
+pub use providers::streaming_response::{
+    ProviderStreamResponse, ProviderStreamResponseType
 };
 
 //TODO: Refactor such that commons doesn't depend on Hermes. For now this will clean up strings
 pub const CHAT_COMPLETIONS_PATH: &str = "/v1/chat/completions";
+pub const OPENAI_RESPONSES_API_PATH: &str = "/v1/responses";
 pub const MESSAGES_PATH: &str = "/v1/messages";
 
 #[cfg(test)]
@@ -42,9 +45,9 @@ mod tests {
     data: [DONE]
     "#;
 
-        use crate::clients::endpoints::SupportedAPIs;
+        use crate::clients::endpoints::SupportedAPIsFromClient;
         let client_api =
-            SupportedAPIs::OpenAIChatCompletions(crate::apis::OpenAIApi::ChatCompletions);
+            SupportedAPIsFromClient::OpenAIChatCompletions(crate::apis::OpenAIApi::ChatCompletions);
         let upstream_api =
             SupportedUpstreamAPIs::OpenAIChatCompletions(crate::apis::OpenAIApi::ChatCompletions);
 
@@ -79,9 +82,16 @@ mod tests {
         assert_eq!(stream_response.content_delta(), Some("Hello"));
         assert!(!stream_response.is_final());
 
-        // Test that stream ends properly with [DONE] (SseStreamIter should stop before [DONE])
+        // Test that stream ends properly with [DONE]
+        // The iterator should return the [DONE] event, then None
+        let done_event = streaming_iter.next();
+        assert!(done_event.is_some(), "Should get [DONE] event");
+        let done_event = done_event.unwrap();
+        assert!(done_event.is_done(), "[DONE] event should be marked as done");
+
+        // After [DONE], iterator should return None
         let final_event = streaming_iter.next();
-        assert!(final_event.is_none()); // Should be None because iterator stops at [DONE]
+        assert!(final_event.is_none(), "Iterator should return None after [DONE]");
     }
 
     /// Test AWS Event Stream decoding for Bedrock ConverseStream responses.

crates/hermesllm/src/providers/id.rs

@@ -1,5 +1,5 @@
 use crate::apis::{AmazonBedrockApi, AnthropicApi, OpenAIApi};
-use crate::clients::endpoints::{SupportedAPIs, SupportedUpstreamAPIs};
+use crate::clients::endpoints::{SupportedAPIsFromClient, SupportedUpstreamAPIs};
 use std::fmt::Display;
 
 /// Provider identifier enum - simple enum for identifying providers
@@ -51,19 +51,24 @@ impl ProviderId {
     /// Given a client API, return the compatible upstream API for this provider
     pub fn compatible_api_for_client(
         &self,
-        client_api: &SupportedAPIs,
+        client_api: &SupportedAPIsFromClient,
         is_streaming: bool,
     ) -> SupportedUpstreamAPIs {
         match (self, client_api) {
             // Claude/Anthropic providers natively support Anthropic APIs
-            (ProviderId::Anthropic, SupportedAPIs::AnthropicMessagesAPI(_)) => {
+            (ProviderId::Anthropic, SupportedAPIsFromClient::AnthropicMessagesAPI(_)) => {
                 SupportedUpstreamAPIs::AnthropicMessagesAPI(AnthropicApi::Messages)
             }
             (
                 ProviderId::Anthropic,
-                SupportedAPIs::OpenAIChatCompletions(OpenAIApi::ChatCompletions),
+                SupportedAPIsFromClient::OpenAIChatCompletions(_),
             ) => SupportedUpstreamAPIs::OpenAIChatCompletions(OpenAIApi::ChatCompletions),
 
+            // Anthropic doesn't support Responses API, fall back to chat completions
+            (ProviderId::Anthropic, SupportedAPIsFromClient::OpenAIResponsesAPI(_)) => {
+                SupportedUpstreamAPIs::OpenAIChatCompletions(OpenAIApi::ChatCompletions)
+            }
+
             // OpenAI-compatible providers only support OpenAI chat completions
             (
                 ProviderId::OpenAI
@@ -80,7 +85,7 @@ impl ProviderId {
                 | ProviderId::Moonshotai
                 | ProviderId::Zhipu
                 | ProviderId::Qwen,
-                SupportedAPIs::AnthropicMessagesAPI(_),
+                SupportedAPIsFromClient::AnthropicMessagesAPI(_),
             ) => SupportedUpstreamAPIs::OpenAIChatCompletions(OpenAIApi::ChatCompletions),
 
             (
@@ -98,11 +103,16 @@ impl ProviderId {
                 | ProviderId::Moonshotai
                 | ProviderId::Zhipu
                 | ProviderId::Qwen,
-                SupportedAPIs::OpenAIChatCompletions(_),
+                SupportedAPIsFromClient::OpenAIChatCompletions(_),
             ) => SupportedUpstreamAPIs::OpenAIChatCompletions(OpenAIApi::ChatCompletions),
 
+            // OpenAI Responses API - only OpenAI supports this
+            (ProviderId::OpenAI, SupportedAPIsFromClient::OpenAIResponsesAPI(_)) => {
+                SupportedUpstreamAPIs::OpenAIResponsesAPI(OpenAIApi::Responses)
+            }
+
             // Amazon Bedrock natively supports Bedrock APIs
-            (ProviderId::AmazonBedrock, SupportedAPIs::OpenAIChatCompletions(_)) => {
+            (ProviderId::AmazonBedrock, SupportedAPIsFromClient::OpenAIChatCompletions(_)) => {
                 if is_streaming {
                     SupportedUpstreamAPIs::AmazonBedrockConverseStream(
                         AmazonBedrockApi::ConverseStream,
@@ -111,7 +121,7 @@ impl ProviderId {
                     SupportedUpstreamAPIs::AmazonBedrockConverse(AmazonBedrockApi::Converse)
                 }
             }
-            (ProviderId::AmazonBedrock, SupportedAPIs::AnthropicMessagesAPI(_)) => {
+            (ProviderId::AmazonBedrock, SupportedAPIsFromClient::AnthropicMessagesAPI(_)) => {
                 if is_streaming {
                     SupportedUpstreamAPIs::AmazonBedrockConverseStream(
                         AmazonBedrockApi::ConverseStream,
@@ -120,6 +130,20 @@ impl ProviderId {
                     SupportedUpstreamAPIs::AmazonBedrockConverse(AmazonBedrockApi::Converse)
                 }
             }
+            (ProviderId::AmazonBedrock, SupportedAPIsFromClient::OpenAIResponsesAPI(_)) => {
+                if is_streaming {
+                    SupportedUpstreamAPIs::AmazonBedrockConverseStream(
+                        AmazonBedrockApi::ConverseStream,
+                    )
+                } else {
+                    SupportedUpstreamAPIs::AmazonBedrockConverse(AmazonBedrockApi::Converse)
+                }
+            }
+
+            // Non-OpenAI providers: if client requested the Responses API, fall back to Chat Completions
+            (_, SupportedAPIsFromClient::OpenAIResponsesAPI(_)) => {
+                SupportedUpstreamAPIs::OpenAIChatCompletions(OpenAIApi::ChatCompletions)
+            }
         }
     }
 }

crates/hermesllm/src/providers/mod.rs

@@ -6,7 +6,9 @@
 pub mod id;
 pub mod request;
 pub mod response;
+pub mod streaming_response;
 
 pub use id::ProviderId;
 pub use request::{ProviderRequest, ProviderRequestError, ProviderRequestType};
-pub use response::{ProviderResponse, ProviderResponseType, ProviderStreamResponse, TokenUsage};
+pub use response::{ProviderResponse, ProviderResponseType, TokenUsage};
+pub use streaming_response::{ProviderStreamResponse, ProviderStreamResponseType};

crates/hermesllm/src/providers/request.rs

@@ -2,19 +2,21 @@ use crate::apis::anthropic::MessagesRequest;
 use crate::apis::openai::ChatCompletionsRequest;
 
 use crate::apis::amazon_bedrock::{ConverseRequest, ConverseStreamRequest};
-use crate::clients::endpoints::SupportedAPIs;
+use crate::apis::openai_responses::ResponsesAPIRequest;
+use crate::clients::endpoints::SupportedAPIsFromClient;
 use crate::clients::endpoints::SupportedUpstreamAPIs;
 
 use serde_json::Value;
 use std::collections::HashMap;
 use std::error::Error;
 use std::fmt;
-#[derive(Clone)]
+#[derive(Clone, Debug)]
 pub enum ProviderRequestType {
     ChatCompletionsRequest(ChatCompletionsRequest),
     MessagesRequest(MessagesRequest),
     BedrockConverse(ConverseRequest),
     BedrockConverseStream(ConverseStreamRequest),
+    ResponsesAPIRequest(ResponsesAPIRequest),
     //add more request types here
 }
 pub trait ProviderRequest: Send + Sync {
@@ -49,6 +51,7 @@ impl ProviderRequest for ProviderRequestType {
             Self::MessagesRequest(r) => r.model(),
             Self::BedrockConverse(r) => r.model(),
             Self::BedrockConverseStream(r) => r.model(),
+            Self::ResponsesAPIRequest(r) => r.model(),
         }
     }
 
@@ -58,6 +61,7 @@ impl ProviderRequest for ProviderRequestType {
             Self::MessagesRequest(r) => r.set_model(model),
             Self::BedrockConverse(r) => r.set_model(model),
             Self::BedrockConverseStream(r) => r.set_model(model),
+            Self::ResponsesAPIRequest(r) => r.set_model(model),
         }
     }
 
@@ -67,6 +71,7 @@ impl ProviderRequest for ProviderRequestType {
             Self::MessagesRequest(r) => r.is_streaming(),
             Self::BedrockConverse(_) => false,
             Self::BedrockConverseStream(_) => true,
+            Self::ResponsesAPIRequest(r) => r.is_streaming(),
         }
     }
 
@@ -76,6 +81,7 @@ impl ProviderRequest for ProviderRequestType {
             Self::MessagesRequest(r) => r.extract_messages_text(),
             Self::BedrockConverse(r) => r.extract_messages_text(),
             Self::BedrockConverseStream(r) => r.extract_messages_text(),
+            Self::ResponsesAPIRequest(r) => r.extract_messages_text(),
         }
     }
 
@@ -85,6 +91,7 @@ impl ProviderRequest for ProviderRequestType {
             Self::MessagesRequest(r) => r.get_recent_user_message(),
             Self::BedrockConverse(r) => r.get_recent_user_message(),
             Self::BedrockConverseStream(r) => r.get_recent_user_message(),
+            Self::ResponsesAPIRequest(r) => r.get_recent_user_message(),
         }
     }
 
@@ -94,6 +101,7 @@ impl ProviderRequest for ProviderRequestType {
             Self::MessagesRequest(r) => r.to_bytes(),
             Self::BedrockConverse(r) => r.to_bytes(),
             Self::BedrockConverseStream(r) => r.to_bytes(),
+            Self::ResponsesAPIRequest(r) => r.to_bytes(),
         }
     }
 
@@ -103,6 +111,7 @@ impl ProviderRequest for ProviderRequestType {
             Self::MessagesRequest(r) => r.metadata(),
             Self::BedrockConverse(r) => r.metadata(),
             Self::BedrockConverseStream(r) => r.metadata(),
+            Self::ResponsesAPIRequest(r) => r.metadata(),
         }
     }
 
@@ -112,18 +121,19 @@ impl ProviderRequest for ProviderRequestType {
             Self::MessagesRequest(r) => r.remove_metadata_key(key),
             Self::BedrockConverse(r) => r.remove_metadata_key(key),
             Self::BedrockConverseStream(r) => r.remove_metadata_key(key),
+            Self::ResponsesAPIRequest(r) => r.remove_metadata_key(key),
         }
     }
 }
 
 /// Parse the client API from a byte slice.
-impl TryFrom<(&[u8], &SupportedAPIs)> for ProviderRequestType {
+impl TryFrom<(&[u8], &SupportedAPIsFromClient)> for ProviderRequestType {
     type Error = std::io::Error;
 
-    fn try_from((bytes, client_api): (&[u8], &SupportedAPIs)) -> Result<Self, Self::Error> {
+    fn try_from((bytes, client_api): (&[u8], &SupportedAPIsFromClient)) -> Result<Self, Self::Error> {
         // Use SupportedApi to determine the appropriate request type
         match client_api {
-            SupportedAPIs::OpenAIChatCompletions(_) => {
+            SupportedAPIsFromClient::OpenAIChatCompletions(_) => {
                 let chat_completion_request: ChatCompletionsRequest =
                     ChatCompletionsRequest::try_from(bytes)
                         .map_err(|e| std::io::Error::new(std::io::ErrorKind::InvalidData, e))?;
@@ -131,11 +141,20 @@ impl TryFrom<(&[u8], &SupportedAPIs)> for ProviderRequestType {
                     chat_completion_request,
                 ))
             }
-            SupportedAPIs::AnthropicMessagesAPI(_) => {
+            SupportedAPIsFromClient::AnthropicMessagesAPI(_) => {
                 let messages_request: MessagesRequest = MessagesRequest::try_from(bytes)
                     .map_err(|e| std::io::Error::new(std::io::ErrorKind::InvalidData, e))?;
                 Ok(ProviderRequestType::MessagesRequest(messages_request))
             }
+
+            SupportedAPIsFromClient::OpenAIResponsesAPI(_) => {
+                let responses_apirequest: ResponsesAPIRequest =
+                    ResponsesAPIRequest::try_from(bytes)
+                        .map_err(|e| std::io::Error::new(std::io::ErrorKind::InvalidData, e))?;
+                Ok(ProviderRequestType::ResponsesAPIRequest(
+                    responses_apirequest,
+                ))
+            }
         }
     }
 }
@@ -148,17 +167,13 @@ impl TryFrom<(ProviderRequestType, &SupportedUpstreamAPIs)> for ProviderRequestT
         (client_request, upstream_api): (ProviderRequestType, &SupportedUpstreamAPIs),
     ) -> Result<Self, Self::Error> {
         match (client_request, upstream_api) {
-            // Same API - no conversion needed, just clone the reference
+            // ============================================================================
+            // ChatCompletionsRequest conversions
+            // ============================================================================
             (
                 ProviderRequestType::ChatCompletionsRequest(chat_req),
                 SupportedUpstreamAPIs::OpenAIChatCompletions(_),
             ) => Ok(ProviderRequestType::ChatCompletionsRequest(chat_req)),
-            (
-                ProviderRequestType::MessagesRequest(messages_req),
-                SupportedUpstreamAPIs::AnthropicMessagesAPI(_),
-            ) => Ok(ProviderRequestType::MessagesRequest(messages_req)),
-
-            // Cross-API conversion - cloning is necessary for transformation
             (
                 ProviderRequestType::ChatCompletionsRequest(chat_req),
                 SupportedUpstreamAPIs::AnthropicMessagesAPI(_),
@@ -173,7 +188,45 @@ impl TryFrom<(ProviderRequestType, &SupportedUpstreamAPIs)> for ProviderRequestT
                     })?;
                 Ok(ProviderRequestType::MessagesRequest(messages_req))
             }
+            (
+                ProviderRequestType::ChatCompletionsRequest(chat_req),
+                SupportedUpstreamAPIs::AmazonBedrockConverse(_),
+            ) => {
+                let bedrock_req = ConverseRequest::try_from(chat_req)
+                    .map_err(|e| ProviderRequestError {
+                        message: format!("Failed to convert ChatCompletionsRequest to Amazon Bedrock request: {}", e),
+                        source: Some(Box::new(e))
+                    })?;
+                Ok(ProviderRequestType::BedrockConverse(bedrock_req))
+            }
+            (
+                ProviderRequestType::ChatCompletionsRequest(chat_req),
+                SupportedUpstreamAPIs::AmazonBedrockConverseStream(_),
+            ) => {
+                let bedrock_req = ConverseStreamRequest::try_from(chat_req)
+                    .map_err(|e| ProviderRequestError {
+                        message: format!("Failed to convert ChatCompletionsRequest to Amazon Bedrock Stream request: {}", e),
+                        source: Some(Box::new(e))
+                    })?;
+                Ok(ProviderRequestType::BedrockConverseStream(bedrock_req))
+            }
+            (
+                ProviderRequestType::ChatCompletionsRequest(_),
+                SupportedUpstreamAPIs::OpenAIResponsesAPI(_),
+            ) => {
+                Err(ProviderRequestError {
+                    message: "Conversion from ChatCompletionsRequest to ResponsesAPIRequest is not supported. ResponsesAPI can only be used as a client API, not as an upstream API.".to_string(),
+                    source: None,
+                })
+            }
 
