feat: implement capability-aware routing for OpenAI Responses API, enhancing model selection and error handling for unsupported tools

crates/brightstaff/src/handlers/llm.rs

@@ -14,7 +14,7 @@ use hyper::{Request, Response, StatusCode};
 use opentelemetry::global;
 use opentelemetry::trace::get_active_span;
 use opentelemetry_http::HeaderInjector;
-use std::collections::HashMap;
+use std::collections::{HashMap, HashSet};
 use std::sync::Arc;
 use tokio::sync::RwLock;
 use tracing::{debug, info, info_span, warn, Instrument};
@@ -149,6 +149,8 @@ async fn llm_chat_inner(
         client_api,
         Some(SupportedAPIsFromClient::OpenAIResponsesAPI(_))
     );
+    let requires_native_responses_tools =
+        responses_request_uses_non_function_tools(&client_request);
 
     // If model is not specified in the request, resolve from default provider
     let model_from_request = client_request.model().to_string();
@@ -394,14 +396,44 @@ async fn llm_chat_inner(
 
     // Determine final model to use
     // Router returns "none" as a sentinel value when it doesn't select a specific model
-    let router_selected_model = routing_result.model_name;
+    let router_selected_model = routing_result.model_name.clone();
     let resolved_model = if router_selected_model != "none" {
         // Router selected a specific model via routing preferences
-        router_selected_model
+        router_selected_model.clone()
     } else {
         // Router returned "none" sentinel, use validated resolved_model from request
         alias_resolved_model.clone()
     };
+    let resolved_model = if requires_native_responses_tools {
+        match select_capability_compatible_model(
+            &llm_providers,
+            &resolved_model,
+            is_streaming_request,
+        )
+        .await
+        {
+            Some(compatible_model) => {
+                if compatible_model != resolved_model {
+                    warn!(
+                        request_id = %request_id,
+                        selected_model = %resolved_model,
+                        compatible_model = %compatible_model,
+                        "selected model cannot serve responses web/file/computer tools; rerouting to compatible model"
+                    );
+                }
+                compatible_model
+            }
+            None => {
+                let err_msg = "No configured model can serve OpenAI Responses API requests with non-function tools".to_string();
+                warn!(request_id = %request_id, error = %err_msg, "capability-aware routing failed");
+                let mut bad_request = Response::new(full(err_msg));
+                *bad_request.status_mut() = StatusCode::BAD_REQUEST;
+                return Ok(bad_request);
+            }
+        }
+    } else {
+        resolved_model
+    };
     tracing::Span::current().record(tracing_llm::MODEL_NAME, resolved_model.as_str());
 
     let span_name = if model_from_request == resolved_model {
@@ -541,6 +573,81 @@ fn resolve_model_alias(
     model_from_request.to_string()
 }
 
+fn responses_request_uses_non_function_tools(client_request: &ProviderRequestType) -> bool {
+    match client_request {
+        ProviderRequestType::ResponsesAPIRequest(req) => req
+            .tools
+            .as_ref()
+            .map(|tools| {
+                tools
+                    .iter()
+                    .any(|tool| !matches!(tool, ResponsesTool::Function { .. }))
+            })
+            .unwrap_or(false),
+        _ => false,
+    }
+}
+
+async fn model_supports_native_responses_api(
+    llm_providers: &Arc<RwLock<LlmProviders>>,
+    model_name: &str,
+    is_streaming: bool,
+) -> bool {
+    let upstream_path = get_upstream_path(
+        llm_providers,
+        model_name,
+        "/v1/responses",
+        model_name,
+        is_streaming,
+    )
+    .await;
+    matches!(
+        SupportedUpstreamAPIs::from_endpoint(&upstream_path),
+        Some(SupportedUpstreamAPIs::OpenAIResponsesAPI(_))
+    )
+}
+
+async fn select_capability_compatible_model(
+    llm_providers: &Arc<RwLock<LlmProviders>>,
+    preferred_model: &str,
+    is_streaming: bool,
+) -> Option<String> {
+    if model_supports_native_responses_api(llm_providers, preferred_model, is_streaming).await {
+        return Some(preferred_model.to_string());
+    }
+
+    let (default_candidate, ordered_candidates): (Option<String>, Vec<String>) = {
+        let providers = llm_providers.read().await;
+        let default_candidate = providers.default().map(|p| p.name.clone());
+
+        let mut seen = HashSet::new();
+        let mut candidates = Vec::new();
+        for (key, provider) in providers.iter() {
+            if key != &provider.name || provider.internal == Some(true) {
+                continue;
+            }
+            if seen.insert(provider.name.clone()) {
+                candidates.push(provider.name.clone());
+            }
+        }
+        (default_candidate, candidates)
+    };
+
+    if let Some(default_model) = default_candidate {
+        if model_supports_native_responses_api(llm_providers, &default_model, is_streaming).await {
+            return Some(default_model);
+        }
+    }
+
+    for candidate in ordered_candidates {
+        if model_supports_native_responses_api(llm_providers, &candidate, is_streaming).await {
+            return Some(candidate);
+        }
+    }
+
+    None
+}
+
 /// Calculates the upstream path for the provider based on the model name.
 /// Looks up provider configuration, gets the ProviderId and base_url_path_prefix,
 /// then uses target_endpoint_for_provider to calculate the correct upstream path.

