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_downloads/c86f9e8fb1f2994b1ba4a0b98481410e/plano_config_full_reference.yaml
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@ -32,13 +32,45 @@ model_providers:
- model: mistral/ministral-3b-latest - model: mistral/ministral-3b-latest
access_key: $MISTRAL_API_KEY access_key: $MISTRAL_API_KEY
# Example: Passthrough authentication for LiteLLM or similar proxies # routing_preferences: tags a model with named capabilities so Plano's LLM router
# When passthrough_auth is true, client's Authorization header is forwarded # can select the best model for each request based on intent. Requires the
# instead of using the configured access_key # 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 - model: openai/gpt-4o-litellm
base_url: https://litellm.example.com base_url: https://litellm.example.com
passthrough_auth: true 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 - use friendly names instead of full provider model names
model_aliases: model_aliases:
fast-llm: fast-llm:
@ -66,26 +98,38 @@ listeners:
name: model_1 name: model_1
address: 0.0.0.0 address: 0.0.0.0
port: 12000 port: 12000
# Optional: attach input filters for guardrails on direct LLM requests timeout: 30s # Request timeout (e.g. "30s", "60s")
# input_filters: max_retries: 3 # Number of retries on upstream failure
# - input_guards 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) # Prompt listener for function calling (for prompt_targets)
- type: prompt - type: prompt
name: prompt_function_listener name: prompt_function_listener
address: 0.0.0.0 address: 0.0.0.0
port: 10000 port: 10000
# This listener is used for prompt_targets and function calling
# Reusable service endpoints # Reusable service endpoints
endpoints: endpoints:
app_server: app_server:
endpoint: 127.0.0.1:80 endpoint: 127.0.0.1:80
connect_timeout: 0.005s connect_timeout: 0.005s
protocol: http # http or https
mistral_local: mistral_local:
endpoint: 127.0.0.1:8001 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 for function calling and API orchestration
prompt_targets: prompt_targets:
- name: get_current_weather - name: get_current_weather
@ -104,8 +148,75 @@ prompt_targets:
name: app_server name: app_server
path: /weather path: /weather
http_method: POST 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 # OpenTelemetry tracing configuration
tracing: tracing:
# Random sampling percentage (1-100) # Random sampling percentage (1-100)
random_sampling: 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

2
concepts/agents.html
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@ -267,7 +267,7 @@
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concepts/filter_chain.html
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@ -333,7 +333,7 @@ powerful abstraction for evolving your agent workflows over time.</p>
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concepts/listeners.html
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@ -270,7 +270,7 @@ application to LLMs (API-based or hosted) via prompt targets.</p>
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concepts/llm_providers/client_libraries.html
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@ -660,7 +660,7 @@ Implement fallback logic for better reliability:</p>
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concepts/llm_providers/llm_providers.html
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@ -304,7 +304,7 @@ Use your preferred client library without changing existing code (see <a class="
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concepts/llm_providers/model_aliases.html
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@ -434,7 +434,7 @@
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concepts/llm_providers/supported_providers.html
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@ -1140,7 +1140,7 @@ Any provider that implements the OpenAI API interface can be configured using cu
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concepts/prompt_target.html
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@ -473,7 +473,7 @@ that you can test and modify locally for multi-turn RAG scenarios.</p>
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@ -540,7 +540,7 @@
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get_started/intro_to_plano.html
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@ -226,7 +226,7 @@ This gives Plano several advantages:</p>
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get_started/overview.html
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@ -337,7 +337,7 @@
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get_started/quickstart.html
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@ -521,7 +521,7 @@
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@ -372,7 +372,7 @@ on the stuff that matters most.</p>
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guides/llm_router.html
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@ -647,7 +647,7 @@ instead of a file.</p></li>
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guides/observability/access_logging.html
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@ -248,7 +248,7 @@ Access logs can be exported to centralized logging systems (e.g., ELK stack or F
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guides/observability/monitoring.html
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@ -260,7 +260,7 @@ are some sample configuration files for both, respectively.</p>
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guides/observability/tracing.html
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@ -792,7 +792,7 @@ tools like AWS X-Ray and Datadog, enhancing observability and facilitating faste
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@ -1003,7 +1003,7 @@ Plano makes it easy to build and scale these systems by managing the orchestrati
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@ -298,7 +298,7 @@ the agent. If validation fails (<code class="docutils literal notranslate"><span
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<p class="text-sm leading-loose text-center text-muted-foreground md:text-left">© 2025, Katanemo Labs, Inc Last updated: Mar 20, 2026. </p> <p class="text-sm leading-loose text-center text-muted-foreground md:text-left">© 2025, Katanemo Labs, Inc Last updated: Mar 30, 2026. </p>
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@ -453,7 +453,7 @@
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<p class="text-sm leading-loose text-center text-muted-foreground md:text-left">© 2025, Katanemo Labs, Inc Last updated: Mar 20, 2026. </p> <p class="text-sm leading-loose text-center text-muted-foreground md:text-left">© 2025, Katanemo Labs, Inc Last updated: Mar 30, 2026. </p>
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127
includes/llms.txt
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@ -1,6 +1,6 @@
