resolve merge conflict in main.rs

.claude/skills/release/SKILL.md

@@ -25,4 +25,6 @@ Update the version string in ALL of these files:
 
 Do NOT change version strings in `*.lock` files or `Cargo.lock`.
 
+After updating all version strings, run `cd cli && uv lock` to update the lock file with the new version.
+
 After making changes, show a summary of all files modified and the old → new version.

.github/workflows/ci.yml

@@ -133,13 +133,13 @@ jobs:
           load: true
           tags: |
             ${{ env.PLANO_DOCKER_IMAGE }}
-            ${{ env.DOCKER_IMAGE }}:0.4.11
+            ${{ env.DOCKER_IMAGE }}:0.4.12
             ${{ env.DOCKER_IMAGE }}:latest
           cache-from: type=gha
           cache-to: type=gha,mode=max
 
       - name: Save image as artifact
-        run: docker save ${{ env.PLANO_DOCKER_IMAGE }} ${{ env.DOCKER_IMAGE }}:0.4.11 ${{ env.DOCKER_IMAGE }}:latest -o /tmp/plano-image.tar
+        run: docker save ${{ env.PLANO_DOCKER_IMAGE }} ${{ env.DOCKER_IMAGE }}:0.4.12 ${{ env.DOCKER_IMAGE }}:latest -o /tmp/plano-image.tar
 
       - name: Upload image artifact
         uses: actions/upload-artifact@v6

.gitignore

@@ -152,3 +152,4 @@ apps/*/dist/
 
 .cursor/
 .agents
+docs/do/

.pre-commit-config.yaml

@@ -4,6 +4,7 @@ repos:
     hooks:
       - id: check-yaml
         exclude: config/envoy.template*
+        args: [--allow-multiple-documents]
       - id: end-of-file-fixer
       - id: trailing-whitespace
   - repo: local

apps/www/src/components/Hero.tsx

@@ -24,7 +24,7 @@ export function Hero() {
             >
               <div className="inline-flex flex-wrap items-center gap-1.5 sm:gap-2 px-3 sm:px-4 py-1 rounded-full bg-[rgba(185,191,255,0.4)] border border-[var(--secondary)] shadow backdrop-blur hover:bg-[rgba(185,191,255,0.6)] transition-colors cursor-pointer">
                 <span className="text-xs sm:text-sm font-medium text-black/65">
-                  v0.4.11
+                  v0.4.12
                 </span>
                 <span className="text-xs sm:text-sm font-medium text-black ">
                   —

build_filter_image.sh

@@ -1 +1 @@
-docker build  -f Dockerfile . -t katanemo/plano -t katanemo/plano:0.4.11
+docker build  -f Dockerfile . -t katanemo/plano -t katanemo/plano:0.4.12

cli/planoai/__init__.py

@@ -1,3 +1,3 @@
 """Plano CLI - Intelligent Prompt Gateway."""
 
-__version__ = "0.4.11"
+__version__ = "0.4.12"

cli/planoai/config_generator.py

@@ -3,18 +3,17 @@ import os
 from planoai.utils import convert_legacy_listeners
 from jinja2 import Environment, FileSystemLoader
 import yaml
-from jsonschema import validate
+from jsonschema import validate, ValidationError
 from urllib.parse import urlparse
 from copy import deepcopy
 from planoai.consts import DEFAULT_OTEL_TRACING_GRPC_ENDPOINT
 
-
 SUPPORTED_PROVIDERS_WITH_BASE_URL = [
     "azure_openai",
     "ollama",
     "qwen",
     "amazon_bedrock",
-    "arch",
+    "plano",
 ]
 
 SUPPORTED_PROVIDERS_WITHOUT_BASE_URL = [
@@ -368,47 +367,52 @@ def validate_and_render_schema():
                     llms_with_endpoint.append(model_provider)
                     llms_with_endpoint_cluster_names.add(cluster_name)
 
-    if len(model_usage_name_keys) > 0:
+    overrides_config = config_yaml.get("overrides", {})
-        routing_model_provider = config_yaml.get("routing", {}).get(
+    # Build lookup of model names (already prefix-stripped by config processing)
-            "model_provider", None
+    model_name_set = {mp.get("model") for mp in updated_model_providers}
+
+    # Auto-add arch-router provider if routing preferences exist and no provider matches the router model
+    router_model = overrides_config.get("llm_routing_model", "Arch-Router")
+    # Strip provider prefix for comparison since config processing strips prefixes from model names
+    router_model_id = (
+        router_model.split("/", 1)[1] if "/" in router_model else router_model
+    )
+    if len(model_usage_name_keys) > 0 and router_model_id not in model_name_set:
+        updated_model_providers.append(
+            {
+                "name": "arch-router",
+                "provider_interface": "plano",
+                "model": router_model_id,
+                "internal": True,
+            }
         )
-        if (
-            routing_model_provider
-            and routing_model_provider not in model_provider_name_set
-        ):
-            raise Exception(
-                f"Routing model_provider {routing_model_provider} is not defined in model_providers"
-            )
-        if (
-            routing_model_provider is None
-            and "arch-router" not in model_provider_name_set
-        ):
-            updated_model_providers.append(
-                {
-                    "name": "arch-router",
-                    "provider_interface": "arch",
-                    "model": config_yaml.get("routing", {}).get("model", "Arch-Router"),
-                    "internal": True,
-                }
-            )
 
     # Always add arch-function model provider if not already defined
     if "arch-function" not in model_provider_name_set:
         updated_model_providers.append(
             {
                 "name": "arch-function",
-                "provider_interface": "arch",
+                "provider_interface": "plano",
                 "model": "Arch-Function",
                 "internal": True,
             }
         )
 
-    if "plano-orchestrator" not in model_provider_name_set:
+    # Auto-add plano-orchestrator provider if no provider matches the orchestrator model
+    orchestrator_model = overrides_config.get(
+        "agent_orchestration_model", "Plano-Orchestrator"
+    )
+    orchestrator_model_id = (
+        orchestrator_model.split("/", 1)[1]
+        if "/" in orchestrator_model
+        else orchestrator_model
+    )
+    if orchestrator_model_id not in model_name_set:
         updated_model_providers.append(
             {
-                "name": "plano-orchestrator",
+                "name": "plano/orchestrator",
-                "provider_interface": "arch",
+                "provider_interface": "plano",
-                "model": "Plano-Orchestrator",
+                "model": orchestrator_model_id,
                 "internal": True,
             }
         )
@@ -513,11 +517,15 @@ def validate_prompt_config(plano_config_file, plano_config_schema_file):
 
     try:
         validate(config_yaml, config_schema_yaml)
-    except Exception as e:
+    except ValidationError as e:
-        print(
+        path = (
-            f"Error validating plano_config file: {plano_config_file}, schema file: {plano_config_schema_file}, error: {e}"
+            " → ".join(str(p) for p in e.absolute_path) if e.absolute_path else "root"
         )
-        raise e
+        raise ValidationError(
+            f"{e.message}\n  Location: {path}\n  Value: {e.instance}"
+        ) from None
+    except Exception as e:
+        raise
 
 
 if __name__ == "__main__":

cli/planoai/consts.py

@@ -5,7 +5,7 @@ PLANO_COLOR = "#969FF4"
 
 SERVICE_NAME_ARCHGW = "plano"
 PLANO_DOCKER_NAME = "plano"
-PLANO_DOCKER_IMAGE = os.getenv("PLANO_DOCKER_IMAGE", "katanemo/plano:0.4.11")
+PLANO_DOCKER_IMAGE = os.getenv("PLANO_DOCKER_IMAGE", "katanemo/plano:0.4.12")
 DEFAULT_OTEL_TRACING_GRPC_ENDPOINT = "http://localhost:4317"
 
 # Native mode constants

cli/planoai/native_runner.py

@@ -420,9 +420,16 @@ def native_validate_config(plano_config_file):
     with _temporary_env(overrides):
         from planoai.config_generator import validate_and_render_schema
 
-        # Suppress verbose print output from config_generator
+        # Suppress verbose print output from config_generator but capture errors
-        with contextlib.redirect_stdout(io.StringIO()):
+        captured = io.StringIO()
-            validate_and_render_schema()
+        try:
+            with contextlib.redirect_stdout(captured):
+                validate_and_render_schema()
+        except SystemExit:
+            # validate_and_render_schema calls exit(1) on failure after
+            # printing to stdout; re-raise so the caller gets a useful message.
+            output = captured.getvalue().strip()
+            raise Exception(output) if output else Exception("Config validation failed")
 
 
 def native_logs(debug=False, follow=False):

cli/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "planoai"
-version = "0.4.11"
+version = "0.4.12"
 description = "Python-based CLI tool to manage Plano."
 authors = [{name = "Katanemo Labs, Inc."}]
 readme = "README.md"

cli/uv.lock

@@ -337,7 +337,7 @@ wheels = [
 
 [[package]]
 name = "planoai"
-version = "0.4.9"
+version = "0.4.12"
 source = { editable = "." }
 dependencies = [
     { name = "click" },

config/envoy.template.yaml

@@ -594,13 +594,13 @@ static_resources:
 
   clusters:
 
