From ec5600b29a97287a00419adf4342bc2618374f31 Mon Sep 17 00:00:00 2001
From: David Sanchez <64162682+dsfaccini@users.noreply.github.com>
Date: Fri, 8 May 2026 16:35:32 -0500
Subject: [PATCH 01/11] Add `pydantic_ai` bridge submodule (port from
 pydantic-ai _a2a.py)

Port of pydantic-ai's `Agent.to_a2a()` integration into this package, in
advance of the v2 deprecation on the pydantic-ai side. This is a near-verbatim
port of `pydantic_ai_slim/pydantic_ai/_a2a.py` so the API can be redesigned
in-tree if the maintainers prefer a different shape.
---
 fasta2a/pydantic_ai/__init__.py |  23 +++
 fasta2a/pydantic_ai/_bridge.py  | 241 ++++++++++++++++++++++++++++++++
 pyproject.toml                  |   1 +
 3 files changed, 265 insertions(+)
 create mode 100644 fasta2a/pydantic_ai/__init__.py
 create mode 100644 fasta2a/pydantic_ai/_bridge.py

diff --git a/fasta2a/pydantic_ai/__init__.py b/fasta2a/pydantic_ai/__init__.py
new file mode 100644
index 0000000..e990e16
--- /dev/null
+++ b/fasta2a/pydantic_ai/__init__.py
@@ -0,0 +1,23 @@
+"""Pydantic AI bridge for FastA2A.
+
+Port of `pydantic_ai._a2a` into the FastA2A package, so users can keep using
+`Agent.to_a2a()` after the corresponding wrapper is removed from pydantic-ai
+in v2. See https://github.com/pydantic/pydantic-ai for the upstream
+deprecation context.
+
+Usage:
+
+    from pydantic_ai import Agent
+    from fasta2a.pydantic_ai import agent_to_a2a
+
+    agent = Agent('openai:gpt-4o')
+    app = agent_to_a2a(agent, name='my-agent', url='http://localhost:8000')
+
+Install the integration with the `pydantic-ai` extra:
+
+    pip install 'fasta2a[pydantic-ai]'
+"""
+
+from ._bridge import AgentWorker, agent_to_a2a, worker_lifespan
+
+__all__ = ['AgentWorker', 'agent_to_a2a', 'worker_lifespan']
diff --git a/fasta2a/pydantic_ai/_bridge.py b/fasta2a/pydantic_ai/_bridge.py
new file mode 100644
index 0000000..2ccd188
--- /dev/null
+++ b/fasta2a/pydantic_ai/_bridge.py
@@ -0,0 +1,241 @@
+from __future__ import annotations
+
+import base64
+import uuid
+from collections.abc import AsyncIterator, Sequence
+from contextlib import asynccontextmanager
+from dataclasses import dataclass
+from functools import partial
+from typing import Any, Generic, TypeVar
+
+from pydantic import TypeAdapter
+from typing_extensions import assert_never
+
+try:
+    from pydantic_ai import (
+        AudioUrl,
+        BinaryContent,
+        DocumentUrl,
+        ImageUrl,
+        ModelMessage,
+        ModelRequest,
+        ModelRequestPart,
+        ModelResponse,
+        ModelResponsePart,
+        TextPart,
+        ThinkingPart,
+        ToolCallPart,
+        UserPromptPart,
+        VideoUrl,
+    )
+    from pydantic_ai.agent import AbstractAgent, AgentDepsT, OutputDataT
+except ImportError as _e:
+    raise ImportError(
+        'Please install the `pydantic-ai` package to use `fasta2a.pydantic_ai`, '
+        "e.g. `pip install 'fasta2a[pydantic-ai]'`"
+    ) from _e
+
+from fasta2a.applications import FastA2A
+from fasta2a.broker import Broker, InMemoryBroker
+from fasta2a.schema import (
+    AgentProvider,
+    Artifact,
+    DataPart,
+    Message,
+    Part,
+    Skill,
+    TaskIdParams,
+    TaskSendParams,
+    TextPart as A2ATextPart,
+)
+from fasta2a.storage import InMemoryStorage, Storage
+from fasta2a.worker import Worker
+from starlette.middleware import Middleware
+from starlette.routing import Route
+from starlette.types import ExceptionHandler, Lifespan
+
+WorkerOutputT = TypeVar('WorkerOutputT')
+
+
+@asynccontextmanager
+async def worker_lifespan(
+    app: FastA2A, worker: Worker, agent: AbstractAgent[AgentDepsT, OutputDataT]
+) -> AsyncIterator[None]:
+    """Lifespan that runs the worker during application startup."""
+    async with app.task_manager, agent:
+        async with worker.run():
+            yield
+
+
+def agent_to_a2a(
+    agent: AbstractAgent[AgentDepsT, OutputDataT],
+    *,
+    storage: Storage | None = None,
+    broker: Broker | None = None,
+    name: str | None = None,
+    url: str = 'http://localhost:8000',
+    version: str = '1.0.0',
+    description: str | None = None,
+    provider: AgentProvider | None = None,
+    skills: list[Skill] | None = None,
+    debug: bool = False,
+    routes: Sequence[Route] | None = None,
+    middleware: Sequence[Middleware] | None = None,
+    exception_handlers: dict[Any, ExceptionHandler] | None = None,
+    lifespan: Lifespan[FastA2A] | None = None,
+) -> FastA2A:
+    """Create a FastA2A server from a pydantic-ai agent."""
+    storage = storage or InMemoryStorage()
+    broker = broker or InMemoryBroker()
+    worker = AgentWorker(agent=agent, broker=broker, storage=storage)
+
+    lifespan = lifespan or partial(worker_lifespan, worker=worker, agent=agent)
+
+    return FastA2A(
+        storage=storage,
+        broker=broker,
+        name=name or agent.name,
+        url=url,
+        version=version,
+        description=description,
+        provider=provider,
+        skills=skills,
+        debug=debug,
+        routes=routes,
+        middleware=middleware,
+        exception_handlers=exception_handlers,
+        lifespan=lifespan,
+    )
+
+
+@dataclass
+class AgentWorker(Worker[list[ModelMessage]], Generic[WorkerOutputT, AgentDepsT]):
+    """A worker that uses a pydantic-ai agent to execute tasks."""
+
+    agent: AbstractAgent[AgentDepsT, WorkerOutputT]
+
+    async def run_task(self, params: TaskSendParams) -> None:
+        task = await self.storage.load_task(params['id'])
+        if task is None:
+            raise ValueError(f'Task {params["id"]} not found')
+
+        if task['status']['state'] != 'submitted':
+            raise ValueError(
