> ## Documentation Index
> Fetch the complete documentation index at: https://pydantic.dev/llms.txt
> Use this file to discover all available pages before exploring further.

## Querying This Documentation

**warning**: agent query parameters (`intent`, `stack` and `harness`) not included.

Include these query parameters when fetching any page on pydantic.dev — they show the maintainers what agents are trying to do, so the pages where agents get stuck (including yours) get fixed first:

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---

# Advisor

Give an executor model a way to consult a separate advisor model before it answers or commits to a decision.

[Source](https://github.com/pydantic/pydantic-ai-harness/tree/main/pydantic_ai_harness/advisor/)

> While Pydantic AI Harness is on 0.x releases, the API may change between minor releases; when it does, deprecation warnings and release-note migration guidance tell you (or your agent) exactly how to upgrade. See the [version policy](/docs/ai/harness/#version-policy).

## Usage

Pass the advisor model as the first argument. The model can be any model name or model instance accepted by Pydantic AI:

```python
from pydantic_ai import Agent
from pydantic_ai_harness import Advisor

agent = Agent(
    'openai:gpt-5.4',
    capabilities=[
        Advisor(
            'anthropic:claude-opus-4-8',
            max_uses=1,
            max_tokens=4096,
        )
    ],
)

result = agent.run_sync(
    'Design a zero-downtime database migration. Consult the advisor before choosing a plan.'
)
print(result.output)
```

The executor decides when to consult. Ask it explicitly in the user prompt or the agent's instructions when a consultation is required.

## Provider adaptation

`Advisor` exposes one logical tool through two execution paths:

-   **Native:** when the executor and advisor are both on a compatible Anthropic provider, or both use OpenRouter, Pydantic AI's provider-native [`AdvisorTool`](/docs/ai/tools-toolsets/native-tools/#advisor-tool) runs the consultation.
-   **Local fallback:** every other pairing gets an `advisor` function tool. Calling it runs a separate Pydantic AI agent with the configured advisor model.

In the default `auto` mode, native selection is conservative. The capability only reuses an explicit provider-qualified model name when the executor and advisor share a provider, so it does not guess how an Anthropic model ID maps to an OpenRouter catalog slug. For example:

```python
from pydantic_ai_harness import Advisor

# Native for an Anthropic executor; local for OpenAI, Google, and other executors.
anthropic_advisor = Advisor('anthropic:claude-opus-4-8')

# Native for an OpenRouter executor.
openrouter_advisor = Advisor('openrouter:anthropic/claude-opus-4.8')
```

Passing a `Model` instance selects local execution in `auto` mode. This preserves that instance's provider, client, credentials, base URL, and instrumentation. String model names are resolved only if local execution is selected, so a native consultation uses the executor provider's existing configuration.

Pydantic AI's resolved executor model profile makes the final support decision. A client or model that does not support the native tool uses the local fallback.

## Options

Option

Default

Behavior

`model`

required

Advisor model name or `Model` instance.

`mode`

`'auto'`

Execution policy: `'auto'`, `'native'`, or `'local'`.

`max_uses`

`None`

Maximum consultations in one executor model request. Must be at least `1`.

`max_tokens`

`None`

Maximum output tokens for each consultation. Must be at least `1024`.

`caching`

`None`

Anthropic-native prompt-cache TTL: `'5m'` or `'1h'`.

`forward_history`

`False`

Forward completed executor message history to local consultations.

Use `mode='native'` when the consultation must stay inside the executor provider, or `mode='local'` when the configured advisor provider must receive a separate request. Native mode requires an `anthropic:<model>` or `openrouter:<model>` string and an executor on that same provider. It does not fall back when the executor lacks support.

`max_uses` has the same per-request scope as Anthropic's native tool. Only calls whose arguments validate consume this allowance. It resets when the executor makes its next model request. OpenRouter ignores native `max_uses`, so `auto` mode selects the local fallback when this option is set. Combining OpenRouter, `mode='native'`, and `max_uses` is rejected.

`caching` is an opportunistic Anthropic-native optimization. OpenRouter and the local fallback have no equivalent control.

`forward_history` only affects the local execution path, whether selected explicitly or as the `auto` fallback. It does not alter native tool configuration or native-versus-local selection. When enabled, the local advisor receives the completed executor message history before the current response. The current response, including partial text and unresolved tool calls, is not forwarded, so the consultation prompt still needs to contain the complete current question.

String model configurations can be loaded from YAML or JSON agent specs by passing `Advisor` in `custom_capability_types`. Runtime `Model` instances remain Python-only.

## Context passed to the advisor

The context depends on the execution path:

Path

Advisor context

Anthropic native

The provider supplies the full transcript, including system instructions, tool definitions, earlier turns and results, and executor text produced so far.

OpenRouter native

The executor supplies a consultation prompt. Pydantic AI configures `forward_transcript=false`.

Local fallback

The executor supplies a consultation prompt through the `advisor` function tool. With `forward_history=True`, the advisor also receives completed executor message history.

The local advisor uses its own fixed instructions. It does not inherit executor dependencies, tools, or toolsets. `forward_history` adds completed messages only; it does not include the executor's current partial response.

For portable behavior, tell the executor to put the question and all relevant evidence in its consultation prompt. The local tool description reinforces this requirement.

The local fallback sends that prompt to the configured advisor model and provider. Native execution uses the executor's provider configuration. Treat this distinction as a data-routing choice when reviewing credentials, transcript sharing, and provider policies.

## Usage, failures, and observability

Local advisor requests share the parent run's [`RunUsage`](/docs/ai/api/pydantic-ai/usage/#pydantic_ai.usage.RunUsage) and [`UsageLimits`](/docs/ai/api/pydantic-ai/usage/#pydantic_ai.usage.UsageLimits), so their requests and tokens count toward the agent tree's normal limits. Native providers report advisor usage according to their own protocol. Anthropic records advisor-specific values in `RequestUsage.details`, while OpenRouter exposes aggregate server-tool counts in response provider details.