Plano Docs v0.4.14 Plano Docs v0.4.14
llms.txt (auto-generated) 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 Table of contents
- Agents (concepts/agents) - Agents (concepts/agents)
@ -6328,13 +6328,45 @@ model_providers:
- model: mistral/ministral-3b-latest - model: mistral/ministral-3b-latest
access_key: $MISTRAL_API_KEY access_key: $MISTRAL_API_KEY
# Example: Passthrough authentication for LiteLLM or similar proxies # routing_preferences: tags a model with named capabilities so Plano's LLM router
# When passthrough_auth is true, client's Authorization header is forwarded # can select the best model for each request based on intent. Requires the
# instead of using the configured access_key # 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 - model: openai/gpt-4o-litellm
base_url: https://litellm.example.com base_url: https://litellm.example.com
passthrough_auth: true 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 - use friendly names instead of full provider model names
model_aliases: model_aliases:
fast-llm: fast-llm:
@ -6362,26 +6394,38 @@ listeners:
name: model_1 name: model_1
address: 0.0.0.0 address: 0.0.0.0
port: 12000 port: 12000
# Optional: attach input filters for guardrails on direct LLM requests timeout: 30s # Request timeout (e.g. "30s", "60s")
# input_filters: max_retries: 3 # Number of retries on upstream failure
# - input_guards 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) # Prompt listener for function calling (for prompt_targets)
- type: prompt - type: prompt
name: prompt_function_listener name: prompt_function_listener
address: 0.0.0.0 address: 0.0.0.0
port: 10000 port: 10000
# This listener is used for prompt_targets and function calling
# Reusable service endpoints # Reusable service endpoints
endpoints: endpoints:
app_server: app_server:
endpoint: 127.0.0.1:80 endpoint: 127.0.0.1:80
connect_timeout: 0.005s connect_timeout: 0.005s
protocol: http # http or https
mistral_local: mistral_local:
endpoint: 127.0.0.1:8001 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 for function calling and API orchestration
prompt_targets: prompt_targets:
- name: get_current_weather - name: get_current_weather
@ -6400,11 +6444,78 @@ prompt_targets:
name: app_server name: app_server
path: /weather path: /weather
http_method: POST 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 # OpenTelemetry tracing configuration
tracing: tracing:
# Random sampling percentage (1-100) # Random sampling percentage (1-100)
random_sampling: 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
--- ---

2
index.html
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@ -247,7 +247,7 @@ Resources</label><div class="sd-tab-content docutils">
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<p class="text-sm leading-loose text-center text-muted-foreground md:text-left">© 2025, Katanemo Labs, Inc Last updated: Mar 20, 2026. </p> <p class="text-sm leading-loose text-center text-muted-foreground md:text-left">© 2025, Katanemo Labs, Inc Last updated: Mar 30, 2026. </p>
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@ -437,7 +437,7 @@ Use this page as the canonical source for command syntax, options, and recommend
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<p class="text-sm leading-loose text-center text-muted-foreground md:text-left">© 2025, Katanemo Labs, Inc Last updated: Mar 20, 2026. </p> <p class="text-sm leading-loose text-center text-muted-foreground md:text-left">© 2025, Katanemo Labs, Inc Last updated: Mar 30, 2026. </p>
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265
resources/configuration_reference.html
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@ -201,83 +201,194 @@ where prompts get routed to, apply guardrails, and enable critical agent observa
</span><span id="line-32"><span class="linenos"> 32</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">model</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">mistral/ministral-3b-latest</span> </span><span id="line-32"><span class="linenos"> 32</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">model</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">mistral/ministral-3b-latest</span>
</span><span id="line-33"><span class="linenos"> 33</span><span class="w"> </span><span class="nt">access_key</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">$MISTRAL_API_KEY</span> </span><span id="line-33"><span class="linenos"> 33</span><span class="w"> </span><span class="nt">access_key</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">$MISTRAL_API_KEY</span>
</span><span id="line-34"><span class="linenos"> 34</span> </span><span id="line-34"><span class="linenos"> 34</span>
</span><span id="line-35"><span class="linenos"> 35</span><span class="w"> </span><span class="c1"># Example: Passthrough authentication for LiteLLM or similar proxies</span> </span><span id="line-35"><span class="linenos"> 35</span><span class="w"> </span><span class="c1"># routing_preferences: tags a model with named capabilities so Plano's LLM router</span>
</span><span id="line-36"><span class="linenos"> 36</span><span class="w"> </span><span class="c1"># When passthrough_auth is true, client's Authorization header is forwarded</span> </span><span id="line-36"><span class="linenos"> 36</span><span class="w"> </span><span class="c1"># can select the best model for each request based on intent. Requires the</span>
</span><span id="line-37"><span class="linenos"> 37</span><span class="w"> </span><span class="c1"># instead of using the configured access_key</span> </span><span id="line-37"><span class="linenos"> 37</span><span class="w"> </span><span class="c1"># Arch-Router model (or equivalent) to be configured in overrides.llm_routing_model.</span>
</span><span id="line-38"><span class="linenos"> 38</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">model</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">openai/gpt-4o-litellm</span> </span><span id="line-38"><span class="linenos"> 38</span><span class="w"> </span><span class="c1"># Each preference has a name (short label) and a description (used for intent matching).</span>
</span><span id="line-39"><span class="linenos"> 39</span><span class="w"> </span><span class="nt">base_url</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">https://litellm.example.com</span> </span><span id="line-39"><span class="linenos"> 39</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">model</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">openai/gpt-4o</span>
</span><span id="line-40"><span class="linenos"> 40</span><span class="w"> </span><span class="nt">passthrough_auth</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span> </span><span id="line-40"><span class="linenos"> 40</span><span class="w"> </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">gpt-4o-coding</span><span class="w"> </span><span class="c1"># Optional friendly name to distinguish multiple entries for same model</span>
