--- title: "Advisor Tool" description: "Pair a faster executor model with a higher-intelligence advisor model that provides strategic guidance mid-generation." type: skill canonical_url: https://claudary.paisolsolutions.com/skills/anthropic-advisor-tool source: "Claudary" difficulty: intermediate author: "Claude Code Knowledge Pack" date: 2026-07-10T11:07:35.101Z license: CC-BY-4.0 attribution: "Advisor Tool — Claudary (https://claudary.paisolsolutions.com/skills/anthropic-advisor-tool)" --- # Advisor Tool Pair a faster executor model with a higher-intelligence advisor model that provides strategic guidance mid-generation. ## Overview # Advisor Tool Pair a faster executor model with a higher-intelligence advisor model that provides strategic guidance mid-generation. The advisor tool lets a fast, lower-cost executor model (Sonnet or Haiku) consult a high-intelligence advisor model (Opus 4.6) mid-generation. The advisor reads the full conversation and produces a plan or course correction — typically 400–700 text tokens — and the executor continues with the task. This pattern is well-suited for long-horizon agentic workloads (coding agents, computer use, multi-step research) where most turns are mechanical but having an excellent plan is crucial. You get close to advisor-solo quality while the bulk of token generation happens at executor-model rates. :::info Beta The advisor tool is in beta. Include `anthropic-beta: advisor-tool-2026-03-01` in your requests — LiteLLM adds this automatically when it detects the advisor tool in your `tools` array. ::: ## Supported Providers | Provider | Chat Completions API | Messages API | Notes | |----------|---------------------|--------------|-------| | **Anthropic API** | ✅ | ✅ | Native — runs server-side | | **OpenAI / Azure OpenAI** | ✅ | ✅ | LiteLLM orchestration loop | | **Amazon Bedrock** | ✅ | ✅ | LiteLLM orchestration loop | | **Google Vertex AI** | ✅ | ✅ | LiteLLM orchestration loop | | **Groq / Mistral / others** | ✅ | ✅ | LiteLLM orchestration loop | ## How it works (LiteLLM native orchestration) For non-Anthropic providers, LiteLLM implements the advisor loop itself. The API you call is identical — LiteLLM handles everything transparently. When a request arrives with an `advisor_20260301` tool and a non-Anthropic provider, `AdvisorOrchestrationHandler` intercepts it. It translates the advisor tool into a regular function tool the provider understands, then runs an orchestration loop: ```mermaid flowchart TD A["Your request\\ntools: advisor_20260301\\nmodel: e.g. openai/gpt-4.1-mini"] --> B["AdvisorOrchestrationHandler\\ntranslates advisor → regular fn tool"] B --> C["EXECUTOR CALL\\nopenai / bedrock / vertex / etc."] C --> D{"executor calls\\nadvisor tool?"} D -->|"yes — tool_use\\nname=advisor"| E{"max_uses\\nexceeded?"} E -->|no| F["ADVISOR SUB-CALL\\nclaude-opus-4-6\\nfull transcript forwarded\\nno tools"] F --> G["Inject advice as\\ntool_result into history"] G --> C E -->|yes| H["AdvisorMaxIterationsError"] D -->|"no — end_turn\\nor other stop reason"| I["Clean final response\\nno advisor blocks in output"] ``` **What LiteLLM does for you:** - Strips `advisor_20260301` from the outgoing request — the provider only sees a standard function tool named `advisor` - When the executor calls it, intercepts before the result reaches you, runs the advisor sub-call, and injects the advice - Strips any `advisor_tool_result` / `server_tool_use` blocks from message history on re-send so non-Anthropic providers never see Anthropic-specific types - Wraps the final response in an SSE stream if you requested `stream=True` - Enforces `max_uses` as a hard cap — `AdvisorMaxIterationsError` is raised if exceeded; `max_uses=0` disables the advisor entirely ## Model Compatibility The executor and advisor models must form a valid pair. Currently the only supported advisor model is `claude-opus-4-6`. | Executor | Advisor | |----------|---------| | `claude-haiku-4-5-20251001` | `claude-opus-4-6` | | `claude-sonnet-4-6` | `claude-opus-4-6` | | `claude-opus-4-6` | `claude-opus-4-6` | --- ## Chat Completions API ### SDK Usage #### Basic Example ```python showLineNumbers title="Advisor Tool — litellm.completion()" import litellm response = litellm.completion( model="anthropic/claude-sonnet-4-6", messages=[ {"role": "user", "content": "Build a concurrent worker pool in Go with graceful shutdown."} ], tools=[ { "type": "advisor_20260301", "name": "advisor", "model": "claude-opus-4-6", } ], max_tokens=4096, ) print(response.choices[0].message.content) ``` #### With Optional Parameters ```python showLineNumbers title="Advisor Tool with max_uses and caching" import litellm response = litellm.completion( model="anthropic/claude-sonnet-4-6", messages=[ {"role": "user", "content": "Build a REST API with authentication in Python."