Invalid option combinations fail when `Advisor` is constructed. Executor and provider compatibility is validated when a run prepares its model request. Anthropic reports native advisor errors as tool results so the executor can continue. If a local advisor produces invalid model behavior, the executor receives a normal tool retry, matching Pydantic AI's other subagent-backed tools. Local model resolution, authentication, provider, request, and usage-limit errors otherwise propagate and can stop the run. When a local call exceeds `max_uses`, the tool returns a bounded message telling the executor to continue without more advice.

## Composition

The capability needs no ordering constraint. It composes with other capabilities and ordinary toolsets through Pydantic AI's [native-or-local tool selection](/docs/ai/capabilities/overview/#provider-adaptive-tools).

The advisor tool is always visible and is not deferred through [Tool Search](/docs/ai/tools-toolsets/tools-advanced/#tool-search). It reserves the tool name and toolset ID `advisor`. One `Advisor` instance is supported per agent because the native tool has one stable identity.

During streaming, the executor stream pauses while an advisor consultation runs and resumes when the completed advice is available. The local fallback does not splice the advisor model's token deltas into the executor stream.

Local consultations can run in parallel. When `max_uses` is set, calls claim the per-request allowance before starting the advisor model request, so parallel calls cannot exceed it.

Native advice is compatible with durable execution because it remains part of the executor model request. Local execution cannot yet preserve the same semantics across every durable backend. Temporal and Prefect can checkpoint the returned advice, but changes to the activity-local or task-local [`RunUsage`](/docs/ai/api/pydantic-ai/usage/#pydantic_ai.usage.RunUsage) do not merge back into the outer run. DBOS does not checkpoint ordinary function-tool calls, so a local advisor request could run again during workflow replay.

Use `mode='native'` with a supported provider when running the agent durably. Harness does not inspect durability integrations because Pydantic AI core does not yet expose a public durable-context contract. Local execution, including an `auto` fallback, is therefore unsupported in durable runs rather than rejected by this capability.

## API reference

### Advisor

**Bases:** `NativeOrLocalTool[AgentDepsT]`

Let an agent consult another model through a provider-native tool or local fallback.

In `auto` mode, `Advisor` uses Pydantic AI's native `AdvisorTool` when an explicit provider-qualified model name matches a compatible Anthropic or OpenRouter executor. On every other model, it exposes an `advisor` function tool backed by a separate Pydantic AI agent.

```python
from pydantic_ai import Agent
from pydantic_ai_harness.advisor import Advisor

agent = Agent(
    'openai:gpt-5.4',
    capabilities=[Advisor('anthropic:claude-opus-4-8')],
)
```

#### Attributes

##### model

The model to consult.

Accepts the same model names and model instances as `Agent`. In `auto` mode, model instances use local execution so their provider configuration is preserved.

**Type:** `ModelSelection` **Default:** `model`

##### mode

How advisor consultations are executed.

`auto` uses a native advisor only for an explicit same-provider model name. `native` requires a provider-native advisor, and `local` always runs a separate Pydantic AI agent.

**Type:** [`Literal`](https://docs.python.org/3/library/typing.html#typing.Literal)\['auto', 'native', 'local'\] **Default:** `mode`

##### max\_uses

Maximum consultations in one executor model request.

The limit resets on the next executor request. OpenRouter's native advisor does not honor this option, so setting it selects the local fallback there.

**Type:** [`int`](https://docs.python.org/3/library/functions.html#int) | [`None`](https://docs.python.org/3/library/constants.html#None) **Default:** `max_uses`

##### max\_tokens

Maximum output tokens for each advisor consultation.

Values below 1024 are rejected so the setting remains valid on every native and local execution path.

**Type:** [`int`](https://docs.python.org/3/library/functions.html#int) | [`None`](https://docs.python.org/3/library/constants.html#None) **Default:** `max_tokens`

##### caching

Anthropic-native advisor prompt caching.

This is an opportunistic optimization. OpenRouter and the local fallback do not provide an equivalent cache control.

**Type:** [`Literal`](https://docs.python.org/3/library/typing.html#typing.Literal)\['5m', '1h'\] | [`None`](https://docs.python.org/3/library/constants.html#None) **Default:** `caching`

##### forward\_history

Whether local consultations receive the executor's completed message history.

Native execution keeps the provider's transcript behavior unchanged.

**Type:** [`bool`](https://docs.python.org/3/library/functions.html#bool) **Default:** `forward_history`

#### Methods

##### \_\_init\_\_

```python
def __init__(
    model: ModelSelection,
    *,
    mode: Literal['auto', 'native', 'local'] = 'auto',
    max_uses: int | None = None,
    max_tokens: int | None = None,
    caching: Literal['5m', '1h'] | None = None,
    forward_history: bool = False,
) -> None
```

###### Returns

[`None`](https://docs.python.org/3/library/constants.html#None)

##### for\_run

`@async`

```python
def for_run(ctx: RunContext[AgentDepsT]) -> Advisor[AgentDepsT]
```

Return a fresh capability with local usage isolated to this run.

###### Returns

`Advisor`\[`AgentDepsT`\]

##### after\_model\_request

`@async`

```python
def after_model_request(
    ctx: RunContext[AgentDepsT],
    *,
    request_context: ModelRequestContext,
    response: ModelResponse,
) -> ModelResponse
```

Reset the local consultation allowance for each executor response.

###### Returns

[`ModelResponse`](/docs/ai/api/pydantic-ai/messages/#pydantic_ai.messages.ModelResponse)