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---

# Runtime Capability Creation

Runtime capability creation lets an agent write, validate, and persist Pydantic AI capabilities during one run for activation on the next. `CapabilityCreation` exposes tools that let the model write an `AbstractCapability` subclass to disk as Python source and validate it immediately; the orchestrator loads active authored capabilities into the next `agent.run(...)`.

`CapabilityCreation` is for capabilities authored by the agent as Python source. For capabilities written or selected by application code, see [Building Custom Capabilities](/docs/ai/capabilities/custom/).

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

> 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

A coding agent often discovers, mid-task, that it wants a behavior its host does not yet have: a guardrail, an extra instruction, a tool, a request hook. The capability surface to express that already exists -- but normally only a developer can write a capability class, wire it into the agent, and restart. Without runtime capability creation, the agent cannot author that extension during a run and make it available to the next run.

## The solution

`CapabilityCreation` exposes three tools:

-   `author_capability(name, code)` -- write `code` to `<directory>/<name>.py`, import it, and validate it. Validation requires exactly one `pydantic_ai.capabilities.AbstractCapability` subclass that constructs with no arguments; the side-effect-free static getters (`get_instructions`, `get_toolset`, `get_native_tools`, `get_model_settings`, `get_serialization_name`) are exercised. The async lifecycle hooks are not run -- they need a live `RunContext`.
-   `list_authored_capabilities()` -- list authored capabilities with their status and any validation error.
-   `disable_authored_capability(name)` -- stop a capability from being injected on the next run.

A "hook" is not a standalone object in pydantic-ai -- it is a method on a capability. So authoring a hook means authoring a capability that overrides one lifecycle method. A single overridden hook is a valid capability.

## Usage

Construct `CapabilityCreation` with a `directory` for the authored files, then add it to the agent's `capabilities`:

```python
from pathlib import Path

from pydantic_ai import Agent
from pydantic_ai_harness import CapabilityCreation

creation = CapabilityCreation(directory=Path('.authored'))
agent = Agent('anthropic:claude-sonnet-4-6', capabilities=[creation])
```

The agent can now call `author_capability`, `list_authored_capabilities`, and `disable_authored_capability`. `CapabilityCreation` also contributes static, cache-stable system-prompt guidance explaining these tools. Leave `guidance=None` for the default text, or pass your own string; set `guidance=''` to omit it entirely.

## Activation boundary

Writing and validation happen in the current run; activation happens on the next run. A capability **cannot** be added to a live, already-executing run. pydantic-ai resolves the effective capability set once at the start of each run (the run's root capability is fixed; there is no setter). So an authored capability is live on the **next** `agent.run(...)`, not the run that authored it. Authoring writes and validates the capability immediately, but its tools and hooks only exist once the next run's toolset and capability chain are assembled at run start.

### Integration contract

The orchestrator drives the loop, so it owns the one-line contract: thread the store's active capabilities into each run via `agent.run(..., capabilities=...)`. With that in place, the authored capability is live on the very next loop iteration -- no process restart:

```python
from pathlib import Path

from pydantic_ai import Agent
from pydantic_ai_harness import CapabilityCreation

creation = CapabilityCreation(directory=Path('.authored'))
agent = Agent('anthropic:claude-sonnet-4-6', capabilities=[creation])

history = None
done = False
next_prompt = 'Start the task.'
while not done:
    extra = creation.store.load_active()
    result = await agent.run(next_prompt, message_history=history, capabilities=extra)
    history = result.all_messages()
    # ... decide `next_prompt` and `done` from `result` ...
```

`creation.store` is the disk-backed `CapabilityStore` over the same `directory`. `store.load_active()` re-imports and re-constructs every active authored capability for injection into the next run. Entries that fail to load (corrupt source, construction error) are skipped, not raised, so one bad capability never blocks the rest.

## Persistence

Authored capabilities persist to disk: each is one `<directory>/<name>.py` file, indexed by a sibling `manifest.json`. A fresh process picks them up by constructing a new `CapabilityCreation` over the same `directory` and calling `store.load_active()`.

`manifest.json` records each capability's name, module file, class name, status (`active` or `disabled`), and last validation error. That is the surface a UI can read to show what the agent has authored. The manifest is written atomically (temp file plus `os.replace`), so a crash mid-write never leaves a partial file that reads back as "no capabilities".

Capability names must be lowercase letters, digits, and underscores, starting with a letter. Reusing a name replaces the previous capability of that name. A code that imports but fails validation is still written to disk (so it can be inspected) and recorded with its `last_error` set; `load_active()` skips it.

