diff --git a/_downloads/c86f9e8fb1f2994b1ba4a0b98481410e/plano_config_full_reference.yaml b/_downloads/c86f9e8fb1f2994b1ba4a0b98481410e/plano_config_full_reference.yaml index 103a5096..6d76224a 100755 --- a/_downloads/c86f9e8fb1f2994b1ba4a0b98481410e/plano_config_full_reference.yaml +++ b/_downloads/c86f9e8fb1f2994b1ba4a0b98481410e/plano_config_full_reference.yaml @@ -32,13 +32,45 @@ model_providers: - model: mistral/ministral-3b-latest access_key: $MISTRAL_API_KEY - # Example: Passthrough authentication for LiteLLM or similar proxies - # When passthrough_auth is true, client's Authorization header is forwarded - # instead of using the configured access_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: openai/gpt-4o + name: gpt-4o-coding # Optional friendly name to distinguish multiple entries for same model + access_key: $OPENAI_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 + + - model: anthropic/claude-sonnet-4-0 + name: claude-sonnet-reasoning + access_key: $ANTHROPIC_API_KEY + routing_preferences: + - name: reasoning + description: complex multi-step reasoning, math, logic puzzles, and analytical tasks + + # 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 + # provider_interface: specifies the API format when the provider doesn't match + # the default inferred from the model name. Supported: openai, claude, gemini, + # mistral, groq, deepseek, plano + - model: groq/llama-3.3-70b-versatile + access_key: $GROQ_API_KEY + provider_interface: groq + + # 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: @@ -66,26 +98,38 @@ listeners: name: model_1 address: 0.0.0.0 port: 12000 - # Optional: attach input filters for guardrails on direct LLM requests - # input_filters: - # - input_guards + 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 - # This listener is used for prompt_targets and function calling # 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 @@ -104,8 +148,75 @@ prompt_targets: 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: true + # 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 + +# 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 diff --git a/concepts/agents.html b/concepts/agents.html index 66d4486d..78e6ad5a 100755 --- a/concepts/agents.html +++ b/concepts/agents.html @@ -267,7 +267,7 @@ diff --git a/concepts/filter_chain.html b/concepts/filter_chain.html index 6b46b488..3aa6ed9c 100755 --- a/concepts/filter_chain.html +++ b/concepts/filter_chain.html @@ -333,7 +333,7 @@ powerful abstraction for evolving your agent workflows over time.

diff --git a/concepts/listeners.html b/concepts/listeners.html index 3799e6e2..dbb53c2e 100755 --- a/concepts/listeners.html +++ b/concepts/listeners.html @@ -270,7 +270,7 @@ application to LLMs (API-based or hosted) via prompt targets.

diff --git a/concepts/llm_providers/client_libraries.html b/concepts/llm_providers/client_libraries.html index aa9d0e1f..5cde15b0 100755 --- a/concepts/llm_providers/client_libraries.html +++ b/concepts/llm_providers/client_libraries.html @@ -660,7 +660,7 @@ Implement fallback logic for better reliability:

diff --git a/concepts/llm_providers/llm_providers.html b/concepts/llm_providers/llm_providers.html index 74ae63a5..1cab9426 100755 --- a/concepts/llm_providers/llm_providers.html +++ b/concepts/llm_providers/llm_providers.html @@ -304,7 +304,7 @@ Use your preferred client library without changing existing code (see
-

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diff --git a/concepts/llm_providers/model_aliases.html b/concepts/llm_providers/model_aliases.html index 3a9227e2..454d906d 100755 --- a/concepts/llm_providers/model_aliases.html +++ b/concepts/llm_providers/model_aliases.html @@ -434,7 +434,7 @@ diff --git a/concepts/llm_providers/supported_providers.html b/concepts/llm_providers/supported_providers.html index 80f8f893..1c2c0d71 100755 --- a/concepts/llm_providers/supported_providers.html +++ b/concepts/llm_providers/supported_providers.html @@ -1140,7 +1140,7 @@ Any provider that implements the OpenAI API interface can be configured using cu diff --git a/concepts/prompt_target.html b/concepts/prompt_target.html index abfbf949..13735047 100755 --- a/concepts/prompt_target.html +++ b/concepts/prompt_target.html @@ -473,7 +473,7 @@ that you can test and modify locally for multi-turn RAG scenarios.