} ], tools=[ { "type": "advisor_20260301", "name": "advisor", "model": "claude-opus-4-6", "max_uses": 3, # cap advisor calls per request "caching": {"type": "ephemeral", "ttl": "5m"}, # enable for 3+ calls per conversation } ], max_tokens=4096, ) ``` #### Streaming ```python showLineNumbers title="Streaming with Advisor Tool" import litellm response = litellm.completion( model="anthropic/claude-sonnet-4-6", messages=[ {"role": "user", "content": "Implement a distributed rate limiter."} ], tools=[ { "type": "advisor_20260301", "name": "advisor", "model": "claude-opus-4-6", } ], max_tokens=4096, stream=True, ) for chunk in response: if chunk.choices[0].delta.content: print(chunk.choices[0].delta.content, end="") ``` :::note Streaming behavior The advisor sub-inference does not stream. The executor's stream pauses while the advisor runs, then the full advisor result arrives in a single event. Executor output resumes streaming afterward. ::: #### Multi-Turn Conversation ```python showLineNumbers title="Multi-Turn with Advisor Tool" import litellm tools = [ { "type": "advisor_20260301", "name": "advisor", "model": "claude-opus-4-6", } ] messages = [ {"role": "user", "content": "Build a concurrent worker pool in Go with graceful shutdown."} ] response = litellm.completion( model="anthropic/claude-sonnet-4-6", messages=messages, tools=tools, max_tokens=4096, ) # Append the full response (includes server_tool_use + advisor_tool_result blocks) messages.append({"role": "assistant", "content": response.choices[0].message.content}) # Continue the conversation — keep the same tools array messages.append({"role": "user", "content": "Now add a max-in-flight limit of 10."}) response2 = litellm.completion( model="anthropic/claude-sonnet-4-6", messages=messages, tools=tools, max_tokens=4096, ) ``` :::tip Auto-strip on follow-up turns LiteLLM automatically strips `advisor_tool_result` blocks from message history when the advisor tool is not present in the current request. This prevents the Anthropic 400 error that would otherwise occur. ::: ### AI Gateway Usage #### Proxy Configuration ```yaml showLineNumbers title="config.yaml" model_list: - model_name: claude-sonnet litellm_params: model: anthropic/claude-sonnet-4-6 api_key: os.environ/ANTHROPIC_API_KEY ``` #### Client Request via Proxy ```python showLineNumbers title="Advisor Tool via AI Gateway" from openai import OpenAI client = OpenAI( api_key="your-litellm-proxy-key", base_url="http://0.0.0.0:4000/v1" ) response = client.chat.completions.create( model="claude-sonnet", messages=[ {"role": "user", "content": "Implement a distributed rate limiter in Python."} ], tools=[ { "type": "advisor_20260301", "name": "advisor", "model": "claude-opus-4-6", } ], max_tokens=4096, ) ``` --- ## Messages API ### SDK Usage #### Basic Example ```python showLineNumbers title="Advisor Tool — litellm.anthropic.messages" import asyncio import litellm async def main(): response = await litellm.anthropic.messages.acreate( model="anthropic/claude-sonnet-4-6", messages=[ {"role": "user", "content": "Build a concurrent worker pool in Go with graceful shutdown."} ], tools=[ { "type": "advisor_20260301", "name": "advisor", "model": "claude-opus-4-6", } ], max_tokens=4096, ) print(response) asyncio.run(main()) ``` #### Streaming ```python showLineNumbers title="Messages API Streaming with Advisor Tool" import asyncio import json import litellm async def main(): response = await litellm.anthropic.messages.acreate( model="anthropic/claude-sonnet-4-6", messages=[ {"role": "user", "content": "Implement a distributed rate limiter."} ], tools=[ { "type": "advisor_20260301", "name": "advisor", "model": "claude-opus-4-6", } ], max_tokens=4096, stream=True, ) async for chunk in response: if isinstance(chunk, bytes): for line in chunk.decode("utf-8").split("\\n"): if line.startswith("data: "): try: print(json.loads(line[6:])) except json.JSONDecodeError: pass asyncio.run(main()) ``` ### AI Gateway Usage #### Proxy Configuration ```yaml showLineNumbers title="config.yaml" model_list: - model_name: claude-sonnet litellm_params: model: anthropic/claude-sonnet-4-6 api_key: os.environ/ANTHROPIC_API_KEY ``` #### Client Request via Proxy (Anthropic SDK) ```python showLineNumbers title="Advisor Tool via AI Gateway (Anthropic SDK)" import anthropic client = anthropic.Anthropic( api_key="your-litellm-proxy-key", base_url="http://0.0.0.0:4000" ) response = client.beta.messages.create( model="claude-sonnet", max_tokens=4096, betas=["advisor-tool-2026-03-01"], messages=[ {"role": "user", "content": "Build a concurrent worker pool in Go with graceful shutdown."