+            // ============================================================================
+            // MessagesRequest conversions
+            // ============================================================================
+            (
+                ProviderRequestType::MessagesRequest(messages_req),
+                SupportedUpstreamAPIs::AnthropicMessagesAPI(_),
+            ) => Ok(ProviderRequestType::MessagesRequest(messages_req)),
             (
                 ProviderRequestType::MessagesRequest(messages_req),
                 SupportedUpstreamAPIs::OpenAIChatCompletions(_),
@@ -189,31 +242,6 @@ impl TryFrom<(ProviderRequestType, &SupportedUpstreamAPIs)> for ProviderRequestT
                 })?;
                 Ok(ProviderRequestType::ChatCompletionsRequest(chat_req))
             }
-
-            // Cross-API conversions: OpenAI/Anthropic to Amazon Bedrock
-            (
-                ProviderRequestType::ChatCompletionsRequest(chat_req),
-                SupportedUpstreamAPIs::AmazonBedrockConverse(_),
-            ) => {
-                let bedrock_req = ConverseRequest::try_from(chat_req)
-                    .map_err(|e| ProviderRequestError {
-                        message: format!("Failed to convert ChatCompletionsRequest to Amazon Bedrock request: {}", e),
-                        source: Some(Box::new(e))
-                    })?;
-                Ok(ProviderRequestType::BedrockConverse(bedrock_req))
-            }
-
-            (
-                ProviderRequestType::ChatCompletionsRequest(chat_req),
-                SupportedUpstreamAPIs::AmazonBedrockConverseStream(_),
-            ) => {
-                let bedrock_req = ConverseStreamRequest::try_from(chat_req)
-                    .map_err(|e| ProviderRequestError {
-                        message: format!("Failed to convert ChatCompletionsRequest to Amazon Bedrock request: {}", e),
-                        source: Some(Box::new(e))
-                    })?;
-                Ok(ProviderRequestType::BedrockConverse(bedrock_req))
-            }
             (
                 ProviderRequestType::MessagesRequest(messages_req),
                 SupportedUpstreamAPIs::AmazonBedrockConverse(_),
@@ -235,7 +263,97 @@ impl TryFrom<(ProviderRequestType, &SupportedUpstreamAPIs)> for ProviderRequestT
                 let bedrock_req = ConverseStreamRequest::try_from(messages_req).map_err(|e| {
                     ProviderRequestError {
                         message: format!(
-                            "Failed to convert MessagesRequest to Amazon Bedrock request: {}",
+                            "Failed to convert MessagesRequest to Amazon Bedrock Stream request: {}",
+                            e
+                        ),
+                        source: Some(Box::new(e)),
+                    }
+                })?;
+                Ok(ProviderRequestType::BedrockConverseStream(bedrock_req))
+            }
+            (
+                ProviderRequestType::MessagesRequest(_),
+                SupportedUpstreamAPIs::OpenAIResponsesAPI(_),
+            ) => {
+                Err(ProviderRequestError {
+                    message: "Conversion from MessagesRequest to ResponsesAPIRequest is not supported. ResponsesAPI can only be used as a client API, not as an upstream API.".to_string(),
+                    source: None,
+                })
+            }
+
+            // ============================================================================
+            // ResponsesAPIRequest conversions (only converts TO other formats)
+            // ============================================================================
+            (
+                ProviderRequestType::ResponsesAPIRequest(responses_req),
+                SupportedUpstreamAPIs::OpenAIResponsesAPI(_),
+            ) => Ok(ProviderRequestType::ResponsesAPIRequest(responses_req)),
+
+            // ResponsesAPI -> ChatCompletions (direct conversion)
+            (
+                ProviderRequestType::ResponsesAPIRequest(responses_req),
+                SupportedUpstreamAPIs::OpenAIChatCompletions(_),
+            ) => {
+                let chat_req = ChatCompletionsRequest::try_from(responses_req).map_err(|e| {
+                    ProviderRequestError {
+                        message: format!(
+                            "Failed to convert ResponsesAPIRequest to ChatCompletionsRequest: {}",
+                            e
+                        ),
+                        source: Some(Box::new(e)),
+                    }
+                })?;
+                Ok(ProviderRequestType::ChatCompletionsRequest(chat_req))
+            }
+
+            // ResponsesAPI -> Anthropic Messages (via ChatCompletions)
+            (
+                ProviderRequestType::ResponsesAPIRequest(responses_req),
+                SupportedUpstreamAPIs::AnthropicMessagesAPI(_),
+            ) => {
+                // Chain: ResponsesAPI -> ChatCompletions -> MessagesRequest
+                let chat_req = ChatCompletionsRequest::try_from(responses_req).map_err(|e| {
+                    ProviderRequestError {
+                        message: format!(
+                            "Failed to convert ResponsesAPIRequest to ChatCompletionsRequest: {}",
+                            e
+                        ),
+                        source: Some(Box::new(e)),
+                    }
+                })?;
+
+                let messages_req = MessagesRequest::try_from(chat_req).map_err(|e| {
+                    ProviderRequestError {
+                        message: format!(
+                            "Failed to convert ChatCompletionsRequest to MessagesRequest: {}",
+                            e
+                        ),
+                        source: Some(Box::new(e)),
+                    }
+                })?;
+                Ok(ProviderRequestType::MessagesRequest(messages_req))
+            }
+
+            // ResponsesAPI -> Bedrock Converse (via ChatCompletions)
+            (
+                ProviderRequestType::ResponsesAPIRequest(responses_req),
+                SupportedUpstreamAPIs::AmazonBedrockConverse(_),
+            ) => {
+                // Chain: ResponsesAPI -> ChatCompletions -> ConverseRequest
+                let chat_req = ChatCompletionsRequest::try_from(responses_req).map_err(|e| {
+                    ProviderRequestError {
+                        message: format!(
+                            "Failed to convert ResponsesAPIRequest to ChatCompletionsRequest: {}",
+                            e
+                        ),
+                        source: Some(Box::new(e)),
+                    }
+                })?;
+
+                let bedrock_req = ConverseRequest::try_from(chat_req).map_err(|e| {
+                    ProviderRequestError {
+                        message: format!(
+                            "Failed to convert ChatCompletionsRequest to Amazon Bedrock request: {}",
                             e
                         ),
                         source: Some(Box::new(e)),
@@ -244,13 +362,50 @@ impl TryFrom<(ProviderRequestType, &SupportedUpstreamAPIs)> for ProviderRequestT
                 Ok(ProviderRequestType::BedrockConverse(bedrock_req))
             }
 
-            // Amazon Bedrock to other APIs conversions
+            // ResponsesAPI -> Bedrock Converse Stream (via ChatCompletions)
+            (
+                ProviderRequestType::ResponsesAPIRequest(responses_req),
+                SupportedUpstreamAPIs::AmazonBedrockConverseStream(_),
+            ) => {
+                // Chain: ResponsesAPI -> ChatCompletions -> ConverseStreamRequest
+                let chat_req = ChatCompletionsRequest::try_from(responses_req).map_err(|e| {
+                    ProviderRequestError {
+                        message: format!(
+                            "Failed to convert ResponsesAPIRequest to ChatCompletionsRequest: {}",
+                            e
+                        ),
+                        source: Some(Box::new(e)),
+                    }
+                })?;
+
+                let bedrock_req = ConverseStreamRequest::try_from(chat_req).map_err(|e| {
+                    ProviderRequestError {
+                        message: format!(
+                            "Failed to convert ChatCompletionsRequest to Amazon Bedrock Stream request: {}",
+                            e
+                        ),
+                        source: Some(Box::new(e)),
+                    }
+                })?;
+                Ok(ProviderRequestType::BedrockConverseStream(bedrock_req))
+            }
+
+            // ============================================================================
+            // Amazon Bedrock conversions (not supported as client API)
+            // ============================================================================
+
             (ProviderRequestType::BedrockConverse(_), _) => {
-                todo!("Amazon Bedrock to ChatCompletionsRequest conversion not implemented yet")
+                Err(ProviderRequestError {
+                    message: "Amazon Bedrock Converse is not supported as a client API. Only OpenAI ChatCompletions, Anthropic Messages, and OpenAI Responses APIs are supported as client APIs.".to_string(),
+                    source: None,
+                })
             }
 
             (ProviderRequestType::BedrockConverseStream(_), _) => {
-                todo!("Amazon Bedrock Stream to ChatCompletionsRequest conversion not implemented yet")
+                Err(ProviderRequestError {
+                    message: "Amazon Bedrock Converse Stream is not supported as a client API. Only OpenAI ChatCompletions, Anthropic Messages, and OpenAI Responses APIs are supported as client APIs.".to_string(),
+                    source: None,
+                })
             }
         }
     }
@@ -284,7 +439,7 @@ mod tests {
     use crate::apis::anthropic::MessagesRequest as AnthropicMessagesRequest;
     use crate::apis::openai::ChatCompletionsRequest;
     use crate::apis::openai::OpenAIApi::ChatCompletions;
-    use crate::clients::endpoints::SupportedAPIs;
+    use crate::clients::endpoints::SupportedAPIsFromClient;
     use crate::transforms::lib::ExtractText;
     use serde_json::json;
 
@@ -298,7 +453,7 @@ mod tests {
             ]
         });
         let bytes = serde_json::to_vec(&req).unwrap();
-        let api = SupportedAPIs::OpenAIChatCompletions(ChatCompletions);
+        let api = SupportedAPIsFromClient::OpenAIChatCompletions(ChatCompletions);
         let result = ProviderRequestType::try_from((bytes.as_slice(), &api));
         assert!(result.is_ok());
         match result.unwrap() {
@@ -321,7 +476,7 @@ mod tests {
             ]
         });
         let bytes = serde_json::to_vec(&req).unwrap();
-        let endpoint = SupportedAPIs::AnthropicMessagesAPI(Messages);
+        let endpoint = SupportedAPIsFromClient::AnthropicMessagesAPI(Messages);
         let result = ProviderRequestType::try_from((bytes.as_slice(), &endpoint));
         assert!(result.is_ok());
         match result.unwrap() {
@@ -343,7 +498,7 @@ mod tests {
             ]
         });
         let bytes = serde_json::to_vec(&req).unwrap();
-        let endpoint = SupportedAPIs::OpenAIChatCompletions(ChatCompletions);
+        let endpoint = SupportedAPIsFromClient::OpenAIChatCompletions(ChatCompletions);
         let result = ProviderRequestType::try_from((bytes.as_slice(), &endpoint));
         assert!(result.is_ok());
         match result.unwrap() {
@@ -366,7 +521,7 @@ mod tests {
         });
         let bytes = serde_json::to_vec(&req).unwrap();
         // Intentionally use OpenAI endpoint for Anthropic payload
-        let endpoint = SupportedAPIs::OpenAIChatCompletions(ChatCompletions);
+        let endpoint = SupportedAPIsFromClient::OpenAIChatCompletions(ChatCompletions);
         let result = ProviderRequestType::try_from((bytes.as_slice(), &endpoint));
         // Should parse as ChatCompletionsRequest, not error
         assert!(result.is_ok());
@@ -486,4 +641,272 @@ mod tests {
         let roundtrip_max_tokens = openai_req2.max_completion_tokens.or(openai_req2.max_tokens);
         assert_eq!(original_max_tokens, roundtrip_max_tokens);
     }
+
+    #[test]
+    fn test_responses_api_request_from_bytes() {
+        use crate::apis::openai::OpenAIApi::Responses;
+
+        let req = json!({
+            "model": "gpt-4o",
+            "input": "Hello, how are you?"
+        });
+        let bytes = serde_json::to_vec(&req).unwrap();
+        let api = SupportedAPIsFromClient::OpenAIResponsesAPI(Responses);
+        let result = ProviderRequestType::try_from((bytes.as_slice(), &api));
+        assert!(result.is_ok());
+        match result.unwrap() {
+            ProviderRequestType::ResponsesAPIRequest(r) => {
+                assert_eq!(r.model, "gpt-4o");
+            }
+            _ => panic!("Expected ResponsesAPIRequest variant"),
+        }
+    }
+
+    #[test]
+    fn test_responses_api_to_chat_completions_conversion() {
+        use crate::apis::openai::OpenAIApi::ChatCompletions;
+        use crate::apis::openai_responses::{InputParam, ResponsesAPIRequest};
+
+        let responses_req = ResponsesAPIRequest {
+            model: "gpt-4o".to_string(),
+            input: InputParam::Text("Hello, world!".to_string()),
+            temperature: Some(0.7),
+            top_p: Some(0.9),
+            max_output_tokens: Some(100),
+            stream: Some(false),
+            metadata: None,
+            tools: None,
+            tool_choice: None,
+            parallel_tool_calls: None,
+            instructions: None,
+            modalities: None,
+            user: None,
+            store: None,
+            reasoning_effort: None,
+            include: None,
+            audio: None,
+            text: None,
+            service_tier: None,
+            top_logprobs: None,
+            stream_options: None,
+            truncation: None,
+            conversation: None,
+            previous_response_id: None,
+            max_tool_calls: None,
+            background: None,
+        };
+
+        let upstream_api = SupportedUpstreamAPIs::OpenAIChatCompletions(ChatCompletions);
+        let result = ProviderRequestType::try_from((
+            ProviderRequestType::ResponsesAPIRequest(responses_req),
+            &upstream_api,
+        ));
+
+        assert!(result.is_ok());
+        match result.unwrap() {
+            ProviderRequestType::ChatCompletionsRequest(chat_req) => {
+                assert_eq!(chat_req.model, "gpt-4o");
+                assert_eq!(chat_req.temperature, Some(0.7));
+                assert_eq!(chat_req.top_p, Some(0.9));
+                assert_eq!(chat_req.max_completion_tokens, Some(100));
+                assert_eq!(chat_req.messages.len(), 1);
+            }
+            _ => panic!("Expected ChatCompletionsRequest variant"),
+        }
+    }
+
+    #[test]
+    fn test_responses_api_to_anthropic_messages_conversion() {
+        use crate::apis::anthropic::AnthropicApi::Messages;
+        use crate::apis::openai_responses::{InputParam, ResponsesAPIRequest};
+
+        let responses_req = ResponsesAPIRequest {
+            model: "gpt-4o".to_string(),
+            input: InputParam::Text("Hello, Claude!".to_string()),
+            temperature: Some(0.8),
+            max_output_tokens: Some(150),
+            stream: Some(false),
+            metadata: None,
+            tools: None,
+            tool_choice: None,
+            parallel_tool_calls: None,
+            instructions: Some("You are a helpful assistant".to_string()),
+            modalities: None,
+            user: None,
+            store: None,
+            reasoning_effort: None,
+            include: None,
+            audio: None,
+            text: None,
+            service_tier: None,
+            top_p: None,
+            top_logprobs: None,
+            stream_options: None,
+            truncation: None,
+            conversation: None,
+            previous_response_id: None,
+            max_tool_calls: None,
+            background: None,
+        };
+
+        let upstream_api = SupportedUpstreamAPIs::AnthropicMessagesAPI(Messages);
+        let result = ProviderRequestType::try_from((
+            ProviderRequestType::ResponsesAPIRequest(responses_req),
+            &upstream_api,
+        ));
+
+        assert!(result.is_ok());
+        match result.unwrap() {
+            ProviderRequestType::MessagesRequest(messages_req) => {
+                assert_eq!(messages_req.model, "gpt-4o");
+                assert_eq!(messages_req.temperature, Some(0.8));
+                assert_eq!(messages_req.max_tokens, 150);
+                // Instructions should be converted to system prompt via ChatCompletions conversion
+                // The conversion chain: ResponsesAPI -> ChatCompletions (system message) -> Anthropic (system prompt)
+                // But we need to check if the system prompt was actually set
+                assert_eq!(messages_req.messages.len(), 1);
+            }
+            _ => panic!("Expected MessagesRequest variant"),
+        }
+    }
+
+    #[test]
+    fn test_responses_api_to_bedrock_conversion() {
+        use crate::apis::amazon_bedrock::AmazonBedrockApi::Converse;
+        use crate::apis::openai_responses::{InputParam, ResponsesAPIRequest};
+
+        let responses_req = ResponsesAPIRequest {
+            model: "gpt-4o".to_string(),
+            input: InputParam::Text("Hello, Bedrock!".to_string()),
+            temperature: Some(0.5),
+            max_output_tokens: Some(200),
+            stream: Some(false),
+            metadata: None,
+            tools: None,
+            tool_choice: None,
+            parallel_tool_calls: None,
+            instructions: None,
+            modalities: None,
+            user: None,
+            store: None,
+            reasoning_effort: None,
+            include: None,
+            audio: None,
+            text: None,
+            service_tier: None,
+            top_p: None,
+            top_logprobs: None,
+            stream_options: None,
+            truncation: None,
+            conversation: None,
+            previous_response_id: None,
+            max_tool_calls: None,
+            background: None,
+        };
+
+        let upstream_api = SupportedUpstreamAPIs::AmazonBedrockConverse(Converse);
+        let result = ProviderRequestType::try_from((
+            ProviderRequestType::ResponsesAPIRequest(responses_req),
+            &upstream_api,
+        ));
+
+        assert!(result.is_ok());
+        match result.unwrap() {
+            ProviderRequestType::BedrockConverse(bedrock_req) => {
+                assert_eq!(bedrock_req.model_id, "gpt-4o");
+                // Bedrock receives the converted request through ChatCompletions
+                assert!(!bedrock_req.messages.is_none());
+            }
+            _ => panic!("Expected BedrockConverse variant"),
+        }
+    }
+
+    #[test]
+    fn test_chat_completions_to_responses_api_not_supported() {
+        use crate::apis::openai::OpenAIApi::Responses;
+        use crate::apis::openai::{Message, MessageContent, Role};
+
+        let chat_req = ChatCompletionsRequest {
+            model: "gpt-4".to_string(),
+            messages: vec![Message {
+                role: Role::User,
+                content: MessageContent::Text("Hello!".to_string()),
+                name: None,
+                tool_calls: None,
+                tool_call_id: None,
+            }],
+            ..Default::default()
+        };
+
+        let upstream_api = SupportedUpstreamAPIs::OpenAIResponsesAPI(Responses);
+        let result = ProviderRequestType::try_from((
+            ProviderRequestType::ChatCompletionsRequest(chat_req),
+            &upstream_api,
+        ));
+
+        assert!(result.is_err());
+        let err = result.unwrap_err();
+        assert!(err.message.contains("ResponsesAPI can only be used as a client API"));
+    }
+
+    #[test]
+    fn test_anthropic_messages_to_responses_api_not_supported() {
+        use crate::apis::anthropic::MessagesRequest as AnthropicMessagesRequest;
+        use crate::apis::openai::OpenAIApi::Responses;
+
+        let messages_req = AnthropicMessagesRequest {
+            model: "claude-3-sonnet".to_string(),
+            messages: vec![crate::apis::anthropic::MessagesMessage {
+                role: crate::apis::anthropic::MessagesRole::User,
+                content: crate::apis::anthropic::MessagesMessageContent::Single(
+                    "Hello!".to_string(),
+                ),
+            }],
+            max_tokens: 100,
+            container: None,
+            mcp_servers: None,
+            service_tier: None,
+            thinking: None,
+            temperature: None,
+            top_p: None,
+            top_k: None,
+            stream: None,
+            stop_sequences: None,
+            system: None,
+            tools: None,
+            tool_choice: None,
+            metadata: None,
+        };
+
+        let upstream_api = SupportedUpstreamAPIs::OpenAIResponsesAPI(Responses);
+        let result = ProviderRequestType::try_from((
+            ProviderRequestType::MessagesRequest(messages_req),
+            &upstream_api,
+        ));
+
+        assert!(result.is_err());
+        let err = result.unwrap_err();
+        assert!(err.message.contains("ResponsesAPI can only be used as a client API"));
+    }
+
+    #[test]
+    fn test_bedrock_as_client_api_not_supported() {
+        use crate::apis::openai::OpenAIApi::ChatCompletions;
+
+        // Create a simple Bedrock request (we'll use Default if available, or minimal construction)
+        let bedrock_req = ConverseRequest::default();
+
+        let upstream_api = SupportedUpstreamAPIs::OpenAIChatCompletions(ChatCompletions);
+        let result = ProviderRequestType::try_from((
+            ProviderRequestType::BedrockConverse(bedrock_req),
+            &upstream_api,
+        ));
+
+        assert!(result.is_err());
+        let err = result.unwrap_err();
+        assert!(err.message.contains("not supported as a client API"));
+        assert!(err
+            .message
+            .contains("OpenAI ChatCompletions, Anthropic Messages, and OpenAI Responses"));
+    }
 }

crates/hermesllm/src/transforms/mod.rs

@@ -11,11 +11,13 @@
 pub mod lib;
 pub mod request;
 pub mod response;
+pub mod response_streaming;
 