-    - name: arch
+    - name: plano
       connect_timeout: {{ upstream_connect_timeout | default('5s') }}
       type: LOGICAL_DNS
       dns_lookup_family: V4_ONLY
       lb_policy: ROUND_ROBIN
       load_assignment:
-        cluster_name: arch
+        cluster_name: plano
         endpoints:
           - lb_endpoints:
               - endpoint:

config/plano_config_schema.yaml

@@ -181,7 +181,7 @@ properties:
         provider_interface:
           type: string
           enum:
-            - arch
+            - plano
             - claude
             - deepseek
             - groq
@@ -228,7 +228,7 @@ properties:
         provider_interface:
           type: string
           enum:
-            - arch
+            - plano
             - claude
             - deepseek
             - groq
@@ -279,6 +279,12 @@ properties:
       upstream_tls_ca_path:
         type: string
         description: "Path to the trusted CA bundle for upstream TLS verification. Default is '/etc/ssl/certs/ca-certificates.crt'."
+      llm_routing_model:
+        type: string
+        description: "Model name for the LLM router (e.g., 'Arch-Router'). Must match a model in model_providers."
+      agent_orchestration_model:
+        type: string
+        description: "Model name for the agent orchestrator (e.g., 'Plano-Orchestrator'). Must match a model in model_providers."
   system_prompt:
     type: string
   prompt_targets:
@@ -416,14 +422,6 @@ properties:
     enum:
       - llm
       - prompt
-  routing:
-    type: object
-    properties:
-      llm_provider:
-        type: string
-      model:
-        type: string
-    additionalProperties: false
   state_storage:
     type: object
     properties:

crates/brightstaff/src/handlers/agent_selector.rs

@@ -178,6 +178,7 @@ mod tests {
         Arc::new(OrchestratorService::new(
             "http://localhost:8080".to_string(),
             "test-model".to_string(),
+            "plano-orchestrator".to_string(),
         ))
     }
 

crates/brightstaff/src/handlers/integration_tests.rs

@@ -23,6 +23,7 @@ mod tests {
         Arc::new(OrchestratorService::new(
             "http://localhost:8080".to_string(),
             "test-model".to_string(),
+            "plano-orchestrator".to_string(),
         ))
     }
 

crates/brightstaff/src/main.rs

@@ -11,9 +11,7 @@ use brightstaff::state::StateStorage;
 use brightstaff::utils::tracing::init_tracer;
 use bytes::Bytes;
 use common::configuration::{Agent, Configuration, ListenerType};
-use common::consts::{
+use common::consts::{CHAT_COMPLETIONS_PATH, MESSAGES_PATH, OPENAI_RESPONSES_API_PATH};
-    CHAT_COMPLETIONS_PATH, MESSAGES_PATH, OPENAI_RESPONSES_API_PATH, PLANO_ORCHESTRATOR_MODEL_NAME,
-};
 use common::llm_providers::LlmProviders;
 use http_body_util::{combinators::BoxBody, BodyExt, Empty};
 use hyper::body::Incoming;
@@ -36,6 +34,8 @@ pub mod router;
 const BIND_ADDRESS: &str = "0.0.0.0:9091";
 const DEFAULT_ROUTING_LLM_PROVIDER: &str = "arch-router";
 const DEFAULT_ROUTING_MODEL_NAME: &str = "Arch-Router";
+const DEFAULT_ORCHESTRATOR_LLM_PROVIDER: &str = "plano-orchestrator";
+const DEFAULT_ORCHESTRATOR_MODEL_NAME: &str = "Plano-Orchestrator";
 
 // Utility function to extract the context from the incoming request headers
 fn extract_context_from_request(req: &Request<Incoming>) -> Context {
@@ -139,16 +139,21 @@ async fn main() -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
         env::var("LLM_PROVIDER_ENDPOINT").unwrap_or_else(|_| "http://localhost:12001".to_string());
 
     let listener = TcpListener::bind(bind_address).await?;
-    let routing_model_name: String = plano_config
+    let overrides = plano_config.overrides.clone().unwrap_or_default();
-        .routing
+
-        .as_ref()
+    // Strip provider prefix (e.g. "arch/") to get the model ID used in upstream requests
-        .and_then(|r| r.model.clone())
+    let routing_model_name: String = overrides
-        .unwrap_or_else(|| DEFAULT_ROUTING_MODEL_NAME.to_string());
+        .llm_routing_model
+        .as_deref()
+        .map(|m| m.split_once('/').map(|(_, id)| id).unwrap_or(m))
+        .unwrap_or(DEFAULT_ROUTING_MODEL_NAME)
+        .to_string();
 
     let routing_llm_provider = plano_config
-        .routing
+        .model_providers
-        .as_ref()
+        .iter()
-        .and_then(|r| r.model_provider.clone())
+        .find(|p| p.model.as_deref() == Some(routing_model_name.as_str()))
+        .map(|p| p.name.clone())
         .unwrap_or_else(|| DEFAULT_ROUTING_LLM_PROVIDER.to_string());
 
     let router_service: Arc<RouterService> = Arc::new(RouterService::new(
@@ -158,9 +163,25 @@ async fn main() -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
         routing_llm_provider,
     ));
 
+    // Strip provider prefix (e.g. "arch/") to get the model ID used in upstream requests
+    let orchestrator_model_name: String = overrides
+        .agent_orchestration_model
+        .as_deref()
+        .map(|m| m.split_once('/').map(|(_, id)| id).unwrap_or(m))
+        .unwrap_or(DEFAULT_ORCHESTRATOR_MODEL_NAME)
+        .to_string();
+
+    let orchestrator_llm_provider: String = plano_config
+        .model_providers
+        .iter()
+        .find(|p| p.model.as_deref() == Some(orchestrator_model_name.as_str()))
+        .map(|p| p.name.clone())
+        .unwrap_or_else(|| DEFAULT_ORCHESTRATOR_LLM_PROVIDER.to_string());
+
     let orchestrator_service: Arc<OrchestratorService> = Arc::new(OrchestratorService::new(
         format!("{llm_provider_url}{CHAT_COMPLETIONS_PATH}"),
-        PLANO_ORCHESTRATOR_MODEL_NAME.to_string(),
+        orchestrator_model_name,
+        orchestrator_llm_provider,
     ));
 
     let model_aliases = Arc::new(plano_config.model_aliases.clone());

crates/brightstaff/src/router/plano_orchestrator.rs

@@ -2,7 +2,7 @@ use std::{collections::HashMap, sync::Arc};
 
 use common::{
     configuration::{AgentUsagePreference, OrchestrationPreference},
-    consts::{ARCH_PROVIDER_HINT_HEADER, PLANO_ORCHESTRATOR_MODEL_NAME, REQUEST_ID_HEADER},
+    consts::{ARCH_PROVIDER_HINT_HEADER, REQUEST_ID_HEADER},
 };
 use hermesllm::apis::openai::{ChatCompletionsResponse, Message};
 use hyper::header;
@@ -19,6 +19,7 @@ pub struct OrchestratorService {
     orchestrator_url: String,
     client: reqwest::Client,
     orchestrator_model: Arc<dyn OrchestratorModel>,
+    orchestrator_provider_name: String,
 }
 
 #[derive(Debug, Error)]
@@ -36,7 +37,11 @@ pub enum OrchestrationError {
 pub type Result<T> = std::result::Result<T, OrchestrationError>;
 
 impl OrchestratorService {
-    pub fn new(orchestrator_url: String, orchestration_model_name: String) -> Self {
+    pub fn new(
+        orchestrator_url: String,
+        orchestration_model_name: String,
+        orchestrator_provider_name: String,
+    ) -> Self {
         // Empty agent orchestrations - will be provided via usage_preferences in requests
         let agent_orchestrations: HashMap<String, Vec<OrchestrationPreference>> = HashMap::new();
 
@@ -50,6 +55,7 @@ impl OrchestratorService {
             orchestrator_url,
             client: reqwest::Client::new(),
             orchestrator_model,
+            orchestrator_provider_name,
         }
     }
 
@@ -75,12 +81,12 @@ impl OrchestratorService {
         debug!(
             model = %self.orchestrator_model.get_model_name(),
             endpoint = %self.orchestrator_url,
-            "sending request to arch-orchestrator"
+            "sending request to plano-orchestrator"
         );
 
         debug!(
             body = %serde_json::to_string(&orchestrator_request).unwrap(),
-            "arch orchestrator request"
+            "plano orchestrator request"
         );
 
         let mut orchestration_request_headers = header::HeaderMap::new();
@@ -91,7 +97,7 @@ impl OrchestratorService {
 
         orchestration_request_headers.insert(
             header::HeaderName::from_static(ARCH_PROVIDER_HINT_HEADER),
-            header::HeaderValue::from_str(PLANO_ORCHESTRATOR_MODEL_NAME).unwrap(),
+            header::HeaderValue::from_str(&self.orchestrator_provider_name).unwrap(),
         );
 