</span><span id="line-41"><span class="linenos"> 41</span> </span><span id="line-41"><span class="linenos"> 41</span><span class="w"> </span><span class="nt">access_key</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">$OPENAI_API_KEY</span>
</span><span id="line-42"><span class="linenos"> 42</span><span class="c1"># Model aliases - use friendly names instead of full provider model names</span> </span><span id="line-42"><span class="linenos"> 42</span><span class="w"> </span><span class="nt">routing_preferences</span><span class="p">:</span>
</span><span id="line-43"><span class="linenos"> 43</span><span class="nt">model_aliases</span><span class="p">:</span> </span><span id="line-43"><span class="linenos"> 43</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">code generation</span>
</span><span id="line-44"><span class="linenos"> 44</span><span class="w"> </span><span class="nt">fast-llm</span><span class="p">:</span> </span><span id="line-44"><span class="linenos"> 44</span><span class="w"> </span><span class="nt">description</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">generating new code snippets, functions, or boilerplate based on user prompts or requirements</span>
</span><span id="line-45"><span class="linenos"> 45</span><span class="w"> </span><span class="nt">target</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">gpt-4o-mini</span> </span><span id="line-45"><span class="linenos"> 45</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">code review</span>
</span><span id="line-46"><span class="linenos"> 46</span> </span><span id="line-46"><span class="linenos"> 46</span><span class="w"> </span><span class="nt">description</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">reviewing, analyzing, and suggesting improvements to existing code</span>
</span><span id="line-47"><span class="linenos"> 47</span><span class="w"> </span><span class="nt">smart-llm</span><span class="p">:</span> </span><span id="line-47"><span class="linenos"> 47</span>
</span><span id="line-48"><span class="linenos"> 48</span><span class="w"> </span><span class="nt">target</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">gpt-4o</span> </span><span id="line-48"><span class="linenos"> 48</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">model</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">anthropic/claude-sonnet-4-0</span>
</span><span id="line-49"><span class="linenos"> 49</span> </span><span id="line-49"><span class="linenos"> 49</span><span class="w"> </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">claude-sonnet-reasoning</span>
</span><span id="line-50"><span class="linenos"> 50</span><span class="c1"># HTTP listeners - entry points for agent routing, prompt targets, and direct LLM access</span> </span><span id="line-50"><span class="linenos"> 50</span><span class="w"> </span><span class="nt">access_key</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">$ANTHROPIC_API_KEY</span>
</span><span id="line-51"><span class="linenos"> 51</span><span class="nt">listeners</span><span class="p">:</span> </span><span id="line-51"><span class="linenos"> 51</span><span class="w"> </span><span class="nt">routing_preferences</span><span class="p">:</span>
</span><span id="line-52"><span class="linenos"> 52</span><span class="w"> </span><span class="c1"># Agent listener for routing requests to multiple agents</span> </span><span id="line-52"><span class="linenos"> 52</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">reasoning</span>
</span><span id="line-53"><span class="linenos"> 53</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">type</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">agent</span> </span><span id="line-53"><span class="linenos"> 53</span><span class="w"> </span><span class="nt">description</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">complex multi-step reasoning, math, logic puzzles, and analytical tasks</span>
</span><span id="line-54"><span class="linenos"> 54</span><span class="w"> </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">travel_booking_service</span> </span><span id="line-54"><span class="linenos"> 54</span>
</span><span id="line-55"><span class="linenos"> 55</span><span class="w"> </span><span class="nt">port</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">8001</span> </span><span id="line-55"><span class="linenos"> 55</span><span class="w"> </span><span class="c1"># passthrough_auth: forwards the client's Authorization header upstream instead of</span>
</span><span id="line-56"><span class="linenos"> 56</span><span class="w"> </span><span class="nt">router</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">plano_orchestrator_v1</span> </span><span id="line-56"><span class="linenos"> 56</span><span class="w"> </span><span class="c1"># using the configured access_key. Useful for LiteLLM or similar proxy setups.</span>
</span><span id="line-57"><span class="linenos"> 57</span><span class="w"> </span><span class="nt">address</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">0.0.0.0</span> </span><span id="line-57"><span class="linenos"> 57</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">model</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">openai/gpt-4o-litellm</span>
</span><span id="line-58"><span class="linenos"> 58</span><span class="w"> </span><span class="nt">agents</span><span class="p">:</span> </span><span id="line-58"><span class="linenos"> 58</span><span class="w"> </span><span class="nt">base_url</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">https://litellm.example.com</span>
</span><span id="line-59"><span class="linenos"> 59</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">id</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">rag_agent</span> </span><span id="line-59"><span class="linenos"> 59</span><span class="w"> </span><span class="nt">passthrough_auth</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
</span><span id="line-60"><span class="linenos"> 60</span><span class="w"> </span><span class="nt">description</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">virtual assistant for retrieval augmented generation tasks</span> </span><span id="line-60"><span class="linenos"> 60</span>
</span><span id="line-61"><span class="linenos"> 61</span><span class="w"> </span><span class="nt">input_filters</span><span class="p">:</span> </span><span id="line-61"><span class="linenos"> 61</span><span class="w"> </span><span class="c1"># provider_interface: specifies the API format when the provider doesn't match</span>
</span><span id="line-62"><span class="linenos"> 62</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">input_guards</span> </span><span id="line-62"><span class="linenos"> 62</span><span class="w"> </span><span class="c1"># the default inferred from the model name. Supported: openai, claude, gemini,</span>