 // Re-export commonly used items for convenience
 pub use lib::*;
 pub use request::*;
 pub use response::*;
+pub use response_streaming::*;
 
 // ============================================================================
 // CONSTANTS

crates/hermesllm/src/transforms/request/from_openai.rs

@@ -12,6 +12,10 @@ use crate::apis::anthropic::{
 use crate::apis::openai::{
     ChatCompletionsRequest, Message, MessageContent, Role, Tool, ToolChoice, ToolChoiceType,
 };
+
+use crate::apis::openai_responses::{
+    ResponsesAPIRequest, InputContent, InputItem, InputParam, MessageRole, Modality, ReasoningEffort, Tool as ResponsesTool, ToolChoice as ResponsesToolChoice
+};
 use crate::clients::TransformError;
 use crate::transforms::lib::ExtractText;
 use crate::transforms::lib::*;
@@ -244,6 +248,202 @@ impl TryFrom<Message> for BedrockMessage {
     }
 }
 
+impl TryFrom<ResponsesAPIRequest> for ChatCompletionsRequest {
+    type Error = TransformError;
+
+    fn try_from(req: ResponsesAPIRequest) -> Result<Self, Self::Error> {
+
+        // Convert input to messages
+        let messages = match req.input {
+            InputParam::Text(text) => {
+                // Simple text input becomes a user message
+                vec![Message {
+                    role: Role::User,
+                    content: MessageContent::Text(text),
+                    name: None,
+                    tool_call_id: None,
+                    tool_calls: None,
+                }]
+            }
+            InputParam::Items(items) => {
+                // Convert input items to messages
+                let mut converted_messages = Vec::new();
+
+                // Add instructions as system message if present
+                if let Some(instructions) = &req.instructions {
+                    converted_messages.push(Message {
+                        role: Role::System,
+                        content: MessageContent::Text(instructions.clone()),
+                        name: None,
+                        tool_call_id: None,
+                        tool_calls: None,
+                    });
+                }
+
+                // Convert each input item
+                for item in items {
+                    match item {
+                        InputItem::Message(input_msg) => {
+                            let role = match input_msg.role {
+                                MessageRole::User => Role::User,
+                                MessageRole::Assistant => Role::Assistant,
+                                MessageRole::System => Role::System,
+                                MessageRole::Developer => Role::System, // Map developer to system
+                            };
+
+                            // Convert content blocks
+                            let content = if input_msg.content.len() == 1 {
+                                // Single content item - check if it's simple text
+                                match &input_msg.content[0] {
+                                    InputContent::InputText { text } => MessageContent::Text(text.clone()),
+                                    _ => {
+                                        // Convert to parts for non-text content
+                                        MessageContent::Parts(
+                                            input_msg.content.iter()
+                                                .filter_map(|c| match c {
+                                                    InputContent::InputText { text } => {
+                                                        Some(crate::apis::openai::ContentPart::Text { text: text.clone() })
+                                                    }
+                                                    InputContent::InputImage { image_url, .. } => {
+                                                        Some(crate::apis::openai::ContentPart::ImageUrl {
+                                                            image_url: crate::apis::openai::ImageUrl {
+                                                                url: image_url.clone(),
+                                                                detail: None,
+                                                            }
+                                                        })
+                                                    }
+                                                    InputContent::InputFile { .. } => None, // Skip files for now
+                                                    InputContent::InputAudio { .. } => None, // Skip audio for now
+                                                })
+                                                .collect()
+                                        )
+                                    }
+                                }
+                            } else {
+                                // Multiple content items - convert to parts
+                                MessageContent::Parts(
+                                    input_msg.content.iter()
+                                        .filter_map(|c| match c {
+                                            InputContent::InputText { text } => {
+                                                Some(crate::apis::openai::ContentPart::Text { text: text.clone() })
+                                            }
+                                            InputContent::InputImage { image_url, .. } => {
+                                                Some(crate::apis::openai::ContentPart::ImageUrl {
+                                                    image_url: crate::apis::openai::ImageUrl {
+                                                        url: image_url.clone(),
+                                                        detail: None,
+                                                    }
+                                                })
+                                            }
+                                            InputContent::InputFile { .. } => None, // Skip files for now
+                                            InputContent::InputAudio { .. } => None, // Skip audio for now
+                                        })
+                                        .collect()
+                                )
+                            };
+
+                            converted_messages.push(Message {
+                                role,
+                                content,
+                                name: None,
+                                tool_call_id: None,
+                                tool_calls: None,
+                            });
+                        }
+                    }
+                }
+
+                converted_messages
+            }
+        };
+
+        // Build the ChatCompletionsRequest
+        Ok(ChatCompletionsRequest {
+            model: req.model,
+            messages,
+            temperature: req.temperature,
+            top_p: req.top_p,
+            max_completion_tokens: req.max_output_tokens.map(|t| t as u32),
+            stream: req.stream,
+            metadata: req.metadata,
+            user: req.user,
+            store: req.store,
+            service_tier: req.service_tier,
+            top_logprobs: req.top_logprobs.map(|t| t as u32),
+            modalities: req.modalities.map(|mods| {
+                mods.into_iter().map(|m| {
+                    match m {
+                        Modality::Text => "text".to_string(),
+                        Modality::Audio => "audio".to_string(),
+                    }
+                }).collect()
+            }),
+            stream_options: req.stream_options.map(|opts| {
+                crate::apis::openai::StreamOptions {
+                    include_usage: opts.include_usage,
+                }
+            }),
+            reasoning_effort: req.reasoning_effort.map(|effort| {
+                match effort {
+                    ReasoningEffort::Low => "low".to_string(),
+                    ReasoningEffort::Medium => "medium".to_string(),
+                    ReasoningEffort::High => "high".to_string(),
+                }
+            }),
+            tools: req.tools.map(|tools| {
+                tools.into_iter().map(|tool| {
+
+                    // Only convert Function tools - other types are not supported in ChatCompletions
+                    match tool {
+                        ResponsesTool::Function { name, description, parameters, strict } => Ok(Tool {
+                            tool_type: "function".to_string(),
+                            function: crate::apis::openai::Function {
+                                name,
+                                description,
+                                parameters: parameters.unwrap_or_else(|| serde_json::json!({
+                                    "type": "object",
+                                    "properties": {}
+                                })),
+                                strict,
+                            }
+                        }),
+                        ResponsesTool::FileSearch { .. } => Err(TransformError::UnsupportedConversion(
+                            "FileSearch tool is not supported in ChatCompletions API. Only function tools are supported.".to_string()
+                        )),
+                        ResponsesTool::WebSearchPreview { .. } => Err(TransformError::UnsupportedConversion(
+                            "WebSearchPreview tool is not supported in ChatCompletions API. Only function tools are supported.".to_string()
+                        )),
+                        ResponsesTool::CodeInterpreter => Err(TransformError::UnsupportedConversion(
+                            "CodeInterpreter tool is not supported in ChatCompletions API. Only function tools are supported.".to_string()
+                        )),
+                        ResponsesTool::Computer { .. } => Err(TransformError::UnsupportedConversion(
+                            "Computer tool is not supported in ChatCompletions API. Only function tools are supported.".to_string()
+                        )),
+                    }
+                }).collect::<Result<Vec<_>, _>>()
+            }).transpose()?,
+            tool_choice: req.tool_choice.map(|choice| {
+                match choice {
+                    ResponsesToolChoice::String(s) => {
+                        match s.as_str() {
+                            "auto" => ToolChoice::Type(ToolChoiceType::Auto),
+                            "required" => ToolChoice::Type(ToolChoiceType::Required),
+                            "none" => ToolChoice::Type(ToolChoiceType::None),
+                            _ => ToolChoice::Type(ToolChoiceType::Auto), // Default to auto for unknown strings
+                        }
+                    }
+                    ResponsesToolChoice::Named { function, .. } => ToolChoice::Function {
+                        choice_type: "function".to_string(),
+                        function: crate::apis::openai::FunctionChoice { name: function.name }
+                    }
+                }
+            }),
+            parallel_tool_calls: req.parallel_tool_calls,
+            ..Default::default()
+        })
+    }
+}
+
 impl TryFrom<ChatCompletionsRequest> for AnthropicMessagesRequest {
     type Error = TransformError;
 

crates/hermesllm/src/transforms/response/to_anthropic.rs

@@ -1,16 +1,11 @@
-use crate::apis::amazon_bedrock::{
+use crate::apis::amazon_bedrock::{ConverseOutput, ConverseResponse, StopReason};
-    ContentBlockDelta, ConverseOutput, ConverseResponse, ConverseStreamEvent, StopReason,
-};
 use crate::apis::anthropic::{
-    MessagesContentBlock, MessagesContentDelta, MessagesMessageDelta, MessagesResponse,
+    MessagesContentBlock, MessagesResponse,
-    MessagesRole, MessagesStopReason, MessagesStreamEvent, MessagesStreamMessage, MessagesUsage,
+    MessagesRole, MessagesStopReason, MessagesUsage,
-};
-use crate::apis::openai::{
-    ChatCompletionsResponse, ChatCompletionsStreamResponse, Role, ToolCallDelta,
 };
+use crate::apis::openai::ChatCompletionsResponse;
 use crate::clients::TransformError;
 use crate::transforms::lib::*;
-use serde_json::Value;
 
 // ============================================================================
 // STANDARD RUST TRAIT IMPLEMENTATIONS - Using Into/TryFrom for convenience
@@ -120,289 +115,6 @@ impl TryFrom<ConverseResponse> for MessagesResponse {
     }
 }
 
-impl TryFrom<ChatCompletionsStreamResponse> for MessagesStreamEvent {
-    type Error = TransformError;
-
-    fn try_from(resp: ChatCompletionsStreamResponse) -> Result<Self, Self::Error> {
-        if resp.choices.is_empty() {
-            return Ok(MessagesStreamEvent::Ping);
-        }
-
-        let choice = &resp.choices[0];
-
-        // Handle final chunk with usage
-        let has_usage = resp.usage.is_some();
-        if let Some(usage) = resp.usage {
-            if let Some(finish_reason) = &choice.finish_reason {
-                let anthropic_stop_reason: MessagesStopReason = finish_reason.clone().into();
-                return Ok(MessagesStreamEvent::MessageDelta {
-                    delta: MessagesMessageDelta {
-                        stop_reason: anthropic_stop_reason,
-                        stop_sequence: None,
-                    },
-                    usage: usage.into(),
-                });
-            }
-        }
-
-        // Handle role start
-        if let Some(Role::Assistant) = choice.delta.role {
-            return Ok(MessagesStreamEvent::MessageStart {
-                message: MessagesStreamMessage {
-                    id: resp.id,
-                    obj_type: "message".to_string(),
-                    role: MessagesRole::Assistant,
-                    content: vec![],
-                    model: resp.model,
-                    stop_reason: None,
-                    stop_sequence: None,
-                    usage: MessagesUsage {
-                        input_tokens: 0,
-                        output_tokens: 0,
-                        cache_creation_input_tokens: None,
-                        cache_read_input_tokens: None,
-                    },
-                },
-            });
-        }
-
-        // Handle content delta
-        if let Some(content) = &choice.delta.content {
-            if !content.is_empty() {
-                return Ok(MessagesStreamEvent::ContentBlockDelta {
-                    index: 0,
-                    delta: MessagesContentDelta::TextDelta {
-                        text: content.clone(),
-                    },
-                });
-            }
-        }
-
-        // Handle tool calls
-        if let Some(tool_calls) = &choice.delta.tool_calls {
-            return convert_tool_call_deltas(tool_calls.clone());
-        }
-
-        // Handle finish reason - generate MessageDelta only (MessageStop comes later)
-        if let Some(finish_reason) = &choice.finish_reason {
-            // If we have usage data, it was already handled above
-            // If not, we need to generate MessageDelta with default usage
-            if !has_usage {
-                let anthropic_stop_reason: MessagesStopReason = finish_reason.clone().into();
-                return Ok(MessagesStreamEvent::MessageDelta {
-                    delta: MessagesMessageDelta {
-                        stop_reason: anthropic_stop_reason,
-                        stop_sequence: None,
-                    },
-                    usage: MessagesUsage {
-                        input_tokens: 0,
-                        output_tokens: 0,
-                        cache_creation_input_tokens: None,
-                        cache_read_input_tokens: None,
-                    },
-                });
-            }
-            // If usage was already handled above, we don't need to do anything more here
-            // MessageStop will be handled when [DONE] is encountered
-        }
-
-        // Default to ping for unhandled cases
-        Ok(MessagesStreamEvent::Ping)
-    }
-}
-
-impl Into<String> for MessagesStreamEvent {
-    fn into(self) -> String {
-        let transformed_json = serde_json::to_string(&self).unwrap_or_default();
-        let event_type = match &self {
-            MessagesStreamEvent::MessageStart { .. } => "message_start",
-            MessagesStreamEvent::ContentBlockStart { .. } => "content_block_start",
-            MessagesStreamEvent::ContentBlockDelta { .. } => "content_block_delta",
-            MessagesStreamEvent::ContentBlockStop { .. } => "content_block_stop",
-            MessagesStreamEvent::MessageDelta { .. } => "message_delta",
-            MessagesStreamEvent::MessageStop => "message_stop",
-            MessagesStreamEvent::Ping => "ping",
-        };
-
-        let event = format!("event: {}\n", event_type);
-        let data = format!("data: {}\n\n", transformed_json);
-        event + &data
-    }
-}
-
-impl TryFrom<ConverseStreamEvent> for MessagesStreamEvent {
-    type Error = TransformError;
-
-    fn try_from(event: ConverseStreamEvent) -> Result<Self, Self::Error> {
-        match event {
-            // MessageStart - convert to Anthropic MessageStart
-            ConverseStreamEvent::MessageStart(start_event) => {
-                let role = match start_event.role {
-                    crate::apis::amazon_bedrock::ConversationRole::User => MessagesRole::User,
-                    crate::apis::amazon_bedrock::ConversationRole::Assistant => {
-                        MessagesRole::Assistant
-                    }
-                };
-
-                Ok(MessagesStreamEvent::MessageStart {
-                    message: MessagesStreamMessage {
-                        id: format!(
-                            "bedrock-stream-{}",
-                            std::time::SystemTime::now()
-                                .duration_since(std::time::UNIX_EPOCH)
-                                .unwrap_or_default()
-                                .as_nanos()
-                        ),
-                        obj_type: "message".to_string(),
-                        role,
-                        content: vec![],
-                        model: "bedrock-model".to_string(),
-                        stop_reason: None,
-                        stop_sequence: None,
-                        usage: MessagesUsage {
-                            input_tokens: 0,
-                            output_tokens: 0,
-                            cache_creation_input_tokens: None,
-                            cache_read_input_tokens: None,
-                        },
-                    },
-                })
-            }
-
-            // ContentBlockStart - convert to Anthropic ContentBlockStart
-            ConverseStreamEvent::ContentBlockStart(start_event) => {
-                // Note: Bedrock sends tool_use_id and name at start, with input coming in subsequent deltas
-                // Anthropic expects the same pattern, so we initialize with an empty input object
-                match start_event.start {
-                    crate::apis::amazon_bedrock::ContentBlockStart::ToolUse { tool_use } => {
-                        Ok(MessagesStreamEvent::ContentBlockStart {
-                            index: start_event.content_block_index as u32,
-                            content_block: MessagesContentBlock::ToolUse {
-                                id: tool_use.tool_use_id,
-                                name: tool_use.name,
-                                input: Value::Object(serde_json::Map::new()), // Empty - will be filled by deltas
-                                cache_control: None,
-                            },
-                        })
-                    }
-                }
-            }
-
-            // ContentBlockDelta - convert to Anthropic ContentBlockDelta
-            ConverseStreamEvent::ContentBlockDelta(delta_event) => {
-                let delta = match delta_event.delta {
-                    ContentBlockDelta::Text { text } => MessagesContentDelta::TextDelta { text },
-                    ContentBlockDelta::ToolUse { tool_use } => {
-                        MessagesContentDelta::InputJsonDelta {
-                            partial_json: tool_use.input,
-                        }
-                    }
-                };
-
-                Ok(MessagesStreamEvent::ContentBlockDelta {
-                    index: delta_event.content_block_index as u32,
-                    delta,
-                })
-            }
-
-            // ContentBlockStop - convert to Anthropic ContentBlockStop
-            ConverseStreamEvent::ContentBlockStop(stop_event) => {
-                Ok(MessagesStreamEvent::ContentBlockStop {
-                    index: stop_event.content_block_index as u32,
-                })
-            }
-
-            // MessageStop - convert to Anthropic MessageDelta with stop reason + MessageStop
-            ConverseStreamEvent::MessageStop(stop_event) => {
-                let anthropic_stop_reason = match stop_event.stop_reason {
-                    StopReason::EndTurn => MessagesStopReason::EndTurn,
-                    StopReason::ToolUse => MessagesStopReason::ToolUse,
-                    StopReason::MaxTokens => MessagesStopReason::MaxTokens,
-                    StopReason::StopSequence => MessagesStopReason::EndTurn,
-                    StopReason::GuardrailIntervened => MessagesStopReason::Refusal,
-                    StopReason::ContentFiltered => MessagesStopReason::Refusal,
-                };
-
-                // Return MessageDelta (MessageStop will be sent separately by the streaming handler)
-                Ok(MessagesStreamEvent::MessageDelta {
-                    delta: MessagesMessageDelta {
-                        stop_reason: anthropic_stop_reason,
-                        stop_sequence: None,
-                    },
-                    usage: MessagesUsage {
-                        input_tokens: 0,
-                        output_tokens: 0,
-                        cache_creation_input_tokens: None,
-                        cache_read_input_tokens: None,
-                    },
-                })
-            }
-
-            // Metadata - convert usage information to MessageDelta
-            ConverseStreamEvent::Metadata(metadata_event) => {
-                Ok(MessagesStreamEvent::MessageDelta {
-                    delta: MessagesMessageDelta {
-                        stop_reason: MessagesStopReason::EndTurn,
-                        stop_sequence: None,
-                    },
-                    usage: MessagesUsage {
-                        input_tokens: metadata_event.usage.input_tokens,
-                        output_tokens: metadata_event.usage.output_tokens,
-                        cache_creation_input_tokens: metadata_event.usage.cache_write_input_tokens,
-                        cache_read_input_tokens: metadata_event.usage.cache_read_input_tokens,
-                    },
-                })
-            }
-
-            // Exception events - convert to Ping (could be enhanced to return error events)
-            ConverseStreamEvent::InternalServerException(_)
-            | ConverseStreamEvent::ModelStreamErrorException(_)
-            | ConverseStreamEvent::ServiceUnavailableException(_)
-            | ConverseStreamEvent::ThrottlingException(_)
-            | ConverseStreamEvent::ValidationException(_) => {
-                // TODO: Consider adding proper error handling/events
-                Ok(MessagesStreamEvent::Ping)
-            }
-        }
-    }
-}
-
-/// Convert tool call deltas to Anthropic stream events
-fn convert_tool_call_deltas(
-    tool_calls: Vec<ToolCallDelta>,
-) -> Result<MessagesStreamEvent, TransformError> {
-    for tool_call in tool_calls {
-        if let Some(id) = &tool_call.id {
-            // Tool call start
-            if let Some(function) = &tool_call.function {
-                if let Some(name) = &function.name {
-                    return Ok(MessagesStreamEvent::ContentBlockStart {
-                        index: tool_call.index,
-                        content_block: MessagesContentBlock::ToolUse {
-                            id: id.clone(),
-                            name: name.clone(),
-                            input: Value::Object(serde_json::Map::new()),
-                            cache_control: None,
-                        },
-                    });
-                }
-            }
-        } else if let Some(function) = &tool_call.function {
-            if let Some(arguments) = &function.arguments {
-                // Tool arguments delta
-                return Ok(MessagesStreamEvent::ContentBlockDelta {
-                    index: tool_call.index,
-                    delta: MessagesContentDelta::InputJsonDelta {
-                        partial_json: arguments.clone(),
-                    },
-                });
-            }
-        }
-    }
-
-    // Fallback to ping if no valid tool call found
-    Ok(MessagesStreamEvent::Ping)
-}
 