+                f'Task {params["id"]} has already been processed (state: {task["status"]["state"]})'
+            )
+
+        await self.storage.update_task(task['id'], state='working')
+
+        message_history = await self.storage.load_context(task['context_id']) or []
+        message_history.extend(self.build_message_history(task.get('history', [])))
+
+        try:
+            result = await self.agent.run(message_history=message_history)  # type: ignore
+
+            await self.storage.update_context(task['context_id'], result.all_messages())
+
+            a2a_messages: list[Message] = []
+            for message in result.new_messages():
+                if isinstance(message, ModelRequest):
+                    continue
+                a2a_parts = self._response_parts_to_a2a(message.parts)
+                if a2a_parts:
+                    a2a_messages.append(
+                        Message(role='agent', parts=a2a_parts, kind='message', message_id=str(uuid.uuid4()))
+                    )
+
+            artifacts = self.build_artifacts(result.output)
+        except Exception:
+            await self.storage.update_task(task['id'], state='failed')
+            raise
+        else:
+            await self.storage.update_task(
+                task['id'], state='completed', new_artifacts=artifacts, new_messages=a2a_messages
+            )
+
+    async def cancel_task(self, params: TaskIdParams) -> None:
+        pass
+
+    def build_artifacts(self, result: WorkerOutputT) -> list[Artifact]:
+        artifact_id = str(uuid.uuid4())
+        part = self._convert_result_to_part(result)
+        return [Artifact(artifact_id=artifact_id, name='result', parts=[part])]
+
+    def _convert_result_to_part(self, result: WorkerOutputT) -> Part:
+        if isinstance(result, str):
+            return A2ATextPart(kind='text', text=result)
+        output_type = type(result)
+        type_adapter = TypeAdapter(output_type)
+        data = type_adapter.dump_python(result, mode='json')
+        json_schema = type_adapter.json_schema(mode='serialization')
+        return DataPart(kind='data', data={'result': data}, metadata={'json_schema': json_schema})
+
+    def build_message_history(self, history: list[Message]) -> list[ModelMessage]:
+        model_messages: list[ModelMessage] = []
+        for message in history:
+            if message['role'] == 'user':
+                model_messages.append(ModelRequest(parts=self._request_parts_from_a2a(message['parts'])))
+            else:
+                model_messages.append(ModelResponse(parts=self._response_parts_from_a2a(message['parts'])))
+        return model_messages
+
+    def _request_parts_from_a2a(self, parts: list[Part]) -> list[ModelRequestPart]:
+        model_parts: list[ModelRequestPart] = []
+        for part in parts:
+            if part['kind'] == 'text':
+                model_parts.append(UserPromptPart(content=part['text']))
+            elif part['kind'] == 'file':
+                file_content = part['file']
+                if 'bytes' in file_content:
+                    data = base64.b64decode(file_content['bytes'])
+                    mime_type = file_content.get('mime_type', 'application/octet-stream')
+                    content = BinaryContent(data=data, media_type=mime_type)
+                    model_parts.append(UserPromptPart(content=[content]))
+                else:
+                    url = file_content['uri']
+                    for url_cls in (DocumentUrl, AudioUrl, ImageUrl, VideoUrl):
+                        content = url_cls(url=url)
+                        try:
+                            content.media_type
+                        except ValueError:
+                            continue
+                        else:
+                            break
+                    else:
+                        raise ValueError(f'Unsupported file type: {url}')
+                    model_parts.append(UserPromptPart(content=[content]))
+            elif part['kind'] == 'data':
+                raise NotImplementedError('Data parts are not supported yet.')
+            else:
+                assert_never(part)
+        return model_parts
+
+    def _response_parts_from_a2a(self, parts: list[Part]) -> list[ModelResponsePart]:
+        model_parts: list[ModelResponsePart] = []
+        for part in parts:
+            if part['kind'] == 'text':
+                model_parts.append(TextPart(content=part['text']))
+            elif part['kind'] == 'file':
+                raise NotImplementedError('File parts are not supported yet.')
+            elif part['kind'] == 'data':
+                raise NotImplementedError('Data parts are not supported yet.')
+            else:
+                assert_never(part)
+        return model_parts
+
+    def _response_parts_to_a2a(self, parts: Sequence[ModelResponsePart]) -> list[Part]:
+        a2a_parts: list[Part] = []
+        for part in parts:
+            if isinstance(part, TextPart):
+                a2a_parts.append(A2ATextPart(kind='text', text=part.content))
+            elif isinstance(part, ThinkingPart):
+                a2a_parts.append(
+                    A2ATextPart(
+                        kind='text',
+                        text=part.content,
+                        metadata={'type': 'thinking', 'thinking_id': part.id, 'signature': part.signature},
+                    )
+                )
+            elif isinstance(part, ToolCallPart):
+                pass
+        return a2a_parts
diff --git a/pyproject.toml b/pyproject.toml
index 2af3b7d..f5977c7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -52,6 +52,7 @@ dependencies = [
 