## Trust boundary

`CapabilityCreation` executes arbitrary Python in-process at import, construction, and run time. That is the same trust boundary an agent that already runs shell commands and edits files operates under, which is the deliberate choice here. Do not point it at a directory whose contents you would not run yourself, and treat authored capabilities as code the agent is executing on your host.

Because authored capabilities hold live code, they are not spec-serializable (`get_serialization_name()` returns `None`) and are persisted as source rather than as an [agent spec](/docs/ai/core-concepts/agent-spec/).

## Typing

Imported authored code is dynamic, but nothing typed `Any` crosses back into the harness: every value pulled from an authored module is narrowed with `isinstance`/`issubclass` before use, and loaded instances are typed `AbstractCapability[object]`. Because `AgentDepsT` is contravariant, an `AbstractCapability[object]` is accepted by any agent's `capabilities=` parameter.

## API reference

### CapabilityCreation

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

Create Pydantic AI capabilities during one run for activation on the next.

Exposes `author_capability(name, code)`, `list_authored_capabilities`, and `disable_authored_capability`. Authoring writes Python source to `directory`, imports it, and validates it (exactly one `AbstractCapability` subclass that constructs with no arguments and whose static getters run). Authored capabilities hold live code, so they are not spec-serializable and are persisted as source rather than as a spec.

Activation boundary: a capability cannot be added to a live, already-executing run -- pydantic-ai resolves the capability set once at the start of each run. The authored capability becomes usable on the next `agent.run(...)`. The integration contract is one line on the orchestrator side: thread the store's active capabilities into the next run.

```python
from pathlib import Path

from pydantic_ai import Agent
from pydantic_ai_harness.capability_creation import CapabilityCreation

creation = CapabilityCreation(directory=Path('.authored'))
agent = Agent('anthropic:claude-sonnet-4-6', capabilities=[creation])

# Loop: each iteration injects whatever the agent has authored so far.
result = await agent.run('build a logging capability', capabilities=creation.store.load_active())
```

This executes authored Python in-process -- the same trust boundary an agent that already runs shell commands and edits files operates under.

#### Attributes

##### directory

Directory holding the authored `<name>.py` files and the `manifest.json` index.

**Type:** `Path`

##### guidance

Static system-prompt guidance on authoring. Cache-stable. Leave `None` for the default, or set `''` to omit guidance entirely.

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

##### store

The disk-backed store. Call `store.load_active()` to inject authored capabilities into the next run.

**Type:** `CapabilityStore`

#### Methods

##### get\_instructions

```python
def get_instructions() -> AgentInstructions[AgentDepsT] | None
```

Static, cache-stable guidance on the authoring tools.

###### Returns

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

##### get\_toolset

```python
def get_toolset() -> AgentToolset[AgentDepsT] | None
```

Toolset providing the authoring tools over this capability's store.

###### Returns

[`AgentToolset`](/docs/ai/api/pydantic-ai/toolsets/#pydantic_ai.toolsets.AgentToolset)\[`AgentDepsT`\] | [`None`](https://docs.python.org/3/library/constants.html#None)

##### get\_serialization\_name

`@classmethod`

```python
def get_serialization_name(cls) -> str | None
```

Not spec-serializable: the capability holds a live, disk-backed store.

###### Returns

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

### CapabilityStore

Read/write index of authored capability `.py` files under `directory`.

#### Methods

##### write

```python
def write(name: str, code: str) -> AuthoredCapability
```

Write `code` to `<name>.py`, validate it, and upsert the manifest entry.

Raises `ValueError` for an invalid name (before writing anything). A code that imports but fails validation is still written (so it can be inspected) and recorded with `last_error` set; `load_active` skips it.

###### Returns

`AuthoredCapability`

##### disable

```python
def disable(name: str) -> bool
```

Mark the named capability disabled so `load_active` stops returning it. Returns whether it existed.

###### Returns

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

##### list\_all

```python
def list_all() -> list[AuthoredCapability]
```

Return every manifest entry, in insertion order.

###### Returns

[`list`](https://docs.python.org/3/glossary.html#term-list)\[`AuthoredCapability`\]

##### load\_active

```python
def load_active() -> list[AbstractCapability[object]]
```

Construct every active authored capability for per-run injection.

Re-imports and re-constructs each active entry. Entries that fail to load (corrupt source, construction error) are skipped, not raised, so one bad capability never blocks the rest. A load outcome that disagrees with the record's `last_error` is persisted back to the manifest: a newly broken entry records its error, a re-fixed entry clears it, so the manifest stays truthful about which capabilities are actually active.

###### Returns

[`list`](https://docs.python.org/3/glossary.html#term-list)\[`AbstractCapability`\[[`object`](https://docs.python.org/3/glossary.html#term-object)\]\]