 /// Convert Bedrock Message to Anthropic content blocks
 ///

crates/hermesllm/src/transforms/response/to_openai.rs

@@ -1,15 +1,13 @@
 use crate::apis::amazon_bedrock::{
-    ConverseOutput, ConverseResponse, ConverseStreamEvent, StopReason,
+    ConverseOutput, ConverseResponse, StopReason,
 };
 use crate::apis::anthropic::{
-    MessagesContentBlock, MessagesContentDelta, MessagesResponse, MessagesStopReason,
+    MessagesContentBlock, MessagesResponse, MessagesUsage,
-    MessagesStreamEvent, MessagesUsage,
 };
 use crate::apis::openai::{
-    ChatCompletionsResponse, ChatCompletionsStreamResponse, Choice, FinishReason,
+    ChatCompletionsResponse, Choice, FinishReason, MessageContent, ResponseMessage, Role, Usage,
-    FunctionCallDelta, MessageContent, MessageDelta, ResponseMessage, Role, StreamChoice,
-    ToolCallDelta, Usage,
 };
+use crate::apis::openai_responses::ResponsesAPIResponse;
 use crate::clients::TransformError;
 use crate::transforms::lib::*;
 
@@ -30,6 +28,163 @@ impl Into<Usage> for MessagesUsage {
     }
 }
 
+impl TryFrom<ChatCompletionsResponse> for ResponsesAPIResponse {
+    type Error = TransformError;
+
+    fn try_from(resp: ChatCompletionsResponse) -> Result<Self, Self::Error> {
+        use crate::apis::openai_responses::{
+            IncompleteDetails, IncompleteReason, OutputContent, OutputItem, OutputItemStatus,
+            ResponseStatus, ResponseUsage, ResponsesAPIResponse,
+        };
+
+        // Convert the first choice's message to output items
+        let output = if let Some(choice) = resp.choices.first() {
+            let mut items = Vec::new();
+
+            // Create a message output item from the response message
+            let mut content = Vec::new();
+
+            // Add text content if present
+            if let Some(text) = &choice.message.content {
+                content.push(OutputContent::OutputText {
+                    text: text.clone(),
+                    annotations: vec![],
+                    logprobs: None,
+                });
+            }
+
+            // Add audio content if present (audio is a Value, need to handle it carefully)
+            if let Some(audio) = &choice.message.audio {
+                // Audio is serde_json::Value, try to extract data and transcript
+                if let Some(audio_obj) = audio.as_object() {
+                    let data = audio_obj
+                        .get("data")
+                        .and_then(|v| v.as_str())
+                        .map(|s| s.to_string());
+                    let transcript = audio_obj
+                        .get("transcript")
+                        .and_then(|v| v.as_str())
+                        .map(|s| s.to_string());
+
+                    content.push(OutputContent::OutputAudio { data, transcript });
+                }
+            }
+
+            // Add refusal content if present
+            if let Some(refusal) = &choice.message.refusal {
+                content.push(OutputContent::Refusal {
+                    refusal: refusal.clone(),
+                });
+            }
+
+            // Only add the message item if there's actual content (text, audio, or refusal)
+            // Don't add empty message items when there are only tool calls
+            if !content.is_empty() {
+                items.push(OutputItem::Message {
+                    id: format!("msg_{}", resp.id),
+                    status: OutputItemStatus::Completed,
+                    role: match choice.message.role {
+                        Role::User => "user".to_string(),
+                        Role::Assistant => "assistant".to_string(),
+                        Role::System => "system".to_string(),
+                        Role::Tool => "tool".to_string(),
+                    },
+                    content,
+                });
+            }
+
+            // Add tool calls as function call items if present
+            if let Some(tool_calls) = &choice.message.tool_calls {
+                for tool_call in tool_calls {
+                    items.push(OutputItem::FunctionCall {
+                        id: format!("func_{}", tool_call.id),
+                        status: OutputItemStatus::Completed,
+                        call_id: tool_call.id.clone(),
+                        name: Some(tool_call.function.name.clone()),
+                        arguments: Some(tool_call.function.arguments.clone()),
+                    });
+                }
+            }
+
+            items
+        } else {
+            vec![]
+        };
+
+        // Convert finish_reason to status
+        let status = if let Some(choice) = resp.choices.first() {
+            match choice.finish_reason {
+                Some(FinishReason::Stop) => ResponseStatus::Completed,
+                Some(FinishReason::ToolCalls) => ResponseStatus::Completed,
+                Some(FinishReason::Length) => ResponseStatus::Incomplete,
+                Some(FinishReason::ContentFilter) => ResponseStatus::Failed,
+                _ => ResponseStatus::Completed,
+            }
+        } else {
+            ResponseStatus::Completed
+        };
+
+        // Convert usage
+        let usage = ResponseUsage {
+            input_tokens: resp.usage.prompt_tokens as i32,
+            output_tokens: resp.usage.completion_tokens as i32,
+            total_tokens: resp.usage.total_tokens as i32,
+            input_tokens_details: resp.usage.prompt_tokens_details.map(|details| {
+                crate::apis::openai_responses::TokenDetails {
+                    cached_tokens: details.cached_tokens.unwrap_or(0) as i32,
+                }
+            }),
+            output_tokens_details: resp.usage.completion_tokens_details.map(|details| {
+                crate::apis::openai_responses::OutputTokenDetails {
+                    reasoning_tokens: details.reasoning_tokens.unwrap_or(0) as i32,
+                }
+            }),
+        };
+
+        // Set incomplete_details if status is incomplete
+        let incomplete_details = if matches!(status, ResponseStatus::Incomplete) {
+            Some(IncompleteDetails {
+                reason: IncompleteReason::MaxOutputTokens,
+            })
+        } else {
+            None
+        };
+
+        Ok(ResponsesAPIResponse {
+            id: resp.id,
+            object: "response".to_string(),
+            created_at: resp.created as i64,
+            status,
+            background: Some(false),
+            error: None,
+            incomplete_details,
+            instructions: None,
+            max_output_tokens: None,
+            max_tool_calls: None,
+            model: resp.model,
+            output,
+            usage: Some(usage),
+            parallel_tool_calls: true,
+            conversation: None,
+            previous_response_id: None,
+            tools: vec![],
+            tool_choice: "auto".to_string(),
+            temperature: 1.0,
+            top_p: 1.0,
+            metadata: resp.metadata.unwrap_or_default(),
+            truncation: None,
+            reasoning: None,
+            store: None,
+            text: None,
+            audio: None,
+            modalities: None,
+            service_tier: resp.service_tier,
+            top_logprobs: None,
+        })
+    }
+}
+
+
 impl TryFrom<MessagesResponse> for ChatCompletionsResponse {
     type Error = TransformError;
 
@@ -173,416 +328,6 @@ impl TryFrom<ConverseResponse> for ChatCompletionsResponse {
     }
 }
 
-// ============================================================================
-// STREAMING TRANSFORMATIONS
-// ============================================================================
-
-impl TryFrom<MessagesStreamEvent> for ChatCompletionsStreamResponse {
-    type Error = TransformError;
-
-    fn try_from(event: MessagesStreamEvent) -> Result<Self, Self::Error> {
-        match event {
-            MessagesStreamEvent::MessageStart { message } => Ok(create_openai_chunk(
-                &message.id,
-                &message.model,
-                MessageDelta {
-                    role: Some(Role::Assistant),
-                    content: None,
-                    refusal: None,
-                    function_call: None,
-                    tool_calls: None,
-                },
-                None,
-                None,
-            )),
-
-            MessagesStreamEvent::ContentBlockStart { content_block, .. } => {
-                convert_content_block_start(content_block)
-            }
-
-            MessagesStreamEvent::ContentBlockDelta { delta, .. } => convert_content_delta(delta),
-
-            MessagesStreamEvent::ContentBlockStop { .. } => Ok(create_empty_openai_chunk()),
-
-            MessagesStreamEvent::MessageDelta { delta, usage } => {
-                let finish_reason: Option<FinishReason> = Some(delta.stop_reason.into());
-                let openai_usage: Option<Usage> = Some(usage.into());
-
-                Ok(create_openai_chunk(
-                    "stream",
-                    "unknown",
-                    MessageDelta {
-                        role: None,
-                        content: None,
-                        refusal: None,
-                        function_call: None,
-                        tool_calls: None,
-                    },
-                    finish_reason,
-                    openai_usage,
-                ))
-            }
-
-            MessagesStreamEvent::MessageStop => Ok(create_openai_chunk(
-                "stream",
-                "unknown",
-                MessageDelta {
-                    role: None,
-                    content: None,
-                    refusal: None,
-                    function_call: None,
-                    tool_calls: None,
-                },
-                Some(FinishReason::Stop),
-                None,
-            )),
-
-            MessagesStreamEvent::Ping => Ok(ChatCompletionsStreamResponse {
-                id: "stream".to_string(),
-                object: Some("chat.completion.chunk".to_string()),
-                created: current_timestamp(),
-                model: "unknown".to_string(),
-                choices: vec![],
-                usage: None,
-                system_fingerprint: None,
-                service_tier: None,
-            }),
-        }
-    }
-}
-
-impl TryFrom<ConverseStreamEvent> for ChatCompletionsStreamResponse {
-    type Error = TransformError;
-
-    fn try_from(event: ConverseStreamEvent) -> Result<Self, Self::Error> {
-        match event {
-            ConverseStreamEvent::MessageStart(start_event) => {
-                let role = match start_event.role {
-                    crate::apis::amazon_bedrock::ConversationRole::User => Role::User,
-                    crate::apis::amazon_bedrock::ConversationRole::Assistant => Role::Assistant,
-                };
-
-                Ok(create_openai_chunk(
-                    "stream",
-                    "unknown",
-                    MessageDelta {
-                        role: Some(role),
-                        content: None,
-                        refusal: None,
-                        function_call: None,
-                        tool_calls: None,
-                    },
-                    None,
-                    None,
-                ))
-            }
-
-            ConverseStreamEvent::ContentBlockStart(start_event) => {
-                use crate::apis::amazon_bedrock::ContentBlockStart;
-
-                match start_event.start {
-                    ContentBlockStart::ToolUse { tool_use } => Ok(create_openai_chunk(
-                        "stream",
-                        "unknown",
-                        MessageDelta {
-                            role: None,
-                            content: None,
-                            refusal: None,
-                            function_call: None,
-                            tool_calls: Some(vec![ToolCallDelta {
-                                index: start_event.content_block_index as u32,
-                                id: Some(tool_use.tool_use_id),
-                                call_type: Some("function".to_string()),
-                                function: Some(FunctionCallDelta {
-                                    name: Some(tool_use.name),
-                                    arguments: Some("".to_string()),
-                                }),
-                            }]),
-                        },
-                        None,
-                        None,
-                    )),
-                }
-            }
-
-            ConverseStreamEvent::ContentBlockDelta(delta_event) => {
-                use crate::apis::amazon_bedrock::ContentBlockDelta;
-
-                match delta_event.delta {
-                    ContentBlockDelta::Text { text } => Ok(create_openai_chunk(
-                        "stream",
-                        "unknown",
-                        MessageDelta {
-                            role: None,
-                            content: Some(text),
-                            refusal: None,
-                            function_call: None,
-                            tool_calls: None,
-                        },
-                        None,
-                        None,
-                    )),
-                    ContentBlockDelta::ToolUse { tool_use } => Ok(create_openai_chunk(
-                        "stream",
-                        "unknown",
-                        MessageDelta {
-                            role: None,
-                            content: None,
-                            refusal: None,
-                            function_call: None,
-                            tool_calls: Some(vec![ToolCallDelta {
-                                index: delta_event.content_block_index as u32,
-                                id: None,
-                                call_type: None,
-                                function: Some(FunctionCallDelta {
-                                    name: None,
-                                    arguments: Some(tool_use.input),
-                                }),
-                            }]),
-                        },
-                        None,
-                        None,
-                    )),
-                }
-            }
-
-            ConverseStreamEvent::ContentBlockStop(_) => Ok(create_empty_openai_chunk()),
-
-            ConverseStreamEvent::MessageStop(stop_event) => {
-                let finish_reason = match stop_event.stop_reason {
-                    StopReason::EndTurn => FinishReason::Stop,
-                    StopReason::ToolUse => FinishReason::ToolCalls,
-                    StopReason::MaxTokens => FinishReason::Length,
-                    StopReason::StopSequence => FinishReason::Stop,
-                    StopReason::GuardrailIntervened => FinishReason::ContentFilter,
-                    StopReason::ContentFiltered => FinishReason::ContentFilter,
-                };
-
-                Ok(create_openai_chunk(
-                    "stream",
-                    "unknown",
-                    MessageDelta {
-                        role: None,
-                        content: None,
-                        refusal: None,
-                        function_call: None,
-                        tool_calls: None,
-                    },
-                    Some(finish_reason),
-                    None,
-                ))
-            }
-
-            ConverseStreamEvent::Metadata(metadata_event) => {
-                let usage = Usage {
-                    prompt_tokens: metadata_event.usage.input_tokens,
-                    completion_tokens: metadata_event.usage.output_tokens,
-                    total_tokens: metadata_event.usage.total_tokens,
-                    prompt_tokens_details: None,
-                    completion_tokens_details: None,
-                };
-
-                Ok(create_openai_chunk(
-                    "stream",
-                    "unknown",
-                    MessageDelta {
-                        role: None,
-                        content: None,
-                        refusal: None,
-                        function_call: None,
-                        tool_calls: None,
-                    },
-                    None,
-                    Some(usage),
-                ))
-            }
-
-            // Error events - convert to empty chunks (errors should be handled elsewhere)
-            ConverseStreamEvent::InternalServerException(_)
-            | ConverseStreamEvent::ModelStreamErrorException(_)
-            | ConverseStreamEvent::ServiceUnavailableException(_)
-            | ConverseStreamEvent::ThrottlingException(_)
-            | ConverseStreamEvent::ValidationException(_) => Ok(create_empty_openai_chunk()),
-        }
-    }
-}
-
-/// Convert content block start to OpenAI chunk
-fn convert_content_block_start(
-    content_block: MessagesContentBlock,
-) -> Result<ChatCompletionsStreamResponse, TransformError> {
-    match content_block {
-        MessagesContentBlock::Text { .. } => {
-            // No immediate output for text block start
-            Ok(create_empty_openai_chunk())
-        }
-        MessagesContentBlock::ToolUse { id, name, .. }
-        | MessagesContentBlock::ServerToolUse { id, name, .. }
-        | MessagesContentBlock::McpToolUse { id, name, .. } => {
-            // Tool use start → OpenAI chunk with tool_calls
-            Ok(create_openai_chunk(
-                "stream",
-                "unknown",
-                MessageDelta {
-                    role: None,
-                    content: None,
-                    refusal: None,
-                    function_call: None,
-                    tool_calls: Some(vec![ToolCallDelta {
-                        index: 0,
-                        id: Some(id),
-                        call_type: Some("function".to_string()),
-                        function: Some(FunctionCallDelta {
-                            name: Some(name),
-                            arguments: Some("".to_string()),
-                        }),
-                    }]),
-                },
-                None,
-                None,
-            ))
-        }
-        _ => Err(TransformError::UnsupportedContent(
-            "Unsupported content block type in stream start".to_string(),
-        )),
-    }
-}
-
-/// Convert content delta to OpenAI chunk
-fn convert_content_delta(
-    delta: MessagesContentDelta,
-) -> Result<ChatCompletionsStreamResponse, TransformError> {
-    match delta {
-        MessagesContentDelta::TextDelta { text } => Ok(create_openai_chunk(
-            "stream",
-            "unknown",
-            MessageDelta {
-                role: None,
-                content: Some(text),
-                refusal: None,
-                function_call: None,
-                tool_calls: None,
-            },
-            None,
-            None,
-        )),
-        MessagesContentDelta::ThinkingDelta { thinking } => Ok(create_openai_chunk(
-            "stream",
-            "unknown",
-            MessageDelta {
-                role: None,
-                content: Some(format!("thinking: {}", thinking)),
-                refusal: None,
-                function_call: None,
-                tool_calls: None,
-            },
-            None,
-            None,
-        )),
-        MessagesContentDelta::InputJsonDelta { partial_json } => Ok(create_openai_chunk(
-            "stream",
-            "unknown",
-            MessageDelta {
-                role: None,
-                content: None,
-                refusal: None,
-                function_call: None,
-                tool_calls: Some(vec![ToolCallDelta {
-                    index: 0,
-                    id: None,
-                    call_type: None,
-                    function: Some(FunctionCallDelta {
-                        name: None,
-                        arguments: Some(partial_json),
-                    }),
-                }]),
-            },
-            None,
-            None,
-        )),
-    }
-}
-
-/// Helper to create OpenAI streaming chunk
-fn create_openai_chunk(
-    id: &str,
-    model: &str,
-    delta: MessageDelta,
-    finish_reason: Option<FinishReason>,
-    usage: Option<Usage>,
-) -> ChatCompletionsStreamResponse {
-    ChatCompletionsStreamResponse {
-        id: id.to_string(),
-        object: Some("chat.completion.chunk".to_string()),
-        created: current_timestamp(),
-        model: model.to_string(),
-        choices: vec![StreamChoice {
-            index: 0,
-            delta,
-            finish_reason,
-            logprobs: None,
-        }],
-        usage,
-        system_fingerprint: None,
-        service_tier: None,
-    }
-}
-
-/// Helper to create empty OpenAI streaming chunk
-fn create_empty_openai_chunk() -> ChatCompletionsStreamResponse {
-    create_openai_chunk(
-        "stream",
-        "unknown",
-        MessageDelta {
-            role: None,
-            content: None,
-            refusal: None,
-            function_call: None,
-            tool_calls: None,
-        },
-        None,
-        None,
-    )
-}
-
-/// Convert Anthropic content blocks to OpenAI message content
-fn convert_anthropic_content_to_openai(
-    content: &[MessagesContentBlock],
-) -> Result<MessageContent, TransformError> {
-    let mut text_parts = Vec::new();
-
-    for block in content {
-        match block {
-            MessagesContentBlock::Text { text, .. } => {
-                text_parts.push(text.clone());
-            }
-            MessagesContentBlock::Thinking { thinking, .. } => {
-                text_parts.push(format!("thinking: {}", thinking));
-            }
-            _ => {
-                // Skip other content types for basic text conversion
-                continue;
-            }
-        }
-    }
-
-    Ok(MessageContent::Text(text_parts.join("\n")))
-}
-
-// Stop Reason Conversions
-impl Into<FinishReason> for MessagesStopReason {
-    fn into(self) -> FinishReason {
-        match self {
-            MessagesStopReason::EndTurn => FinishReason::Stop,
-            MessagesStopReason::MaxTokens => FinishReason::Length,
-            MessagesStopReason::StopSequence => FinishReason::Stop,
-            MessagesStopReason::ToolUse => FinishReason::ToolCalls,
-            MessagesStopReason::PauseTurn => FinishReason::Stop,
-            MessagesStopReason::Refusal => FinishReason::ContentFilter,
-        }
-    }
-}
-
 /// Convert Bedrock Message to OpenAI content and tool calls
 /// This function extracts text content and tool calls from a Bedrock message
 fn convert_bedrock_message_to_openai(
@@ -627,6 +372,31 @@ fn convert_bedrock_message_to_openai(
     Ok((content, tool_calls))
 }
 
