> ## Documentation Index
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---

# Managed Prompt

`ManagedPrompt` backs an agent's instructions with a [Logfire-managed prompt](https://logfire.pydantic.dev/docs/reference/advanced/prompt-management/), so you can iterate on your system prompt from the Logfire UI -- versioned, labelled, and rolled out -- without touching code or redeploying. It's a Pydantic AI [capability](/docs/ai/harness/), so you wire it in through the `capabilities=` parameter on `Agent`.

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

Install the `logfire` extra:

Terminal

```bash
uv add "pydantic-ai-harness[logfire]"
```

A first-party `Managed` capability is in flight

A broader, first-party `Managed` capability is being built in [pydantic-ai#5107](https://github.com/pydantic/pydantic-ai/pull/5107) and will eventually be importable as `pydantic_ai.managed.logfire.Managed` -- covering instructions, model settings, and whole-spec variables. Until then, `ManagedPrompt` is the supported path for backing an agent's instructions with a Logfire-managed prompt.

> 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).

## The problem it solves

Prompts are critical to agent behavior, but iterating on them through the normal edit -> review -> deploy loop is slow. You can't easily A/B test a change, and you can't roll it back the moment it misbehaves in production without shipping a new build.

`ManagedPrompt` moves the prompt out of your codebase and into Logfire's managed-variable store. It declares the backing managed variable for you and resolves it **once per run**, feeding the resolved value into the agent's instructions. Resolution happens inside the run's [`wrap_run`](/docs/ai/api/pydantic-ai/capabilities/#pydantic_ai.capabilities.AbstractCapability.wrap_run) hook, using the [`ResolvedVariable`](https://logfire.pydantic.dev/docs/reference/advanced/managed-variables/) as a context manager that stays open for the whole run -- so the selected label and version are attached as baggage to every child span of the agent run. You get a direct correlation between a run's behavior and the exact prompt version that produced it, plus instant iteration and rollback from the Logfire UI.

## Usage

Pass the prompt name and a default value. The name `support_agent` is declared as the managed variable `prompt__support_agent` -- the naming Logfire's Prompt management uses (hyphens in a name become underscores). The `default` keeps the agent working until a remote value is published, so your code always runs even before you create the prompt in Logfire.

```python
import logfire
from pydantic_ai import Agent

from pydantic_ai_harness import ManagedPrompt

logfire.configure()

agent = Agent(
    'openai:gpt-5',
    capabilities=[
        ManagedPrompt(
            'support_agent',
            default='You are a helpful customer support agent. Be friendly and concise.',
            label='production',
        )
    ],
)

result = agent.run_sync('My order never arrived.')
print(result.output)
```

Pinning `label='production'` is the recommended default: the resolved value only changes on a deliberate prompt rollout, which keeps the provider prompt cache hot (see [Prompt-cache trade-off](#prompt-cache-trade-off) below).

## Targeting

For deterministic A/B assignment (the same user always sees the same label), pass a `targeting_key`. It can be a static string or a callable that derives the key from the [`RunContext`](/docs/ai/api/pydantic-ai/tools/#pydantic_ai.tools.RunContext) -- handy when the key lives in your agent's `deps`:

```python
from dataclasses import dataclass

from pydantic_ai import Agent

from pydantic_ai_harness import ManagedPrompt


@dataclass
class Deps:
    user_id: str


agent = Agent(
    'openai:gpt-5',
    deps_type=Deps,
    capabilities=[
        ManagedPrompt(
            'support_agent',
            default='You are a helpful customer support agent.',
            targeting_key=lambda ctx: ctx.deps.user_id,
        ),
    ],
)
```

Pass `attributes` (a mapping, or a callable returning one) for condition-based targeting rules. When `label` is omitted, the variable's rollout and targeting rules pick the label. When both `targeting_key` and `attributes` are omitted, Logfire falls back to its own targeting context and then to the active trace id.

For Logfire-side targeting that lives outside the agent (e.g. set once per request handler), use Logfire's [`targeting_context`](https://logfire.pydantic.dev/docs/reference/advanced/managed-variables/) in an outer scope; `ManagedPrompt` only needs `targeting_key` / `attributes` when the key comes from the agent's `RunContext`.

## Templating with deps

By default the resolved prompt is used verbatim. Pass `render_template=True` to render it as a Handlebars template against the agent's `deps` -- the same mechanism as [`TemplateStr`](/docs/ai/api/pydantic-ai/agent/) -- so `{{field}}` is filled from `deps`:

```python
from dataclasses import dataclass

from pydantic_ai import Agent

from pydantic_ai_harness import ManagedPrompt


@dataclass
class Deps:
    customer_name: str


agent = Agent(
    'openai:gpt-5',
    deps_type=Deps,
    capabilities=[
        ManagedPrompt(
            'support_agent',
            default='You are helping {{customer_name}}. Be friendly and concise.',
            render_template=True,
        ),
    ],
)
```

Rendering requires `pydantic-handlebars` (install `pydantic-ai-slim[spec]`). It is off by default.