} ], tools=[ { "type": "advisor_20260301", "name": "advisor", "model": "claude-opus-4-6", } ], ) print(response) ``` #### Non-Anthropic Provider (LiteLLM orchestration loop) ```python showLineNumbers title="Advisor Tool with OpenAI executor" import asyncio import litellm async def main(): # executor: openai/gpt-4.1-mini | advisor: claude-opus-4-6 # LiteLLM runs the orchestration loop automatically response = await litellm.anthropic.messages.acreate( model="openai/gpt-4.1-mini", messages=[ {"role": "user", "content": "Implement a Python LRU cache with O(1) get and put."} ], tools=[ { "type": "advisor_20260301", "name": "advisor", "model": "claude-opus-4-6", "max_uses": 3, } ], max_tokens=1024, custom_llm_provider="openai", ) # Final response is clean — no advisor tool_use blocks print(response["content"][0]["text"]) asyncio.run(main()) ``` --- ## Response Structure A successful advisor call returns `server_tool_use` and `advisor_tool_result` blocks in the assistant content: ```json title="Response with advisor blocks" { "role": "assistant", "content": [ { "type": "text", "text": "Let me consult the advisor on this." }, { "type": "server_tool_use", "id": "srvtoolu_abc123", "name": "advisor", "input": {} }, { "type": "advisor_tool_result", "tool_use_id": "srvtoolu_abc123", "content": { "type": "advisor_result", "text": "Use a channel-based coordination pattern. The tricky part is draining in-flight work during shutdown: close the input channel first, then wait on a WaitGroup..." } }, { "type": "text", "text": "Here's the implementation using a channel-based coordination pattern..." } ] } ``` Pass the full assistant content, including advisor blocks, back on subsequent turns. LiteLLM handles this automatically through `provider_specific_fields`. --- ## Cost Control Advisor calls run as a separate sub-inference billed at the advisor model's rates. Usage is reported in `usage.iterations[]`: ```json title="Usage with advisor sub-inference" { "usage": { "input_tokens": 412, "output_tokens": 531, "iterations": [ { "type": "message", "input_tokens": 412, "output_tokens": 89 }, { "type": "advisor_message", "model": "claude-opus-4-6", "input_tokens": 823, "output_tokens": 1612 }, { "type": "message", "input_tokens": 1348, "output_tokens": 442 } ] } } ``` Top-level `usage` reflects executor tokens only. Advisor tokens appear in `iterations` entries with `type: "advisor_message"` and are billed at Opus rates. **Tips:** - Enable `caching` on the tool definition only when you expect 3+ advisor calls per conversation; it costs more than it saves below that threshold. - Use `max_uses` to cap advisor calls per request. Once reached, the executor continues without further advice. - For conversation-level caps, count advisor calls client-side. When you reach your limit, remove the advisor tool from `tools`. --- ## Recommended System Prompt For coding and agent tasks, Anthropic recommends prepending these blocks to your system prompt for consistent advisor timing and optimal cost/quality: ```text title="Timing guidance (prepend to system prompt)" You have access to an `advisor` tool backed by a stronger reviewer model. It takes NO parameters — when you call advisor(), your entire conversation history is automatically forwarded. They see the task, every tool call you've made, every result you've seen. Call advisor BEFORE substantive work — before writing, before committing to an interpretation, before building on an assumption. If the task requires orientation first (finding files, fetching a source, seeing what's there), do that, then call advisor. Orientation is not substantive work. Writing, editing, and declaring an answer are. Also call advisor: - When you believe the task is complete. BEFORE this call, make your deliverable durable: write the file, save the result, commit the change. - When stuck — errors recurring, approach not converging, results that don't fit. - When considering a change of approach. On tasks longer than a few steps, call advisor at least once before committing to an approach and once before declaring done. On short reactive tasks where the next action is dictated by tool output you just read, you don't need to keep calling. ``` ```text title="Advice weight guidance (add after timing block)" Give the advice serious weight. If you follow a step and it fails empirically, or you have primary-source evidence that contradicts a specific claim, adapt. A passing self-test is not evidence the advice is wrong. If you've already retrieved data pointing one way and the advisor points another: don't sile --- Source: [Claudary](https://claudary.paisolsolutions.com/skills/anthropic-advisor-tool) · https://claudary.paisolsolutions.com