diff --git a/concepts/signals.html b/concepts/signals.html index 3406d85c..87a31b40 100755 --- a/concepts/signals.html +++ b/concepts/signals.html @@ -540,7 +540,7 @@ diff --git a/get_started/intro_to_plano.html b/get_started/intro_to_plano.html index 5d0b0970..5a5e90fd 100755 --- a/get_started/intro_to_plano.html +++ b/get_started/intro_to_plano.html @@ -226,7 +226,7 @@ This gives Plano several advantages:

diff --git a/get_started/overview.html b/get_started/overview.html index afe97a45..78b52b71 100755 --- a/get_started/overview.html +++ b/get_started/overview.html @@ -337,7 +337,7 @@ diff --git a/get_started/quickstart.html b/get_started/quickstart.html index ef869860..6a11f37b 100755 --- a/get_started/quickstart.html +++ b/get_started/quickstart.html @@ -521,7 +521,7 @@ diff --git a/guides/function_calling.html b/guides/function_calling.html index 6a0e3d83..703c1b0b 100755 --- a/guides/function_calling.html +++ b/guides/function_calling.html @@ -372,7 +372,7 @@ on the stuff that matters most.

diff --git a/guides/llm_router.html b/guides/llm_router.html index 6a258980..b0ba9d5c 100755 --- a/guides/llm_router.html +++ b/guides/llm_router.html @@ -647,7 +647,7 @@ instead of a file.

diff --git a/guides/observability/access_logging.html b/guides/observability/access_logging.html index 4b2bb460..8fe6be0f 100755 --- a/guides/observability/access_logging.html +++ b/guides/observability/access_logging.html @@ -248,7 +248,7 @@ Access logs can be exported to centralized logging systems (e.g., ELK stack or F diff --git a/guides/observability/monitoring.html b/guides/observability/monitoring.html index b114c300..a9f0e320 100755 --- a/guides/observability/monitoring.html +++ b/guides/observability/monitoring.html @@ -260,7 +260,7 @@ are some sample configuration files for both, respectively.

diff --git a/guides/observability/observability.html b/guides/observability/observability.html index fc6052c3..1ababfb7 100755 --- a/guides/observability/observability.html +++ b/guides/observability/observability.html @@ -216,7 +216,7 @@ diff --git a/guides/observability/tracing.html b/guides/observability/tracing.html index 43aa95b8..4915f3f9 100755 --- a/guides/observability/tracing.html +++ b/guides/observability/tracing.html @@ -792,7 +792,7 @@ tools like AWS X-Ray and Datadog, enhancing observability and facilitating faste diff --git a/guides/orchestration.html b/guides/orchestration.html index 8e444311..32000d7d 100755 --- a/guides/orchestration.html +++ b/guides/orchestration.html @@ -1003,7 +1003,7 @@ Plano makes it easy to build and scale these systems by managing the orchestrati diff --git a/guides/prompt_guard.html b/guides/prompt_guard.html index 29e25258..85bf050b 100755 --- a/guides/prompt_guard.html +++ b/guides/prompt_guard.html @@ -298,7 +298,7 @@ the agent. If validation fails (
-

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diff --git a/guides/state.html b/guides/state.html index 82edfcca..e04b8f70 100755 --- a/guides/state.html +++ b/guides/state.html @@ -453,7 +453,7 @@
-

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+

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diff --git a/includes/llms.txt b/includes/llms.txt index 19878a32..5ec38db5 100755 --- a/includes/llms.txt +++ b/includes/llms.txt @@ -1,6 +1,6 @@ Plano Docs v0.4.14 llms.txt (auto-generated) -Generated (UTC): 2026-03-20T07:52:01.895282+00:00 +Generated (UTC): 2026-03-30T19:25:35.189116+00:00 Table of contents - Agents (concepts/agents) @@ -6328,13 +6328,45 @@ model_providers: - model: mistral/ministral-3b-latest access_key: $MISTRAL_API_KEY - # Example: Passthrough authentication for LiteLLM or similar proxies - # When passthrough_auth is true, client's Authorization header is forwarded - # instead of using the configured access_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: openai/gpt-4o + name: gpt-4o-coding # Optional friendly name to distinguish multiple entries for same model + access_key: $OPENAI_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 + + - model: anthropic/claude-sonnet-4-0 + name: claude-sonnet-reasoning + access_key: $ANTHROPIC_API_KEY + routing_preferences: + - name: reasoning + description: complex multi-step reasoning, math, logic puzzles, and analytical tasks + + # 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 + # provider_interface: specifies the API format when the provider doesn't match + # the default inferred from the model name. Supported: openai, claude, gemini, + # mistral, groq, deepseek, plano + - model: groq/llama-3.3-70b-versatile + access_key: $GROQ_API_KEY + provider_interface: groq + + # 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: @@ -6362,26 +6394,38 @@ listeners: name: model_1 address: 0.0.0.0 port: 12000 - # Optional: attach input filters for guardrails on direct LLM requests - # input_filters: - # - input_guards + 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 - # This listener is used for prompt_targets and function calling # 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 @@ -6400,11 +6444,78 @@ prompt_targets: 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: true + # 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 + +# 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 --- diff --git a/index.html b/index.html index d2701502..78a2ac5f 100755 --- a/index.html +++ b/index.html @@ -247,7 +247,7 @@ Resources
-

