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# 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 |
|----------|---------------------|--------------|
| **Anthropic API** | ✅ | ✅ |
| **Azure Anthropic** | ❌ (coming soon) | ❌ (coming soon) |
| **Google Cloud Vertex AI** | ❌ (coming soon) | ❌ (coming soon) |
| **Amazon Bedrock** | ❌ (coming soon) | ❌ (coming soon) |
## 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)
```
---
## 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 silently switch. Surface the conflict in one more advisor call — "I found X, you suggest Y, which constraint breaks the tie?"
```
To reduce advisor output length by 35–45% without losing quality, add:
```text title="Cost reduction (optional, add before timing block)"
The advisor should respond in under 100 words and use enumerated steps, not explanations.
```
---
## Additional Resources
- [Anthropic Advisor Tool Documentation](https://platform.claude.com/docs/en/agents-and-tools/tool-use/advisor-tool)
- [LiteLLM Tool Calling Guide](https://docs.litellm.ai/docs/completion/function_call)

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]
},
"providers/anthropic",
"providers/anthropic_advisor_tool",
"providers/anthropic_tool_search",
"providers/aws_sagemaker",
{
type: "category",