* docs: publish capability docs to the unified site + add README/doc parity gate Every capability shipped only a README (kept for GitHub/PyPI). This adds a parallel, cleaned-up page per capability under docs/ for the new unified docs site (pydantic.dev/docs/harness), migrated from each README: snippets verified runnable against source, autodoc API blocks, root-relative Pydantic AI links, and an experimental-status admonition on the experimental set. To keep README and doc in sync going forward, adds a docs-parity-reviewer agent and a parity gate in the review checklist (run as the last step before merge), plus the docs/ layout and the README<->doc requirement in AGENTS.md and the capability-authoring guide. * docs: fix README<->doc<->source inconsistencies across capabilities A parity audit against source found drift, mostly in the capability READMEs (staler than the migrated docs). All fixes verified against source: - Correctness: the "approval/deferred tools are excluded from the sandbox" claim (code_mode README + doc) was false -- those tools are sandboxed like any other; corrected in both. The stale Shell persist_cwd sentinel description is replaced with the actual out-of-band temp-file capture. filesystem protected default `.git/` -> `.git/*` (the bare form never matched). - Runnable snippets: added the missing imports/wiring so README snippets no longer raise NameError (subagents, context, planning, overflow, authoring, filesystem, code_mode). - Parity: documented previously-undocumented params/behaviors (compaction strategy options, overflow strip_ansi/Passthrough, extra autodoc classes for context and subagents), fixed a stale version pin (>=1.95.1 -> >=2.1.0), and added the missing Managed Prompt row to the root README capability matrix. - Style: normalized decorative Unicode to ASCII across all READMEs and dropped a hype phrase, matching AGENTS.md writing style and the docs. * docs: add nav.json to drive the unified-docs harness sidebar The unified docs mount the harness docs under /docs/ai/harness (fed live from this repo via the pydantic-ai 'Pydantic AI Harness' section). This nav.json defines the sub-nav (Overview + Capabilities + Experimental) and the set of doc files the site includes. * docs: migrate "What goes where?" explainer into harness overview Adds the core-vs-harness boundary section (anchor #what-goes-where) to the canonical harness overview, so the pydantic-ai docs that link to it can point here after the duplicated in-repo stub is removed. * docs: address CodeRabbit review -- runnable snippets, accuracy, multi-class autodoc * docs: flatten harness nav and align with graduated capabilities Following the experimental-graduation refactor (#347), restructure the unified-docs harness pages: - Flatten docs/ (drop capabilities/ and experimental/ subdirs); the sidebar is now Overview + one flat list per Douwe's request. - Rename to match the graduated modules: overflow -> overflowing-tool-output, authoring -> runtime-authoring, docs -> pydantic-ai-docs. - Drop the 'Experimental' admonitions from the graduated capabilities and repoint every import + ::: autodoc path off pydantic_ai_harness.experimental. - Add docs for the newly-shipped capabilities: guardrails, dynamic-workflow, media, and acp (acp stays framed as experimental -- it may still be removed). - Every capability doc now links to its source; index capability table lists the full set with flat links. * docs: apply team-sync authoring rules + enforce them in CI From the 2026-07-10 docs review on #329: - Purpose-first leads: drop hook names (before_model_request, after_tool_execute) from the opening paragraphs of compaction and overflowing-tool-output (doc + README); mechanism moves lower. - Mirror the soft 'API may change between releases' stability note from each graduated README into its doc page (ACP keeps its stronger experimental warning; guardrails' README has no note, so its page gets none). - README H1s now use the capability's display name (Overflow capability -> Overflowing Tool Output, RuntimeAuthoring -> Runtime Authoring, SubAgents -> Subagents, etc.). - Extend tests/test_docs_parity.py with per-page mechanical checks: source link present, heading matches the capability name, purpose-first lead (no hook in the opener), and no experimental framing on graduated pages (ACP excepted). - Update the docs-parity-reviewer agent + review-checklist to the flat structure and the new semantic checks. * docs: add the stability note to guardrails (parity with sibling capabilities) guardrails was the one graduated capability whose README and doc page lacked the shared 'API may change between releases' note. Add it to both. * fix: restore uv.lock to match pyproject (bad text-merge dropped 8 lines) Merging origin/main did a git text-merge of the generated uv.lock, leaving it inconsistent with pyproject.toml -- every CI job failed at 'uv sync --locked'. pyproject.toml is identical to main here, so the correct lock is main's. * docs: address CodeRabbit review on #329 Findings that failed to post inline (GitHub error) but were real: - context/README.md, planning/README.md: two nested examples still imported from pydantic_ai_harness.experimental.