+/// Convert Anthropic content blocks to OpenAI message content
+fn convert_anthropic_content_to_openai(
+    content: &[MessagesContentBlock],
+) -> Result<MessageContent, TransformError> {
+    let mut text_parts = Vec::new();
+
+    for block in content {
+        match block {
+            MessagesContentBlock::Text { text, .. } => {
+                text_parts.push(text.clone());
+            }
+            MessagesContentBlock::Thinking { thinking, .. } => {
+                text_parts.push(format!("thinking: {}", thinking));
+            }
+            _ => {
+                // Skip other content types for basic text conversion
+                continue;
+            }
+        }
+    }
+
+    Ok(MessageContent::Text(text_parts.join("\n")))
+}
+
+
 #[cfg(test)]
 mod tests {
     use super::*;
@@ -1166,4 +936,212 @@ mod tests {
         assert!(content.contains("Here's the analysis:"));
         // Note: Image blocks are not converted to text in the current implementation
     }
+
+    #[test]
+    fn test_chat_completions_to_responses_api_basic() {
+        use crate::apis::openai_responses::{OutputContent, OutputItem, ResponsesAPIResponse};
+
+        let chat_response = ChatCompletionsResponse {
+            id: "chatcmpl-123".to_string(),
+            object: Some("chat.completion".to_string()),
+            created: 1677652288,
+            model: "gpt-4".to_string(),
+            choices: vec![Choice {
+                index: 0,
+                message: crate::apis::openai::ResponseMessage {
+                    role: Role::Assistant,
+                    content: Some("Hello! How can I help you?".to_string()),
+                    refusal: None,
+                    annotations: None,
+                    audio: None,
+                    function_call: None,
+                    tool_calls: None,
+                },
+                finish_reason: Some(FinishReason::Stop),
+                logprobs: None,
+            }],
+            usage: Usage {
+                prompt_tokens: 10,
+                completion_tokens: 20,
+                total_tokens: 30,
+                prompt_tokens_details: None,
+                completion_tokens_details: None,
+            },
+            system_fingerprint: None,
+            service_tier: Some("default".to_string()),
+            metadata: None,
+        };
+
+        let responses_api: ResponsesAPIResponse = chat_response.try_into().unwrap();
+
+        assert_eq!(responses_api.id, "chatcmpl-123");
+        assert_eq!(responses_api.object, "response");
+        assert_eq!(responses_api.model, "gpt-4");
+
+        // Check usage conversion
+        let usage = responses_api.usage.unwrap();
+        assert_eq!(usage.input_tokens, 10);
+        assert_eq!(usage.output_tokens, 20);
+        assert_eq!(usage.total_tokens, 30);
+
+        // Check output items
+        assert_eq!(responses_api.output.len(), 1);
+        match &responses_api.output[0] {
+            OutputItem::Message {
+                role,
+                content,
+                ..
+            } => {
+                assert_eq!(role, "assistant");
+                assert_eq!(content.len(), 1);
+                match &content[0] {
+                    OutputContent::OutputText { text, .. } => {
+                        assert_eq!(text, "Hello! How can I help you?");
+                    }
+                    _ => panic!("Expected OutputText content"),
+                }
+            }
+            _ => panic!("Expected Message output item"),
+        }
+    }
+
+    #[test]
+    fn test_chat_completions_to_responses_api_with_tool_calls() {
+        use crate::apis::openai::{FunctionCall, ToolCall};
+        use crate::apis::openai_responses::{OutputItem, ResponsesAPIResponse};
+
+        let chat_response = ChatCompletionsResponse {
+            id: "chatcmpl-456".to_string(),
+            object: Some("chat.completion".to_string()),
+            created: 1677652300,
+            model: "gpt-4".to_string(),
+            choices: vec![Choice {
+                index: 0,
+                message: crate::apis::openai::ResponseMessage {
+                    role: Role::Assistant,
+                    content: Some("Let me check the weather.".to_string()),
+                    refusal: None,
+                    annotations: None,
+                    audio: None,
+                    function_call: None,
+                    tool_calls: Some(vec![ToolCall {
+                        id: "call_abc123".to_string(),
+                        call_type: "function".to_string(),
+                        function: FunctionCall {
+                            name: "get_weather".to_string(),
+                            arguments: r#"{"location":"San Francisco"}"#.to_string(),
+                        },
+                    }]),
+                },
+                finish_reason: Some(FinishReason::ToolCalls),
+                logprobs: None,
+            }],
+            usage: Usage {
+                prompt_tokens: 15,
+                completion_tokens: 25,
+                total_tokens: 40,
+                prompt_tokens_details: None,
+                completion_tokens_details: None,
+            },
+            system_fingerprint: None,
+            service_tier: None,
+            metadata: None,
+        };
+
+        let responses_api: ResponsesAPIResponse = chat_response.try_into().unwrap();
+
+        // Should have 2 output items: message + function call
+        assert_eq!(responses_api.output.len(), 2);
+
+        // Check message item
+        match &responses_api.output[0] {
+            OutputItem::Message { content, .. } => {
+                assert_eq!(content.len(), 1);
+            }
+            _ => panic!("Expected Message output item"),
+        }
+
+        // Check function call item
+        match &responses_api.output[1] {
+            OutputItem::FunctionCall {
+                call_id,
+                name,
+                arguments,
+                ..
+            } => {
+                assert_eq!(call_id, "call_abc123");
+                assert_eq!(name.as_ref().unwrap(), "get_weather");
+                assert!(arguments.as_ref().unwrap().contains("San Francisco"));
+            }
+            _ => panic!("Expected FunctionCall output item"),
+        }
+    }
+
+    #[test]
+    fn test_chat_completions_to_responses_api_tool_calls_only() {
+        use crate::apis::openai::{FunctionCall, ToolCall};
+        use crate::apis::openai_responses::{OutputItem, ResponsesAPIResponse};
+
+        // Test the real-world case where content is null and there are only tool calls
+        let chat_response = ChatCompletionsResponse {
+            id: "chatcmpl-789".to_string(),
+            object: Some("chat.completion".to_string()),
+            created: 1764023939,
+            model: "gpt-4o-2024-08-06".to_string(),
+            choices: vec![Choice {
+                index: 0,
+                message: crate::apis::openai::ResponseMessage {
+                    role: Role::Assistant,
+                    content: None, // No text content, only tool calls
+                    refusal: None,
+                    annotations: None,
+                    audio: None,
+                    function_call: None,
+                    tool_calls: Some(vec![ToolCall {
+                        id: "call_oJBtqTJmRfBGlFS55QhMfUUV".to_string(),
+                        call_type: "function".to_string(),
+                        function: FunctionCall {
+                            name: "get_weather".to_string(),
+                            arguments: r#"{"location":"San Francisco, CA"}"#.to_string(),
+                        },
+                    }]),
+                },
+                finish_reason: Some(FinishReason::ToolCalls),
+                logprobs: None,
+            }],
+            usage: Usage {
+                prompt_tokens: 84,
+                completion_tokens: 17,
+                total_tokens: 101,
+                prompt_tokens_details: None,
+                completion_tokens_details: None,
+            },
+            system_fingerprint: Some("fp_7eeb46f068".to_string()),
+            service_tier: Some("default".to_string()),
+            metadata: None,
+        };
+
+        let responses_api: ResponsesAPIResponse = chat_response.try_into().unwrap();
+
+        // Should have only 1 output item: function call (no empty message item)
+        assert_eq!(responses_api.output.len(), 1);
+
+        // Check function call item
+        match &responses_api.output[0] {
+            OutputItem::FunctionCall {
+                call_id,
+                name,
+                arguments,
+                ..
+            } => {
+                assert_eq!(call_id, "call_oJBtqTJmRfBGlFS55QhMfUUV");
+                assert_eq!(name.as_ref().unwrap(), "get_weather");
+                assert!(arguments.as_ref().unwrap().contains("San Francisco, CA"));
+            }
+            _ => panic!("Expected FunctionCall output item as first item"),
+        }
+
+        // Verify status is Completed for tool_calls finish reason
+        assert!(matches!(responses_api.status, crate::apis::openai_responses::ResponseStatus::Completed));
+    }
 }

crates/hermesllm/src/transforms/response_streaming/mod.rs

@@ -0,0 +1,2 @@
+pub mod to_anthropic_streaming;
+pub mod to_openai_streaming;

crates/hermesllm/src/transforms/response_streaming/to_anthropic_streaming.rs

@@ -0,0 +1,281 @@
+use crate::apis::amazon_bedrock::{
+    ContentBlockDelta, ConverseStreamEvent,
+};
+use crate::apis::anthropic::{
+    MessagesContentBlock, MessagesContentDelta, MessagesMessageDelta,
+    MessagesRole, MessagesStopReason, MessagesStreamEvent, MessagesStreamMessage, MessagesUsage,
+};
+use crate::apis::openai::{ ChatCompletionsStreamResponse, ToolCallDelta,
+};
+use crate::clients::TransformError;
+use serde_json::Value;
+
+impl TryFrom<ChatCompletionsStreamResponse> for MessagesStreamEvent {
+    type Error = TransformError;
+
+    fn try_from(resp: ChatCompletionsStreamResponse) -> Result<Self, Self::Error> {
+        if resp.choices.is_empty() {
+            return Ok(MessagesStreamEvent::Ping);
+        }
+
+        let choice = &resp.choices[0];
+
+        // Handle final chunk with usage
+        let has_usage = resp.usage.is_some();
+        if let Some(usage) = resp.usage {
+            if let Some(finish_reason) = &choice.finish_reason {
+                let anthropic_stop_reason: MessagesStopReason = finish_reason.clone().into();
+                return Ok(MessagesStreamEvent::MessageDelta {
+                    delta: MessagesMessageDelta {
+                        stop_reason: anthropic_stop_reason,
+                        stop_sequence: None,
+                    },
+                    usage: usage.into(),
+                });
+            }
+        }
+
+        // NOTE: We do NOT emit MessageStart here anymore!
+        // The AnthropicMessagesStreamBuffer will inject message_start and content_block_start
+        // when it sees the first content_block_delta. This solves the problem where OpenAI
+        // sends both role and content in the same chunk - we can only return one event here,
+        // so we prioritize the content and let the buffer handle lifecycle events.
+
+        // Handle content delta (even if role is present in the same chunk)
+        if let Some(content) = &choice.delta.content {
+            if !content.is_empty() {
+                return Ok(MessagesStreamEvent::ContentBlockDelta {
+                    index: 0,
+                    delta: MessagesContentDelta::TextDelta {
+                        text: content.clone(),
+                    },
+                });
+            }
+        }
+
+        // Handle tool calls
+        if let Some(tool_calls) = &choice.delta.tool_calls {
+            return convert_tool_call_deltas(tool_calls.clone());
+        }
+
+        // Handle finish reason - generate MessageDelta only (MessageStop comes later)
+        if let Some(finish_reason) = &choice.finish_reason {
+            // If we have usage data, it was already handled above
+            // If not, we need to generate MessageDelta with default usage
+            if !has_usage {
+                let anthropic_stop_reason: MessagesStopReason = finish_reason.clone().into();
+                return Ok(MessagesStreamEvent::MessageDelta {
+                    delta: MessagesMessageDelta {
+                        stop_reason: anthropic_stop_reason,
+                        stop_sequence: None,
+                    },
+                    usage: MessagesUsage {
+                        input_tokens: 0,
+                        output_tokens: 0,
+                        cache_creation_input_tokens: None,
+                        cache_read_input_tokens: None,
+                    },
+                });
+            }
+            // If usage was already handled above, we don't need to do anything more here
+            // MessageStop will be handled when [DONE] is encountered
+        }
+
+        // Default to ping for unhandled cases
+        Ok(MessagesStreamEvent::Ping)
+    }
+}
+
+impl Into<String> for MessagesStreamEvent {
+    fn into(self) -> String {
+        let transformed_json = serde_json::to_string(&self).unwrap_or_default();
+        let event_type = match &self {
+            MessagesStreamEvent::MessageStart { .. } => "message_start",
+            MessagesStreamEvent::ContentBlockStart { .. } => "content_block_start",
+            MessagesStreamEvent::ContentBlockDelta { .. } => "content_block_delta",
+            MessagesStreamEvent::ContentBlockStop { .. } => "content_block_stop",
+            MessagesStreamEvent::MessageDelta { .. } => "message_delta",
+            MessagesStreamEvent::MessageStop => "message_stop",
+            MessagesStreamEvent::Ping => "ping",
+        };
+
+        let event = format!("event: {}\n", event_type);
+        let data = format!("data: {}\n\n", transformed_json);
+        event + &data
+    }
+}
+
+impl TryFrom<ConverseStreamEvent> for MessagesStreamEvent {
+    type Error = TransformError;
+
+    fn try_from(event: ConverseStreamEvent) -> Result<Self, Self::Error> {
+        match event {
+            // MessageStart - convert to Anthropic MessageStart
+            ConverseStreamEvent::MessageStart(start_event) => {
+                let role = match start_event.role {
+                    crate::apis::amazon_bedrock::ConversationRole::User => MessagesRole::User,
+                    crate::apis::amazon_bedrock::ConversationRole::Assistant => {
+                        MessagesRole::Assistant
+                    }
+                };
+
+                Ok(MessagesStreamEvent::MessageStart {
+                    message: MessagesStreamMessage {
+                        id: format!(
+                            "bedrock-stream-{}",
+                            std::time::SystemTime::now()
+                                .duration_since(std::time::UNIX_EPOCH)
+                                .unwrap_or_default()
+                                .as_nanos()
+                        ),
+                        obj_type: "message".to_string(),
+                        role,
+                        content: vec![],
+                        model: "bedrock-model".to_string(),
+                        stop_reason: None,
+                        stop_sequence: None,
+                        usage: MessagesUsage {
+                            input_tokens: 0,
+                            output_tokens: 0,
+                            cache_creation_input_tokens: None,
+                            cache_read_input_tokens: None,
+                        },
+                    },
+                })
+            }
+
+            // ContentBlockStart - convert to Anthropic ContentBlockStart
+            ConverseStreamEvent::ContentBlockStart(start_event) => {
+                // Note: Bedrock sends tool_use_id and name at start, with input coming in subsequent deltas
+                // Anthropic expects the same pattern, so we initialize with an empty input object
+                match start_event.start {
+                    crate::apis::amazon_bedrock::ContentBlockStart::ToolUse { tool_use } => {
+                        Ok(MessagesStreamEvent::ContentBlockStart {
+                            index: start_event.content_block_index as u32,
+                            content_block: MessagesContentBlock::ToolUse {
+                                id: tool_use.tool_use_id,
+                                name: tool_use.name,
+                                input: Value::Object(serde_json::Map::new()), // Empty - will be filled by deltas
+                                cache_control: None,
+                            },
+                        })
+                    }
+                }
+            }
+
+            // ContentBlockDelta - convert to Anthropic ContentBlockDelta
+            ConverseStreamEvent::ContentBlockDelta(delta_event) => {
+                let delta = match delta_event.delta {
+                    ContentBlockDelta::Text { text } => MessagesContentDelta::TextDelta { text },
+                    ContentBlockDelta::ToolUse { tool_use } => {
+                        MessagesContentDelta::InputJsonDelta {
+                            partial_json: tool_use.input,
+                        }
+                    }
+                };
+
+                Ok(MessagesStreamEvent::ContentBlockDelta {
+                    index: delta_event.content_block_index as u32,
+                    delta,
+                })
+            }
+
+            // ContentBlockStop - convert to Anthropic ContentBlockStop
+            ConverseStreamEvent::ContentBlockStop(stop_event) => {
+                Ok(MessagesStreamEvent::ContentBlockStop {
+                    index: stop_event.content_block_index as u32,
+                })
+            }
+
+            // MessageStop - convert to Anthropic MessageDelta with stop reason
+            // Note: Bedrock sends Metadata separately with usage info, creating a second MessageDelta
+            // The client should merge these or use the final one with complete usage
+            ConverseStreamEvent::MessageStop(stop_event) => {
+                let anthropic_stop_reason = match stop_event.stop_reason {
+                    crate::apis::amazon_bedrock::StopReason::EndTurn => MessagesStopReason::EndTurn,
+                    crate::apis::amazon_bedrock::StopReason::ToolUse => MessagesStopReason::ToolUse,
+                    crate::apis::amazon_bedrock::StopReason::MaxTokens => MessagesStopReason::MaxTokens,
+                    crate::apis::amazon_bedrock::StopReason::StopSequence => MessagesStopReason::EndTurn,
+                    crate::apis::amazon_bedrock::StopReason::GuardrailIntervened => MessagesStopReason::Refusal,
+                    crate::apis::amazon_bedrock::StopReason::ContentFiltered => MessagesStopReason::Refusal,
+                };
+
+                Ok(MessagesStreamEvent::MessageDelta {
+                    delta: MessagesMessageDelta {
+                        stop_reason: anthropic_stop_reason,
+                        stop_sequence: None,
+                    },
+                    usage: MessagesUsage {
+                        input_tokens: 0,
+                        output_tokens: 0,
+                        cache_creation_input_tokens: None,
+                        cache_read_input_tokens: None,
+                    },
+                })
+            }
+
+            // Metadata - convert usage information to MessageDelta
+            ConverseStreamEvent::Metadata(metadata_event) => {
+                Ok(MessagesStreamEvent::MessageDelta {
+                    delta: MessagesMessageDelta {
+                        stop_reason: MessagesStopReason::EndTurn,
+                        stop_sequence: None,
+                    },
+                    usage: MessagesUsage {
+                        input_tokens: metadata_event.usage.input_tokens,
+                        output_tokens: metadata_event.usage.output_tokens,
+                        cache_creation_input_tokens: metadata_event.usage.cache_write_input_tokens,
+                        cache_read_input_tokens: metadata_event.usage.cache_read_input_tokens,
+                    },
+                })
+            }
+
+            // Exception events - convert to Ping (could be enhanced to return error events)
+            ConverseStreamEvent::InternalServerException(_)
+            | ConverseStreamEvent::ModelStreamErrorException(_)
+            | ConverseStreamEvent::ServiceUnavailableException(_)
+            | ConverseStreamEvent::ThrottlingException(_)
+            | ConverseStreamEvent::ValidationException(_) => {
+                // TODO: Consider adding proper error handling/events
+                Ok(MessagesStreamEvent::Ping)
+            }
+        }
+    }
+}
+
+/// Convert tool call deltas to Anthropic stream events
+fn convert_tool_call_deltas(
+    tool_calls: Vec<ToolCallDelta>,
+) -> Result<MessagesStreamEvent, TransformError> {
+    for tool_call in tool_calls {
+        if let Some(id) = &tool_call.id {
+            // Tool call start
+            if let Some(function) = &tool_call.function {
+                if let Some(name) = &function.name {
+                    return Ok(MessagesStreamEvent::ContentBlockStart {
+                        index: tool_call.index,
+                        content_block: MessagesContentBlock::ToolUse {
+                            id: id.clone(),
+                            name: name.clone(),
+                            input: Value::Object(serde_json::Map::new()),
+                            cache_control: None,
+                        },
+                    });
+                }
+            }
+        } else if let Some(function) = &tool_call.function {
+            if let Some(arguments) = &function.arguments {
+                // Tool arguments delta
+                return Ok(MessagesStreamEvent::ContentBlockDelta {
+                    index: tool_call.index,
+                    delta: MessagesContentDelta::InputJsonDelta {
+                        partial_json: arguments.clone(),
+                    },
+                });
+            }
+        }
+    }
+
+    // Fallback to ping if no valid tool call found
+    Ok(MessagesStreamEvent::Ping)
+}