         // Inject OpenTelemetry trace context from current span
@@ -110,7 +116,7 @@ impl OrchestratorService {
 
         orchestration_request_headers.insert(
             header::HeaderName::from_static("model"),
-            header::HeaderValue::from_static(PLANO_ORCHESTRATOR_MODEL_NAME),
+            header::HeaderValue::from_str(&self.orchestrator_provider_name).unwrap(),
         );
 
         let start_time = std::time::Instant::now();

crates/common/src/configuration.rs

@@ -7,12 +7,6 @@ use crate::api::open_ai::{
     ChatCompletionTool, FunctionDefinition, FunctionParameter, FunctionParameters, ParameterType,
 };
 
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct Routing {
-    pub model_provider: Option<String>,
-    pub model: Option<String>,
-}
-
 #[derive(Debug, Clone, Serialize, Deserialize)]
 pub struct ModelAlias {
     pub target: String,
@@ -84,7 +78,6 @@ pub struct Configuration {
     pub ratelimits: Option<Vec<Ratelimit>>,
     pub tracing: Option<Tracing>,
     pub mode: Option<GatewayMode>,
-    pub routing: Option<Routing>,
     pub agents: Option<Vec<Agent>>,
     pub filters: Option<Vec<Agent>>,
     pub listeners: Vec<Listener>,
@@ -96,6 +89,8 @@ pub struct Overrides {
     pub prompt_target_intent_matching_threshold: Option<f64>,
     pub optimize_context_window: Option<bool>,
     pub use_agent_orchestrator: Option<bool>,
+    pub llm_routing_model: Option<String>,
+    pub agent_orchestration_model: Option<String>,
 }
 
 #[derive(Debug, Clone, Serialize, Deserialize, Default)]
@@ -219,8 +214,6 @@ pub struct EmbeddingProviver {
 
 #[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq, Hash)]
 pub enum LlmProviderType {
-    #[serde(rename = "arch")]
-    Arch,
     #[serde(rename = "anthropic")]
     Anthropic,
     #[serde(rename = "deepseek")]
@@ -249,12 +242,13 @@ pub enum LlmProviderType {
     Qwen,
     #[serde(rename = "amazon_bedrock")]
     AmazonBedrock,
+    #[serde(rename = "plano")]
+    Plano,
 }
 
 impl Display for LlmProviderType {
     fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
         match self {
-            LlmProviderType::Arch => write!(f, "arch"),
             LlmProviderType::Anthropic => write!(f, "anthropic"),
             LlmProviderType::Deepseek => write!(f, "deepseek"),
             LlmProviderType::Groq => write!(f, "groq"),
@@ -269,6 +263,7 @@ impl Display for LlmProviderType {
             LlmProviderType::Zhipu => write!(f, "zhipu"),
             LlmProviderType::Qwen => write!(f, "qwen"),
             LlmProviderType::AmazonBedrock => write!(f, "amazon_bedrock"),
+            LlmProviderType::Plano => write!(f, "plano"),
         }
     }
 }
@@ -603,14 +598,14 @@ mod test {
             },
             LlmProvider {
                 name: "arch-router".to_string(),
-                provider_interface: LlmProviderType::Arch,
+                provider_interface: LlmProviderType::Plano,
                 model: Some("Arch-Router".to_string()),
                 internal: Some(true),
                 ..Default::default()
             },
             LlmProvider {
                 name: "plano-orchestrator".to_string(),
-                provider_interface: LlmProviderType::Arch,
+                provider_interface: LlmProviderType::Plano,
                 model: Some("Plano-Orchestrator".to_string()),
                 internal: Some(true),
                 ..Default::default()

crates/common/src/consts.rs

@@ -33,5 +33,4 @@ pub const OTEL_COLLECTOR_HTTP: &str = "opentelemetry_collector_http";
 pub const LLM_ROUTE_HEADER: &str = "x-arch-llm-route";
 pub const ENVOY_RETRY_HEADER: &str = "x-envoy-max-retries";
 pub const BRIGHT_STAFF_SERVICE_NAME: &str = "brightstaff";
-pub const PLANO_ORCHESTRATOR_MODEL_NAME: &str = "Plano-Orchestrator";
+pub const PLANO_FC_CLUSTER: &str = "plano";
-pub const ARCH_FC_CLUSTER: &str = "arch";