 [project.optional-dependencies]
 logfire = ["logfire>=2.3"]
+pydantic-ai = ["pydantic-ai-slim>=0.5"]
 
 [dependency-groups]
 dev = [

From bcfd4b67160062c4984f181ab3cf3ce443577286 Mon Sep 17 00:00:00 2001
From: David Sanchez <64162682+dsfaccini@users.noreply.github.com>
Date: Fri, 8 May 2026 17:19:12 -0500
Subject: [PATCH 02/11] Pin pydantic-ai-slim floor to >=1.92 (latest minor at
 port time)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

The previous >=0.5 floor was a typo from copying the plan-doc — pydantic-ai is
on v1.92.0 and the bridge depends on symbols (AbstractAgent, BinaryContent,
VideoUrl, …) that don't exist below 1.x.
---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index f5977c7..003f822 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -52,7 +52,7 @@ dependencies = [
 
 [project.optional-dependencies]
 logfire = ["logfire>=2.3"]
-pydantic-ai = ["pydantic-ai-slim>=0.5"]
+pydantic-ai = ["pydantic-ai-slim>=1.92"]
 
 [dependency-groups]
 dev = [

From 581dc3ac8e3b3cc3ce67af74bca30d8e4232a5dd Mon Sep 17 00:00:00 2001
From: David Sanchez <64162682+dsfaccini@users.noreply.github.com>
Date: Thu, 14 May 2026 13:22:36 -0500
Subject: [PATCH 03/11] Fix ruff format + update README 'Using PydanticAI'
 section

- Collapse 3-line ValueError back to one line under fasta2a's 120-char limit
- Rewrite 'Using PydanticAI' to use the new bridge: install 'fasta2a[pydantic-ai]'
  and call 'agent_to_a2a(agent)' instead of 'agent.to_a2a()'
- Add note that 'Agent.to_a2a()' continues to work in pydantic-ai 1.x with a
  deprecation warning, and is removed in v2
---
 README.md                      | 15 ++++++++++++---
 fasta2a/pydantic_ai/_bridge.py |  4 +---
 2 files changed, 13 insertions(+), 6 deletions(-)

diff --git a/README.md b/README.md
index 8fc7dd9..7271da1 100644
--- a/README.md
+++ b/README.md
@@ -107,21 +107,30 @@ we've decided to move it to a separate repository.
 > [!NOTE]
 > Other agentic frameworks are welcome to implement the `Worker` component, and we'll be happy add the reference here.
 
-For reference, you can [check the PydanticAI implementation of the `Worker`](https://github.com/pydantic/pydantic-ai/blob/3ef42ed9a1a2c799bb94a5a69c80aa9e8968ca72/pydantic_ai_slim/pydantic_ai/_a2a.py#L115-L304).
+Install the integration with the `pydantic-ai` extra:
 
-Let's see how to use it in practice:
+```bash
+pip install 'fasta2a[pydantic-ai]'  # or `uv add 'fasta2a[pydantic-ai]'`
+```
+
+Then turn any `pydantic_ai.Agent` into an A2A-compatible ASGI app:
 
 ```python
 from pydantic_ai import Agent
+from fasta2a.pydantic_ai import agent_to_a2a
 
 agent = Agent('openai:gpt-4.1')
-app = agent.to_a2a()
+app = agent_to_a2a(agent)
 ```
 
 _You can run this example as is with `uvicorn main:app --reload`._
 
 As you see, it's pretty easy from the point of view of the developer using your agentic framework.
 
+> [!NOTE]
+> In PydanticAI 1.x, `Agent.to_a2a()` continues to work but emits a deprecation
+> warning pointing here. It will be removed in PydanticAI v2.
+
 ## Design
 
 **FastA2A** is built on top of [Starlette](https://www.starlette.io/), which means it's fully compatible
diff --git a/fasta2a/pydantic_ai/_bridge.py b/fasta2a/pydantic_ai/_bridge.py
index 2ccd188..ff21fa8 100644
--- a/fasta2a/pydantic_ai/_bridge.py
+++ b/fasta2a/pydantic_ai/_bridge.py
@@ -120,9 +120,7 @@ async def run_task(self, params: TaskSendParams) -> None:
             raise ValueError(f'Task {params["id"]} not found')
 
         if task['status']['state'] != 'submitted':
-            raise ValueError(
-                f'Task {params["id"]} has already been processed (state: {task["status"]["state"]})'
-            )
+            raise ValueError(f'Task {params["id"]} has already been processed (state: {task["status"]["state"]})')
 
         await self.storage.update_task(task['id'], state='working')
 

From 535b5d2d112917aed8540f0d8c6c3f051422e4f2 Mon Sep 17 00:00:00 2001
From: David Sanchez <64162682+dsfaccini@users.noreply.github.com>
Date: Thu, 14 May 2026 13:28:38 -0500
Subject: [PATCH 04/11] Sort imports in _bridge.py (ruff I001)

---
 fasta2a/pydantic_ai/_bridge.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/fasta2a/pydantic_ai/_bridge.py b/fasta2a/pydantic_ai/_bridge.py
index ff21fa8..3e058ed 100644
--- a/fasta2a/pydantic_ai/_bridge.py
+++ b/fasta2a/pydantic_ai/_bridge.py
@@ -35,6 +35,10 @@
         "e.g. `pip install 'fasta2a[pydantic-ai]'`"
     ) from _e
 
+from starlette.middleware import Middleware
+from starlette.routing import Route
+from starlette.types import ExceptionHandler, Lifespan
+
 from fasta2a.applications import FastA2A
 from fasta2a.broker import Broker, InMemoryBroker
 from fasta2a.schema import (
@@ -50,9 +54,6 @@
 )
 from fasta2a.storage import InMemoryStorage, Storage
 from fasta2a.worker import Worker
-from starlette.middleware import Middleware
-from starlette.routing import Route
-from starlette.types import ExceptionHandler, Lifespan
 
 WorkerOutputT = TypeVar('WorkerOutputT')
 

From 2f06d2dce91a91f406e7932c226f192c4e4b3259 Mon Sep 17 00:00:00 2001
From: David Sanchez <64162682+dsfaccini@users.noreply.github.com>
Date: Thu, 14 May 2026 13:31:19 -0500
Subject: [PATCH 05/11] Gate pydantic-ai extra to Python >= 3.10

pydantic-ai-slim requires Python>=3.10 but fasta2a supports >=3.9; without
the marker uv can't compute a universal lockfile (the resolver tries to pull
pydantic-ai-slim for Python 3.9 too). Added the marker on the optional dep
and regenerated uv.lock.
---
 pyproject.toml |   2 +-
 uv.lock        | 270 +++++++++++++++++++++++++++++++++++++++++++++++--
 2 files changed, 262 insertions(+), 10 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 003f822..19ce3c8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -52,7 +52,7 @@ dependencies = [
 
 [project.optional-dependencies]
 logfire = ["logfire>=2.3"]
-pydantic-ai = ["pydantic-ai-slim>=1.92"]
+pydantic-ai = ["pydantic-ai-slim>=1.92; python_version >= '3.10'"]
 
 [dependency-groups]
 dev = [
diff --git a/uv.lock b/uv.lock
index a77fc88..88d0c66 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,11 +1,18 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.9"
 resolution-markers = [
     "python_full_version >= '3.10'",
     "python_full_version < '3.10'",
 ]
 
+[options]
+exclude-newer = "2026-05-11T18:30:53.086364Z"
+exclude-newer-span = "P3D"
+
+[options.exclude-newer-package]
+plaine = "2099-01-01T00:00:00Z"
+
 [[package]]
 name = "annotated-types"
 version = "0.7.0"
@@ -438,7 +445,8 @@ source = { editable = "." }
 dependencies = [
     { name = "eval-type-backport", marker = "python_full_version < '3.10'" },
     { name = "opentelemetry-api" },
-    { name = "pydantic" },
+    { name = "pydantic", version = "2.11.7", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
+    { name = "pydantic", version = "2.13.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
     { name = "starlette" },
 ]
 