</span><span id="line-63"><span class="linenos"> 63</span> </span><span id="line-63"><span class="linenos"> 63</span><span class="w"> </span><span class="c1"># mistral, groq, deepseek, plano</span>
</span><span id="line-64"><span class="linenos"> 64</span><span class="w"> </span><span class="c1"># Model listener for direct LLM access</span> </span><span id="line-64"><span class="linenos"> 64</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">model</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">groq/llama-3.3-70b-versatile</span>
</span><span id="line-65"><span class="linenos"> 65</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">type</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">model</span> </span><span id="line-65"><span class="linenos"> 65</span><span class="w"> </span><span class="nt">access_key</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">$GROQ_API_KEY</span>
</span><span id="line-66"><span class="linenos"> 66</span><span class="w"> </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">model_1</span> </span><span id="line-66"><span class="linenos"> 66</span><span class="w"> </span><span class="nt">provider_interface</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">groq</span>
</span><span id="line-67"><span class="linenos"> 67</span><span class="w"> </span><span class="nt">address</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">0.0.0.0</span> </span><span id="line-67"><span class="linenos"> 67</span>
</span><span id="line-68"><span class="linenos"> 68</span><span class="w"> </span><span class="nt">port</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">12000</span> </span><span id="line-68"><span class="linenos"> 68</span><span class="w"> </span><span class="c1"># Custom/self-hosted endpoint with explicit http_host override</span>
</span><span id="line-69"><span class="linenos"> 69</span><span class="w"> </span><span class="c1"># Optional: attach input filters for guardrails on direct LLM requests</span> </span><span id="line-69"><span class="linenos"> 69</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">model</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">openai/llama-3.3-70b</span>
</span><span id="line-70"><span class="linenos"> 70</span><span class="w"> </span><span class="c1"># input_filters:</span> </span><span id="line-70"><span class="linenos"> 70</span><span class="w"> </span><span class="nt">base_url</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">https://api.custom-provider.com</span>
</span><span id="line-71"><span class="linenos"> 71</span><span class="w"> </span><span class="c1"># - input_guards</span> </span><span id="line-71"><span class="linenos"> 71</span><span class="w"> </span><span class="nt">http_host</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">api.custom-provider.com</span>
</span><span id="line-72"><span class="linenos"> 72</span> </span><span id="line-72"><span class="linenos"> 72</span><span class="w"> </span><span class="nt">access_key</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">$CUSTOM_API_KEY</span>
</span><span id="line-73"><span class="linenos"> 73</span><span class="w"> </span><span class="c1"># Prompt listener for function calling (for prompt_targets)</span> </span><span id="line-73"><span class="linenos"> 73</span>
</span><span id="line-74"><span class="linenos"> 74</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">type</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">prompt</span> </span><span id="line-74"><span class="linenos"> 74</span><span class="c1"># Model aliases - use friendly names instead of full provider model names</span>
</span><span id="line-75"><span class="linenos"> 75</span><span class="w"> </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">prompt_function_listener</span> </span><span id="line-75"><span class="linenos"> 75</span><span class="nt">model_aliases</span><span class="p">:</span>
</span><span id="line-76"><span class="linenos"> 76</span><span class="w"> </span><span class="nt">address</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">0.0.0.0</span> </span><span id="line-76"><span class="linenos"> 76</span><span class="w"> </span><span class="nt">fast-llm</span><span class="p">:</span>
</span><span id="line-77"><span class="linenos"> 77</span><span class="w"> </span><span class="nt">port</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">10000</span> </span><span id="line-77"><span class="linenos"> 77</span><span class="w"> </span><span class="nt">target</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">gpt-4o-mini</span>
</span><span id="line-78"><span class="linenos"> 78</span><span class="w"> </span><span class="c1"># This listener is used for prompt_targets and function calling</span> </span><span id="line-78"><span class="linenos"> 78</span>
</span><span id="line-79"><span class="linenos"> 79</span> </span><span id="line-79"><span class="linenos"> 79</span><span class="w"> </span><span class="nt">smart-llm</span><span class="p">:</span>
</span><span id="line-80"><span class="linenos"> 80</span><span class="c1"># Reusable service endpoints</span> </span><span id="line-80"><span class="linenos"> 80</span><span class="w"> </span><span class="nt">target</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">gpt-4o</span>
</span><span id="line-81"><span class="linenos"> 81</span><span class="nt">endpoints</span><span class="p">:</span> </span><span id="line-81"><span class="linenos"> 81</span>
</span><span id="line-82"><span class="linenos"> 82</span><span class="w"> </span><span class="nt">app_server</span><span class="p">:</span> </span><span id="line-82"><span class="linenos"> 82</span><span class="c1"># HTTP listeners - entry points for agent routing, prompt targets, and direct LLM access</span>
</span><span id="line-83"><span class="linenos"> 83</span><span class="w"> </span><span class="nt">endpoint</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">127.0.0.1:80</span> </span><span id="line-83"><span class="linenos"> 83</span><span class="nt">listeners</span><span class="p">:</span>
</span><span id="line-84"><span class="linenos"> 84</span><span class="w"> </span><span class="nt">connect_timeout</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">0.005s</span> </span><span id="line-84"><span class="linenos"> 84</span><span class="w"> </span><span class="c1"># Agent listener for routing requests to multiple agents</span>
</span><span id="line-85"><span class="linenos"> 85</span> </span><span id="line-85"><span class="linenos"> 85</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">type</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">agent</span>
</span><span id="line-86"><span class="linenos"> 86</span><span class="w"> </span><span class="nt">mistral_local</span><span class="p">:</span> </span><span id="line-86"><span class="linenos"> 86</span><span class="w"> </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">travel_booking_service</span>