crates/hermesllm/src/bin/provider_models.yaml

@@ -1,183 +1,16 @@
 version: '1.0'
 source: canonical-apis
 providers:
-  mistralai:
-  - mistralai/mistral-medium-2505
-  - mistralai/mistral-medium-2508
-  - mistralai/mistral-medium-latest
-  - mistralai/mistral-medium
-  - mistralai/mistral-vibe-cli-with-tools
-  - mistralai/open-mistral-nemo
-  - mistralai/open-mistral-nemo-2407
-  - mistralai/mistral-tiny-2407
-  - mistralai/mistral-tiny-latest
-  - mistralai/mistral-large-2411
-  - mistralai/pixtral-large-2411
-  - mistralai/pixtral-large-latest
-  - mistralai/mistral-large-pixtral-2411
-  - mistralai/codestral-2508
-  - mistralai/codestral-latest
-  - mistralai/devstral-small-2507
-  - mistralai/devstral-medium-2507
-  - mistralai/devstral-2512
-  - mistralai/mistral-vibe-cli-latest
-  - mistralai/devstral-medium-latest
-  - mistralai/devstral-latest
-  - mistralai/labs-devstral-small-2512
-  - mistralai/devstral-small-latest
-  - mistralai/mistral-small-2506
-  - mistralai/mistral-small-latest
-  - mistralai/labs-mistral-small-creative
-  - mistralai/magistral-medium-2509
-  - mistralai/magistral-medium-latest
-  - mistralai/magistral-small-2509
-  - mistralai/magistral-small-latest
-  - mistralai/mistral-large-2512
-  - mistralai/mistral-large-latest
-  - mistralai/ministral-3b-2512
-  - mistralai/ministral-3b-latest
-  - mistralai/ministral-8b-2512
-  - mistralai/ministral-8b-latest
-  - mistralai/ministral-14b-2512
-  - mistralai/ministral-14b-latest
-  - mistralai/mistral-small-2501
-  - mistralai/mistral-embed-2312
-  - mistralai/mistral-embed
-  - mistralai/codestral-embed
-  - mistralai/codestral-embed-2505
-  openai:
-  - openai/gpt-4-0613
-  - openai/gpt-4
-  - openai/gpt-3.5-turbo
-  - openai/gpt-5.2-codex
-  - openai/gpt-3.5-turbo-instruct
-  - openai/gpt-3.5-turbo-instruct-0914
-  - openai/gpt-4-1106-preview
-  - openai/gpt-3.5-turbo-1106
-  - openai/gpt-4-0125-preview
-  - openai/gpt-4-turbo-preview
-  - openai/gpt-3.5-turbo-0125
-  - openai/gpt-4-turbo
-  - openai/gpt-4-turbo-2024-04-09
-  - openai/gpt-4o
-  - openai/gpt-4o-2024-05-13
-  - openai/gpt-4o-mini-2024-07-18
-  - openai/gpt-4o-mini
-  - openai/gpt-4o-2024-08-06
-  - openai/chatgpt-4o-latest
-  - openai/o1-2024-12-17
-  - openai/o1
-  - openai/computer-use-preview
-  - openai/o3-mini
-  - openai/o3-mini-2025-01-31
-  - openai/gpt-4o-2024-11-20
-  - openai/computer-use-preview-2025-03-11
-  - openai/gpt-4o-search-preview-2025-03-11
-  - openai/gpt-4o-search-preview
-  - openai/gpt-4o-mini-search-preview-2025-03-11
-  - openai/gpt-4o-mini-search-preview
-  - openai/o1-pro-2025-03-19
-  - openai/o1-pro
-  - openai/o3-2025-04-16
-  - openai/o4-mini-2025-04-16
-  - openai/o3
-  - openai/o4-mini
-  - openai/gpt-4.1-2025-04-14
-  - openai/gpt-4.1
-  - openai/gpt-4.1-mini-2025-04-14
-  - openai/gpt-4.1-mini
-  - openai/gpt-4.1-nano-2025-04-14
-  - openai/gpt-4.1-nano
-  - openai/o3-pro
-  - openai/o3-pro-2025-06-10
-  - openai/o4-mini-deep-research
-  - openai/o3-deep-research
-  - openai/o3-deep-research-2025-06-26
-  - openai/o4-mini-deep-research-2025-06-26
-  - openai/gpt-5-chat-latest
-  - openai/gpt-5-2025-08-07
-  - openai/gpt-5
-  - openai/gpt-5-mini-2025-08-07
-  - openai/gpt-5-mini
-  - openai/gpt-5-nano-2025-08-07
-  - openai/gpt-5-nano
-  - openai/gpt-5-codex
-  - openai/gpt-5-pro-2025-10-06
-  - openai/gpt-5-pro
-  - openai/gpt-5-search-api
-  - openai/gpt-5-search-api-2025-10-14
-  - openai/gpt-5.1-chat-latest
-  - openai/gpt-5.1-2025-11-13
-  - openai/gpt-5.1
-  - openai/gpt-5.1-codex
-  - openai/gpt-5.1-codex-mini
-  - openai/gpt-5.1-codex-max
-  - openai/gpt-5.2-2025-12-11
-  - openai/gpt-5.2
-  - openai/gpt-5.2-pro-2025-12-11
-  - openai/gpt-5.2-pro
-  - openai/gpt-5.2-chat-latest
-  - openai/gpt-3.5-turbo-16k
-  - openai/ft:gpt-3.5-turbo-0613:katanemo::8CMZbm0P
   deepseek:
   - deepseek/deepseek-chat
   - deepseek/deepseek-reasoner
-  x-ai:
-  - x-ai/grok-2-vision-1212
-  - x-ai/grok-3
-  - x-ai/grok-3-mini
-  - x-ai/grok-4-0709
-  - x-ai/grok-4-1-fast-non-reasoning
-  - x-ai/grok-4-1-fast-reasoning
-  - x-ai/grok-4-fast-non-reasoning
-  - x-ai/grok-4-fast-reasoning
-  - x-ai/grok-code-fast-1
-  - x-ai/grok-imagine-image
-  - x-ai/grok-imagine-video
-  moonshotai:
-  - moonshotai/kimi-k2-thinking
-  - moonshotai/kimi-k2.5
-  - moonshotai/moonshot-v1-128k-vision-preview
-  - moonshotai/moonshot-v1-8k
-  - moonshotai/kimi-k2-turbo-preview
-  - moonshotai/moonshot-v1-128k
-  - moonshotai/moonshot-v1-32k-vision-preview
-  - moonshotai/kimi-k2-thinking-turbo
-  - moonshotai/kimi-latest
-  - moonshotai/moonshot-v1-32k
-  - moonshotai/moonshot-v1-auto
-  - moonshotai/kimi-k2-0711-preview
-  - moonshotai/kimi-k2-0905-preview
-  - moonshotai/moonshot-v1-8k-vision-preview
-  anthropic:
-  - anthropic/claude-opus-4-6
-  - anthropic/claude-opus-4-5-20251101
-  - anthropic/claude-opus-4-5
-  - anthropic/claude-haiku-4-5-20251001
-  - anthropic/claude-haiku-4-5
-  - anthropic/claude-sonnet-4-5-20250929
-  - anthropic/claude-sonnet-4-5
-  - anthropic/claude-opus-4-1-20250805
-  - anthropic/claude-opus-4-1
-  - anthropic/claude-opus-4-20250514
-  - anthropic/claude-opus-4
-  - anthropic/claude-sonnet-4-20250514
-  - anthropic/claude-sonnet-4
-  - anthropic/claude-3-7-sonnet-20250219
-  - anthropic/claude-3-7-sonnet
-  - anthropic/claude-3-5-haiku-20241022
-  - anthropic/claude-3-5-haiku
-  - anthropic/claude-3-haiku-20240307
-  - anthropic/claude-3-haiku
   google:
   - google/gemini-2.5-flash
   - google/gemini-2.5-pro
   - google/gemini-2.0-flash
   - google/gemini-2.0-flash-001
-  - google/gemini-2.0-flash-exp-image-generation
   - google/gemini-2.0-flash-lite-001
   - google/gemini-2.0-flash-lite
-  - google/gemini-exp-1206
   - google/gemini-2.5-flash-preview-tts
   - google/gemini-2.5-pro-preview-tts
   - google/gemma-3-1b-it
@@ -191,12 +24,15 @@ providers:
   - google/gemini-pro-latest
   - google/gemini-2.5-flash-lite
   - google/gemini-2.5-flash-image
-  - google/gemini-2.5-flash-preview-09-2025
   - google/gemini-2.5-flash-lite-preview-09-2025
   - google/gemini-3-pro-preview
   - google/gemini-3-flash-preview
+  - google/gemini-3.1-pro-preview
+  - google/gemini-3.1-pro-preview-customtools
+  - google/gemini-3.1-flash-lite-preview
   - google/gemini-3-pro-image-preview
   - google/nano-banana-pro-preview
+  - google/gemini-3.1-flash-image-preview
   - google/gemini-robotics-er-1.5-preview
   - google/gemini-2.5-computer-use-preview-10-2025
   - google/deep-research-pro-preview-12-2025
@@ -212,7 +48,37 @@ providers:
   - amazon/amazon.nova-premier-v1:0
   - amazon/amazon.nova-lite-v1:0
   - amazon/amazon.nova-micro-v1:0
+  x-ai:
+  - x-ai/grok-3
+  - x-ai/grok-3-mini
+  - x-ai/grok-4-0709
+  - x-ai/grok-4-1-fast-non-reasoning
+  - x-ai/grok-4-1-fast-reasoning
+  - x-ai/grok-4-fast-non-reasoning
+  - x-ai/grok-4-fast-reasoning
+  - x-ai/grok-4.20-beta-0309-non-reasoning
+  - x-ai/grok-4.20-beta-0309-reasoning
+  - x-ai/grok-4.20-multi-agent-beta-0309
+  - x-ai/grok-code-fast-1
+  - x-ai/grok-imagine-image
+  - x-ai/grok-imagine-video
+  z-ai:
+  - z-ai/glm-4.5
+  - z-ai/glm-4.5-air
+  - z-ai/glm-4.6
+  - z-ai/glm-4.7
+  - z-ai/glm-5
   qwen:
+  - qwen/qwen3-asr-flash-2026-02-10
+  - qwen/qwen3.5-flash-2026-02-23
+  - qwen/qwen3.5-flash
+  - qwen/qwen3.5-122b-a10b
+  - qwen/qwen3.5-35b-a3b
+  - qwen/qwen3.5-27b
+  - qwen/qwen3-coder-next
+  - qwen/qwen3.5-397b-a17b
+  - qwen/qwen3.5-plus-2026-02-15
+  - qwen/qwen3.5-plus
   - qwen/qwen3-vl-flash-2026-01-22
   - qwen/qwen3-max-2026-01-23
   - qwen/qwen-plus-character
@@ -294,13 +160,161 @@ providers:
   - qwen/qwen-max
   - qwen/qwen-plus
   - qwen/qwen-turbo
-  z-ai:
+  mistralai:
-  - z-ai/glm-4.5
+  - mistralai/mistral-medium-2505
-  - z-ai/glm-4.5-air
+  - mistralai/mistral-medium-2508
-  - z-ai/glm-4.6
+  - mistralai/mistral-medium-latest
-  - z-ai/glm-4.7
+  - mistralai/mistral-medium
-  - z-ai/glm-5
+  - mistralai/mistral-vibe-cli-with-tools
+  - mistralai/open-mistral-nemo
+  - mistralai/open-mistral-nemo-2407
+  - mistralai/mistral-tiny-2407
+  - mistralai/mistral-tiny-latest
+  - mistralai/codestral-2508
+  - mistralai/codestral-latest
+  - mistralai/devstral-2512
+  - mistralai/mistral-vibe-cli-latest
+  - mistralai/devstral-medium-latest
+  - mistralai/devstral-latest
+  - mistralai/mistral-small-2506
+  - mistralai/mistral-small-latest
+  - mistralai/labs-mistral-small-creative
+  - mistralai/magistral-medium-2509
+  - mistralai/magistral-medium-latest
+  - mistralai/magistral-small-2509
+  - mistralai/magistral-small-latest
+  - mistralai/mistral-large-2512
+  - mistralai/mistral-large-latest
+  - mistralai/ministral-3b-2512
+  - mistralai/ministral-3b-latest
+  - mistralai/ministral-8b-2512
+  - mistralai/ministral-8b-latest
+  - mistralai/ministral-14b-2512
+  - mistralai/ministral-14b-latest
+  - mistralai/mistral-large-2411
+  - mistralai/pixtral-large-2411
+  - mistralai/pixtral-large-latest
+  - mistralai/mistral-large-pixtral-2411
+  - mistralai/devstral-small-2507
+  - mistralai/devstral-medium-2507
+  - mistralai/labs-devstral-small-2512
+  - mistralai/devstral-small-latest
+  - mistralai/mistral-squarepoint-2602
+  - mistralai/mistral-embed-2312
+  - mistralai/mistral-embed
+  - mistralai/codestral-embed
+  - mistralai/codestral-embed-2505
+  moonshotai:
+  - moonshotai/kimi-k2.5
+  - moonshotai/kimi-k2-0905-preview
+  - moonshotai/moonshot-v1-32k
+  - moonshotai/moonshot-v1-128k
+  - moonshotai/kimi-k2-thinking-turbo
+  - moonshotai/moonshot-v1-8k-vision-preview
+  - moonshotai/kimi-k2-0711-preview
+  - moonshotai/moonshot-v1-auto
+  - moonshotai/kimi-k2-thinking
+  - moonshotai/moonshot-v1-128k-vision-preview
+  - moonshotai/kimi-k2-turbo-preview
+  - moonshotai/moonshot-v1-32k-vision-preview
+  - moonshotai/moonshot-v1-8k
+  anthropic:
+  - anthropic/claude-sonnet-4-6
+  - anthropic/claude-opus-4-6
+  - anthropic/claude-opus-4-5-20251101
+  - anthropic/claude-opus-4-5
+  - anthropic/claude-haiku-4-5-20251001
+  - anthropic/claude-haiku-4-5
+  - anthropic/claude-sonnet-4-5-20250929
+  - anthropic/claude-sonnet-4-5
+  - anthropic/claude-opus-4-1-20250805
+  - anthropic/claude-opus-4-1
+  - anthropic/claude-opus-4-20250514
+  - anthropic/claude-opus-4
+  - anthropic/claude-sonnet-4-20250514
+  - anthropic/claude-sonnet-4
+  - anthropic/claude-3-haiku-20240307
+  - anthropic/claude-3-haiku
+  openai:
+  - openai/gpt-4-0613
+  - openai/gpt-4
+  - openai/gpt-3.5-turbo
+  - openai/gpt-5.4
+  - openai/gpt-5.3-chat-latest
+  - openai/gpt-5.4-2026-03-05
+  - openai/gpt-5.4-pro
+  - openai/gpt-5.4-pro-2026-03-05
+  - openai/gpt-3.5-turbo-instruct
+  - openai/gpt-3.5-turbo-instruct-0914
+  - openai/gpt-4-1106-preview
+  - openai/gpt-3.5-turbo-1106
+  - openai/gpt-4-0125-preview
+  - openai/gpt-4-turbo-preview
+  - openai/gpt-3.5-turbo-0125
+  - openai/gpt-4-turbo
+  - openai/gpt-4-turbo-2024-04-09
+  - openai/gpt-4o
+  - openai/gpt-4o-2024-05-13
+  - openai/gpt-4o-mini-2024-07-18
+  - openai/gpt-4o-mini
+  - openai/gpt-4o-2024-08-06
+  - openai/o1-2024-12-17
+  - openai/o1
+  - openai/computer-use-preview
+  - openai/o3-mini
+  - openai/o3-mini-2025-01-31
+  - openai/gpt-4o-2024-11-20
+  - openai/computer-use-preview-2025-03-11
+  - openai/gpt-4o-mini-search-preview-2025-03-11
+  - openai/gpt-4o-mini-search-preview
+  - openai/o1-pro-2025-03-19
+  - openai/o1-pro
+  - openai/o3-2025-04-16
+  - openai/o4-mini-2025-04-16
+  - openai/o3
+  - openai/o4-mini
+  - openai/gpt-4.1-2025-04-14
+  - openai/gpt-4.1
+  - openai/gpt-4.1-mini-2025-04-14
+  - openai/gpt-4.1-mini
+  - openai/gpt-4.1-nano-2025-04-14
+  - openai/gpt-4.1-nano
+  - openai/o3-pro
+  - openai/o3-pro-2025-06-10
+  - openai/o4-mini-deep-research
+  - openai/o3-deep-research
+  - openai/o3-deep-research-2025-06-26
+  - openai/o4-mini-deep-research-2025-06-26
+  - openai/gpt-5-chat-latest
+  - openai/gpt-5-2025-08-07
+  - openai/gpt-5
+  - openai/gpt-5-mini-2025-08-07
+  - openai/gpt-5-mini
+  - openai/gpt-5-nano-2025-08-07
+  - openai/gpt-5-nano
+  - openai/gpt-5-codex
+  - openai/gpt-5-pro-2025-10-06
+  - openai/gpt-5-pro
+  - openai/gpt-5-search-api
+  - openai/gpt-5-search-api-2025-10-14
+  - openai/gpt-5.1-chat-latest
+  - openai/gpt-5.1-2025-11-13
+  - openai/gpt-5.1
+  - openai/gpt-5.1-codex
+  - openai/gpt-5.1-codex-mini
+  - openai/gpt-5.1-codex-max
+  - openai/gpt-5.2-2025-12-11
+  - openai/gpt-5.2
+  - openai/gpt-5.2-pro-2025-12-11
+  - openai/gpt-5.2-pro
+  - openai/gpt-5.2-chat-latest
+  - openai/gpt-5.2-codex
+  - openai/gpt-5.3-codex
+  - openai/gpt-4o-search-preview
+  - openai/gpt-4o-search-preview-2025-03-11
+  - openai/gpt-3.5-turbo-16k
+  - openai/ft:gpt-3.5-turbo-0613:katanemo::8CMZbm0P
 metadata:
   total_providers: 10
-  total_models: 289
+  total_models: 303
-  last_updated: 2026-02-13T22:44:30.413065+00:00
+  last_updated: 2026-03-15T16:47:22.207197+00:00