@@ -446,6 +454,9 @@ dependencies = [
 logfire = [
     { name = "logfire" },
 ]
+pydantic-ai = [
+    { name = "pydantic-ai-slim", marker = "python_full_version >= '3.10'" },
+]
 
 [package.dev-dependencies]
 dev = [
@@ -470,9 +481,10 @@ requires-dist = [
     { name = "logfire", marker = "extra == 'logfire'", specifier = ">=2.3" },
     { name = "opentelemetry-api", specifier = ">=1.28.0" },
     { name = "pydantic", specifier = ">=2.10" },
+    { name = "pydantic-ai-slim", marker = "python_full_version >= '3.10' and extra == 'pydantic-ai'", specifier = ">=1.92" },
     { name = "starlette", specifier = ">0.29.0" },
 ]
-provides-extras = ["logfire"]
+provides-extras = ["logfire", "pydantic-ai"]
 
 [package.metadata.requires-dev]
 dev = [
@@ -491,6 +503,19 @@ docs = [
     { name = "mkdocstrings-python" },
 ]
 
+[[package]]
+name = "genai-prices"
+version = "0.0.59"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "httpx", marker = "python_full_version >= '3.10'" },
+    { name = "pydantic", version = "2.13.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/cd/c8/b61a028b8d8ee286ffab3f9b9f1c9229087184e7d543cea4e349e11375b0/genai_prices-0.0.59.tar.gz", hash = "sha256:3e1c7dcd9b38163589c8cf4a9bcfd286c52ea57a3becdc062a2cbaa8295b08c4", size = 67406, upload-time = "2026-05-07T12:08:40.475Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/11/f9/4693c127f9fab0a8d39c47c198e378ecafcb043463e6dd73df205eacbc13/genai_prices-0.0.59-py3-none-any.whl", hash = "sha256:88fd8818e6807374e5a5c03f293b574ade5f18a3060622080cdd94a03cf43115", size = 70509, upload-time = "2026-05-07T12:08:39.075Z" },
+]
+
 [[package]]
 name = "ghp-import"
 version = "2.1.0"
@@ -527,6 +552,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/58/c6/5c20af38c2a57c15d87f7f38bee77d63c1d2a3689f74fefaf35915dd12b2/griffe-1.7.3-py3-none-any.whl", hash = "sha256:c6b3ee30c2f0f17f30bcdef5068d6ab7a2a4f1b8bf1a3e74b56fffd21e1c5f75", size = 129303, upload-time = "2025-04-23T11:29:07.145Z" },
 ]
 
+[[package]]
+name = "griffelib"
+version = "2.0.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/9d/82/74f4a3310cdabfbb10da554c3a672847f1ed33c6f61dd472681ce7f1fe67/griffelib-2.0.2.tar.gz", hash = "sha256:3cf20b3bc470e83763ffbf236e0076b1211bac1bc67de13daf494640f2de707e", size = 166461, upload-time = "2026-03-27T11:34:51.091Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/11/8c/c9138d881c79aa0ea9ed83cbd58d5ca75624378b38cee225dcf5c42cc91f/griffelib-2.0.2-py3-none-any.whl", hash = "sha256:925c857658fb1ba40c0772c37acbc2ab650bd794d9c1b9726922e36ea4117ea1", size = 142357, upload-time = "2026-03-27T11:34:46.275Z" },
+]
+
 [[package]]
 name = "h11"
 version = "0.16.0"
@@ -641,6 +675,15 @@ wheels = [
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+[[package]]
+name = "pydantic-graph"
+version = "1.93.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "httpx", marker = "python_full_version >= '3.10'" },
+    { name = "logfire-api", marker = "python_full_version >= '3.10'" },
+    { name = "pydantic", version = "2.13.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
+    { name = "typing-inspection", version = "0.4.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/63/40/4addcd3c9d06fbdf6c0776026d8a5b87e7ebb17c9896ac2452e714bb17c6/pydantic_graph-1.93.0.tar.gz", hash = "sha256:17effd900200aa7b72ec0509a79f36d3e161c2a6ef02dda6285a381e867ab195", size = 59250, upload-time = "2026-05-09T00:23:28.081Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2b/bc/5f9eb611c6b9315fb0c6ae58bb82a63d9d9f17340b6c8778319261593fbb/pydantic_graph-1.93.0-py3-none-any.whl", hash = "sha256:ef1b0dcd55a6b5a3544de53a9594216ee8f51ac26bf4be79f7ef310747be598a", size = 73066, upload-time = "2026-05-09T00:23:20.847Z" },
+]
+
 [[package]]
 name = "pygments"
 version = "2.19.2"
@@ -1591,14 +1825,32 @@ wheels = [
 name = "typing-inspection"
 version = "0.4.1"
 source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version < '3.10'",
+]
 dependencies = [
-    { name = "typing-extensions" },
+    { name = "typing-extensions", marker = "python_full_version < '3.10'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/f8/b1/0c11f5058406b3af7609f121aaa6b609744687f1d158b3c3a5bf4cc94238/typing_inspection-0.4.1.tar.gz", hash = "sha256:6ae134cc0203c33377d43188d4064e9b357dba58cff3185f22924610e70a9d28", size = 75726, upload-time = "2025-05-21T18:55:23.885Z" }
 wheels = [
     { url = "https://files.pythonhosted.org/packages/17/69/cd203477f944c353c31bade965f880aa1061fd6bf05ded0726ca845b6ff7/typing_inspection-0.4.1-py3-none-any.whl", hash = "sha256:389055682238f53b04f7badcb49b989835495a96700ced5dab2d8feae4b26f51", size = 14552, upload-time = "2025-05-21T18:55:22.152Z" },
 ]
 
+[[package]]
+name = "typing-inspection"
+version = "0.4.2"
+source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version >= '3.10'",
+]
+dependencies = [
+    { name = "typing-extensions", marker = "python_full_version >= '3.10'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/55/e3/70399cb7dd41c10ac53367ae42139cf4b1ca5f36bb3dc6c9d33acdb43655/typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464", size = 75949, upload-time = "2025-10-01T02:14:41.687Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/dc/9b/47798a6c91d8bdb567fe2698fe81e0c6b7cb7ef4d13da4114b41d239f65d/typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7", size = 14611, upload-time = "2025-10-01T02:14:40.154Z" },
+]
+
 [[package]]
 name = "urllib3"
 version = "2.5.0"