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+

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diff --git a/resources/cli_reference.html b/resources/cli_reference.html index bf4f73c6..474864f2 100755 --- a/resources/cli_reference.html +++ b/resources/cli_reference.html @@ -437,7 +437,7 @@ Use this page as the canonical source for command syntax, options, and recommend
-

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+

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diff --git a/resources/configuration_reference.html b/resources/configuration_reference.html index 65dd8642..fb9e2ace 100755 --- a/resources/configuration_reference.html +++ b/resources/configuration_reference.html @@ -201,83 +201,194 @@ where prompts get routed to, apply guardrails, and enable critical agent observa 32 - model: mistral/ministral-3b-latest 33 access_key: $MISTRAL_API_KEY 34 - 35 # Example: Passthrough authentication for LiteLLM or similar proxies - 36 # When passthrough_auth is true, client's Authorization header is forwarded - 37 # instead of using the configured access_key - 38 - model: openai/gpt-4o-litellm - 39 base_url: https://litellm.example.com - 40 passthrough_auth: true - 41 - 42# Model aliases - use friendly names instead of full provider model names - 43model_aliases: - 44 fast-llm: - 45 target: gpt-4o-mini - 46 - 47 smart-llm: - 48 target: gpt-4o - 49 - 50# HTTP listeners - entry points for agent routing, prompt targets, and direct LLM access - 51listeners: - 52 # Agent listener for routing requests to multiple agents - 53 - type: agent - 54 name: travel_booking_service - 55 port: 8001 - 56 router: plano_orchestrator_v1 - 57 address: 0.0.0.0 - 58 agents: - 59 - id: rag_agent - 60 description: virtual assistant for retrieval augmented generation tasks - 61 input_filters: - 62 - input_guards - 63 - 64 # Model listener for direct LLM access - 65 - type: model - 66 name: model_1 - 67 address: 0.0.0.0 - 68 port: 12000 - 69 # Optional: attach input filters for guardrails on direct LLM requests - 70 # input_filters: - 71 # - input_guards - 72 - 73 # Prompt listener for function calling (for prompt_targets) - 74 - type: prompt - 75 name: prompt_function_listener - 76 address: 0.0.0.0 - 77 port: 10000 - 78 # This listener is used for prompt_targets and function calling - 79 - 80# Reusable service endpoints - 81endpoints: - 82 app_server: - 83 endpoint: 127.0.0.1:80 - 84 connect_timeout: 0.005s - 85 - 86 mistral_local: - 87 endpoint: 127.0.0.1:8001 - 88 - 89# Prompt targets for function calling and API orchestration - 90prompt_targets: - 91 - name: get_current_weather - 92 description: Get current weather at a location. - 93 parameters: - 94 - name: location - 95 description: The location to get the weather for - 96 required: true - 97 type: string - 98 format: City, State - 99 - name: days -100 description: the number of days for the request -101 required: true -102 type: int -103 endpoint: -104 name: app_server -105 path: /weather -106 http_method: POST + 35 # routing_preferences: tags a model with named capabilities so Plano's LLM router + 36 # can select the best model for each request based on intent. Requires the + 37 # Arch-Router model (or equivalent) to be configured in overrides.llm_routing_model. + 38 # Each preference has a name (short label) and a description (used for intent matching). + 39 - model: openai/gpt-4o + 40 name: gpt-4o-coding # Optional friendly name to distinguish multiple entries for same model + 41 access_key: $OPENAI_API_KEY + 42 routing_preferences: + 43 - name: code generation + 44 description: generating new code snippets, functions, or boilerplate based on user prompts or requirements + 45 - name: code review + 46 description: reviewing, analyzing, and suggesting improvements to existing code + 47 + 48 - model: anthropic/claude-sonnet-4-0 + 49 name: claude-sonnet-reasoning + 50 access_key: $ANTHROPIC_API_KEY + 51 routing_preferences: + 52 - name: reasoning + 53 description: complex multi-step reasoning, math, logic puzzles, and analytical tasks + 54 + 55 # passthrough_auth: forwards the client's Authorization header upstream instead of + 56 # using the configured access_key. Useful for LiteLLM or similar proxy setups. + 57 - model: openai/gpt-4o-litellm + 58 base_url: https://litellm.example.com + 59 passthrough_auth: true + 60 + 61 # provider_interface: specifies the API format when the provider doesn't match + 62 # the default inferred from the model name. Supported: openai, claude, gemini, + 63 # mistral, groq, deepseek, plano + 64 - model: groq/llama-3.3-70b-versatile + 65 access_key: $GROQ_API_KEY + 66 provider_interface: groq + 67 + 68 # Custom/self-hosted endpoint with explicit http_host override + 69 - model: openai/llama-3.3-70b + 70 base_url: https://api.custom-provider.com + 71 http_host: api.custom-provider.com + 72 access_key: $CUSTOM_API_KEY + 73 + 74# Model aliases - use friendly names instead of full provider model names + 75model_aliases: + 76 fast-llm: + 77 target: gpt-4o-mini + 78 + 79 smart-llm: + 80 target: gpt-4o + 81 + 82# HTTP listeners - entry points for agent routing, prompt targets, and direct LLM access + 83listeners: + 84 # Agent listener for routing requests to multiple agents + 85 - type: agent + 86 name: travel_booking_service + 87 port: 8001 + 88 router: plano_orchestrator_v1 + 89 address: 0.0.0.0 + 90 agents: + 91 - id: rag_agent + 92 description: virtual assistant for retrieval augmented generation tasks + 93 input_filters: + 94 - input_guards + 95 + 96 # Model listener for direct LLM access + 97 - type: model + 98 name: model_1 + 99 address: 0.0.0.0 +100 port: 12000 +101 timeout: 30s # Request timeout (e.g. "30s", "60s") +102 max_retries: 3 # Number of retries on upstream failure +103 input_filters: # Filters applied before forwarding to LLM +104 - input_guards +105 output_filters: # Filters applied to LLM responses before returning to client +106 - input_guards 107 -108# OpenTelemetry tracing configuration -109tracing: -110 # Random sampling percentage (1-100) -111 random_sampling: 100 +108 # Prompt listener for function calling (for prompt_targets) +109 - type: prompt +110 name: prompt_function_listener +111 address: 0.0.0.0 +112 port: 10000 +113 +114# Reusable service endpoints +115endpoints: +116 app_server: +117 endpoint: 127.0.0.1:80 +118 connect_timeout: 0.005s +119 protocol: http # http or https +120 +121 mistral_local: +122 endpoint: 127.0.0.1:8001 +123 +124 secure_service: +125 endpoint: api.example.com:443 +126 protocol: https +127 http_host: api.example.com # Override the Host header sent upstream +128 +129# Optional top-level system prompt applied to all prompt_targets +130system_prompt: | +131 You are a helpful assistant. Always respond concisely and accurately. +132 +133# Prompt targets for function calling and API orchestration +134prompt_targets: +135 - name: get_current_weather +136 description: Get current weather at a location. +137 parameters: +138 - name: location +139 description: The location to get the weather for +140 required: true +141 type: string +142 format: City, State +143 - name: days +144 description: the number of days for the request +145 required: true +146 type: int +147 endpoint: +148 name: app_server +149 path: /weather +150 http_method: POST +151 # Per-target system prompt (overrides top-level system_prompt for this target) +152 system_prompt: You are a weather expert. Provide accurate and concise weather information. +153 # auto_llm_dispatch_on_response: when true, the LLM is called again with the +154 # function response to produce a final natural-language answer for the user +155 auto_llm_dispatch_on_response: true +156 +157# Rate limits - control token usage per model and request selector +158ratelimits: +159 - model: openai/gpt-4o +160 selector: +161 key: x-user-id # HTTP header key used to identify the rate-limit subject +162 value: "*" # Wildcard matches any value; use a specific string to target one +163 limit: +164 tokens: 100000 # Maximum tokens