crates/hermesllm/src/lib.rs

@@ -35,7 +35,7 @@ mod tests {
             ProviderId::Mistral
         );
         assert_eq!(ProviderId::try_from("groq").unwrap(), ProviderId::Groq);
-        assert_eq!(ProviderId::try_from("arch").unwrap(), ProviderId::Arch);
+        assert_eq!(ProviderId::try_from("plano").unwrap(), ProviderId::Plano);
 
         // Test aliases
         assert_eq!(ProviderId::try_from("google").unwrap(), ProviderId::Gemini);

crates/hermesllm/src/providers/id.rs

@@ -34,7 +34,7 @@ pub enum ProviderId {
     Gemini,
     Anthropic,
     GitHub,
-    Arch,
+    Plano,
     AzureOpenAI,
     XAI,
     TogetherAI,
@@ -58,7 +58,7 @@ impl TryFrom<&str> for ProviderId {
             "google" => Ok(ProviderId::Gemini), // alias
             "anthropic" => Ok(ProviderId::Anthropic),
             "github" => Ok(ProviderId::GitHub),
-            "arch" => Ok(ProviderId::Arch),
+            "plano" => Ok(ProviderId::Plano),
             "azure_openai" => Ok(ProviderId::AzureOpenAI),
             "xai" => Ok(ProviderId::XAI),
             "together_ai" => Ok(ProviderId::TogetherAI),
@@ -135,7 +135,7 @@ impl ProviderId {
                 | ProviderId::Groq
                 | ProviderId::Mistral
                 | ProviderId::Deepseek
-                | ProviderId::Arch
+                | ProviderId::Plano
                 | ProviderId::Gemini
                 | ProviderId::GitHub
                 | ProviderId::AzureOpenAI
@@ -153,7 +153,7 @@ impl ProviderId {
                 | ProviderId::Groq
                 | ProviderId::Mistral
                 | ProviderId::Deepseek
-                | ProviderId::Arch
+                | ProviderId::Plano
                 | ProviderId::Gemini
                 | ProviderId::GitHub
                 | ProviderId::AzureOpenAI
@@ -219,7 +219,7 @@ impl Display for ProviderId {
             ProviderId::Gemini => write!(f, "Gemini"),
             ProviderId::Anthropic => write!(f, "Anthropic"),
             ProviderId::GitHub => write!(f, "GitHub"),
-            ProviderId::Arch => write!(f, "Arch"),
+            ProviderId::Plano => write!(f, "Plano"),
             ProviderId::AzureOpenAI => write!(f, "azure_openai"),
             ProviderId::XAI => write!(f, "xai"),
             ProviderId::TogetherAI => write!(f, "together_ai"),

crates/llm_gateway/src/stream_context.rs

@@ -873,7 +873,7 @@ impl HttpContext for StreamContext {
                 // ensure that the provider has an endpoint if the access key is missing else return a bad request
                 if self.llm_provider.as_ref().unwrap().endpoint.is_none()
                     && self.llm_provider.as_ref().unwrap().provider_interface
-                        != LlmProviderType::Arch
+                        != LlmProviderType::Plano
                 {
                     self.send_server_error(error, Some(StatusCode::BAD_REQUEST));
                 }

demos/agent_orchestration/travel_agents/README.md

@@ -123,6 +123,42 @@ Each agent:
 
 Both agents run as native local processes and communicate with Plano running natively on the host.
 