From 989b68532a4401101ef9179aa03becb8b8c4c509 Mon Sep 17 00:00:00 2001
From: David Sanchez <64162682+dsfaccini@users.noreply.github.com>
Date: Thu, 14 May 2026 13:35:48 -0500
Subject: [PATCH 06/11] Use 'Pydantic AI' wording + gpt-5.5; relax pyright on
 bridge

- README: rename 'Using PydanticAI' section + body to 'Pydantic AI' (two
  words), per Pydantic team's standard wording
- Bump example model openai:gpt-4.1 / gpt-4o -> gpt-5.5 in both README and
  bridge docstring
- pyproject.toml: add executionEnvironments override setting typeCheckingMode
  to 'basic' for the fasta2a/pydantic_ai directory. The bridge is a
  near-verbatim port of pydantic_ai._a2a and inherits its looser typing
  posture (118 strict-mode pyright errors in a 1:1 port). Can be tightened
  in a follow-up.
---
 README.md                       | 12 ++++++------
 fasta2a/pydantic_ai/__init__.py |  2 +-
 pyproject.toml                  |  6 ++++++
 3 files changed, 13 insertions(+), 7 deletions(-)

diff --git a/README.md b/README.md
index 7271da1..19f1181 100644
--- a/README.md
+++ b/README.md
@@ -7,7 +7,7 @@
 [![license](https://img.shields.io/github/license/pydantic/pydantic-ai.svg)](https://github.com/pydantic/pydantic-ai/blob/main/LICENSE)
 
 **FastA2A** is an agentic framework agnostic implementation of the A2A protocol in Python.
-The library is designed to be used with any agentic framework, and is not exclusive to PydanticAI.
+The library is designed to be used with any agentic framework, and is not exclusive to Pydantic AI.
 
 ![Interactive Chat](./assets/docs.png)
 
@@ -99,9 +99,9 @@ app = FastA2A(storage=storage, broker=broker, lifespan=lifespan)
 
 _You can run this example as is with `uvicorn main:app --reload`._
 
-### Using PydanticAI
+### Using Pydantic AI
 
-Initially, this **FastA2A** lived under **PydanticAI** repository, but since we received community feedback,
+Initially, this **FastA2A** lived under **Pydantic AI** repository, but since we received community feedback,
 we've decided to move it to a separate repository.
 
 > [!NOTE]
@@ -119,7 +119,7 @@ Then turn any `pydantic_ai.Agent` into an A2A-compatible ASGI app:
 from pydantic_ai import Agent
 from fasta2a.pydantic_ai import agent_to_a2a
 
-agent = Agent('openai:gpt-4.1')
+agent = Agent('openai:gpt-5.5')
 app = agent_to_a2a(agent)
 ```
 
@@ -128,8 +128,8 @@ _You can run this example as is with `uvicorn main:app --reload`._
 As you see, it's pretty easy from the point of view of the developer using your agentic framework.
 
 > [!NOTE]
-> In PydanticAI 1.x, `Agent.to_a2a()` continues to work but emits a deprecation
-> warning pointing here. It will be removed in PydanticAI v2.
+> In Pydantic AI 1.x, `Agent.to_a2a()` continues to work but emits a deprecation
+> warning pointing here. It will be removed in Pydantic AI v2.
 
 ## Design
 
diff --git a/fasta2a/pydantic_ai/__init__.py b/fasta2a/pydantic_ai/__init__.py
index e990e16..072da09 100644
--- a/fasta2a/pydantic_ai/__init__.py
+++ b/fasta2a/pydantic_ai/__init__.py
@@ -10,7 +10,7 @@
     from pydantic_ai import Agent
     from fasta2a.pydantic_ai import agent_to_a2a
 
-    agent = Agent('openai:gpt-4o')
+    agent = Agent('openai:gpt-5.5')
     app = agent_to_a2a(agent, name='my-agent', url='http://localhost:8000')
 
 Install the integration with the `pydantic-ai` extra:
diff --git a/pyproject.toml b/pyproject.toml
index 19ce3c8..3641533 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -121,3 +121,9 @@ include = [
     "fasta2a",
     "tests",
 ]
+
+# The pydantic-ai bridge is a near-verbatim port of `pydantic_ai._a2a` and
+# inherits its (looser) typing posture; tighten in a follow-up if desired.
+[[tool.pyright.executionEnvironments]]
+root = "fasta2a/pydantic_ai"
+typeCheckingMode = "basic"

From ffb66dcc9f6ca08cbeeafa84c3e9b102e98a2937 Mon Sep 17 00:00:00 2001
From: David Sanchez <64162682+dsfaccini@users.noreply.github.com>
Date: Thu, 14 May 2026 13:44:11 -0500
Subject: [PATCH 07/11] Revert pyright executionEnvironments override
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

The strict-mode 'errors' on the bridge aren't a typing posture mismatch — they
are real bugs: the verbatim port is written against fasta2a's pre-A2A-v1-spec
schema (discriminated-union Part with kind/file fields), but upstream
collapsed Part into a single flat TypedDict in PRs #46/#47. Pyright is
correctly catching that the imported TextPart/DataPart symbols don't exist
and that part['kind']/part['file'] don't either. Fix is to rewrite the
part-conversion methods, not to silence pyright.
---
 pyproject.toml | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 3641533..19ce3c8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -121,9 +121,3 @@ include = [
     "fasta2a",
     "tests",
 ]
-
-# The pydantic-ai bridge is a near-verbatim port of `pydantic_ai._a2a` and
-# inherits its (looser) typing posture; tighten in a follow-up if desired.
-[[tool.pyright.executionEnvironments]]
-root = "fasta2a/pydantic_ai"
-typeCheckingMode = "basic"

From a6cbd84e0dd6ff7f4c57f19bb694b8ca537800bd Mon Sep 17 00:00:00 2001
From: David SF <64162682+dsfaccini@users.noreply.github.com>
Date: Fri, 15 May 2026 15:07:36 -0500
Subject: [PATCH 08/11] Backport pydantic-ai bridge submodule onto 0.6 line
 (#56)

Adds the `fasta2a.pydantic_ai` bridge (`agent_to_a2a`, `AgentWorker`, `worker_lifespan`) on top of the pre-A2A-v1-spec line so we can cut a 0.6.1 maintenance release giving Pydantic AI 1.x users a migration target for `Agent.to_a2a()` before the v2 cut. Companion: pydantic/pydantic-ai#5426.
---
 README.md                       |  23 ++-
 fasta2a/pydantic_ai/__init__.py |  23 +++
 fasta2a/pydantic_ai/_bridge.py  | 242 +++++++++++++++++++++++++++++
 pyproject.toml                  |   1 +
 uv.lock                         | 263 ++++++++++++++++++++++++++++++--
 5 files changed, 536 insertions(+), 16 deletions(-)
 create mode 100644 fasta2a/pydantic_ai/__init__.py
 create mode 100644 fasta2a/pydantic_ai/_bridge.py

diff --git a/README.md b/README.md
index 8fc7dd9..19f1181 100644
--- a/README.md
+++ b/README.md
@@ -7,7 +7,7 @@
 [![license](https://img.shields.io/github/license/pydantic/pydantic-ai.svg)](https://github.com/pydantic/pydantic-ai/blob/main/LICENSE)
 
 **FastA2A** is an agentic framework agnostic implementation of the A2A protocol in Python.
-The library is designed to be used with any agentic framework, and is not exclusive to PydanticAI.
+The library is designed to be used with any agentic framework, and is not exclusive to Pydantic AI.
 
 ![Interactive Chat](./assets/docs.png)
 
@@ -99,29 +99,38 @@ app = FastA2A(storage=storage, broker=broker, lifespan=lifespan)
 
 _You can run this example as is with `uvicorn main:app --reload`._
 
-### Using PydanticAI
+### Using Pydantic AI
 
-Initially, this **FastA2A** lived under **PydanticAI** repository, but since we received community feedback,
+Initially, this **FastA2A** lived under **Pydantic AI** repository, but since we received community feedback,
 we've decided to move it to a separate repository.
 
 > [!NOTE]
 > Other agentic frameworks are welcome to implement the `Worker` component, and we'll be happy add the reference here.
 
-For reference, you can [check the PydanticAI implementation of the `Worker`](https://github.com/pydantic/pydantic-ai/blob/3ef42ed9a1a2c799bb94a5a69c80aa9e8968ca72/pydantic_ai_slim/pydantic_ai/_a2a.py#L115-L304).
+Install the integration with the `pydantic-ai` extra:
 
-Let's see how to use it in practice:
+```bash
+pip install 'fasta2a[pydantic-ai]'  # or `uv add 'fasta2a[pydantic-ai]'`
+```
+
+Then turn any `pydantic_ai.Agent` into an A2A-compatible ASGI app:
 