## Prompt-cache trade-off

The resolved value lands in the agent's **system instructions**. Provider prompt caches (Anthropic, OpenAI, etc.) key strictly by prefix -- `tools -> system -> messages` -- so any change to the system block invalidates the cached prefix for the affected runs.

Mode

Cache impact

Pinned `label='production'`, no rollout split

**Cache-stable.** The value only changes on a deliberate prompt rollout, which is the same cost as a redeploy.

Percentage rollout across labels (no `label=`)

Different runs land on different labels -> splits the cache into one lane per label.

`targeting_key` per user/tenant with multiple labels in play

Cache lanes per assigned label; deterministic per key but still N lanes overall.

Mid-traffic label flip in the Logfire UI

One-shot cold-invalidation for everyone on that label.

In short: pinning a `label` keeps the cache hot; using `ManagedPrompt` as an A/B platform is opt-in cache cost. If you don't need rollouts, `label='production'` is the recommended default.

## Bringing your own variable

Declaring the same name more than once is fine -- each `ManagedPrompt` builds its own backing variable, so sharing a prompt across several agents just works. Pass an existing [`logfire.variables.Variable`](https://logfire.pydantic.dev/docs/reference/advanced/managed-variables/) as the first argument instead of a name when you want to declare the variable yourself -- for example a template variable, or one registered for `variables_push`:

```python
import logfire
from pydantic_ai import Agent

from pydantic_ai_harness import ManagedPrompt

logfire.configure()

support_prompt = logfire.var(
    name='prompt__support_agent',
    type=str,
    default='You are a helpful customer support agent. Be friendly and concise.',
)

agent = Agent('openai:gpt-5', capabilities=[ManagedPrompt(support_prompt, label='production')])
```

When `name` is a prompt name (not a `Variable`), pass `logfire_instance=` to declare the variable on a specific Logfire instance instead of the module-level default. `default` is required when `name` is a prompt name and is ignored when you pass a `Variable` (which already carries its own default and instance).

## How it composes

-   **Resolves once per run.** A label flip or rollout change that lands in Logfire mid-run is not picked up until the next run starts -- the trade-off for run-stable instructions and a single baggage scope across all child spans.
-   **Runs outermost.** The capability wraps [`Instrumentation`](/docs/ai/api/pydantic-ai/capabilities/#pydantic_ai.capabilities.Instrumentation) so the resolved variable's baggage covers the agent run span as well as its children. On recent Logfire versions both the selected label and the version are propagated as separate baggage attributes.
-   **Concurrency-safe.** Resolution is isolated per run via a context variable, so a single capability instance is safe to share across concurrent runs.
-   **Inspectable mid-run.** `ManagedPrompt.resolved` exposes the active run's `ResolvedVariable` (`value`, `label`, `version`, `reason`) for inspection -- e.g. from inside a tool. It is `None` outside a run.

## API reference

The resolved prompt is a `str`. Pass the bare prompt name (the `prompt__` prefix and hyphen-to-underscore normalization are applied for you) and a `default`, then use `label`, `targeting_key`, `attributes`, `render_template`, and `logfire_instance` to control resolution.

### ManagedPrompt

**Bases:** `AbstractCapability[AgentDepsT]`

Back an agent's instructions with a Logfire-managed prompt.

**Prompt-cache trade-off:** the resolved value lands in the system instructions block, so any Logfire-side change to the prompt (new version rollout, label flip, A/B targeting) invalidates the provider's prompt cache for the affected runs. Pin a `label` (e.g. `'production'`) for the cache-stable path; treat percentage rollouts and per-user targeting as opt-in cache cost. See the README's "Prompt-cache trade-off" section for the full picture.

Pass the managed prompt name and a default value and the capability declares the backing [managed variable](https://logfire.pydantic.dev/docs/reference/advanced/managed-variables/) for you -- a name of `support_agent` resolves the variable `prompt__support_agent`, matching the naming Logfire's [Prompt management](https://logfire.pydantic.dev/docs/reference/advanced/prompt-management/) uses. You can iterate on the prompt from the Logfire UI -- versioned, labelled, and rolled out -- without redeploying, while the code default keeps the agent working when no remote value is available.

```python
import logfire
from pydantic_ai import Agent

from pydantic_ai_harness.logfire import ManagedPrompt

logfire.configure()

agent = Agent(
    'openai:gpt-5',
    capabilities=[
        ManagedPrompt(
            'support_agent',
            default='You are a helpful customer support agent. Be friendly and concise.',
            label='production',
        )
    ],
)
result = agent.run_sync('My order never arrived.')
```

The prompt value is resolved **once per run**, inside the run's [`wrap_run`](/docs/ai/api/pydantic-ai/capabilities/#pydantic_ai.capabilities.AbstractCapability.wrap_run) hook, using the `ResolvedVariable` as a context manager that stays open for the whole run -- so the selected label and version are attached as baggage to every child span of the agent run.

Declaring the same name more than once is fine -- each `ManagedPrompt` constructs its own backing variable, so sharing a prompt across several agents just works. Pass an existing `logfire.variables.Variable` as `name` instead of a prompt name when you want to use a variable you defined yourself (for example a `template_var`, or one registered for [`variables_push`](https://logfire.pydantic.dev/docs/api/logfire/#logfire.Logfire.variables_push)).