</span><span id="line-87"><span class="linenos"> 87</span><span class="w"> </span><span class="nt">endpoint</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">127.0.0.1:8001</span> </span><span id="line-87"><span class="linenos"> 87</span><span class="w"> </span><span class="nt">port</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">8001</span>
</span><span id="line-88"><span class="linenos"> 88</span> </span><span id="line-88"><span class="linenos"> 88</span><span class="w"> </span><span class="nt">router</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">plano_orchestrator_v1</span>
</span><span id="line-89"><span class="linenos"> 89</span><span class="c1"># Prompt targets for function calling and API orchestration</span> </span><span id="line-89"><span class="linenos"> 89</span><span class="w"> </span><span class="nt">address</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">0.0.0.0</span>
</span><span id="line-90"><span class="linenos"> 90</span><span class="nt">prompt_targets</span><span class="p">:</span> </span><span id="line-90"><span class="linenos"> 90</span><span class="w"> </span><span class="nt">agents</span><span class="p">:</span>
</span><span id="line-91"><span class="linenos"> 91</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">get_current_weather</span> </span><span id="line-91"><span class="linenos"> 91</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">id</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">rag_agent</span>
</span><span id="line-92"><span class="linenos"> 92</span><span class="w"> </span><span class="nt">description</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">Get current weather at a location.</span> </span><span id="line-92"><span class="linenos"> 92</span><span class="w"> </span><span class="nt">description</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">virtual assistant for retrieval augmented generation tasks</span>
</span><span id="line-93"><span class="linenos"> 93</span><span class="w"> </span><span class="nt">parameters</span><span class="p">:</span> </span><span id="line-93"><span class="linenos"> 93</span><span class="w"> </span><span class="nt">input_filters</span><span class="p">:</span>
</span><span id="line-94"><span class="linenos"> 94</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">location</span> </span><span id="line-94"><span class="linenos"> 94</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">input_guards</span>
</span><span id="line-95"><span class="linenos"> 95</span><span class="w"> </span><span class="nt">description</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">The location to get the weather for</span> </span><span id="line-95"><span class="linenos"> 95</span>
</span><span id="line-96"><span class="linenos"> 96</span><span class="w"> </span><span class="nt">required</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span> </span><span id="line-96"><span class="linenos"> 96</span><span class="w"> </span><span class="c1"># Model listener for direct LLM access</span>
</span><span id="line-97"><span class="linenos"> 97</span><span class="w"> </span><span class="nt">type</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">string</span> </span><span id="line-97"><span class="linenos"> 97</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">type</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">model</span>
</span><span id="line-98"><span class="linenos"> 98</span><span class="w"> </span><span class="nt">format</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">City, State</span> </span><span id="line-98"><span class="linenos"> 98</span><span class="w"> </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">model_1</span>
</span><span id="line-99"><span class="linenos"> 99</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">days</span> </span><span id="line-99"><span class="linenos"> 99</span><span class="w"> </span><span class="nt">address</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">0.0.0.0</span>
</span><span id="line-100"><span class="linenos">100</span><span class="w"> </span><span class="nt">description</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">the number of days for the request</span> </span><span id="line-100"><span class="linenos">100</span><span class="w"> </span><span class="nt">port</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">12000</span>
</span><span id="line-101"><span class="linenos">101</span><span class="w"> </span><span class="nt">required</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span> </span><span id="line-101"><span class="linenos">101</span><span class="w"> </span><span class="nt">timeout</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">30s</span><span class="w"> </span><span class="c1"># Request timeout (e.g. "30s", "60s")</span>
</span><span id="line-102"><span class="linenos">102</span><span class="w"> </span><span class="nt">type</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">int</span> </span><span id="line-102"><span class="linenos">102</span><span class="w"> </span><span class="nt">max_retries</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">3</span><span class="w"> </span><span class="c1"># Number of retries on upstream failure</span>
</span><span id="line-103"><span class="linenos">103</span><span class="w"> </span><span class="nt">endpoint</span><span class="p">:</span> </span><span id="line-103"><span class="linenos">103</span><span class="w"> </span><span class="nt">input_filters</span><span class="p">:</span><span class="w"> </span><span class="c1"># Filters applied before forwarding to LLM</span>
</span><span id="line-104"><span class="linenos">104</span><span class="w"> </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">app_server</span> </span><span id="line-104"><span class="linenos">104</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">input_guards</span>
</span><span id="line-105"><span class="linenos">105</span><span class="w"> </span><span class="nt">path</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">/weather</span> </span><span id="line-105"><span class="linenos">105</span><span class="w"> </span><span class="nt">output_filters</span><span class="p">:</span><span class="w"> </span><span class="c1"># Filters applied to LLM responses before returning to client</span>
</span><span id="line-106"><span class="linenos">106</span><span class="w"> </span><span class="nt">http_method</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">POST</span> </span><span id="line-106"><span class="linenos">106</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">input_guards</span>
</span><span id="line-107"><span class="linenos">107</span> </span><span id="line-107"><span class="linenos">107</span>
