Managed Prompt

ManagedPrompt backs an agent’s instructions with a Logfire-managed prompt, 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, so you wire it in through the capabilities= parameter on Agent.

Source

Install the logfire extra:

Terminal

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 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.

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 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. 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.

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.')
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 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 — handy when the key lives in your agent’s deps:

from dataclasses import dataclass

from pydantic_ai import Agent

from pydantic_ai_harness.logfire 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 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 — so {{field}} is filled from deps:

from dataclasses import dataclass

from pydantic_ai import Agent

from pydantic_ai_harness.logfire 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 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:

import logfire
from pydantic_ai import Agent

from pydantic_ai_harness.logfire 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 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 for you — a name of support_agent resolves the variable prompt__support_agent, matching the naming Logfire’s 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.

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 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).

Attributes

name

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

Type: str | Variable[str]

default

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

Type: str | 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 | 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. When None, Logfire falls back to its own targeting context and then the active trace id.

Type: str | Callable[[RunContext[AgentDepsT]], str | None] | None Default: None

attributes

Attributes for condition-based targeting rules, or a callable that derives them from the RunContext.

Type: Mapping[str, Any] | Callable[[RunContext[AgentDepsT]], Mapping[str, Any] | None] | 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); {{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 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 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] | None

Methods

get_ordering

def get_ordering() -> CapabilityOrdering

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

Returns

CapabilityOrdering

get_instructions

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

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

Returns

Callable[[RunContext[AgentDepsT]], str | None]

wrap_run

@async

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[Any]