 ```python
 from pydantic_ai import Agent
+from fasta2a.pydantic_ai import agent_to_a2a
 
-agent = Agent('openai:gpt-4.1')
-app = agent.to_a2a()
+agent = Agent('openai:gpt-5.5')
+app = agent_to_a2a(agent)
 ```
 
 _You can run this example as is with `uvicorn main:app --reload`._
 
 As you see, it's pretty easy from the point of view of the developer using your agentic framework.
 
+> [!NOTE]
+> In Pydantic AI 1.x, `Agent.to_a2a()` continues to work but emits a deprecation
+> warning pointing here. It will be removed in Pydantic AI v2.
+
 ## Design
 
 **FastA2A** is built on top of [Starlette](https://www.starlette.io/), which means it's fully compatible
diff --git a/fasta2a/pydantic_ai/__init__.py b/fasta2a/pydantic_ai/__init__.py
new file mode 100644
index 0000000..072da09
--- /dev/null
+++ b/fasta2a/pydantic_ai/__init__.py
@@ -0,0 +1,23 @@
+"""Pydantic AI bridge for FastA2A.
+
+Port of `pydantic_ai._a2a` into the FastA2A package, so users can keep using
+`Agent.to_a2a()` after the corresponding wrapper is removed from pydantic-ai
+in v2. See https://github.com/pydantic/pydantic-ai for the upstream
+deprecation context.
+
+Usage:
+
+    from pydantic_ai import Agent
+    from fasta2a.pydantic_ai import agent_to_a2a
+
+    agent = Agent('openai:gpt-5.5')
+    app = agent_to_a2a(agent, name='my-agent', url='http://localhost:8000')
+
+Install the integration with the `pydantic-ai` extra:
+
+    pip install 'fasta2a[pydantic-ai]'
+"""
+
+from ._bridge import AgentWorker, agent_to_a2a, worker_lifespan
+
+__all__ = ['AgentWorker', 'agent_to_a2a', 'worker_lifespan']
diff --git a/fasta2a/pydantic_ai/_bridge.py b/fasta2a/pydantic_ai/_bridge.py
new file mode 100644
index 0000000..ff5b115
--- /dev/null
+++ b/fasta2a/pydantic_ai/_bridge.py
@@ -0,0 +1,242 @@
+from __future__ import annotations
+
+import base64
+import uuid
+from collections.abc import AsyncGenerator, Sequence
+from contextlib import asynccontextmanager
+from dataclasses import dataclass
+from functools import partial
+from typing import Any, Generic, TypeVar
+
+from pydantic import TypeAdapter
+from typing_extensions import assert_never
+
+try:
+    from pydantic_ai import (
+        AudioUrl,
+        BinaryContent,
+        DocumentUrl,
+        ImageUrl,
+        ModelMessage,
+        ModelRequest,
+        ModelRequestPart,
+        ModelResponse,
+        ModelResponsePart,
+        TextPart,
+        ThinkingPart,
+        ToolCallPart,
+        UserPromptPart,
+        VideoUrl,
+    )
+    from pydantic_ai._run_context import AgentDepsT
+    from pydantic_ai.agent import AbstractAgent
+    from pydantic_ai.output import OutputDataT
+except ImportError as _e:
+    raise ImportError(
+        'Please install the `pydantic-ai` package to use `fasta2a.pydantic_ai`, '
+        "e.g. `pip install 'fasta2a[pydantic-ai]'`"
+    ) from _e
+
+from starlette.middleware import Middleware
+from starlette.routing import Route
+from starlette.types import ExceptionHandler, Lifespan
+
+from fasta2a.applications import FastA2A
+from fasta2a.broker import Broker, InMemoryBroker
+from fasta2a.schema import (
+    AgentProvider,
+    Artifact,
+    DataPart,
+    Message,
+    Part,
+    Skill,
+    TaskIdParams,
+    TaskSendParams,
+    TextPart as A2ATextPart,
+)
+from fasta2a.storage import InMemoryStorage, Storage
+from fasta2a.worker import Worker
+
+WorkerOutputT = TypeVar('WorkerOutputT')
+
+
+@asynccontextmanager
+async def worker_lifespan(
+    app: FastA2A, worker: Worker, agent: AbstractAgent[AgentDepsT, OutputDataT]
+) -> AsyncGenerator[None]:
+    """Lifespan that runs the worker during application startup."""
+    async with app.task_manager, agent:
+        async with worker.run():
+            yield
+
+
+def agent_to_a2a(
+    agent: AbstractAgent[AgentDepsT, OutputDataT],
+    *,
+    storage: Storage | None = None,
+    broker: Broker | None = None,
+    name: str | None = None,
+    url: str = 'http://localhost:8000',
+    version: str = '1.0.0',
+    description: str | None = None,
+    provider: AgentProvider | None = None,
+    skills: list[Skill] | None = None,
+    debug: bool = False,
+    routes: Sequence[Route] | None = None,
+    middleware: Sequence[Middleware] | None = None,
+    exception_handlers: dict[Any, ExceptionHandler] | None = None,
+    lifespan: Lifespan[FastA2A] | None = None,
+) -> FastA2A:
+    """Create a FastA2A server from a pydantic-ai agent."""
+    storage = storage or InMemoryStorage()
+    broker = broker or InMemoryBroker()
+    worker = AgentWorker(agent=agent, broker=broker, storage=storage)
+
+    lifespan = lifespan or partial(worker_lifespan, worker=worker, agent=agent)
+
+    return FastA2A(
+        storage=storage,
+        broker=broker,
+        name=name or agent.name,
+        url=url,
+        version=version,
+        description=description,
+        provider=provider,
+        skills=skills,
+        debug=debug,
+        routes=routes,
+        middleware=middleware,
+        exception_handlers=exception_handlers,
+        lifespan=lifespan,
+    )
+
+
+@dataclass
+class AgentWorker(Worker[list[ModelMessage]], Generic[WorkerOutputT, AgentDepsT]):
+    """A worker that uses a pydantic-ai agent to execute tasks."""
+
+    agent: AbstractAgent[AgentDepsT, WorkerOutputT]
+
+    async def run_task(self, params: TaskSendParams) -> None:
+        task = await self.storage.load_task(params['id'])
+        if task is None:
+            raise ValueError(f'Task {params["id"]} not found')
+
+        if task['status']['state'] != 'submitted':