crates/hermesllm/src/transforms/response_streaming/to_openai_streaming.rs

@@ -0,0 +1,527 @@
+use crate::apis::amazon_bedrock::{ ConverseStreamEvent, StopReason};
+use crate::apis::anthropic::{
+    MessagesContentBlock, MessagesContentDelta, MessagesStopReason, MessagesStreamEvent};
+use crate::apis::openai::{ ChatCompletionsStreamResponse,FinishReason,
+    FunctionCallDelta, MessageDelta, Role, StreamChoice, ToolCallDelta, Usage,
+};
+use crate::apis::openai_responses::ResponsesAPIStreamEvent;
+
+use crate::clients::TransformError;
+use crate::transforms::lib::*;
+
+// ============================================================================
+// PROVIDER STREAMING TRANSFORMATIONS TO OPENAI FORMAT
+// ============================================================================
+//
+// This module handles business logic for converting streaming events from
+// various providers (Anthropic, Bedrock, etc.) into OpenAI's ChatCompletions format.
+//
+// # Architecture Separation
+//
+// **Provider Transformations** (this module):
+// - Business logic for converting between provider formats
+// - Uses Rust traits (TryFrom, Into) for type-safe conversions
+// - Stateless event-by-event transformation
+// - Example: MessagesStreamEvent → ChatCompletionsStreamResponse
+//
+// **Wire Format Buffering** (`apis/streaming_shapes/`):
+// - SSE protocol handling (data:, event: lines)
+// - State accumulation and lifecycle management
+// - Buffering for stateful APIs (v1/responses)
+// - Example: ChatCompletionsToResponsesTransformer
+//
+// # Flow
+//
+// ```text
+// Anthropic Event → [Provider Transform] → OpenAI Event → [Wire Buffer] → SSE Wire Format
+//    (business)          (this module)        (protocol)    (streaming_shapes)    (network)
+// ```
+//
+// ============================================================================
+
+impl TryFrom<MessagesStreamEvent> for ChatCompletionsStreamResponse {
+    type Error = TransformError;
+
+    fn try_from(event: MessagesStreamEvent) -> Result<Self, Self::Error> {
+        match event {
+            MessagesStreamEvent::MessageStart { message } => Ok(create_openai_chunk(
+                &message.id,
+                &message.model,
+                MessageDelta {
+                    role: Some(Role::Assistant),
+                    content: None,
+                    refusal: None,
+                    function_call: None,
+                    tool_calls: None,
+                },
+                None,
+                None,
+            )),
+
+            MessagesStreamEvent::ContentBlockStart { content_block, .. } => {
+                convert_content_block_start(content_block)
+            }
+
+            MessagesStreamEvent::ContentBlockDelta { delta, .. } => convert_content_delta(delta),
+
+            MessagesStreamEvent::ContentBlockStop { .. } => Ok(create_empty_openai_chunk()),
+
+            MessagesStreamEvent::MessageDelta { delta, usage } => {
+                let finish_reason: Option<FinishReason> = Some(delta.stop_reason.into());
+                let openai_usage: Option<Usage> = Some(usage.into());
+
+                Ok(create_openai_chunk(
+                    "stream",
+                    "unknown",
+                    MessageDelta {
+                        role: None,
+                        content: None,
+                        refusal: None,
+                        function_call: None,
+                        tool_calls: None,
+                    },
+                    finish_reason,
+                    openai_usage,
+                ))
+            }
+
+            MessagesStreamEvent::MessageStop => Ok(create_openai_chunk(
+                "stream",
+                "unknown",
+                MessageDelta {
+                    role: None,
+                    content: None,
+                    refusal: None,
+                    function_call: None,
+                    tool_calls: None,
+                },
+                Some(FinishReason::Stop),
+                None,
+            )),
+
+            MessagesStreamEvent::Ping => Ok(ChatCompletionsStreamResponse {
+                id: "stream".to_string(),
+                object: Some("chat.completion.chunk".to_string()),
+                created: current_timestamp(),
+                model: "unknown".to_string(),
+                choices: vec![],
+                usage: None,
+                system_fingerprint: None,
+                service_tier: None,
+            }),
+        }
+    }
+}
+
+impl TryFrom<ConverseStreamEvent> for ChatCompletionsStreamResponse {
+    type Error = TransformError;
+
+    fn try_from(event: ConverseStreamEvent) -> Result<Self, Self::Error> {
+        match event {
+            ConverseStreamEvent::MessageStart(start_event) => {
+                let role = match start_event.role {
+                    crate::apis::amazon_bedrock::ConversationRole::User => Role::User,
+                    crate::apis::amazon_bedrock::ConversationRole::Assistant => Role::Assistant,
+                };
+
+                Ok(create_openai_chunk(
+                    "stream",
+                    "unknown",
+                    MessageDelta {
+                        role: Some(role),
+                        content: None,
+                        refusal: None,
+                        function_call: None,
+                        tool_calls: None,
+                    },
+                    None,
+                    None,
+                ))
+            }
+
+            ConverseStreamEvent::ContentBlockStart(start_event) => {
+                use crate::apis::amazon_bedrock::ContentBlockStart;
+
+                match start_event.start {
+                    ContentBlockStart::ToolUse { tool_use } => Ok(create_openai_chunk(
+                        "stream",
+                        "unknown",
+                        MessageDelta {
+                            role: None,
+                            content: None,
+                            refusal: None,
+                            function_call: None,
+                            tool_calls: Some(vec![ToolCallDelta {
+                                index: start_event.content_block_index as u32,
+                                id: Some(tool_use.tool_use_id),
+                                call_type: Some("function".to_string()),
+                                function: Some(FunctionCallDelta {
+                                    name: Some(tool_use.name),
+                                    arguments: Some("".to_string()),
+                                }),
+                            }]),
+                        },
+                        None,
+                        None,
+                    )),
+                }
+            }
+
+            ConverseStreamEvent::ContentBlockDelta(delta_event) => {
+                use crate::apis::amazon_bedrock::ContentBlockDelta;
+
+                match delta_event.delta {
+                    ContentBlockDelta::Text { text } => Ok(create_openai_chunk(
+                        "stream",
+                        "unknown",
+                        MessageDelta {
+                            role: None,
+                            content: Some(text),
+                            refusal: None,
+                            function_call: None,
+                            tool_calls: None,
+                        },
+                        None,
+                        None,
+                    )),
+                    ContentBlockDelta::ToolUse { tool_use } => Ok(create_openai_chunk(
+                        "stream",
+                        "unknown",
+                        MessageDelta {
+                            role: None,
+                            content: None,
+                            refusal: None,
+                            function_call: None,
+                            tool_calls: Some(vec![ToolCallDelta {
+                                index: delta_event.content_block_index as u32,
+                                id: None,
+                                call_type: None,
+                                function: Some(FunctionCallDelta {
+                                    name: None,
+                                    arguments: Some(tool_use.input),
+                                }),
+                            }]),
+                        },
+                        None,
+                        None,
+                    )),
+                }
+            }
+
+            ConverseStreamEvent::ContentBlockStop(_) => Ok(create_empty_openai_chunk()),
+
+            ConverseStreamEvent::MessageStop(stop_event) => {
+                let finish_reason = match stop_event.stop_reason {
+                    StopReason::EndTurn => FinishReason::Stop,
+                    StopReason::ToolUse => FinishReason::ToolCalls,
+                    StopReason::MaxTokens => FinishReason::Length,
+                    StopReason::StopSequence => FinishReason::Stop,
+                    StopReason::GuardrailIntervened => FinishReason::ContentFilter,
+                    StopReason::ContentFiltered => FinishReason::ContentFilter,
+                };
+
+                Ok(create_openai_chunk(
+                    "stream",
+                    "unknown",
+                    MessageDelta {
+                        role: None,
+                        content: None,
+                        refusal: None,
+                        function_call: None,
+                        tool_calls: None,
+                    },
+                    Some(finish_reason),
+                    None,
+                ))
+            }
+
+            ConverseStreamEvent::Metadata(metadata_event) => {
+                let usage = Usage {
+                    prompt_tokens: metadata_event.usage.input_tokens,
+                    completion_tokens: metadata_event.usage.output_tokens,
+                    total_tokens: metadata_event.usage.total_tokens,
+                    prompt_tokens_details: None,
+                    completion_tokens_details: None,
+                };
+
+                Ok(create_openai_chunk(
+                    "stream",
+                    "unknown",
+                    MessageDelta {
+                        role: None,
+                        content: None,
+                        refusal: None,
+                        function_call: None,
+                        tool_calls: None,
+                    },
+                    None,
+                    Some(usage),
+                ))
+            }
+
+            // Error events - convert to empty chunks (errors should be handled elsewhere)
+            ConverseStreamEvent::InternalServerException(_)
+            | ConverseStreamEvent::ModelStreamErrorException(_)
+            | ConverseStreamEvent::ServiceUnavailableException(_)
+            | ConverseStreamEvent::ThrottlingException(_)
+            | ConverseStreamEvent::ValidationException(_) => Ok(create_empty_openai_chunk()),
+        }
+    }
+}
+
+/// Convert content block start to OpenAI chunk
+fn convert_content_block_start(
+    content_block: MessagesContentBlock,
+) -> Result<ChatCompletionsStreamResponse, TransformError> {
+    match content_block {
+        MessagesContentBlock::Text { .. } => {
+            // No immediate output for text block start
+            Ok(create_empty_openai_chunk())
+        }
+        MessagesContentBlock::ToolUse { id, name, .. }
+        | MessagesContentBlock::ServerToolUse { id, name, .. }
+        | MessagesContentBlock::McpToolUse { id, name, .. } => {
+            // Tool use start → OpenAI chunk with tool_calls
+            Ok(create_openai_chunk(
+                "stream",
+                "unknown",
+                MessageDelta {
+                    role: None,
+                    content: None,
+                    refusal: None,
+                    function_call: None,
+                    tool_calls: Some(vec![ToolCallDelta {
+                        index: 0,
+                        id: Some(id),
+                        call_type: Some("function".to_string()),
+                        function: Some(FunctionCallDelta {
+                            name: Some(name),
+                            arguments: Some("".to_string()),
+                        }),
+                    }]),
+                },
+                None,
+                None,
+            ))
+        }
+        _ => Err(TransformError::UnsupportedContent(
+            "Unsupported content block type in stream start".to_string(),
+        )),
+    }
+}
+
+/// Convert content delta to OpenAI chunk
+fn convert_content_delta(
+    delta: MessagesContentDelta,
+) -> Result<ChatCompletionsStreamResponse, TransformError> {
+    match delta {
+        MessagesContentDelta::TextDelta { text } => Ok(create_openai_chunk(
+            "stream",
+            "unknown",
+            MessageDelta {
+                role: None,
+                content: Some(text),
+                refusal: None,
+                function_call: None,
+                tool_calls: None,
+            },
+            None,
+            None,
+        )),
+        MessagesContentDelta::ThinkingDelta { thinking } => Ok(create_openai_chunk(
+            "stream",
+            "unknown",
+            MessageDelta {
+                role: None,
+                content: Some(format!("thinking: {}", thinking)),
+                refusal: None,
+                function_call: None,
+                tool_calls: None,
+            },
+            None,
+            None,
+        )),
+        MessagesContentDelta::InputJsonDelta { partial_json } => Ok(create_openai_chunk(
+            "stream",
+            "unknown",
+            MessageDelta {
+                role: None,
+                content: None,
+                refusal: None,
+                function_call: None,
+                tool_calls: Some(vec![ToolCallDelta {
+                    index: 0,
+                    id: None,
+                    call_type: None,
+                    function: Some(FunctionCallDelta {
+                        name: None,
+                        arguments: Some(partial_json),
+                    }),
+                }]),
+            },
+            None,
+            None,
+        )),
+    }
+}
+
+/// Helper to create OpenAI streaming chunk
+fn create_openai_chunk(
+    id: &str,
+    model: &str,
+    delta: MessageDelta,
+    finish_reason: Option<FinishReason>,
+    usage: Option<Usage>,
+) -> ChatCompletionsStreamResponse {
+    ChatCompletionsStreamResponse {
+        id: id.to_string(),
+        object: Some("chat.completion.chunk".to_string()),
+        created: current_timestamp(),
+        model: model.to_string(),
+        choices: vec![StreamChoice {
+            index: 0,
+            delta,
+            finish_reason,
+            logprobs: None,
+        }],
+        usage,
+        system_fingerprint: None,
+        service_tier: None,
+    }
+}
+
+/// Helper to create empty OpenAI streaming chunk
+fn create_empty_openai_chunk() -> ChatCompletionsStreamResponse {
+    create_openai_chunk(
+        "stream",
+        "unknown",
+        MessageDelta {
+            role: None,
+            content: None,
+            refusal: None,
+            function_call: None,
+            tool_calls: None,
+        },
+        None,
+        None,
+    )
+}
+
+// Stop Reason Conversions
+impl Into<FinishReason> for MessagesStopReason {
+    fn into(self) -> FinishReason {
+        match self {
+            MessagesStopReason::EndTurn => FinishReason::Stop,
+            MessagesStopReason::MaxTokens => FinishReason::Length,
+            MessagesStopReason::StopSequence => FinishReason::Stop,
+            MessagesStopReason::ToolUse => FinishReason::ToolCalls,
+            MessagesStopReason::PauseTurn => FinishReason::Stop,
+            MessagesStopReason::Refusal => FinishReason::ContentFilter,
+        }
+    }
+}
+
+impl TryFrom<ChatCompletionsStreamResponse> for ResponsesAPIStreamEvent {
+    type Error = TransformError;
+
+    fn try_from(chunk: ChatCompletionsStreamResponse) -> Result<Self, TransformError> {
+        // Stateless conversion - just extract the delta information
+        // The buffer will manage state, item IDs, and sequence numbers
+
+        // Extract first choice if available
+        if let Some(choice) = chunk.choices.first() {
+            let delta = &choice.delta;
+
+            // Tool call with function name and/or arguments
+            if let Some(tool_calls) = &delta.tool_calls {
+                if let Some(tool_call) = tool_calls.first() {
+                    // Extract call_id and name if available (metadata from initial event)
+                    let call_id = tool_call.id.clone();
+                    let function_name = tool_call.function.as_ref()
+                        .and_then(|f| f.name.clone());
+
+                    // Check if we have function metadata (name, id)
+                    if let Some(function) = &tool_call.function {
+                        // If we have arguments delta, return that
+                        if let Some(args) = &function.arguments {
+                            return Ok(ResponsesAPIStreamEvent::ResponseFunctionCallArgumentsDelta {
+                                output_index: choice.index as i32,
+                                item_id: "".to_string(), // Buffer will fill this
+                                delta: args.clone(),
+                                sequence_number: 0, // Buffer will fill this
+                                call_id,
+                                name: function_name,
+                            });
+                        }
+
+                        // If we have function name but no arguments yet (initial tool call event)
+                        // Return an empty arguments delta so the buffer knows to create the item
+                        if function.name.is_some() {
+                            return Ok(ResponsesAPIStreamEvent::ResponseFunctionCallArgumentsDelta {
+                                output_index: choice.index as i32,
+                                item_id: "".to_string(), // Buffer will fill this
+                                delta: "".to_string(), // Empty delta signals this is the initial event
+                                sequence_number: 0, // Buffer will fill this
+                                call_id,
+                                name: function_name,
+                            });
+                        }
+                    }
+                }
+            }
+
+            // Text content delta
+            if let Some(content) = &delta.content {
+                if !content.is_empty() {
+                    return Ok(ResponsesAPIStreamEvent::ResponseOutputTextDelta {
+                        item_id: "".to_string(), // Buffer will fill this
+                        output_index: choice.index as i32,
+                        content_index: 0,
+                        delta: content.clone(),
+                        logprobs: vec![],
+                        obfuscation: None,
+                        sequence_number: 0, // Buffer will fill this
+                    });
+                }
+            }
+
+            // Handle finish_reason - this is a completion signal
+            // Return an empty delta that the buffer can use to detect completion
+            if choice.finish_reason.is_some() {
+                // Return a minimal text delta to signal completion
+                // The buffer will handle the finish_reason and generate response.completed
+                return Ok(ResponsesAPIStreamEvent::ResponseOutputTextDelta {
+                    item_id: "".to_string(), // Buffer will fill this
+                    output_index: choice.index as i32,
+                    content_index: 0,
+                    delta: "".to_string(), // Empty delta signals completion
+                    logprobs: vec![],
+                    obfuscation: None,
+                    sequence_number: 0, // Buffer will fill this
+                });
+            }
+
+            // Empty delta with role only (common at stream start)
+            if delta.role.is_some() {
+                // This is typically the first chunk establishing the assistant role
+                // Return an empty text delta that the buffer can use to initialize state
+                return Ok(ResponsesAPIStreamEvent::ResponseOutputTextDelta {
+                    item_id: "".to_string(),
+                    output_index: choice.index as i32,
+                    content_index: 0,
+                    delta: "".to_string(),
+                    logprobs: vec![],
+                    obfuscation: None,
+                    sequence_number: 0,
+                });
+            }
+        }
+
+        // Empty chunk or no convertible content (e.g., keep-alive chunks with delta: {})
+        // These are valid in OpenAI streaming and should be silently ignored
+        // Return error so the caller can skip these chunks without warnings
+        Err(TransformError::UnsupportedConversion(
+            "Empty or keep-alive chunk with no convertible content".to_string(),
+        ))
+    }
+}