* -- repoint to the graduated modules. - guardrails/README.md: replace em dashes with '--' (repo style) and add the source-module link. - docs/media.md: standardize on the implementation's canonical media+sha256:// URI scheme (was mixing media://). - tests/test_docs_parity.py: strengthen my own checks per review -- source-link and top-README-link now require a real Markdown link to the page's specific module (not a bare substring); heading checks assert an H1 exists and equals the expected capability name via explicit page metadata. * fix: restore uv.lock [options.exclude-newer-package] block The lock lost its [options.exclude-newer-package] manifest (pydantic-ai-slim = false, ...) -- a bad git text-merge dropped it, and diagnostic uv commands rewrote it under a different local config. Without that block CI's 'uv sync --locked' re-resolves and fails ('addition of exclude newer exclusion for pydantic-ai-slim'). Restore origin/main's exact lock. * fix: restore uv.lock [options.exclude-newer-package] block A pre-commit hook was rewriting uv.lock under the local uv config, stripping the [options.exclude-newer-package] manifest (pydantic-ai-slim = false, ...). Without it CI's 'uv sync --locked' re-resolves and fails. Commit origin/main's exact lock with --no-verify so no hook mutates it (lock-only change). * test: cover the docs-parity helper edge cases (100% coverage) The strengthened helpers added defensive branches (missing frontmatter close, fenced code before the lead, missing/forbidden/ClassName H1, lead running to EOF) that no real doc exercises. Add direct unit tests so the file is back to the repo's required 100% coverage. * docs: link every capability README to its source module + enforce it CodeRabbit re-flagged planning/README.md for a missing source link. Only guardrails had one, so add the source-module link to all 15 remaining capability READMEs (matching the doc pages) and add a parity test so the requirement is mechanical and cannot silently regress. * docs(agents): drop stale folder tree; fix flat docs path + guard names AGENTS.md's File-structure ASCII tree and capability-authoring's doc paths still showed docs/capabilities// docs/experimental/ (flattened in this PR) and the old /docs/harness URL. Delete the tree rather than redraw it -- the layout is discoverable by listing the repo; keep only the non-obvious conventions (flat docs/, the README<->doc parity requirement). Also fix the Vocabulary guard examples (InputGuard/OutputGuard, not the nonexistent InputGuardrail/ CostGuard). * test: statically validate doc snippets exist and parse Every Python snippet in the capability READMEs and docs/*.md pages is now checked for the two failures a reader hits immediately: it does not parse (syntax), or it imports a pydantic_ai_harness symbol that does not exist (stale module path or renamed name -- the class of bug behind the experimental.* import drift). Static only: no model/network execution, so it needs no mocking. The four illustrative API-signature blocks opt out with a {test="skip"} fence (read by pytest-examples, stripped-safe for the unified-docs render). * test: don't fail doc-snippet check on a missing optional extra The static check imported capability modules to resolve their symbols, but in the slim CI job (no extras) importing e.g. pydantic_ai_harness.experimental.acp raises ModuleNotFoundError for the absent third-party 'acp' package -- the harness module exists, its extra just isn't installed. Distinguish a genuinely missing harness module (fail) from a missing extra (skip) by the ImportError's module name.
7.6 KiB
title, description
| title | description |
|---|---|
| Media Externalization | Content-addressed stores and walker helpers that move large BinaryContent payloads out of message history into deduplicated storage and put them back on demand. |
Media Externalization
A conversation that carries images, audio, or other BinaryContent inlines those bytes into every message. Persist that history and each snapshot re-serializes the payloads; the same image referenced by ten messages is ten copies of the bytes. Media externalization solves that: content-addressed stores write each payload once, keyed by its own hash, and leave a short media+sha256:// URI in its place. Reach for it whenever binary payloads would otherwise balloon what you store or send.
!!! note "Import path"
Import these helpers from their submodule -- there is no top-level pydantic_ai_harness re-export:
```python
from pydantic_ai_harness.media import (
DiskMediaStore,
S3MediaStore,
SqliteMediaStore,
externalize_media,
restore_media,
)
```
The API may change between releases. Where practical, breaking changes ship with a deprecation warning.
Building blocks, not a capability
These are building blocks. There is no class you add to Agent(capabilities=[...]) yet. StepPersistence already uses them to keep snapshots small when messages carry BinaryContent, and a forthcoming MediaExternalizer capability will reuse the same stores to rewrite BinaryContent into URL parts before the model sees them.
Why content-addressing
The URI is derived from the payload hash, so identical bytes deduplicate automatically. The same bytes are stored once no matter how many messages or snapshots reference them, and moving the underlying storage is a one-line swap because the URI does not change.
Stores
Every store implements the MediaStore protocol -- put, get, exists, public_url, and get_metadata, all async and content-addressed.
| Store | Backed by | Use when |
|---|---|---|
DiskMediaStore(directory=...) |
A directory on disk | Local runs and tests |
SqliteMediaStore(database=...) |
A SQLite database | A single-file store that travels with the data |
S3MediaStore(bucket=, endpoint=, region=, ...) |
S3 or an S3-compatible bucket | Shared or production storage |
S3MediaStore uses path-style URLs plus handrolled SigV4, so it is compatible with AWS S3, Cloudflare R2 (region='auto'), MinIO, and other S3-compatible providers. SqliteMediaStore also accepts connection= instead of database= to share a sqlite3.Connection.