+            raise ValueError(f'Task {params["id"]} has already been processed (state: {task["status"]["state"]})')
+
+        await self.storage.update_task(task['id'], state='working')
+
+        message_history = await self.storage.load_context(task['context_id']) or []
+        message_history.extend(self.build_message_history(task.get('history', [])))
+
+        try:
+            result = await self.agent.run(message_history=message_history)  # type: ignore
+
+            await self.storage.update_context(task['context_id'], result.all_messages())
+
+            a2a_messages: list[Message] = []
+            for message in result.new_messages():
+                if isinstance(message, ModelRequest):
+                    continue
+                a2a_parts = self._response_parts_to_a2a(message.parts)
+                if a2a_parts:
+                    a2a_messages.append(
+                        Message(role='agent', parts=a2a_parts, kind='message', message_id=str(uuid.uuid4()))
+                    )
+
+            artifacts = self.build_artifacts(result.output)
+        except Exception:
+            await self.storage.update_task(task['id'], state='failed')
+            raise
+        else:
+            await self.storage.update_task(
+                task['id'], state='completed', new_artifacts=artifacts, new_messages=a2a_messages
+            )
+
+    async def cancel_task(self, params: TaskIdParams) -> None:
+        pass
+
+    def build_artifacts(self, result: WorkerOutputT) -> list[Artifact]:
+        artifact_id = str(uuid.uuid4())
+        part = self._convert_result_to_part(result)
+        return [Artifact(artifact_id=artifact_id, name='result', parts=[part])]
+
+    def _convert_result_to_part(self, result: WorkerOutputT) -> Part:
+        if isinstance(result, str):
+            return A2ATextPart(kind='text', text=result)
+        output_type = type(result)
+        type_adapter = TypeAdapter(output_type)
+        data = type_adapter.dump_python(result, mode='json')
+        json_schema = type_adapter.json_schema(mode='serialization')
+        return DataPart(kind='data', data={'result': data}, metadata={'json_schema': json_schema})
+
+    def build_message_history(self, history: list[Message]) -> list[ModelMessage]:
+        model_messages: list[ModelMessage] = []
+        for message in history:
+            if message['role'] == 'user':
+                model_messages.append(ModelRequest(parts=self._request_parts_from_a2a(message['parts'])))
+            else:
+                model_messages.append(ModelResponse(parts=self._response_parts_from_a2a(message['parts'])))
+        return model_messages
+
+    def _request_parts_from_a2a(self, parts: list[Part]) -> list[ModelRequestPart]:
+        model_parts: list[ModelRequestPart] = []
+        for part in parts:
+            if part['kind'] == 'text':
+                model_parts.append(UserPromptPart(content=part['text']))
+            elif part['kind'] == 'file':
+                file_content = part['file']
+                if 'bytes' in file_content:
+                    data = base64.b64decode(file_content['bytes'])
+                    mime_type = file_content.get('mime_type', 'application/octet-stream')
+                    content = BinaryContent(data=data, media_type=mime_type)
+                    model_parts.append(UserPromptPart(content=[content]))
+                else:
+                    url = file_content['uri']
+                    for url_cls in (DocumentUrl, AudioUrl, ImageUrl, VideoUrl):
+                        content = url_cls(url=url)
+                        try:
+                            content.media_type
+                        except ValueError:
+                            continue
+                        else:
+                            break
+                    else:
+                        raise ValueError(f'Unsupported file type: {url}')
+                    model_parts.append(UserPromptPart(content=[content]))
+            elif part['kind'] == 'data':
+                raise NotImplementedError('Data parts are not supported yet.')
+            else:
+                assert_never(part)
+        return model_parts
+
+    def _response_parts_from_a2a(self, parts: list[Part]) -> list[ModelResponsePart]:
+        model_parts: list[ModelResponsePart] = []
+        for part in parts:
+            if part['kind'] == 'text':
+                model_parts.append(TextPart(content=part['text']))
+            elif part['kind'] == 'file':
+                raise NotImplementedError('File parts are not supported yet.')
+            elif part['kind'] == 'data':
+                raise NotImplementedError('Data parts are not supported yet.')
+            else:
+                assert_never(part)
+        return model_parts
+
+    def _response_parts_to_a2a(self, parts: Sequence[ModelResponsePart]) -> list[Part]:
+        a2a_parts: list[Part] = []
+        for part in parts:
+            if isinstance(part, TextPart):
+                a2a_parts.append(A2ATextPart(kind='text', text=part.content))
+            elif isinstance(part, ThinkingPart):
+                a2a_parts.append(
+                    A2ATextPart(
+                        kind='text',
+                        text=part.content,
+                        metadata={'type': 'thinking', 'thinking_id': part.id, 'signature': part.signature},
+                    )
+                )
+            elif isinstance(part, ToolCallPart):
+                pass