crates/llm_gateway/src/stream_context.rs

@@ -22,11 +22,13 @@ use common::ratelimit::Header;
 use common::stats::{IncrementingMetric, RecordingMetric};
 use common::tracing::{Event, Span, TraceData, Traceparent};
 use common::{ratelimit, routing, tokenizer};
-use hermesllm::apis::amazon_bedrock_binary_frame::BedrockBinaryFrameDecoder;
+use hermesllm::apis::streaming_shapes::amazon_bedrock_binary_frame::BedrockBinaryFrameDecoder;
-use hermesllm::apis::anthropic::{MessagesContentBlock, MessagesStreamEvent};
+use hermesllm::apis::streaming_shapes::sse::{
-use hermesllm::apis::sse::{SseEvent, SseStreamIter};
+    SseEvent, SseStreamBuffer, SseStreamBufferTrait, SseStreamIter,
-use hermesllm::clients::endpoints::SupportedAPIs;
+};
+use hermesllm::clients::endpoints::SupportedAPIsFromClient;
 use hermesllm::providers::response::ProviderResponse;
+use hermesllm::providers::streaming_response::ProviderStreamResponse;
 use hermesllm::{
     DecodedFrame, ProviderId, ProviderRequest, ProviderRequestType, ProviderResponseType,
     ProviderStreamResponseType,
@@ -38,7 +40,7 @@ pub struct StreamContext {
     streaming_response: bool,
     response_tokens: usize,
     /// The API that is requested by the client (before compatibility mapping)
-    client_api: Option<SupportedAPIs>,
+    client_api: Option<SupportedAPIsFromClient>,
     /// The API that should be used for the upstream provider (after compatibility mapping)
     resolved_api: Option<SupportedUpstreamAPIs>,
     llm_providers: Rc<LlmProviders>,
@@ -56,6 +58,7 @@ pub struct StreamContext {
     binary_frame_decoder: Option<BedrockBinaryFrameDecoder<bytes::BytesMut>>,
     http_method: Option<String>,
     http_protocol: Option<String>,
+    sse_buffer: Option<SseStreamBuffer>,
 }
 
 impl StreamContext {
@@ -87,6 +90,7 @@ impl StreamContext {
             binary_frame_decoder: None,
             http_method: None,
             http_protocol: None,
+            sse_buffer: None,
         }
     }
 
@@ -172,7 +176,8 @@ impl StreamContext {
             Some(
                 SupportedUpstreamAPIs::OpenAIChatCompletions(_)
                 | SupportedUpstreamAPIs::AmazonBedrockConverse(_)
-                | SupportedUpstreamAPIs::AmazonBedrockConverseStream(_),
+                | SupportedUpstreamAPIs::AmazonBedrockConverseStream(_)
+                | SupportedUpstreamAPIs::OpenAIResponsesAPI(_),
             )
             | None => {
                 // OpenAI and default: use Authorization Bearer token
@@ -476,7 +481,17 @@ impl StreamContext {
                         }
                     };
 
-                let mut response_buffer = Vec::new();
+                // Initialize SSE buffer if not present
+                if self.sse_buffer.is_none() {
+                    self.sse_buffer = match SseStreamBuffer::try_from((&client_api, &upstream_api))
+                    {
+                        Ok(buffer) => Some(buffer),
+                        Err(e) => {
+                            warn!("Failed to create SSE buffer: {}", e);
+                            return Err(Action::Continue);
+                        }
+                    };
+                }
 
                 // Process each SSE event
                 for sse_event in sse_iter {
@@ -527,12 +542,32 @@ impl StreamContext {
                         }
                     }
 
-                    // Add transformed event to response buffer
+                    // Add transformed event to buffer (buffer may inject lifecycle events)
-                    let bytes: Vec<u8> = transformed_event.into();
+                    if let Some(buffer) = self.sse_buffer.as_mut() {
-                    response_buffer.extend_from_slice(&bytes);
+                        buffer.add_transformed_event(transformed_event);
+                    }
                 }
 
-                Ok(response_buffer)
+                // Get accumulated bytes from buffer and return
+                match self.sse_buffer.as_mut() {
+                    Some(buffer) => {
+                        let bytes = buffer.into_bytes();
+                        if !bytes.is_empty() {
+                            let content = String::from_utf8_lossy(&bytes);
+                            debug!(
+                                "[ARCHGW_REQ_ID:{}] UPSTREAM_TRANSFORMED_CLIENT_RESPONSE: size={} content={}",
+                                self.request_identifier(),
+                                bytes.len(),
+                                content
+                            );
+                        }
+                        Ok(bytes)
+                    }
+                    None => {
+                        warn!("SSE buffer unexpectedly missing after initialization");
+                        Err(Action::Continue)
+                    }
+                }
             }
             None => {
                 warn!("Missing client_api for non-streaming response");
@@ -544,7 +579,7 @@ impl StreamContext {
     fn handle_bedrock_binary_stream(
         &mut self,
         body: &[u8],
-        client_api: &SupportedAPIs,
+        client_api: &SupportedAPIsFromClient,
         upstream_api: &SupportedUpstreamAPIs,
     ) -> Result<Vec<u8>, Action> {
         // Initialize decoder if not present
@@ -552,83 +587,57 @@ impl StreamContext {
             self.binary_frame_decoder = Some(BedrockBinaryFrameDecoder::from_bytes(&[]));
         }
 
-        // Add incoming bytes to buffer
+        // Initialize SSE buffer if not present
+        if self.sse_buffer.is_none() {
+            self.sse_buffer = match SseStreamBuffer::try_from((client_api, upstream_api)) {
+                Ok(buffer) => Some(buffer),
+                Err(e) => {
+                    warn!(
+                        "[ARCHGW_REQ_ID:{}] BEDROCK_BUFFER_INIT_ERROR: {}",
+                        self.request_identifier(),
+                        e
+                    );
+                    return Err(Action::Continue);
+                }
+            };
+        }
+
+        // Add incoming bytes to decoder buffer
         let decoder = self.binary_frame_decoder.as_mut().unwrap();
         decoder.buffer_mut().extend_from_slice(body);
 
-        let mut response_buffer = Vec::new();
+        // Process all complete frames
         loop {
             let decoded_frame = self.binary_frame_decoder.as_mut().unwrap().decode_frame();
             match decoded_frame {
                 Some(DecodedFrame::Complete(ref frame_ref)) => {
                     let frame = DecodedFrame::Complete(frame_ref.clone());
+
+                    // Convert frame to provider response type
                     match ProviderStreamResponseType::try_from((&frame, client_api, upstream_api)) {
                         Ok(provider_response) => {
                             self.record_ttft_if_needed();
 
-                            // Handle ContentBlockStart and ContentBlockDelta events
+                            // Track token usage
-                            match &provider_response {
+                            if let Some(content) = provider_response.content_delta() {
-                                ProviderStreamResponseType::MessagesStreamEvent(evt) => {
+                                let estimated_tokens = content.len() / 4;
-                                    match evt {
+                                self.response_tokens += estimated_tokens.max(1);
-                                        MessagesStreamEvent::ContentBlockStart {
+                                debug!(
-                                            index, ..
+                                    "[ARCHGW_REQ_ID:{}] BEDROCK_TOKEN_UPDATE: delta_chars={} estimated_tokens={} total_tokens={}",
-                                        } => {
+                                    self.request_identifier(),
-                                            // Mark that we've seen ContentBlockStart for this index
+                                    content.len(),
-                                            self.binary_frame_decoder
+                                    estimated_tokens.max(1),
-                                                .as_mut()
+                                    self.response_tokens
-                                                .unwrap()
+                                );
-                                                .set_content_block_start_sent(*index as i32);
-                                            debug!(
-                                                "[ARCHGW_REQ_ID:{}] BEDROCK_CONTENT_BLOCK_START_TRACKED: index={}",
-                                                self.request_identifier(),
-                                                *index
-                                            );
-                                        }
-                                        MessagesStreamEvent::ContentBlockDelta {
-                                            index, ..
-                                        } => {
-                                            // Check if ContentBlockStart was sent for this index
-                                            let needs_start = !self
-                                                .binary_frame_decoder
-                                                .as_ref()
-                                                .unwrap()
-                                                .has_content_block_start_been_sent(*index as i32);
-
-                                            if needs_start {
-                                                // Emit empty ContentBlockStart before delta
-                                                let content_block_start =
-                                                    MessagesStreamEvent::ContentBlockStart {
-                                                        index: *index,
-                                                        content_block: MessagesContentBlock::Text {
-                                                            text: String::new(),
-                                                            cache_control: None,
-                                                        },
-                                                    };
-                                                let start_sse: String = content_block_start.into();
-                                                response_buffer
-                                                    .extend_from_slice(start_sse.as_bytes());
-
-                                                // Mark that we've now sent it
-                                                self.binary_frame_decoder
-                                                    .as_mut()
-                                                    .unwrap()
-                                                    .set_content_block_start_sent(*index as i32);
-
-                                                debug!(
-                                                    "[ARCHGW_REQ_ID:{}] BEDROCK_INJECTED_CONTENT_BLOCK_START: index={}",
-                                                    self.request_identifier(),
-                                                    *index
-                                                );
-                                            }
-                                        }
-                                        _ => {}
-                                    }
-                                }
-                                _ => {}
                             }
 
-                            let sse_string: String = provider_response.into();
+                            // Create SseEvent from provider response
-                            response_buffer.extend_from_slice(sse_string.as_bytes());
+                            let event = SseEvent::from_provider_response(provider_response);
+
+                            // Add to buffer (buffer handles all shim logic including ContentBlockStart injection)
+                            if let Some(buffer) = self.sse_buffer.as_mut() {
+                                buffer.add_transformed_event(event);
+                            }
                         }
                         Err(e) => {
                             warn!(
@@ -658,8 +667,29 @@ impl StreamContext {
             }
         }
 
-        // Return accumulated complete frames (may be empty if all frames incomplete)
+        // Get accumulated bytes from buffer and return
-        Ok(response_buffer)
+        match self.sse_buffer.as_mut() {
+            Some(buffer) => {
+                let bytes = buffer.into_bytes();
+                if !bytes.is_empty() {
+                    let content = String::from_utf8_lossy(&bytes);
+                    debug!(
+                        "[ARCHGW_REQ_ID:{}] UPSTREAM_TRANSFORMED_CLIENT_RESPONSE: size={} content={}",
+                        self.request_identifier(),
+                        bytes.len(),
+                        content
+                    );
+                }
+                Ok(bytes)
+            }
+            None => {
+                warn!(
+                    "[ARCHGW_REQ_ID:{}] BEDROCK_BUFFER_MISSING",
+                    self.request_identifier()
+                );
+                Err(Action::Continue)
+            }
+        }
     }
 
     fn handle_non_streaming_response(
@@ -782,13 +812,14 @@ impl HttpContext for StreamContext {
             self.select_llm_provider();
 
             // Check if this is a supported API endpoint
-            if SupportedAPIs::from_endpoint(&request_path).is_none() {
+            if SupportedAPIsFromClient::from_endpoint(&request_path).is_none() {
                 self.send_http_response(404, vec![], Some(b"Unsupported endpoint"));
                 return Action::Continue;
             }
 
             // Get the SupportedApi for routing decisions
-            let supported_api: Option<SupportedAPIs> = SupportedAPIs::from_endpoint(&request_path);
+            let supported_api: Option<SupportedAPIsFromClient> =
+                SupportedAPIsFromClient::from_endpoint(&request_path);
             self.client_api = supported_api;
 
             // Debug: log provider, client API, resolved API, and request path
@@ -1131,8 +1162,9 @@ impl HttpContext for StreamContext {
         }
 
         match self.client_api {
-            Some(SupportedAPIs::OpenAIChatCompletions(_)) => {}
+            Some(SupportedAPIsFromClient::OpenAIChatCompletions(_)) => {}
-            Some(SupportedAPIs::AnthropicMessagesAPI(_)) => {}
+            Some(SupportedAPIsFromClient::AnthropicMessagesAPI(_)) => {}
+            Some(SupportedAPIsFromClient::OpenAIResponsesAPI(_)) => {}
             _ => {
                 let api_info = match &self.client_api {
                     Some(api) => format!("{}", api),

demos/use_cases/model_alias_routing/arch_config_with_aliases.yaml

@@ -47,6 +47,9 @@ llm_providers:
   - model: ollama/llama3.1
     base_url: http://host.docker.internal:11434
 
+  # Grok (xAI) Models
+  - model: xai/grok-4-0709
+    access_key: $GROK_API_KEY
 
 # Model aliases - friendly names that map to actual provider names
 model_aliases:
@@ -83,5 +86,9 @@ model_aliases:
   coding-model:
     target: us.amazon.nova-premier-v1:0
 
+  # Alias for grok testing
+  arch.grok.v1:
+    target: grok-4-0709
+
 tracing:
   random_sampling: 100

demos/use_cases/model_choice_with_test_harness/pyproject.toml

@@ -12,7 +12,7 @@ python = ">=3.10,<3.13.3"
 pydantic = "^2.0"
 openai = "^1.0"
 pyyaml = "^6.0"
-archgw ="^0.3.20"
+archgw ="^0.3.21"
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^8.3"