</span><span id="line-108"><span class="linenos">108</span><span class="c1"># OpenTelemetry tracing configuration</span> </span><span id="line-108"><span class="linenos">108</span><span class="w"> </span><span class="c1"># Prompt listener for function calling (for prompt_targets)</span>
</span><span id="line-109"><span class="linenos">109</span><span class="nt">tracing</span><span class="p">:</span> </span><span id="line-109"><span class="linenos">109</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">type</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">prompt</span>
</span><span id="line-110"><span class="linenos">110</span><span class="w"> </span><span class="c1"># Random sampling percentage (1-100)</span> </span><span id="line-110"><span class="linenos">110</span><span class="w"> </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">prompt_function_listener</span>
</span><span id="line-111"><span class="linenos">111</span><span class="w"> </span><span class="nt">random_sampling</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">100</span> </span><span id="line-111"><span class="linenos">111</span><span class="w"> </span><span class="nt">address</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">0.0.0.0</span>
</span><span id="line-112"><span class="linenos">112</span><span class="w"> </span><span class="nt">port</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">10000</span>
</span><span id="line-113"><span class="linenos">113</span>
</span><span id="line-114"><span class="linenos">114</span><span class="c1"># Reusable service endpoints</span>
</span><span id="line-115"><span class="linenos">115</span><span class="nt">endpoints</span><span class="p">:</span>
</span><span id="line-116"><span class="linenos">116</span><span class="w"> </span><span class="nt">app_server</span><span class="p">:</span>
</span><span id="line-117"><span class="linenos">117</span><span class="w"> </span><span class="nt">endpoint</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">127.0.0.1:80</span>
</span><span id="line-118"><span class="linenos">118</span><span class="w"> </span><span class="nt">connect_timeout</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">0.005s</span>
</span><span id="line-119"><span class="linenos">119</span><span class="w"> </span><span class="nt">protocol</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">http</span><span class="w"> </span><span class="c1"># http or https</span>
</span><span id="line-120"><span class="linenos">120</span>
</span><span id="line-121"><span class="linenos">121</span><span class="w"> </span><span class="nt">mistral_local</span><span class="p">:</span>
</span><span id="line-122"><span class="linenos">122</span><span class="w"> </span><span class="nt">endpoint</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">127.0.0.1:8001</span>
</span><span id="line-123"><span class="linenos">123</span>
</span><span id="line-124"><span class="linenos">124</span><span class="w"> </span><span class="nt">secure_service</span><span class="p">:</span>
</span><span id="line-125"><span class="linenos">125</span><span class="w"> </span><span class="nt">endpoint</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">api.example.com:443</span>
</span><span id="line-126"><span class="linenos">126</span><span class="w"> </span><span class="nt">protocol</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">https</span>
</span><span id="line-127"><span class="linenos">127</span><span class="w"> </span><span class="nt">http_host</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">api.example.com</span><span class="w"> </span><span class="c1"># Override the Host header sent upstream</span>
</span><span id="line-128"><span class="linenos">128</span>
</span><span id="line-129"><span class="linenos">129</span><span class="c1"># Optional top-level system prompt applied to all prompt_targets</span>
</span><span id="line-130"><span class="linenos">130</span><span class="nt">system_prompt</span><span class="p">:</span><span class="w"> </span><span class="p p-Indicator">|</span>
</span><span id="line-131"><span class="linenos">131</span><span class="w"> </span><span class="no">You are a helpful assistant. Always respond concisely and accurately.</span>
</span><span id="line-132"><span class="linenos">132</span>
</span><span id="line-133"><span class="linenos">133</span><span class="c1"># Prompt targets for function calling and API orchestration</span>
</span><span id="line-134"><span class="linenos">134</span><span class="nt">prompt_targets</span><span class="p">:</span>
</span><span id="line-135"><span class="linenos">135</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">get_current_weather</span>
</span><span id="line-136"><span class="linenos">136</span><span class="w"> </span><span class="nt">description</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">Get current weather at a location.</span>
</span><span id="line-137"><span class="linenos">137</span><span class="w"> </span><span class="nt">parameters</span><span class="p">:</span>
</span><span id="line-138"><span class="linenos">138</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">location</span>
</span><span id="line-139"><span class="linenos">139</span><span class="w"> </span><span class="nt">description</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">The location to get the weather for</span>
</span><span id="line-140"><span class="linenos">140</span><span class="w"> </span><span class="nt">required</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
</span><span id="line-141"><span class="linenos">141</span><span class="w"> </span><span class="nt">type</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">string</span>
</span><span id="line-142"><span class="linenos">142</span><span class="w"> </span><span class="nt">format</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">City, State</span>
</span><span id="line-143"><span class="linenos">143</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">days</span>
</span><span id="line-144"><span class="linenos">144</span><span class="w"> </span><span class="nt">description</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">the number of days for the request</span>
</span><span id="line-145"><span class="linenos">145</span><span class="w"> </span><span class="nt">required</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
</span><span id="line-146"><span class="linenos">146</span><span class="w"> </span><span class="nt">type</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">int</span>
</span><span id="line-147"><span class="linenos">147</span><span class="w"> </span><span class="nt">endpoint</span><span class="p">:</span>
</span><span id="line-148"><span class="linenos">148</span><span class="w"> </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">app_server</span>
</span><span id="line-149"><span class="linenos">149</span><span class="w"> </span><span class="nt">path</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">/weather</span>