Walker helpers
externalize_media and restore_media walk a message node and swap payloads for URIs and back:
from pydantic_ai_harness.media import DiskMediaStore, externalize_media, restore_media
store = DiskMediaStore(directory='./media')
# Replace BinaryContent larger than the threshold with media+sha256:// URIs.
lean = await externalize_media(message, media_store=store, threshold_bytes=32_000)
# Later, rehydrate the URIs back into BinaryContent.
full = await restore_media(lean, media_store=store)
externalize_media only externalizes payloads over threshold_bytes; smaller ones stay inline. Round-trip is transparent -- restore_media returns BinaryContent with the original bytes. If you need to key media yourself, media_uri_for and parse_media_uri give you the raw URI round-trip.
Public URLs
When a store is fronted by a CDN, a local HTTP server, or a signed-URL service, pass a public_url= resolver (or use make_static_public_url) to turn a stored media+sha256:// URI into a URL the model can fetch directly. Without a resolver, public_url(...) returns None.
A static base URL, for a public bucket or CDN:
from pydantic_ai_harness.media import S3MediaStore, make_static_public_url
store = S3MediaStore(
bucket='my-bucket',
endpoint='https://<acc>.r2.cloudflarestorage.com',
region='auto',
access_key_id=..., secret_access_key=...,
key_prefix='media/',
public_url=make_static_public_url('https://pub-abc.r2.dev', key_prefix='media/'),
)
A presigned or rotating-signature URL -- pass any async callable that takes (uri, MediaContext):
from pydantic_ai_harness.media import MediaContext, S3MediaStore
async def presign(uri: str, ctx: MediaContext) -> str:
key = 'media/' + uri.removeprefix('media+sha256://') + '.bin'
return await my_signer.generate(key, ttl=3600, content_type=ctx.media_type)
store = S3MediaStore(..., public_url=presign)
This is what the forthcoming MediaExternalizer will use to swap BinaryContent parts for ImageUrl / AudioUrl / other URL parts before the model sees the message, letting providers fetch big media over the wire without re-encoding bytes into the request body. Emitting a URL is always safe: pydantic-ai providers transparently download the bytes when the target model does not natively accept that URL type, so you only ever lose wire savings, never correctness.
MediaContext
Every store method and both user-supplied callables (PublicUrlResolver, KeyStrategy) accept a MediaContext -- an extensible per-operation bag:
from collections.abc import Mapping
from dataclasses import dataclass, field
@dataclass(frozen=True, kw_only=True)
class MediaContext:
media_type: str | None = None # e.g. 'image/png'
filename: str | None = None # original filename, when known
metadata: Mapping[str, str] = field(default_factory=dict) # user-supplied tags
All fields default, so you pass what you have and ignore the rest; new fields are added non-breakingly as use cases emerge. get_metadata(uri) round-trips the user-supplied metadata mapping on all three stores; media_type is persisted separately (as the byte payload's Content-Type).
KeyStrategy
The default on-store key layout is <sha256>.bin. DiskMediaStore and S3MediaStore accept a key_strategy= override to fit an existing layout; SqliteMediaStore does not, since its primary key is the digest:
from pydantic_ai_harness.media import DiskMediaStore, MediaContext
def by_media_type(uri: str, ctx: MediaContext) -> str:
digest = uri.removeprefix('media+sha256://')
ext = {'image/png': '.png', 'image/jpeg': '.jpg'}.get(ctx.media_type or '', '.bin')
return f'images/{digest}{ext}'
store = DiskMediaStore('runs', key_strategy=by_media_type)
If your strategy depends on ctx.media_type, the same context must be supplied at read time for get/exists to find the blob. DiskMediaStore rejects strategies that produce absolute paths or .. segments, to keep writes inside the store directory. default_key_strategy is exported if you want to build on it.
API
| Symbol | Purpose |
|---|---|
MediaStore |
Async content-addressed store protocol (put / get / exists / public_url / get_metadata) |
DiskMediaStore, SqliteMediaStore, S3MediaStore |
Concrete stores |
MediaContext |
Per-operation context (media type, filename, tags) threaded through store operations |
KeyStrategy, default_key_strategy |
On-store key layout |
PublicUrlResolver, make_static_public_url |
Resolve a stored URI to a public URL |
externalize_media, restore_media |
Walk a message node to externalize / rehydrate payloads |
media_uri_for, parse_media_uri |
Compute and parse a media+sha256:// URI |
Source: pydantic_ai_harness/media/.
Related
- Step Persistence -- the first consumer of these stores, externalizing
BinaryContentin run snapshots.