+## Running with local Plano-Orchestrator (via vLLM)
+
+By default, Plano uses a hosted Plano-Orchestrator endpoint. To self-host the orchestrator model locally using vLLM on a server with an NVIDIA GPU:
+
+1. Install vLLM and download the model:
+```bash
+pip install vllm
+```
+
+2. Start the vLLM server with the 4B model:
+```bash
+vllm serve katanemo/Plano-Orchestrator-4B \
+    --host 0.0.0.0 \
+    --port 8000 \
+    --tensor-parallel-size 1 \
+    --gpu-memory-utilization 0.3 \
+    --tokenizer katanemo/Plano-Orchestrator-4B \
+    --chat-template chat_template.jinja \
+    --served-model-name katanemo/Plano-Orchestrator-4B \
+    --enable-prefix-caching
+```
+
+3. Start the demo with the local orchestrator config:
+```bash
+./run_demo.sh --local-orchestrator
+```
+
+4. Test with curl:
+```bash
+curl -X POST http://localhost:8001/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{"model": "gpt-5.2", "messages": [{"role": "user", "content": "What is the weather in Istanbul?"}]}'
+```
+
+You should see Plano use your local orchestrator to route the request to the weather agent.
+
 ## Observability
 
 This demo includes full OpenTelemetry (OTel) compatible distributed tracing to monitor and debug agent interactions:

demos/agent_orchestration/travel_agents/config_local_orchestrator.yaml

@@ -0,0 +1,66 @@
+version: v0.3.0
+
+overrides:
+  agent_orchestration_model: plano/katanemo/Plano-Orchestrator-4B
+
+agents:
+  - id: weather_agent
+    url: http://localhost:10510
+  - id: flight_agent
+    url: http://localhost:10520
+
+model_providers:
+  - model: plano/katanemo/Plano-Orchestrator-4B
+    base_url: http://localhost:8000
+
+  - model: openai/gpt-5.2
+    access_key: $OPENAI_API_KEY
+    default: true
+  - model: openai/gpt-4o-mini
+    access_key: $OPENAI_API_KEY # smaller, faster, cheaper model for extracting entities like location
+
+listeners:
+  - type: agent
+    name: travel_booking_service
+    port: 8001
+    router: plano_orchestrator_v1
+    agents:
+      - id: weather_agent
+        description: |
+
+          WeatherAgent is a specialized AI assistant for real-time weather information and forecasts. It provides accurate weather data for any city worldwide using the Open-Meteo API, helping travelers plan their trips with up-to-date weather conditions.
+
+          Capabilities:
+            * Get real-time weather conditions and multi-day forecasts for any city worldwide using Open-Meteo API (free, no API key needed)
+            * Provides current temperature
+            * Provides multi-day forecasts
+            * Provides weather conditions
+            * Provides sunrise/sunset times
+            * Provides detailed weather information
+            * Understands conversation context to resolve location references from previous messages
+            * Handles weather-related questions including "What's the weather in [city]?", "What's the forecast for [city]?", "How's the weather in [city]?"
+            * When queries include both weather and other travel questions (e.g., flights, currency), this agent answers ONLY the weather part
+
+      - id: flight_agent
+        description: |
+
+          FlightAgent is an AI-powered tool specialized in providing live flight information between airports. It leverages the FlightAware AeroAPI to deliver real-time flight status, gate information, and delay updates.
+
+          Capabilities:
+            * Get live flight information between airports using FlightAware AeroAPI
+            * Shows real-time flight status
+            * Shows scheduled/estimated/actual departure and arrival times
+            * Shows gate and terminal information
+            * Shows delays
+            * Shows aircraft type
+            * Shows flight status
+            * Automatically resolves city names to airport codes (IATA/ICAO)
+            * Understands conversation context to infer origin/destination from follow-up questions
+            * Handles flight-related questions including "What flights go from [city] to [city]?", "Do flights go to [city]?", "Are there direct flights from [city]?"
+            * When queries include both flight and other travel questions (e.g., weather, currency), this agent answers ONLY the flight part
+
+tracing:
+  random_sampling: 100
+  span_attributes:
+    header_prefixes:
+      - x-acme-

demos/agent_orchestration/travel_agents/run_demo.sh

@@ -31,8 +31,13 @@ start_demo() {
   fi
 
   # Step 4: Start Plano
-  echo "Starting Plano with config.yaml..."
+  PLANO_CONFIG="config.yaml"
-  planoai up config.yaml
+  if [ "$1" == "--local-orchestrator" ]; then
+    PLANO_CONFIG="config_local_orchestrator.yaml"
+    echo "Using local orchestrator config..."
+  fi
+  echo "Starting Plano with $PLANO_CONFIG..."
+  planoai up "$PLANO_CONFIG"
 
   # Step 5: Start agents natively
   echo "Starting agents..."

demos/llm_routing/model_routing_service/README.md

@@ -1,6 +1,54 @@
 # Model Routing Service Demo
 
-This demo shows how to use the `/routing/v1/*` endpoints to get routing decisions without proxying requests to an LLM. The endpoint accepts standard LLM request formats and returns which model Plano's router would select.
+Plano is an AI-native proxy and data plane for agentic apps — with built-in orchestration, safety, observability, and intelligent LLM routing.
+
+```
+┌───────────┐      ┌─────────────────────────────────┐      ┌──────────────┐
+│  Client   │ ───► │  Plano                          │ ───► │  OpenAI      │
+│  (any     │      │                                 │      │  Anthropic   │
+│  language)│      │  Arch-Router (1.5B model)       │      │  Any Provider│
+└───────────┘      │  analyzes intent → picks model  │      └──────────────┘
+                   └─────────────────────────────────┘
+```
+
+- **One endpoint, many models** — apps call Plano using standard OpenAI/Anthropic APIs; Plano handles provider selection, keys, and failover
+- **Intelligent routing** — a lightweight 1.5B router model classifies user intent and picks the best model per request
+- **Platform governance** — centralize API keys, rate limits, guardrails, and observability without touching app code
+- **Runs anywhere** — single binary; self-host the router for full data privacy
+
+## How Routing Works
+
+The entire routing configuration is plain YAML — no code:
+
+```yaml
+model_providers:
+  - model: openai/gpt-4o-mini
+    default: true                    # fallback for unmatched requests
+
+  - model: openai/gpt-4o
+    routing_preferences:
+      - name: complex_reasoning
+        description: complex reasoning tasks, multi-step analysis
+
+  - model: anthropic/claude-sonnet-4-20250514
+    routing_preferences:
+      - name: code_generation
+        description: generating new code, writing functions
+```
+
+When a request arrives, Plano sends the conversation and routing preferences to Arch-Router, which classifies the intent and returns the matching route:
+
+```
+1. Request arrives          → "Write binary search in Python"
+2. Preferences serialized   → [{"name":"code_generation", ...}, {"name":"complex_reasoning", ...}]
+3. Arch-Router classifies   → {"route": "code_generation"}
+4. Route → Model lookup     → code_generation → anthropic/claude-sonnet-4-20250514
+5. Request forwarded        → Claude generates the response
+```
+
+No match? Arch-Router returns `other` → Plano falls back to the default model.
+
+The `/routing/v1/*` endpoints return the routing decision **without** forwarding to the LLM — useful for testing and validating routing behavior before going to production.
 
 ## Setup
 
@@ -55,6 +103,69 @@ Response:
 
 The response tells you which model would handle this request and which route was matched, without actually making the LLM call.
 