crates/brightstaff/src/handlers/router_chat.rs

@@ -40,13 +40,16 @@ pub async fn router_chat_get_upstream_model(
 ) -> Result<RoutingResult, RoutingError> {
     // Clone metadata for routing before converting (which consumes client_request)
     let routing_metadata = client_request.metadata().clone();
+    let fallback_messages = client_request.get_messages();
 
-    // Convert to ChatCompletionsRequest for routing (regardless of input type)
+    // Convert to ChatCompletionsRequest for routing when possible.
-    let chat_request = match ProviderRequestType::try_from((
+    // If conversion fails for unsupported Responses tools (e.g. `custom`),
+    // route based on normalized OpenAI messages extracted from the original request.
+    let routing_messages = match ProviderRequestType::try_from((
         client_request,
         &SupportedUpstreamAPIs::OpenAIChatCompletions(hermesllm::apis::OpenAIApi::ChatCompletions),
     )) {
-        Ok(ProviderRequestType::ChatCompletionsRequest(req)) => req,
+        Ok(ProviderRequestType::ChatCompletionsRequest(req)) => req.messages,
         Ok(
             ProviderRequestType::MessagesRequest(_)
             | ProviderRequestType::BedrockConverse(_)
@@ -59,6 +62,14 @@ pub async fn router_chat_get_upstream_model(
             ));
         }
         Err(err) => {
+            let err_text = err.to_string();
+            if err_text.contains("Unsupported conversion") {
+                warn!(
+                    "routing conversion unsupported; falling back to routing with normalized request messages: {}",
+                    err_text
+                );
+                fallback_messages
+            } else {
                 warn!(
                     "failed to convert request to ChatCompletionsRequest: {}",
                     err
@@ -68,11 +79,12 @@ pub async fn router_chat_get_upstream_model(
                     err
                 )));
             }
+        }
     };
 
     debug!(
-        request = %serde_json::to_string(&chat_request).unwrap(),
+        message_count = routing_messages.len(),
-        "router request"
+        "router request prepared"
     );
 
     // Extract usage preferences from metadata
@@ -87,10 +99,7 @@ pub async fn router_chat_get_upstream_model(
         .and_then(|s| serde_yaml::from_str(s).ok());
 
     // Prepare log message with latest message from chat request
-    let latest_message_for_log = chat_request
+    let latest_message_for_log = routing_messages.last().map_or("None".to_string(), |msg| {
-        .messages
-        .last()
-        .map_or("None".to_string(), |msg| {
         msg.content
             .as_ref()
             .map_or("None".to_string(), |c| c.to_string().replace('\n', "\\n"))
@@ -120,7 +129,7 @@ pub async fn router_chat_get_upstream_model(
     // Attempt to determine route using the router service
     let routing_result = router_service
         .determine_route(
-            &chat_request.messages,
+            &routing_messages,
             traceparent,
             usage_preferences,
             request_id,

crates/hermesllm/src/apis/openai_responses.rs

@@ -209,10 +209,13 @@ pub struct AudioConfig {
 }
 
 /// Text configuration
+#[skip_serializing_none]
 #[derive(Debug, Clone, Serialize, Deserialize)]
 pub struct TextConfig {
     /// Text format configuration
-    pub format: TextFormat,
+    pub format: Option<TextFormat>,
+    /// Controls response verbosity for models that support it.
+    pub verbosity: Option<String>,
 }
 
 /// Text format
@@ -302,6 +305,12 @@ pub enum Tool {
         display_height_px: Option<i32>,
         display_number: Option<i32>,
     },
+    /// Custom tool (forward-compatible passthrough for provider-specific tools)
+    #[serde(rename = "custom")]
+    Custom {
+        #[serde(flatten)]
+        config: serde_json::Value,
+    },
 }
 
 /// Ranking options for file search

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

@@ -236,7 +236,8 @@ impl ResponsesAPIStreamBuffer {
             }),
             store: Some(true),
             text: Some(TextConfig {
-                format: TextFormat::Text,
+                format: Some(TextFormat::Text),
+                verbosity: None,
             }),
             audio: None,
             modalities: None,

