plano/docs/source/resources/includes/plano_config_full_reference.yaml

# Plano Gateway configuration version
version: v0.3.0

# External HTTP agents - API type is controlled by request path (/v1/responses, /v1/messages, /v1/chat/completions)
agents:
  - id: weather_agent # Example agent for weather
    url: http://localhost:10510

  - id: flight_agent # Example agent for flights
    url: http://localhost:10520

# MCP filters applied to requests/responses (e.g., input validation, query rewriting)
filters:
  - id: input_guards # Example filter for input validation
    url: http://localhost:10500
    # type: mcp (default)
    # transport: streamable-http (default)
    # tool: input_guards (default - same as filter id)

# LLM provider configurations with API keys and model routing
model_providers:
  - model: openai/gpt-4o
    access_key: $OPENAI_API_KEY
    default: true

  - model: openai/gpt-4o-mini
    access_key: $OPENAI_API_KEY

  - model: anthropic/claude-sonnet-4-0
    access_key: $ANTHROPIC_API_KEY

  - model: mistral/ministral-3b-latest
    access_key: $MISTRAL_API_KEY

  # routing_preferences: tags a model with named capabilities so Plano's LLM router
  # can select the best model for each request based on intent. Requires the
  # Arch-Router model (or equivalent) to be configured in overrides.llm_routing_model.
  # Each preference has a name (short label) and a description (used for intent matching).
  - model: groq/llama-3.3-70b-versatile
    access_key: $GROQ_API_KEY
    routing_preferences:
      - name: code generation
        description: generating new code snippets, functions, or boilerplate based on user prompts or requirements
      - name: code review
        description: reviewing, analyzing, and suggesting improvements to existing code

  # passthrough_auth: forwards the client's Authorization header upstream instead of
  # using the configured access_key. Useful for LiteLLM or similar proxy setups.
  - model: openai/gpt-4o-litellm
    base_url: https://litellm.example.com
    passthrough_auth: true

  # Custom/self-hosted endpoint with explicit http_host override
  - model: openai/llama-3.3-70b
    base_url: https://api.custom-provider.com
    http_host: api.custom-provider.com
    access_key: $CUSTOM_API_KEY

# Model aliases - use friendly names instead of full provider model names
model_aliases:
  fast-llm:
    target: gpt-4o-mini

  smart-llm:
    target: gpt-4o

# HTTP listeners - entry points for agent routing, prompt targets, and direct LLM access
listeners:
  # Agent listener for routing requests to multiple agents
  - type: agent
    name: travel_booking_service
    port: 8001
    router: plano_orchestrator_v1
    address: 0.0.0.0
    agents:
      - id: rag_agent
        description: virtual assistant for retrieval augmented generation tasks
        input_filters:
          - input_guards

  # Model listener for direct LLM access
  - type: model
    name: model_1
    address: 0.0.0.0
    port: 12000
    timeout: 30s          # Request timeout (e.g. "30s", "60s")
    max_retries: 3        # Number of retries on upstream failure
    input_filters:        # Filters applied before forwarding to LLM
      - input_guards
    output_filters:       # Filters applied to LLM responses before returning to client
      - input_guards

  # Prompt listener for function calling (for prompt_targets)
  - type: prompt
    name: prompt_function_listener
    address: 0.0.0.0
    port: 10000

# Reusable service endpoints
endpoints:
  app_server:
    endpoint: 127.0.0.1:80
    connect_timeout: 0.005s
    protocol: http        # http or https

  mistral_local:
    endpoint: 127.0.0.1:8001

  secure_service:
    endpoint: api.example.com:443
    protocol: https
    http_host: api.example.com  # Override the Host header sent upstream

# Optional top-level system prompt applied to all prompt_targets
system_prompt: |
  You are a helpful assistant. Always respond concisely and accurately.

# Prompt targets for function calling and API orchestration
prompt_targets:
  - name: get_current_weather
    description: Get current weather at a location.
    parameters:
      - name: location
        description: The location to get the weather for
        required: true
        type: string
        format: City, State
      - name: days
        description: the number of days for the request
        required: true
        type: int
    endpoint:
      name: app_server
      path: /weather
      http_method: POST
    # Per-target system prompt (overrides top-level system_prompt for this target)
    system_prompt: You are a weather expert. Provide accurate and concise weather information.
    # auto_llm_dispatch_on_response: when true, the LLM is called again with the
    # function response to produce a final natural-language answer for the user
    auto_llm_dispatch_on_response: true

# Rate limits - control token usage per model and request selector
ratelimits:
  - model: openai/gpt-4o
    selector:
      key: x-user-id       # HTTP header key used to identify the rate-limit subject
      value: "*"           # Wildcard matches any value; use a specific string to target one
    limit:
      tokens: 100000       # Maximum tokens allowed in the given time unit
      unit: hour           # Time unit: "minute", "hour", or "day"

  - model: openai/gpt-4o-mini
    selector:
      key: x-org-id
      value: acme-corp
    limit:
      tokens: 500000
      unit: day

# Global behavior overrides
overrides:
  # Threshold for routing a request to a prompt_target (0.0–1.0). Lower = more permissive.
  prompt_target_intent_matching_threshold: 0.7
  # Trim conversation history to fit within the model's context window
  optimize_context_window: true
  # Use Plano's agent orchestrator for multi-agent request routing
  use_agent_orchestrator: false
  # Connect timeout for upstream provider clusters (e.g., "5s", "10s"). Default: "5s"
  upstream_connect_timeout: 10s
  # Path to the trusted CA bundle for upstream TLS verification
  upstream_tls_ca_path: /etc/ssl/certs/ca-certificates.crt
  # Model used for intent-based LLM routing (must be listed in model_providers)
  llm_routing_model: Arch-Router
  # Model used for agent orchestration (must be listed in model_providers)
  agent_orchestration_model: Plano-Orchestrator

# Model affinity — pin routing decisions for agentic loops
routing:
  session_ttl_seconds: 600    # How long a pinned session lasts (default: 600s / 10 min)
  session_max_entries: 10000  # Max cached sessions before eviction (upper limit: 10000)

# State storage for multi-turn conversation history
state_storage:
  type: memory            # "memory" (in-process) or "postgres" (persistent)
  # connection_string is required when type is postgres.
  # Supports environment variable substitution: $VAR or ${VAR}
  # connection_string: postgresql://user:$DB_PASS@localhost:5432/plano

# Input guardrails applied globally to all incoming requests
prompt_guards:
  input_guards:
    jailbreak:
      on_exception:
        message: "I'm sorry, I can't help with that request."

# OpenTelemetry tracing configuration
tracing:
  # Random sampling percentage (1-100)
  random_sampling: 100
  # Include internal Plano spans in traces
  trace_arch_internal: false
  # gRPC endpoint for OpenTelemetry collector (e.g., Jaeger, Tempo)
  opentracing_grpc_endpoint: http://localhost:4317
  span_attributes:
    # Propagate request headers whose names start with these prefixes as span attributes
    header_prefixes:
      - x-user-
      - x-org-
    # Static key/value pairs added to every span
    static:
      environment: production
      service.team: platform