# Plano Gateway configuration version version: v0.4.0 # 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 - model: groq/llama-3.3-70b-versatile access_key: $GROQ_API_KEY # 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 # routing_preferences: top-level list that tags named task categories with an # ordered pool of candidate models. Plano's LLM router matches incoming requests # against these descriptions and returns an ordered list of models; the client # uses models[0] as primary and retries with models[1], models[2]... on 429/5xx. # Requires overrides.llm_routing_model to point at Plano-Orchestrator (or equivalent). # Each model in `models` must be declared in model_providers above. # selection_policy is optional: {prefer: cheapest|fastest|none} lets the router # reorder candidates using live cost/latency data from model_metrics_sources. routing_preferences: - name: code generation description: generating new code snippets, functions, or boilerplate based on user prompts or requirements models: - anthropic/claude-sonnet-4-0 - openai/gpt-4o - groq/llama-3.3-70b-versatile - name: code review description: reviewing, analyzing, and suggesting improvements to existing code models: - anthropic/claude-sonnet-4-0 - groq/llama-3.3-70b-versatile selection_policy: prefer: cheapest # 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: Plano-Orchestrator # Model used for agent orchestration (must be listed in model_providers) agent_orchestration_model: Plano-Orchestrator # Disable agentic signal analysis (frustration, repetition, escalation, etc.) # on LLM responses to save CPU. Default: false. disable_signals: false # 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) # session_cache controls the backend used to store affinity state. # "memory" (default) is in-process and works for single-instance deployments. # "redis" shares state across replicas — required for multi-replica / Kubernetes setups. session_cache: type: memory # "memory" (default) or "redis" # url is required when type is "redis". Supports redis:// and rediss:// (TLS). # url: redis://localhost:6379 # tenant_header: x-org-id # optional; when set, keys are scoped as plano:affinity:{tenant_id}:{session_id} # 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