+## Kubernetes Deployment (Self-hosted Arch-Router on GPU)
+
+To run Arch-Router in-cluster using vLLM instead of the default hosted endpoint:
+
+**0. Check your GPU node labels and taints**
+
+```bash
+kubectl get nodes --show-labels | grep -i gpu
+kubectl get node <gpu-node-name> -o jsonpath='{.spec.taints}'
+```
+
+GPU nodes commonly have a `nvidia.com/gpu:NoSchedule` taint — `vllm-deployment.yaml` includes a matching toleration. If you have multiple GPU node pools and need to pin to a specific one, uncomment and set the `nodeSelector` in `vllm-deployment.yaml` using the label for your cloud provider.
+
+**1. Deploy Arch-Router and Plano:**
+
+```bash
+
+# arch-router deployment
+kubectl apply -f vllm-deployment.yaml
+
+# plano deployment
+kubectl create secret generic plano-secrets \
+  --from-literal=OPENAI_API_KEY=$OPENAI_API_KEY \
+  --from-literal=ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY
+
+kubectl create configmap plano-config \
+  --from-file=plano_config.yaml=config_k8s.yaml \
+  --dry-run=client -o yaml | kubectl apply -f -
+
+kubectl apply -f plano-deployment.yaml
+```
+
+**3. Wait for both pods to be ready:**
+
+```bash
+# Arch-Router downloads the model (~1 min) then vLLM loads it (~2 min)
+kubectl get pods -l app=arch-router -w
+kubectl rollout status deployment/plano
+```
+
+**4. Test:**
+
+```bash
+kubectl port-forward svc/plano 12000:12000
+./demo.sh
+```
+
+To confirm requests are hitting your in-cluster Arch-Router (not just health checks):
+
+```bash
+kubectl logs -l app=arch-router -f --tail=0
+# Look for POST /v1/chat/completions entries
+```
+
+**Updating the config:**
+
+```bash
+kubectl create configmap plano-config \
+  --from-file=plano_config.yaml=config_k8s.yaml \
+  --dry-run=client -o yaml | kubectl apply -f -
+kubectl rollout restart deployment/plano
+```
+
 ## Demo Output
 
 ```

demos/llm_routing/model_routing_service/config_k8s.yaml

@@ -0,0 +1,33 @@
+version: v0.3.0
+
+overrides:
+  llm_routing_model: plano/Arch-Router
+
+listeners:
+  - type: model
+    name: model_listener
+    port: 12000
+
+model_providers:
+
+  - model: plano/Arch-Router
+    base_url: http://arch-router:10000
+
+  - model: openai/gpt-4o-mini
+    access_key: $OPENAI_API_KEY
+    default: true
+
+  - model: openai/gpt-4o
+    access_key: $OPENAI_API_KEY
+    routing_preferences:
+      - name: complex_reasoning
+        description: complex reasoning tasks, multi-step analysis, or detailed explanations
+
+  - model: anthropic/claude-sonnet-4-20250514
+    access_key: $ANTHROPIC_API_KEY
+    routing_preferences:
+      - name: code_generation
+        description: generating new code, writing functions, or creating boilerplate
+
+tracing:
+  random_sampling: 100

demos/llm_routing/model_routing_service/plano-deployment.yaml

@@ -0,0 +1,68 @@
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: plano
+  labels:
+    app: plano
+spec:
+  replicas: 1
+  selector:
+    matchLabels:
+      app: plano
+  template:
+    metadata:
+      labels:
+        app: plano
+    spec:
+      containers:
+        - name: plano
+          image: katanemo/plano:0.4.12
+          ports:
+            - containerPort: 12000  # LLM gateway (chat completions, model routing)
+              name: llm-gateway
+          envFrom:
+            - secretRef:
+                name: plano-secrets
+          env:
+            - name: LOG_LEVEL
+              value: "info"
+          volumeMounts:
+            - name: plano-config
+              mountPath: /app/plano_config.yaml
+              subPath: plano_config.yaml
+              readOnly: true
+          readinessProbe:
+            httpGet:
+              path: /healthz
+              port: 12000
+            initialDelaySeconds: 5
+            periodSeconds: 10
+          livenessProbe:
+            httpGet:
+              path: /healthz
+              port: 12000
+            initialDelaySeconds: 10
+            periodSeconds: 30
+          resources:
+            requests:
+              memory: "256Mi"
+              cpu: "250m"
+            limits:
+              memory: "512Mi"
+              cpu: "1000m"
+      volumes:
+        - name: plano-config
+          configMap:
+            name: plano-config
+---
+apiVersion: v1
+kind: Service
+metadata:
+  name: plano
+spec:
+  selector:
+    app: plano
+  ports:
+    - name: llm-gateway
+      port: 12000
+      targetPort: 12000

demos/llm_routing/model_routing_service/test.rest

@@ -0,0 +1,36 @@
+### Code generation query (OpenAI format) — expects anthropic/claude-sonnet
+POST http://localhost:12000/routing/v1/chat/completions
+Content-Type: application/json
+
+{
+  "model": "gpt-4o-mini",
+  "messages": [{"role": "user", "content": "Write a Python function for binary search"}]
+}
+
+### Complex reasoning query (OpenAI format) — expects openai/gpt-4o
+POST http://localhost:12000/routing/v1/chat/completions
+Content-Type: application/json
+
+{
+  "model": "gpt-4o-mini",
+  "messages": [{"role": "user", "content": "Analyze the trade-offs between microservices and monolithic architecture"}]
+}
+
+### Simple query — no routing match, expects default model
+POST http://localhost:12000/routing/v1/chat/completions
+Content-Type: application/json
+
+{
+  "model": "gpt-4o-mini",
+  "messages": [{"role": "user", "content": "Hello"}]
+}
+
+### Code generation query (Anthropic format)
+POST http://localhost:12000/routing/v1/messages
+Content-Type: application/json
+
+{
+  "model": "claude-sonnet-4-20250514",
+  "max_tokens": 1024,
+  "messages": [{"role": "user", "content": "Write a REST API in Go using Gin"}]
+}

demos/llm_routing/model_routing_service/vllm-deployment.yaml

@@ -0,0 +1,104 @@
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: arch-router
+  labels:
+    app: arch-router
+spec:
+  replicas: 1
+  selector:
+    matchLabels:
+      app: arch-router
+  template:
+    metadata:
+      labels:
+        app: arch-router
+    spec:
+      tolerations:
+        - key: nvidia.com/gpu
+          operator: Exists
+          effect: NoSchedule
+      # Optional: add a nodeSelector to pin to a specific GPU node pool.
+      # The nvidia.com/gpu resource request below is sufficient for most clusters.
+      # nodeSelector:
+      #   DigitalOcean: doks.digitalocean.com/gpu-model: l40s
+      #   GKE:          cloud.google.com/gke-accelerator: nvidia-l4
+      #   EKS:          eks.amazonaws.com/nodegroup: gpu-nodes
+      #   AKS:          kubernetes.azure.com/agentpool: gpupool
+      initContainers:
+        - name: download-model
+          image: python:3.11-slim
+          command:
+            - sh
+            - -c
+            - |
+              pip install huggingface_hub[cli] && \
+              python -c "from huggingface_hub import snapshot_download; snapshot_download('katanemo/Arch-Router-1.5B.gguf', local_dir='/models/Arch-Router-1.5B.gguf')"
+          volumeMounts:
+            - name: model-cache
+              mountPath: /models
+      containers:
+        - name: vllm
+          image: vllm/vllm-openai:latest
+          command:
+            - vllm
+            - serve
+            - /models/Arch-Router-1.5B.gguf/Arch-Router-1.5B-Q4_K_M.gguf
+            - "--host"
+            - "0.0.0.0"
+            - "--port"
+            - "10000"
+            - "--load-format"
+            - "gguf"
+            - "--tokenizer"
+            - "katanemo/Arch-Router-1.5B"
+            - "--served-model-name"
+            - "Arch-Router"
+            - "--gpu-memory-utilization"
+            - "0.3"
+            - "--tensor-parallel-size"
+            - "1"
+            - "--enable-prefix-caching"
+          ports:
+            - name: http
+              containerPort: 10000
+              protocol: TCP
+          resources:
+            requests:
+              cpu: "1"
+              memory: "4Gi"
+              nvidia.com/gpu: "1"
+            limits:
+              cpu: "4"
+              memory: "8Gi"
+              nvidia.com/gpu: "1"
+          volumeMounts:
+            - name: model-cache
+              mountPath: /models
+          readinessProbe:
+            httpGet:
+              path: /health
+              port: 10000
+            initialDelaySeconds: 60
+            periodSeconds: 10
+          livenessProbe:
+            httpGet:
+              path: /health
+              port: 10000
+            initialDelaySeconds: 180
+            periodSeconds: 30
+      volumes:
+        - name: model-cache
+          emptyDir: {}
+---
+apiVersion: v1
+kind: Service
+metadata:
+  name: arch-router
+spec:
+  selector:
+    app: arch-router
+  ports:
+    - name: http
+      port: 10000
+      targetPort: 10000

demos/llm_routing/openclaw_routing/config.yaml

@@ -1,8 +1,7 @@
 version: v0.1.0
 
-routing:
+overrides:
-  model: Arch-Router
+  llm_routing_model: Arch-Router