demos/use_cases/preference_based_routing/README.md

@@ -14,9 +14,9 @@ Make sure your machine is up to date with [latest version of archgw]([url](https
 2. start archgw in the foreground
 ```bash
 (venv) $ archgw up --service archgw --foreground
-2025-05-30 18:00:09,953 - cli.main - INFO - Starting archgw cli version: 0.3.20
+2025-05-30 18:00:09,953 - cli.main - INFO - Starting archgw cli version: 0.3.21
 2025-05-30 18:00:09,953 - cli.main - INFO - Validating /Users/adilhafeez/src/intelligent-prompt-gateway/demos/use_cases/preference_based_routing/arch_config.yaml
-2025-05-30 18:00:10,422 - cli.core - INFO - Starting arch gateway, image name: archgw, tag: katanemo/archgw:0.3.20
+2025-05-30 18:00:10,422 - cli.core - INFO - Starting arch gateway, image name: archgw, tag: katanemo/archgw:0.3.21
 2025-05-30 18:00:10,662 - cli.core - INFO - archgw status: running, health status: starting
 2025-05-30 18:00:11,712 - cli.core - INFO - archgw status: running, health status: starting
 2025-05-30 18:00:12,761 - cli.core - INFO - archgw is running and is healthy!

docs/source/conf.py

@@ -15,7 +15,7 @@ from sphinxawesome_theme.postprocess import Icons
 project = "Arch Docs"
 copyright = "2025, Katanemo Labs, Inc"
 author = "Katanemo Labs, Inc"
-release = " v0.3.20"
+release = " v0.3.21"
 
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration

docs/source/get_started/quickstart.rst

@@ -25,7 +25,7 @@ Arch's CLI allows you to manage and interact with the Arch gateway efficiently.
 
    $ python -m venv venv
    $ source venv/bin/activate   # On Windows, use: venv\Scripts\activate
-   $ pip install archgw==0.3.20
+   $ pip install archgw==0.3.21
 
 
 Build AI Agent with Arch Gateway

docs/source/resources/deployment.rst

@@ -25,7 +25,7 @@ Create a ``docker-compose.yml`` file with the following configuration:
    # docker-compose.yml
    services:
      archgw:
-       image: katanemo/archgw:0.3.20
+       image: katanemo/archgw:0.3.21
        container_name: archgw
        ports:
          - "10000:10000" # ingress (client -> arch)

tests/e2e/run_e2e_tests.sh

@@ -65,6 +65,10 @@ log running e2e tests for model alias routing
 log ========================================
 poetry run pytest test_model_alias_routing.py
 
+log running e2e tests for openai responses api client
+log ========================================
+poetry run pytest test_openai_responses_api_client.py
+
 log shutting down the weather_forecast demo
 log =======================================
 cd ../../demos/samples_python/weather_forecast

tests/e2e/test_openai_responses_api_client.py

@@ -0,0 +1,630 @@
+import openai
+import pytest
+import os
+import logging
+import sys
+
+# Set up logging
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+    handlers=[logging.StreamHandler(sys.stdout)],
+)
+logger = logging.getLogger(__name__)
+
+LLM_GATEWAY_ENDPOINT = os.getenv(
+    "LLM_GATEWAY_ENDPOINT", "http://localhost:12000/v1/chat/completions"
+)
+
+
+# -----------------------
+# v1/responses API tests
+# -----------------------
+def test_openai_responses_api_non_streaming_passthrough():
+    """Build a v1/responses API request (pass-through) and ensure gateway accepts it"""
+    base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
+    client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1")
+
+    # Simple responses API request using a direct model (pass-through)
+    resp = client.responses.create(
+        model="gpt-4o", input="Hello via responses passthrough"
+    )
+
+    # Print the response content - handle both responses format and chat completions format
+    print(f"\n{'='*80}")
+    print(f"Model: {resp.model}")
+    print(f"Output: {resp.output_text}")
+    print(f"{'='*80}\n")
+
+    # Minimal sanity checks
+    assert resp is not None
+    assert (
+        getattr(resp, "id", None) is not None
+        or getattr(resp, "output", None) is not None
+    )
+
+
+def test_openai_responses_api_with_streaming_passthrough():
+    """Build a v1/responses API streaming request (pass-through) and ensure gateway accepts it"""
+    base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
+    client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1")
+
+    # Simple streaming responses API request using a direct model (pass-through)
+    stream = client.responses.create(
+        model="gpt-4o",
+        input="Write a short haiku about coding",
+        stream=True,
+    )
+
+    # Collect streamed content using the official Responses API streaming shape
+    text_chunks = []
+    final_message = None
+
+    for event in stream:
+        # The Python SDK surfaces a high-level Responses streaming interface.
+        # We rely on its typed helpers instead of digging into model_extra.
+        if getattr(event, "type", None) == "response.output_text.delta" and getattr(
+            event, "delta", None
+        ):
+            # Each delta contains a text fragment
+            text_chunks.append(event.delta)
+
+        # Track the final response message if provided by the SDK
+        if getattr(event, "type", None) == "response.completed" and getattr(
+            event, "response", None
+        ):
+            final_message = event.response
+
+    full_content = "".join(text_chunks)
+
+    # Print the streaming response
+    print(f"\n{'='*80}")
+    print(
+        f"Model: {getattr(final_message, 'model', 'unknown') if final_message else 'unknown'}"
+    )
+    print(f"Streamed Output: {full_content}")
+    print(f"{'='*80}\n")
+
+    assert len(text_chunks) > 0, "Should have received streaming text deltas"
+    assert len(full_content) > 0, "Should have received content"
+
+
+def test_openai_responses_api_non_streaming_with_tools_passthrough():
+    """Responses API with a function/tool definition (pass-through)"""
+    base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
+    client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1", max_retries=0)
+
+    # Define a simple tool/function for the Responses API
+    tools = [
+        {
+            "type": "function",
+            "name": "echo_tool",
+            "description": "Echo back the provided input",
+            "parameters": {
+                "type": "object",
+                "properties": {"text": {"type": "string"}},
+                "required": ["text"],
+            },
+        }
+    ]
+
+    resp = client.responses.create(
+        model="gpt-5",
+        input="Call the echo tool",
+        tools=tools,
+    )
+
+    assert resp is not None
+    assert (
+        getattr(resp, "id", None) is not None
+        or getattr(resp, "output", None) is not None
+    )
+
+
+def test_openai_responses_api_with_streaming_with_tools_passthrough():
+    """Responses API with a function/tool definition (streaming, pass-through)"""
+    base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
+    client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1", max_retries=0)
+
+    tools = [
+        {
+            "type": "function",
+            "name": "echo_tool",
+            "description": "Echo back the provided input",
+            "parameters": {
+                "type": "object",
+                "properties": {"text": {"type": "string"}},
+                "required": ["text"],
+            },
+        }
+    ]
+
+    stream = client.responses.create(
+        model="gpt-5",
+        input="Call the echo tool",
+        tools=tools,
+        stream=True,
+    )
+
+    text_chunks = []
+    tool_calls = []
+
+    for event in stream:
+        etype = getattr(event, "type", None)
+
+        # Collect streamed text output
+        if etype == "response.output_text.delta" and getattr(event, "delta", None):
+            text_chunks.append(event.delta)
+
+        # Collect streamed tool call arguments
+        if etype == "response.function_call_arguments.delta" and getattr(
+            event, "delta", None
+        ):
+            tool_calls.append(event.delta)
+
+    full_text = "".join(text_chunks)
+
+    print(f"\n{'='*80}")
+    print("Responses tools streaming test")
+    print(f"Streamed text: {full_text}")
+    print(f"Tool call argument chunks: {len(tool_calls)}")
+    print(f"{'='*80}\n")
+
+    # We expect either streamed text output or streamed tool-call arguments
+    assert (
+        full_text or tool_calls
+    ), "Expected streamed text or tool call argument deltas from Responses tools stream"
+
+
+def test_openai_responses_api_non_streaming_upstream_chat_completions():
+    """Send a v1/responses request using the grok alias to verify translation/routing"""
+    base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
+    client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1")
+
+    resp = client.responses.create(
+        model="arch.grok.v1", input="Hello, translate this via grok alias"
+    )
+
+    # Print the response content - handle both responses format and chat completions format
+    print(f"\n{'='*80}")
+    print(f"Model: {resp.model}")
+    print(f"Output: {resp.output_text}")
+    print(f"{'='*80}\n")
+
+    assert resp is not None
+    assert resp.id is not None
+
+
+def test_openai_responses_api_with_streaming_upstream_chat_completions():
+    """Build a v1/responses API streaming request (pass-through) and ensure gateway accepts it"""
+    base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
+    client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1")
+
+    # Simple streaming responses API request using a direct model (pass-through)
+    stream = client.responses.create(
+        model="arch.grok.v1",
+        input="Write a short haiku about coding",
+        stream=True,
+    )
+
+    # Collect streamed content using the official Responses API streaming shape
+    text_chunks = []
+    final_message = None
+
+    for event in stream:
+        # The Python SDK surfaces a high-level Responses streaming interface.
+        # We rely on its typed helpers instead of digging into model_extra.
+        if getattr(event, "type", None) == "response.output_text.delta" and getattr(
+            event, "delta", None
+        ):
+            # Each delta contains a text fragment
+            text_chunks.append(event.delta)
+
+        # Track the final response message if provided by the SDK
+        if getattr(event, "type", None) == "response.completed" and getattr(
+            event, "response", None
+        ):
+            final_message = event.response
+
+    full_content = "".join(text_chunks)
+
+    # Print the streaming response
+    print(f"\n{'='*80}")
+    print(
+        f"Model: {getattr(final_message, 'model', 'unknown') if final_message else 'unknown'}"
+    )
+    print(f"Streamed Output: {full_content}")
+    print(f"{'='*80}\n")
+
+    assert len(text_chunks) > 0, "Should have received streaming text deltas"
+    assert len(full_content) > 0, "Should have received content"
+
+
+def test_openai_responses_api_non_streaming_with_tools_upstream_chat_completions():
+    """Responses API wioutputling routed to grok via alias"""
+    base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
+    client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1")
+
+    tools = [
+        {
+            "type": "function",
+            "name": "echo_tool",
+            "description": "Echo back the provided input",
+            "parameters": {
+                "type": "object",
+                "properties": {"text": {"type": "string"}},
+                "required": ["text"],
+            },
+        }
+    ]
+
+    resp = client.responses.create(
+        model="arch.grok.v1",
+        input="Call the echo tool",
+        tools=tools,
+    )
+
+    assert resp.id is not None
+
+    print(f"\n{'='*80}")
+    print(f"Model: {resp.model}")
+    print(f"Output: {resp.output_text}")
+    print(f"{'='*80}\n")
+
+
+def test_openai_responses_api_streaming_with_tools_upstream_chat_completions():
+    """Responses API with a function/tool definition (streaming, pass-through)"""
+    base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
+    client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1", max_retries=0)
+
+    tools = [
+        {
+            "type": "function",
+            "name": "echo_tool",
+            "description": "Echo back the provided input",
+            "parameters": {
+                "type": "object",
+                "properties": {"text": {"type": "string"}},
+                "required": ["text"],
+            },
+        }
+    ]
+
+    stream = client.responses.create(
+        model="arch.grok.v1",
+        input="Call the echo tool",
+        tools=tools,
+        stream=True,
+    )
+
+    text_chunks = []
+    tool_calls = []
+
+    for event in stream:
+        etype = getattr(event, "type", None)
+
+        # Collect streamed text output
+        if etype == "response.output_text.delta" and getattr(event, "delta", None):
+            text_chunks.append(event.delta)
+
+        # Collect streamed tool call arguments
+        if etype == "response.function_call_arguments.delta" and getattr(
+            event, "delta", None
+        ):
+            tool_calls.append(event.delta)
+
+    full_text = "".join(text_chunks)
+
+    print(f"\n{'='*80}")
+    print("Responses tools streaming test")
+    print(f"Streamed text: {full_text}")
+    print(f"Tool call argument chunks: {len(tool_calls)}")
+    print(f"{'='*80}\n")
+
+    # We expect either streamed text output or streamed tool-call arguments
+    assert (
+        full_text or tool_calls
+    ), "Expected streamed text or tool call argument deltas from Responses tools stream"
+
+
+def test_openai_responses_api_non_streaming_upstream_bedrock():
+    """Send a v1/responses request using the coding-model alias to verify Bedrock translation/routing"""
+    base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
+    client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1")
+
+    resp = client.responses.create(
+        model="coding-model",
+        input="Hello, translate this via coding-model alias to Bedrock",
+    )
+
+    # Print the response content - handle both responses format and chat completions format
+    print(f"\n{'='*80}")
+    print(f"Model: {resp.model}")
+    print(f"Output: {resp.output_text}")
+    print(f"{'='*80}\n")
+
+    assert resp is not None
+    assert resp.id is not None
+
+
+def test_openai_responses_api_with_streaming_upstream_bedrock():
+    """Build a v1/responses API streaming request routed to Bedrock via coding-model alias"""
+    base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
+    client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1")
+
+    # Simple streaming responses API request using coding-model alias
+    stream = client.responses.create(
+        model="coding-model",
+        input="Write a short haiku about coding",
+        stream=True,
+    )
+
+    # Collect streamed content using the official Responses API streaming shape
+    text_chunks = []
+    final_message = None
+
+    for event in stream:
+        # The Python SDK surfaces a high-level Responses streaming interface.
+        # We rely on its typed helpers instead of digging into model_extra.
+        if getattr(event, "type", None) == "response.output_text.delta" and getattr(
+            event, "delta", None
+        ):
+            # Each delta contains a text fragment
+            text_chunks.append(event.delta)
+
+        # Track the final response message if provided by the SDK
+        if getattr(event, "type", None) == "response.completed" and getattr(
+            event, "response", None
+        ):
+            final_message = event.response
+
+    full_content = "".join(text_chunks)
+
+    # Print the streaming response
+    print(f"\n{'='*80}")
+    print(
+        f"Model: {getattr(final_message, 'model', 'unknown') if final_message else 'unknown'}"
+    )
+    print(f"Streamed Output: {full_content}")
+    print(f"{'='*80}\n")
+
+    assert len(text_chunks) > 0, "Should have received streaming text deltas"
+    assert len(full_content) > 0, "Should have received content"
+
+
+def test_openai_responses_api_non_streaming_with_tools_upstream_bedrock():
+    """Responses API with tools routed to Bedrock via coding-model alias"""
+    base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
+    client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1")
+
+    tools = [
+        {
+            "type": "function",
+            "name": "echo_tool",
+            "description": "Echo back the provided input",
+            "parameters": {
+                "type": "object",
+                "properties": {"text": {"type": "string"}},
+                "required": ["text"],
+            },
+        }
+    ]
+
+    resp = client.responses.create(
+        model="coding-model",
+        input="Call the echo tool",
+        tools=tools,
+    )
+
+    assert resp.id is not None
+
+    print(f"\n{'='*80}")
+    print(f"Model: {resp.model}")
+    print(f"Output: {resp.output_text}")
+    print(f"{'='*80}\n")
+
+
+def test_openai_responses_api_streaming_with_tools_upstream_bedrock():
+    """Responses API with a function/tool definition streaming to Bedrock via coding-model alias"""
+    base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
+    client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1", max_retries=0)
+
+    tools = [
+        {
+            "type": "function",
+            "name": "echo_tool",
+            "description": "Echo back the provided input",
+            "parameters": {
+                "type": "object",
+                "properties": {"text": {"type": "string"}},
+                "required": ["text"],
+            },
+        }
+    ]
+
+    stream = client.responses.create(
+        model="coding-model",
+        input="Call the echo tool",
+        tools=tools,
+        stream=True,
+    )
+
+    text_chunks = []
+    tool_calls = []
+
+    for event in stream:
+        etype = getattr(event, "type", None)
+
+        # Collect streamed text output
+        if etype == "response.output_text.delta" and getattr(event, "delta", None):
+            text_chunks.append(event.delta)
+
+        # Collect streamed tool call arguments
+        if etype == "response.function_call_arguments.delta" and getattr(
+            event, "delta", None
+        ):
+            tool_calls.append(event.delta)
+
+    full_text = "".join(text_chunks)
+
+    print(f"\n{'='*80}")
+    print("Responses tools streaming test (Bedrock)")
+    print(f"Streamed text: {full_text}")
+    print(f"Tool call argument chunks: {len(tool_calls)}")
+    print(f"{'='*80}\n")
+
+    # We expect either streamed text output or streamed tool-call arguments
+    assert (
+        full_text or tool_calls
+    ), "Expected streamed text or tool call argument deltas from Responses tools stream"
+
+
+def test_openai_responses_api_non_streaming_upstream_anthropic():
+    """Send a v1/responses request using the grok alias to verify translation/routing"""
+    base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
+    client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1")
+
+    resp = client.responses.create(
+        model="claude-sonnet-4-20250514", input="Hello, translate this via grok alias"
+    )
+
+    # Print the response content - handle both responses format and chat completions format
+    print(f"\n{'='*80}")
+    print(f"Model: {resp.model}")
+    print(f"Output: {resp.output_text}")
+    print(f"{'='*80}\n")
+
+    assert resp is not None
+    assert resp.id is not None
+
+
+def test_openai_responses_api_with_streaming_upstream_anthropic():
+    """Build a v1/responses API streaming request (pass-through) and ensure gateway accepts it"""
+    base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
+    client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1")
+
+    # Simple streaming responses API request using a direct model (pass-through)
+    stream = client.responses.create(
+        model="claude-sonnet-4-20250514",
+        input="Write a short haiku about coding",
+        stream=True,
+    )
+
+    # Collect streamed content using the official Responses API streaming shape
+    text_chunks = []
+    final_message = None
+
+    for event in stream:
+        # The Python SDK surfaces a high-level Responses streaming interface.
+        # We rely on its typed helpers instead of digging into model_extra.
+        if getattr(event, "type", None) == "response.output_text.delta" and getattr(
+            event, "delta", None
+        ):
+            # Each delta contains a text fragment
+            text_chunks.append(event.delta)
+
+        # Track the final response message if provided by the SDK
+        if getattr(event, "type", None) == "response.completed" and getattr(
+            event, "response", None
+        ):
+            final_message = event.response
+
+    full_content = "".join(text_chunks)
+
+    # Print the streaming response
+    print(f"\n{'='*80}")
+    print(
+        f"Model: {getattr(final_message, 'model', 'unknown') if final_message else 'unknown'}"
+    )
+    print(f"Streamed Output: {full_content}")
+    print(f"{'='*80}\n")
+
+    assert len(text_chunks) > 0, "Should have received streaming text deltas"
+    assert len(full_content) > 0, "Should have received content"
+
+
+def test_openai_responses_api_non_streaming_with_tools_upstream_anthropic():
+    """Responses API with tools routed to grok via alias"""
+    base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
+    client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1")
+
+    tools = [
+        {
+            "type": "function",
+            "name": "echo_tool",
+            "description": "Echo back the provided input: hello_world",
+            "parameters": {
+                "type": "object",
+                "properties": {"text": {"type": "string"}},
+                "required": ["text"],
+            },
+        }
+    ]
+
+    resp = client.responses.create(
+        model="claude-sonnet-4-20250514",
+        input="Call the echo tool",
+        tools=tools,
+    )
+
+    assert resp.id is not None
+
+    print(f"\n{'='*80}")
+    print(f"Model: {resp.model}")
+    print(f"Output: {resp.output_text}")
+    print(f"{'='*80}\n")
+
+
+def test_openai_responses_api_streaming_with_tools_upstream_anthropic():
+    """Responses API with a function/tool definition (streaming, pass-through)"""
+    base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
+    client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1", max_retries=0)
+
+    tools = [
+        {
+            "type": "function",
+            "name": "echo_tool",
+            "description": "Echo back the provided input: hello_world",
+            "parameters": {
+                "type": "object",
+                "properties": {"text": {"type": "string"}},
+                "required": ["text"],
+            },
+        }
+    ]
+
+    stream = client.responses.create(
+        model="claude-sonnet-4-20250514",
+        input="Call the echo tool",
+        tools=tools,
+        stream=True,
+    )
+
+    text_chunks = []
+    tool_calls = []
+
+    for event in stream:
+        etype = getattr(event, "type", None)
+
+        # Collect streamed text output
+        if etype == "response.output_text.delta" and getattr(event, "delta", None):
+            text_chunks.append(event.delta)
+
+        # Collect streamed tool call arguments
+        if etype == "response.function_call_arguments.delta" and getattr(
+            event, "delta", None
+        ):
+            tool_calls.append(event.delta)
+
+    full_text = "".join(text_chunks)
+
+    print(f"\n{'='*80}")
+    print("Responses tools streaming test")
+    print(f"Streamed text: {full_text}")
+    print(f"Tool call argument chunks: {len(tool_calls)}")
+    print(f"{'='*80}\n")
+
+    # We expect either streamed text output or streamed tool-call arguments
+    assert (
+        full_text or tool_calls
+    ), "Expected streamed text or tool call argument deltas from Responses tools stream"