</span><span id="line-150"><span class="linenos">150</span><span class="w"> </span><span class="nt">http_method</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">POST</span>
</span><span id="line-151"><span class="linenos">151</span><span class="w"> </span><span class="c1"># Per-target system prompt (overrides top-level system_prompt for this target)</span>
</span><span id="line-152"><span class="linenos">152</span><span class="w"> </span><span class="nt">system_prompt</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">You are a weather expert. Provide accurate and concise weather information.</span>
</span><span id="line-153"><span class="linenos">153</span><span class="w"> </span><span class="c1"># auto_llm_dispatch_on_response: when true, the LLM is called again with the</span>
</span><span id="line-154"><span class="linenos">154</span><span class="w"> </span><span class="c1"># function response to produce a final natural-language answer for the user</span>
</span><span id="line-155"><span class="linenos">155</span><span class="w"> </span><span class="nt">auto_llm_dispatch_on_response</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
</span><span id="line-156"><span class="linenos">156</span>
</span><span id="line-157"><span class="linenos">157</span><span class="c1"># Rate limits - control token usage per model and request selector</span>
</span><span id="line-158"><span class="linenos">158</span><span class="nt">ratelimits</span><span class="p">:</span>
</span><span id="line-159"><span class="linenos">159</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">model</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">openai/gpt-4o</span>
</span><span id="line-160"><span class="linenos">160</span><span class="w"> </span><span class="nt">selector</span><span class="p">:</span>
</span><span id="line-161"><span class="linenos">161</span><span class="w"> </span><span class="nt">key</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">x-user-id</span><span class="w"> </span><span class="c1"># HTTP header key used to identify the rate-limit subject</span>
</span><span id="line-162"><span class="linenos">162</span><span class="w"> </span><span class="nt">value</span><span class="p">:</span><span class="w"> </span><span class="s">"*"</span><span class="w"> </span><span class="c1"># Wildcard matches any value; use a specific string to target one</span>
</span><span id="line-163"><span class="linenos">163</span><span class="w"> </span><span class="nt">limit</span><span class="p">:</span>
</span><span id="line-164"><span class="linenos">164</span><span class="w"> </span><span class="nt">tokens</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">100000</span><span class="w"> </span><span class="c1"># Maximum tokens allowed in the given time unit</span>
</span><span id="line-165"><span class="linenos">165</span><span class="w"> </span><span class="nt">unit</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">hour</span><span class="w"> </span><span class="c1"># Time unit: "minute", "hour", or "day"</span>
</span><span id="line-166"><span class="linenos">166</span>
</span><span id="line-167"><span class="linenos">167</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">model</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">openai/gpt-4o-mini</span>
</span><span id="line-168"><span class="linenos">168</span><span class="w"> </span><span class="nt">selector</span><span class="p">:</span>
</span><span id="line-169"><span class="linenos">169</span><span class="w"> </span><span class="nt">key</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">x-org-id</span>
</span><span id="line-170"><span class="linenos">170</span><span class="w"> </span><span class="nt">value</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">acme-corp</span>
</span><span id="line-171"><span class="linenos">171</span><span class="w"> </span><span class="nt">limit</span><span class="p">:</span>
</span><span id="line-172"><span class="linenos">172</span><span class="w"> </span><span class="nt">tokens</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">500000</span>
</span><span id="line-173"><span class="linenos">173</span><span class="w"> </span><span class="nt">unit</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">day</span>
</span><span id="line-174"><span class="linenos">174</span>
</span><span id="line-175"><span class="linenos">175</span><span class="c1"># Global behavior overrides</span>
</span><span id="line-176"><span class="linenos">176</span><span class="nt">overrides</span><span class="p">:</span>
</span><span id="line-177"><span class="linenos">177</span><span class="w"> </span><span class="c1"># Threshold for routing a request to a prompt_target (0.0–1.0). Lower = more permissive.</span>
</span><span id="line-178"><span class="linenos">178</span><span class="w"> </span><span class="nt">prompt_target_intent_matching_threshold</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">0.7</span>
</span><span id="line-179"><span class="linenos">179</span><span class="w"> </span><span class="c1"># Trim conversation history to fit within the model's context window</span>
</span><span id="line-180"><span class="linenos">180</span><span class="w"> </span><span class="nt">optimize_context_window</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
</span><span id="line-181"><span class="linenos">181</span><span class="w"> </span><span class="c1"># Use Plano's agent orchestrator for multi-agent request routing</span>
</span><span id="line-182"><span class="linenos">182</span><span class="w"> </span><span class="nt">use_agent_orchestrator</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">true</span>
</span><span id="line-183"><span class="linenos">183</span><span class="w"> </span><span class="c1"># Connect timeout for upstream provider clusters (e.g., "5s", "10s"). Default: "5s"</span>
</span><span id="line-184"><span class="linenos">184</span><span class="w"> </span><span class="nt">upstream_connect_timeout</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">10s</span>
</span><span id="line-185"><span class="linenos">185</span><span class="w"> </span><span class="c1"># Path to the trusted CA bundle for upstream TLS verification</span>
</span><span id="line-186"><span class="linenos">186</span><span class="w"> </span><span class="nt">upstream_tls_ca_path</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">/etc/ssl/certs/ca-certificates.crt</span>
</span><span id="line-187"><span class="linenos">187</span><span class="w"> </span><span class="c1"># Model used for intent-based LLM routing (must be listed in model_providers)</span>
</span><span id="line-188"><span class="linenos">188</span><span class="w"> </span><span class="nt">llm_routing_model</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">Arch-Router</span>
</span><span id="line-189"><span class="linenos">189</span><span class="w"> </span><span class="c1"># Model used for agent orchestration (must be listed in model_providers)</span>
</span><span id="line-190"><span class="linenos">190</span><span class="w"> </span><span class="nt">agent_orchestration_model</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">Plano-Orchestrator</span>
</span><span id="line-191"><span class="linenos">191</span>
</span><span id="line-192"><span class="linenos">192</span><span class="c1"># State storage for multi-turn conversation history</span>
</span><span id="line-193"><span class="linenos">193</span><span class="nt">state_storage</span><span class="p">:</span>
</span><span id="line-194"><span class="linenos">194</span><span class="w"> </span><span class="nt">type</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">memory</span><span class="w"> </span><span class="c1"># "memory" (in-process) or "postgres" (persistent)</span>
</span><span id="line-195"><span class="linenos">195</span><span class="w"> </span><span class="c1"># connection_string is required when type is postgres.</span>
</span><span id="line-196"><span class="linenos">196</span><span class="w"> </span><span class="c1"># Supports environment variable substitution: $VAR or ${VAR}</span>
</span><span id="line-197"><span class="linenos">197</span><span class="w"> </span><span class="c1"># connection_string: postgresql://user:$DB_PASS@localhost:5432/plano</span>
</span><span id="line-198"><span class="linenos">198</span>
</span><span id="line-199"><span class="linenos">199</span><span class="c1"># Input guardrails applied globally to all incoming requests</span>
</span><span id="line-200"><span class="linenos">200</span><span class="nt">prompt_guards</span><span class="p">:</span>
</span><span id="line-201"><span class="linenos">201</span><span class="w"> </span><span class="nt">input_guards</span><span class="p">:</span>
</span><span id="line-202"><span class="linenos">202</span><span class="w"> </span><span class="nt">jailbreak</span><span class="p">:</span>
</span><span id="line-203"><span class="linenos">203</span><span class="w"> </span><span class="nt">on_exception</span><span class="p">:</span>
</span><span id="line-204"><span class="linenos">204</span><span class="w"> </span><span class="nt">message</span><span class="p">:</span><span class="w"> </span><span class="s">"I'm</span><span class="nv"> </span><span class="s">sorry,</span><span class="nv"> </span><span class="s">I</span><span class="nv"> </span><span class="s">can't</span><span class="nv"> </span><span class="s">help</span><span class="nv"> </span><span class="s">with</span><span class="nv"> </span><span class="s">that</span><span class="nv"> </span><span class="s">request."</span>
</span><span id="line-205"><span class="linenos">205</span>
</span><span id="line-206"><span class="linenos">206</span><span class="c1"># OpenTelemetry tracing configuration</span>
</span><span id="line-207"><span class="linenos">207</span><span class="nt">tracing</span><span class="p">:</span>
</span><span id="line-208"><span class="linenos">208</span><span class="w"> </span><span class="c1"># Random sampling percentage (1-100)</span>
</span><span id="line-209"><span class="linenos">209</span><span class="w"> </span><span class="nt">random_sampling</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">100</span>
</span><span id="line-210"><span class="linenos">210</span><span class="w"> </span><span class="c1"># Include internal Plano spans in traces</span>
</span><span id="line-211"><span class="linenos">211</span><span class="w"> </span><span class="nt">trace_arch_internal</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">false</span>
</span><span id="line-212"><span class="linenos">212</span><span class="w"> </span><span class="c1"># gRPC endpoint for OpenTelemetry collector (e.g., Jaeger, Tempo)</span>
</span><span id="line-213"><span class="linenos">213</span><span class="w"> </span><span class="nt">opentracing_grpc_endpoint</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">http://localhost:4317</span>
</span><span id="line-214"><span class="linenos">214</span><span class="w"> </span><span class="nt">span_attributes</span><span class="p">:</span>
</span><span id="line-215"><span class="linenos">215</span><span class="w"> </span><span class="c1"># Propagate request headers whose names start with these prefixes as span attributes</span>
</span><span id="line-216"><span class="linenos">216</span><span class="w"> </span><span class="nt">header_prefixes</span><span class="p">:</span>
</span><span id="line-217"><span class="linenos">217</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">x-user-</span>
</span><span id="line-218"><span class="linenos">218</span><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">x-org-</span>
</span><span id="line-219"><span class="linenos">219</span><span class="w"> </span><span class="c1"># Static key/value pairs added to every span</span>
</span><span id="line-220"><span class="linenos">220</span><span class="w"> </span><span class="nt">static</span><span class="p">:</span>
</span><span id="line-221"><span class="linenos">221</span><span class="w"> </span><span class="nt">environment</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">production</span>
</span><span id="line-222"><span class="linenos">222</span><span class="w"> </span><span class="nt">service.team</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">platform</span>
</span></code></pre></div> </span></code></pre></div>
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@ -305,7 +416,7 @@ where prompts get routed to, apply guardrails, and enable critical agent observa
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@ -199,7 +199,7 @@ own deployments), and Plano reaches them via HTTP.</p>
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@ -485,7 +485,7 @@ processing request headers and then finalized by the HCM during post-request pro
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@ -200,7 +200,7 @@ hardware threads on the machine.</p>
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@ -221,7 +221,7 @@
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<div class="container flex flex-col items-center justify-between gap-4 md:h-24 md:flex-row"> <div class="container flex flex-col items-center justify-between gap-4 md:h-24 md:flex-row">
<div class="flex flex-col items-center gap-4 px-8 md:flex-row md:gap-2 md:px-0"> <div class="flex flex-col items-center gap-4 px-8 md:flex-row md:gap-2 md:px-0">
<p class="text-sm leading-loose text-center text-muted-foreground md:text-left">© 2025, Katanemo Labs, Inc&nbsp;Last updated: Mar 20, 2026.&nbsp;</p> <p class="text-sm leading-loose text-center text-muted-foreground md:text-left">© 2025, Katanemo Labs, Inc&nbsp;Last updated: Mar 30, 2026.&nbsp;</p>
</div> </div>
</div> </div>
</footer> </footer>

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searchindex.js
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