-  llm_provider: arch-router
 
 listeners:
   egress_traffic:

demos/llm_routing/preference_based_routing/plano_config_local.yaml

@@ -1,8 +1,7 @@
 version: v0.3.0
 
-routing:
+overrides:
-  model: Arch-Router
+  llm_routing_model: plano/hf.co/katanemo/Arch-Router-1.5B.gguf:Q4_K_M
-  llm_provider: arch-router
 
 listeners:
   - type: model
@@ -11,8 +10,7 @@ listeners:
 
 model_providers:
 
-  - name: arch-router
+  - model: plano/hf.co/katanemo/Arch-Router-1.5B.gguf:Q4_K_M
-    model: arch/hf.co/katanemo/Arch-Router-1.5B.gguf:Q4_K_M
     base_url: http://localhost:11434
 
   - model: openai/gpt-4o-mini

docs/source/conf.py

@@ -17,7 +17,7 @@ from sphinxawesome_theme.postprocess import Icons
 project = "Plano Docs"
 copyright = "2025, Katanemo Labs, Inc"
 author = "Katanemo Labs, Inc"
-release = " v0.4.11"
+release = " v0.4.12"
 
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration

docs/source/get_started/quickstart.rst

@@ -43,7 +43,7 @@ Plano's CLI allows you to manage and interact with the Plano efficiently. To ins
 
 .. code-block:: console
 
-   $ uv tool install planoai==0.4.11
+   $ uv tool install planoai==0.4.12
 
 **Option 2: Install with pip (Traditional)**
 
@@ -51,7 +51,7 @@ Plano's CLI allows you to manage and interact with the Plano efficiently. To ins
 
    $ python -m venv venv
    $ source venv/bin/activate   # On Windows, use: venv\Scripts\activate
-   $ pip install planoai==0.4.11
+   $ pip install planoai==0.4.12
 
 
 .. _llm_routing_quickstart:

docs/source/guides/llm_router.rst

@@ -253,13 +253,11 @@ Using Ollama (recommended for local development)
 
    .. code-block:: yaml
 
-       routing:
+       overrides:
-         model: Arch-Router
+         llm_routing_model: plano/hf.co/katanemo/Arch-Router-1.5B.gguf:Q4_K_M
-         llm_provider: arch-router
 
        model_providers:
-         - name: arch-router
+         - model: plano/hf.co/katanemo/Arch-Router-1.5B.gguf:Q4_K_M
-           model: arch/hf.co/katanemo/Arch-Router-1.5B.gguf:Q4_K_M
            base_url: http://localhost:11434
 
          - model: openai/gpt-5.2
@@ -324,13 +322,11 @@ vLLM provides higher throughput and GPU optimizations suitable for production de
 
    .. code-block:: yaml
 
-       routing:
+       overrides:
-         model: Arch-Router
+         llm_routing_model: plano/Arch-Router
-         llm_provider: arch-router
 
        model_providers:
-         - name: arch-router
+         - model: plano/Arch-Router
-           model: Arch-Router
            base_url: http://<your-server-ip>:10000
 
          - model: openai/gpt-5.2
@@ -351,6 +347,35 @@ vLLM provides higher throughput and GPU optimizations suitable for production de
        curl http://localhost:10000/v1/models
 
 
+Using vLLM on Kubernetes (GPU nodes)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+For teams running Kubernetes, Arch-Router and Plano can be deployed as in-cluster services.
+The ``demos/llm_routing/model_routing_service/`` directory includes ready-to-use manifests:
+
+- ``vllm-deployment.yaml`` — Arch-Router served by vLLM, with an init container to download
+  the model from HuggingFace
+- ``plano-deployment.yaml`` — Plano proxy configured to use the in-cluster Arch-Router
+- ``config_k8s.yaml`` — Plano config with ``llm_routing_model`` pointing at
+  ``http://arch-router:10000`` instead of the default hosted endpoint
+
+Key things to know before deploying:
+
+- GPU nodes commonly have a ``nvidia.com/gpu:NoSchedule`` taint — the ``vllm-deployment.yaml``
+  includes a matching toleration. The ``nvidia.com/gpu: "1"`` resource request is sufficient
+  for scheduling in most clusters; a ``nodeSelector`` is optional and commented out in the
+  manifest for cases where you need to pin to a specific GPU node pool.
+- Model download takes ~1 minute; vLLM loads the model in ~1-2 minutes after that. The
+  ``livenessProbe`` has a 180-second ``initialDelaySeconds`` to avoid premature restarts.
+- The Plano config ConfigMap must use ``--from-file=plano_config.yaml=config_k8s.yaml`` with
+  ``subPath`` in the Deployment — omitting ``subPath`` causes Kubernetes to mount a directory
+  instead of a file.
+
+For the canonical Plano Kubernetes deployment (ConfigMap, Secrets, Deployment YAML), see
+:ref:`deployment`. For full step-by-step commands specific to this demo, see the
+`demo README <https://github.com/katanemo/plano/tree/main/demos/llm_routing/model_routing_service/README.md>`_.
+
+
 Combining Routing Methods
 -------------------------
 

docs/source/guides/orchestration.rst

@@ -335,6 +335,90 @@ Combine RAG agents for documentation lookup with specialized troubleshooting age
       - id: troubleshoot_agent
         description: Diagnoses and resolves technical issues step by step
 
+Self-hosting Plano-Orchestrator
+-------------------------------
+
+By default, Plano uses a hosted Plano-Orchestrator endpoint. To self-host the orchestrator model, you can serve it using **vLLM** on a server with an NVIDIA GPU.
+
+.. note::
+   vLLM requires a Linux server with an NVIDIA GPU (CUDA). For local development on macOS, a GGUF version for Ollama is coming soon.
+
+The following model variants are available on HuggingFace:
+
+* `Plano-Orchestrator-4B <https://huggingface.co/katanemo/Plano-Orchestrator-4B>`_ — lighter model, suitable for development and testing
+* `Plano-Orchestrator-4B-FP8 <https://huggingface.co/katanemo/Plano-Orchestrator-4B-FP8>`_ — FP8 quantized 4B model, lower memory usage
+* `Plano-Orchestrator-30B-A3B <https://huggingface.co/katanemo/Plano-Orchestrator-30B-A3B>`_ — full-size model for production
+* `Plano-Orchestrator-30B-A3B-FP8 <https://huggingface.co/katanemo/Plano-Orchestrator-30B-A3B-FP8>`_ — FP8 quantized 30B model, recommended for production deployments
+
+Using vLLM
+~~~~~~~~~~
+
+1. **Install vLLM**
+
+   .. code-block:: bash
+
+       pip install vllm
+
+2. **Download the model and chat template**
+
+   .. code-block:: bash
+
+       pip install huggingface_hub
+       huggingface-cli download katanemo/Plano-Orchestrator-4B
+
+3. **Start the vLLM server**
+
+   For the 4B model (development):
+
+   .. code-block:: bash
+
+       vllm serve katanemo/Plano-Orchestrator-4B \
+           --host 0.0.0.0 \
+           --port 8000 \
+           --tensor-parallel-size 1 \
+           --gpu-memory-utilization 0.3 \
+           --tokenizer katanemo/Plano-Orchestrator-4B \
+           --chat-template chat_template.jinja \
+           --served-model-name katanemo/Plano-Orchestrator-4B \
+           --enable-prefix-caching
+
+   For the 30B-A3B-FP8 model (production):
+
+   .. code-block:: bash
+
+       vllm serve katanemo/Plano-Orchestrator-30B-A3B-FP8 \
+           --host 0.0.0.0 \
+           --port 8000 \
+           --tensor-parallel-size 1 \
+           --gpu-memory-utilization 0.9 \
+           --tokenizer katanemo/Plano-Orchestrator-30B-A3B-FP8 \
+           --chat-template chat_template.jinja \
+           --max-model-len 32768 \
+           --served-model-name katanemo/Plano-Orchestrator-30B-A3B-FP8 \
+           --enable-prefix-caching
+
+4. **Configure Plano to use the local orchestrator**
+
+   Use the model name matching your ``--served-model-name``:
+
+   .. code-block:: yaml
+
+       overrides:
+         agent_orchestration_model: plano/katanemo/Plano-Orchestrator-4B
+
+       model_providers:
+         - model: katanemo/Plano-Orchestrator-4B
+           provider_interface: plano
+           base_url: http://<your-server-ip>:8000
+
+5. **Verify the server is running**
+
+   .. code-block:: bash
+
+       curl http://localhost:8000/health
+       curl http://localhost:8000/v1/models
+
+
 Next Steps
 ----------
 

docs/source/resources/deployment.rst

@@ -65,7 +65,7 @@ Create a ``docker-compose.yml`` file with the following configuration:
    # docker-compose.yml
    services:
      plano:
-       image: katanemo/plano:0.4.11
+       image: katanemo/plano:0.4.12
        container_name: plano
        ports:
          - "10000:10000" # ingress (client -> plano)
@@ -153,7 +153,7 @@ Create a ``plano-deployment.yaml``:
        spec:
          containers:
            - name: plano
-             image: katanemo/plano:0.4.11
+             image: katanemo/plano:0.4.12
              ports:
                - containerPort: 12000  # LLM gateway (chat completions, model routing)
                  name: llm-gateway

docs/source/resources/includes/plano_config_full_reference_rendered.yaml

@@ -107,11 +107,11 @@ model_providers:
 - internal: true
   model: Arch-Function
   name: arch-function
-  provider_interface: arch
+  provider_interface: plano
 - internal: true
   model: Plano-Orchestrator
-  name: plano-orchestrator
+  name: plano/orchestrator
-  provider_interface: arch
+  provider_interface: plano
 prompt_targets:
 - description: Get current weather at a location.
   endpoint: