plano/demos/llm_routing/model_routing_service

Model Routing Service Demo

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)│      │  Plano-Orchestrator              │      │  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

Routing is configured in top-level routing_preferences (requires version: v0.4.0):

version: v0.4.0

routing_preferences:
  - name: complex_reasoning
    description: complex reasoning tasks, multi-step analysis, or detailed explanations
    models:
      - openai/gpt-4o
      - openai/gpt-4o-mini

  - name: code_generation
    description: generating new code, writing functions, or creating boilerplate
    models:
      - anthropic/claude-sonnet-4-6
      - openai/gpt-4o

When a request arrives, Plano:

1. Sends the conversation + route descriptions to Plano-Orchestrator for intent classification
2. Looks up the matched route and returns its candidate models
3. Returns an ordered list — client uses models[0], falls back to models[1] on 429/5xx

1. Request arrives          → "Write binary search in Python"
2. Plano-Orchestrator classifies → route: "code_generation"
3. Response                 → models: ["anthropic/claude-sonnet-4-6", "openai/gpt-4o"]

No match? Plano-Orchestrator returns an empty route → client falls back to the model in the original request.

The /routing/v1/* endpoints return the routing decision without forwarding to the LLM — useful for testing routing behavior before going to production.

Setup

Make sure you have Plano CLI installed (pip install planoai or uv tool install planoai).

export OPENAI_API_KEY=<your-key>
export ANTHROPIC_API_KEY=<your-key>

Start Plano:

planoai up demos/llm_routing/model_routing_service/config.yaml

Run the demo

./demo.sh

Endpoints

All three LLM API formats are supported:

Endpoint	Format
POST /routing/v1/chat/completions	OpenAI Chat Completions
POST /routing/v1/messages	Anthropic Messages
POST /routing/v1/responses	OpenAI Responses API

Example

curl http://localhost:12000/routing/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4o-mini",
    "messages": [{"role": "user", "content": "Write a Python function for binary search"}]
  }'

Response:

{
    "models": ["anthropic/claude-sonnet-4-6", "openai/gpt-4o"],
    "route": "code_generation",
    "trace_id": "c16d1096c1af4a17abb48fb182918a88"
}

The response contains the model list — your client should try models[0] first and fall back to models[1] on 429 or 5xx errors.

Session Pinning

Send an X-Model-Affinity header to give a session a stable identity. Routing still runs on every request — pinning means the session sticks to its anchor model while the session is warm (recently used), so the provider-side prompt cache stays hot. The pinned field in the response signals a warm, stuck session. If a routing_budget is configured, a proposed switch away from the anchor is additionally gated by cost (see the routing_budget demo).

# First call — creates the session binding
curl http://localhost:12000/routing/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "X-Model-Affinity: my-session-123" \
  -d '{
    "model": "gpt-4o-mini",
    "messages": [{"role": "user", "content": "Write a Python function for binary search"}]
  }'

Response (first call — session is new, not yet warm):

{
    "models": ["anthropic/claude-sonnet-4-6", "openai/gpt-4o"],
    "route": "code_generation",
    "trace_id": "c16d1096c1af4a17abb48fb182918a88",
    "session_id": "my-session-123",
    "pinned": false
}

# Second call — same session, sticks to the anchor while warm
curl http://localhost:12000/routing/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "X-Model-Affinity: my-session-123" \
  -d '{
    "model": "gpt-4o-mini",
    "messages": [{"role": "user", "content": "Now explain merge sort"}]
  }'

Response (warm session — pinned: true, the anchor model leads the list):

{
    "models": ["anthropic/claude-sonnet-4-6", "openai/gpt-4o"],
    "route": "code_generation",
    "trace_id": "a1b2c3d4e5f6...",
    "session_id": "my-session-123",
    "pinned": true
}

Session TTL and max cache size are configurable in config.yaml:

routing:
  session_ttl_seconds: 600      # default: 600 (10 minutes)
  session_max_entries: 10000    # default: 10000

Without the X-Model-Affinity header, sessions can still be pinned implicitly when prompt caching or a routing budget is enabled — a session key is derived from the system prompt + tools + first user message. With neither enabled (as in this demo's config), every request routes fresh (no breaking change).

Kubernetes Deployment (Self-hosted Plano-Orchestrator on GPU)

To run Plano-Orchestrator in-cluster using vLLM instead of the default hosted endpoint:

0. Check your GPU node labels and taints

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 Plano-Orchestrator and Plano:

# plano-orchestrator 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:

# Plano-Orchestrator downloads the model (~1 min) then vLLM loads it (~2 min)
kubectl get pods -l app=plano-orchestrator -w
kubectl rollout status deployment/plano

4. Test:

kubectl port-forward svc/plano 12000:12000
./demo.sh

To confirm requests are hitting your in-cluster Plano-Orchestrator (not just health checks):

kubectl logs -l app=plano-orchestrator -f --tail=0
# Look for POST /v1/chat/completions entries

Updating the config:

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

=== Model Routing Service Demo ===

--- 1. Code generation query (OpenAI format) ---
{
    "models": ["anthropic/claude-sonnet-4-6", "openai/gpt-4o"],
    "route": "code_generation",
    "trace_id": "c16d1096c1af4a17abb48fb182918a88"
}

--- 2. Complex reasoning query (OpenAI format) ---
{
    "models": ["openai/gpt-4o", "openai/gpt-4o-mini"],
    "route": "complex_reasoning",
    "trace_id": "30795e228aff4d7696f082ed01b75ad4"
}

--- 3. Simple query - no routing match (OpenAI format) ---
{
    "models": ["none"],
    "route": null,
    "trace_id": "ae0b6c3b220d499fb5298ac63f4eac0e"
}

--- 4. Code generation query (Anthropic format) ---
{
    "models": ["anthropic/claude-sonnet-4-6", "openai/gpt-4o"],
    "route": "code_generation",
    "trace_id": "26be822bbdf14a3ba19fe198e55ea4a9"
}

--- 7. Session pinning - first call (fresh routing decision) ---
{
    "models": ["anthropic/claude-sonnet-4-6", "openai/gpt-4o"],
    "route": "code_generation",
    "trace_id": "f1a2b3c4d5e6f7a8b9c0d1e2f3a4b5c6",
    "session_id": "demo-session-001",
    "pinned": false
}

--- 8. Session pinning - second call (same session, pinned) ---
    Notice: same anchor model returned with "pinned": true (warm session)
{
    "models": ["anthropic/claude-sonnet-4-6", "openai/gpt-4o"],
    "route": "code_generation",
    "trace_id": "a9b8c7d6e5f4a3b2c1d0e9f8a7b6c5d4",
    "session_id": "demo-session-001",
    "pinned": true
}

--- 9. Different session gets its own fresh routing ---
{
    "models": ["openai/gpt-4o", "openai/gpt-4o-mini"],
    "route": "complex_reasoning",
    "trace_id": "1a2b3c4d5e6f7a8b9c0d1e2f3a4b5c6d",
    "session_id": "demo-session-002",
    "pinned": false
}

=== Demo Complete ===