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

@@ -466,6 +466,9 @@ impl TryFrom<ResponsesAPIRequest> for ChatCompletionsRequest {
                         ResponsesTool::Computer { .. } => Err(TransformError::UnsupportedConversion(
                             "Computer tool is not supported in ChatCompletions API. Only function tools are supported.".to_string()
                         )),
+                        ResponsesTool::Custom { .. } => Err(TransformError::UnsupportedConversion(
+                            "Custom tool is not supported in ChatCompletions API. Only function tools are supported.".to_string()
+                        )),
                     }
                 }).collect::<Result<Vec<_>, _>>()
             }).transpose()?,

crates/llm_gateway/src/stream_context.rs

@@ -1139,6 +1139,27 @@ impl HttpContext for StreamContext {
 
         let current_time = get_current_time().unwrap();
         if end_of_stream && body_size == 0 {
+            // Flush any buffered partial SSE event on stream end.
+            // This handles cases where the last logical SSE event (for example, response.completed)
+            // was split across chunks and the tail only arrives at end-of-stream.
+            if self.streaming_response {
+                let provider_id = self.get_provider_id();
+                match self.handle_streaming_response(&[], provider_id) {
+                    Ok(serialized_body) => {
+                        if !serialized_body.is_empty() {
+                            self.set_http_response_body(0, 0, &serialized_body);
+                            debug!(
+                                "request_id={}: flushed buffered streaming bytes on end_of_stream, size={}",
+                                self.request_identifier(),
+                                serialized_body.len()
+                            );
+                        }
+                    }
+                    Err(_) => {
+                        // Ignore flush errors and proceed with end-of-request handling.
+                    }
+                }
+            }
             debug!(
                 "request_id={}: response body complete, total_bytes={}",
                 self.request_identifier(),

demos/llm_routing/codex_router/config.yaml

@@ -7,22 +7,22 @@ listeners:
 
 model_providers:
   # OpenAI Models
-  - model: openai/gpt-5-2025-08-07
+  - model: openai/gpt-5.3-codex
     default: true
     access_key: $OPENAI_API_KEY
     routing_preferences:
       - name: code generation
         description: generating new code snippets, functions, or boilerplate based on user prompts or requirements
 
-  - model: openai/gpt-4.1-2025-04-14
+  - model: openai/gpt-5-2025-08-07
     access_key: $OPENAI_API_KEY
     routing_preferences:
       - name: code understanding
         description: understand and explain existing code snippets, functions, or libraries
 
-  # Anthropic Model
+  # # Anthropic Model
-  - model: anthropic/claude-sonnet-4-6
+  # - model: anthropic/claude-sonnet-4-6
-    access_key: $ANTHROPIC_API_KEY
+  #   access_key: $ANTHROPIC_API_KEY
 
   # Ollama Model (optional local fallback)
   - model: ollama/llama3.1
@@ -32,7 +32,7 @@ model_providers:
 model_aliases:
   # Default model Codex should request when launched by planoai cli-agent codex
   arch.codex.default:
-    target: gpt-5-2025-08-07
+    target: gpt-5.3-codex
 
 tracing:
   random_sampling: 100

demos/llm_routing/opencode_router/config.yaml

@@ -7,22 +7,22 @@ listeners:
 
 model_providers:
   # OpenAI Models
-  - model: openai/gpt-5-2025-08-07
+  - model: openai/gpt-5.3-codex
     default: true
     access_key: $OPENAI_API_KEY
     routing_preferences:
       - name: code generation
         description: generating new code snippets, functions, or boilerplate based on user prompts or requirements
 
-  - model: openai/gpt-4.1-2025-04-14
+  - model: openai/gpt-5-2025-08-07
     access_key: $OPENAI_API_KEY
     routing_preferences:
       - name: code understanding
         description: understand and explain existing code snippets, functions, or libraries
 
-  # Anthropic Model
+  # # Anthropic Model
-  - model: anthropic/claude-sonnet-4-6
+  # - model: anthropic/claude-sonnet-4-6
-    access_key: $ANTHROPIC_API_KEY
+  #   access_key: $ANTHROPIC_API_KEY
 
   # Ollama Model (optional local fallback)
   - model: ollama/llama3.1
@@ -32,7 +32,7 @@ model_providers:
 model_aliases:
   # Default model OpenCode should request when launched by planoai cli-agent opencode
   arch.opencode.default:
-    target: gpt-5-2025-08-07
+    target: gpt-5.3-codex
 
 tracing:
   random_sampling: 100