allowed in the given time unit +165 unit: hour # Time unit: "minute", "hour", or "day" +166 +167 - model: openai/gpt-4o-mini +168 selector: +169 key: x-org-id +170 value: acme-corp +171 limit: +172 tokens: 500000 +173 unit: day +174 +175# Global behavior overrides +176overrides: +177 # Threshold for routing a request to a prompt_target (0.0–1.0). Lower = more permissive. +178 prompt_target_intent_matching_threshold: 0.7 +179 # Trim conversation history to fit within the model's context window +180 optimize_context_window: true +181 # Use Plano's agent orchestrator for multi-agent request routing +182 use_agent_orchestrator: true +183 # Connect timeout for upstream provider clusters (e.g., "5s", "10s"). Default: "5s" +184 upstream_connect_timeout: 10s +185 # Path to the trusted CA bundle for upstream TLS verification +186 upstream_tls_ca_path: /etc/ssl/certs/ca-certificates.crt +187 # Model used for intent-based LLM routing (must be listed in model_providers) +188 llm_routing_model: Arch-Router +189 # Model used for agent orchestration (must be listed in model_providers) +190 agent_orchestration_model: Plano-Orchestrator +191 +192# State storage for multi-turn conversation history +193state_storage: +194 type: memory # "memory" (in-process) or "postgres" (persistent) +195 # connection_string is required when type is postgres. +196 # Supports environment variable substitution: $VAR or ${VAR} +197 # connection_string: postgresql://user:$DB_PASS@localhost:5432/plano +198 +199# Input guardrails applied globally to all incoming requests +200prompt_guards: +201 input_guards: +202 jailbreak: +203 on_exception: +204 message: "I'm sorry, I can't help with that request." +205 +206# OpenTelemetry tracing configuration +207tracing: +208 # Random sampling percentage (1-100) +209 random_sampling: 100 +210 # Include internal Plano spans in traces +211 trace_arch_internal: false +212 # gRPC endpoint for OpenTelemetry collector (e.g., Jaeger, Tempo) +213 opentracing_grpc_endpoint: http://localhost:4317 +214 span_attributes: +215 # Propagate request headers whose names start with these prefixes as span attributes +216 header_prefixes: +217 - x-user- +218 - x-org- +219 # Static key/value pairs added to every span +220 static: +221 environment: production +222 service.team: platform @@ -305,7 +416,7 @@ where prompts get routed to, apply guardrails, and enable critical agent observa diff --git a/resources/deployment.html b/resources/deployment.html index 08c71c99..56ca3c6c 100755 --- a/resources/deployment.html +++ b/resources/deployment.html @@ -542,7 +542,7 @@ diff --git a/resources/llms_txt.html b/resources/llms_txt.html index ca6ae5e2..aac66f30 100755 --- a/resources/llms_txt.html +++ b/resources/llms_txt.html @@ -179,7 +179,7 @@ diff --git a/resources/tech_overview/model_serving.html b/resources/tech_overview/model_serving.html index 156e85a1..4a775289 100755 --- a/resources/tech_overview/model_serving.html +++ b/resources/tech_overview/model_serving.html @@ -199,7 +199,7 @@ own deployments), and Plano reaches them via HTTP.

diff --git a/resources/tech_overview/request_lifecycle.html b/resources/tech_overview/request_lifecycle.html index d27be3f8..2c8c90e7 100755 --- a/resources/tech_overview/request_lifecycle.html +++ b/resources/tech_overview/request_lifecycle.html @@ -485,7 +485,7 @@ processing request headers and then finalized by the HCM during post-request pro diff --git a/resources/tech_overview/tech_overview.html b/resources/tech_overview/tech_overview.html index 53271dfc..7100c5d7 100755 --- a/resources/tech_overview/tech_overview.html +++ b/resources/tech_overview/tech_overview.html @@ -200,7 +200,7 @@ diff --git a/resources/tech_overview/threading_model.html b/resources/tech_overview/threading_model.html index 0b21803e..c118b469 100755 --- a/resources/tech_overview/threading_model.html +++ b/resources/tech_overview/threading_model.html @@ -200,7 +200,7 @@ hardware threads on the machine.

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