#### Attributes

##### name

The managed prompt name (declared as the variable `prompt__<name>`), or a pre-built `logfire.Variable`.

**Type:** [`str`](https://docs.python.org/3/library/stdtypes.html#str) | `Variable`\[[`str`](https://docs.python.org/3/library/stdtypes.html#str)\]

##### default

Code-default prompt text. Required when `name` is a prompt name; ignored when `name` is a `Variable`.

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

##### label

Explicit targeting label on the Logfire managed prompt to resolve (e.g. `'production'`). When `None`, the targeting rules on the managed variable select the label.

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

##### targeting\_key

Stable key that seeds Logfire's deterministic rollout assignment -- the same key always lands in the same percentage bucket, so a given user keeps the same label across runs. Accepts a static value or a callable that derives it from the [`RunContext`](/docs/ai/api/pydantic-ai/tools/#pydantic_ai.tools.RunContext). When `None`, Logfire falls back to its own targeting context and then the active trace id.

**Type:** [`str`](https://docs.python.org/3/library/stdtypes.html#str) | [`Callable`](https://docs.python.org/3/library/typing.html#typing.Callable)\[\[[`RunContext`](/docs/ai/api/pydantic-ai/tools/#pydantic_ai.tools.RunContext)\[`AgentDepsT`\]\], [`str`](https://docs.python.org/3/library/stdtypes.html#str) | [`None`](https://docs.python.org/3/library/constants.html#None)\] | [`None`](https://docs.python.org/3/library/constants.html#None) **Default:** `None`

##### attributes

Attributes for condition-based targeting rules, or a callable that derives them from the [`RunContext`](/docs/ai/api/pydantic-ai/tools/#pydantic_ai.tools.RunContext).

**Type:** [`Mapping`](https://docs.python.org/3/library/typing.html#typing.Mapping)\[[`str`](https://docs.python.org/3/library/stdtypes.html#str), [`Any`](https://docs.python.org/3/library/typing.html#typing.Any)\] | [`Callable`](https://docs.python.org/3/library/typing.html#typing.Callable)\[\[[`RunContext`](/docs/ai/api/pydantic-ai/tools/#pydantic_ai.tools.RunContext)\[`AgentDepsT`\]\], [`Mapping`](https://docs.python.org/3/library/typing.html#typing.Mapping)\[[`str`](https://docs.python.org/3/library/stdtypes.html#str), [`Any`](https://docs.python.org/3/library/typing.html#typing.Any)\] | [`None`](https://docs.python.org/3/library/constants.html#None)\] | [`None`](https://docs.python.org/3/library/constants.html#None) **Default:** `None`

##### render\_template

When `True`, render the resolved prompt as a Handlebars template against the agent's `deps` (the same mechanism as [`TemplateStr`](/docs/ai/api/pydantic-ai/template/#pydantic_ai.template.TemplateStr)); `{{field}}` is filled from `deps`. Requires `pydantic-handlebars` (install `pydantic-ai-slim[spec]`). Defaults to `False`, so the resolved prompt is used verbatim.

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

##### logfire\_instance

Logfire instance to resolve the variable on. When `None`, the global default instance (the one backing the module-level `logfire.var`) is used. Ignored when `name` is a `Variable`.

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

##### resolved

The prompt resolution for the active run, or `None` outside a run.

Exposes the full `ResolvedVariable` (`value`, `label`, `version`, `reason`, ...) so callers can inspect which prompt version is in play.

**Type:** `ResolvedVariable`\[[`str`](https://docs.python.org/3/library/stdtypes.html#str)\] | [`None`](https://docs.python.org/3/library/constants.html#None)

#### Methods

##### get\_ordering

```python
def get_ordering() -> CapabilityOrdering
```

Run outermost so the prompt's baggage envelops the whole run, including the run span.

###### Returns

`CapabilityOrdering`

##### get\_instructions

```python
def get_instructions() -> Callable[[RunContext[AgentDepsT]], str | None]
```

Provide the resolved prompt to the agent's system prompt.

###### Returns

[`Callable`](https://docs.python.org/3/library/typing.html#typing.Callable)\[\[[`RunContext`](/docs/ai/api/pydantic-ai/tools/#pydantic_ai.tools.RunContext)\[`AgentDepsT`\]\], [`str`](https://docs.python.org/3/library/stdtypes.html#str) | [`None`](https://docs.python.org/3/library/constants.html#None)\]

##### wrap\_run

`@async`

```python
def wrap_run(
    ctx: RunContext[AgentDepsT],
    *,
    handler: WrapRunHandler,
) -> AgentRunResult[Any]
```

Resolve the prompt once and keep its baggage active for the duration of the run.

###### Returns

[`AgentRunResult`](/docs/ai/api/pydantic-ai/run/#pydantic_ai.run.AgentRunResult)\[[`Any`](https://docs.python.org/3/library/typing.html#typing.Any)\]