+        return a2a_parts
diff --git a/pyproject.toml b/pyproject.toml
index 2af3b7d..19ce3c8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -52,6 +52,7 @@ dependencies = [
 
 [project.optional-dependencies]
 logfire = ["logfire>=2.3"]
+pydantic-ai = ["pydantic-ai-slim>=1.92; python_version >= '3.10'"]
 
 [dependency-groups]
 dev = [
diff --git a/uv.lock b/uv.lock
index a77fc88..0557720 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.9"
 resolution-markers = [
     "python_full_version >= '3.10'",
@@ -438,7 +438,8 @@ source = { editable = "." }
 dependencies = [
     { name = "eval-type-backport", marker = "python_full_version < '3.10'" },
     { name = "opentelemetry-api" },
-    { name = "pydantic" },
+    { name = "pydantic", version = "2.11.7", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
+    { name = "pydantic", version = "2.13.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
     { name = "starlette" },
 ]
 
@@ -446,6 +447,9 @@ dependencies = [
 logfire = [
     { name = "logfire" },
 ]
+pydantic-ai = [
+    { name = "pydantic-ai-slim", marker = "python_full_version >= '3.10'" },
+]
 
 [package.dev-dependencies]
 dev = [
@@ -470,9 +474,10 @@ requires-dist = [
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     { name = "opentelemetry-api", specifier = ">=1.28.0" },
     { name = "pydantic", specifier = ">=2.10" },
+    { name = "pydantic-ai-slim", marker = "python_full_version >= '3.10' and extra == 'pydantic-ai'", specifier = ">=1.92" },
     { name = "starlette", specifier = ">0.29.0" },
 ]
-provides-extras = ["logfire"]
+provides-extras = ["logfire", "pydantic-ai"]
 
 [package.metadata.requires-dev]
 dev = [
@@ -491,6 +496,19 @@ docs = [
     { name = "mkdocstrings-python" },
 ]
 
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+resolution-markers = [
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+version = "1.93.0"
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+dependencies = [
+    { name = "httpx", marker = "python_full_version >= '3.10'" },
+    { name = "logfire-api", marker = "python_full_version >= '3.10'" },
+    { name = "pydantic", version = "2.13.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
+    { name = "typing-inspection", version = "0.4.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
+]
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+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2b/bc/5f9eb611c6b9315fb0c6ae58bb82a63d9d9f17340b6c8778319261593fbb/pydantic_graph-1.93.0-py3-none-any.whl", hash = "sha256:ef1b0dcd55a6b5a3544de53a9594216ee8f51ac26bf4be79f7ef310747be598a", size = 73066, upload-time = "2026-05-09T00:23:20.847Z" },
+]
+
 [[package]]
 name = "pygments"
 version = "2.19.2"
@@ -1591,14 +1818,32 @@ wheels = [
 name = "typing-inspection"
 version = "0.4.1"
 source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version < '3.10'",
+]
 dependencies = [
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+    { name = "typing-extensions", marker = "python_full_version < '3.10'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/f8/b1/0c11f5058406b3af7609f121aaa6b609744687f1d158b3c3a5bf4cc94238/typing_inspection-0.4.1.tar.gz", hash = "sha256:6ae134cc0203c33377d43188d4064e9b357dba58cff3185f22924610e70a9d28", size = 75726, upload-time = "2025-05-21T18:55:23.885Z" }
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 ]
 
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+name = "typing-inspection"
+version = "0.4.2"
+source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version >= '3.10'",
+]
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From 48d9e1a695d980f5c8f6cd75d13de4692c077758 Mon Sep 17 00:00:00 2001
From: David SF <64162682+dsfaccini@users.noreply.github.com>
Date: Thu, 21 May 2026 15:06:27 -0500
Subject: [PATCH 09/11] Port pydantic-ai's A2A integration tests + wire CI to
 run them (#57)

Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
---
 .github/workflows/main.yml |   36 +-
 pyproject.toml             |    2 +
 tests/assets/kiwi.jpg      |  Bin 0 -> 98572 bytes
 tests/test_pydantic_ai.py  | 1060 ++++++++++++++++++++++++++++++++++++
 uv.lock                    |   13 +
 5 files changed, 1104 insertions(+), 7 deletions(-)
 create mode 100644 tests/assets/kiwi.jpg
 create mode 100644 tests/test_pydantic_ai.py

diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index c1e7580..7709de4 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -2,11 +2,32 @@ name: CI
 
 on:
   push:
-    branches: ["main"]
+    branches: ["main", "release/**"]
   pull_request:
-    branches: ["main"]
+    branches: ["main", "release/**"]
 
 jobs:
+  # `scripts/check` (ruff, pyright, check-sdist, uv lock) runs once on a fixed
+  # interpreter. pyright must see `pydantic-ai-slim` to resolve the bridge's
+  # `pydantic_ai` imports, and that extra is marker-gated to Python 3.10+, so
+  # this job pins 3.12 — running it across the test matrix would fail on 3.9.
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v4
+        with:
+          python-version: "3.12"
+          enable-cache: true
+
+      - name: Install dependencies
+        run: uv sync --frozen --extra pydantic-ai
+
+      - name: Run linters
+        run: scripts/check
+
   test:
     runs-on: ubuntu-latest
     strategy:
@@ -21,11 +42,12 @@ jobs:
           python-version: ${{ matrix.python-version }}
           enable-cache: true
 
+      # `[pydantic-ai]` is the optional extra that pulls `pydantic-ai-slim` for
+      # the `fasta2a.pydantic_ai` bridge tests. Marker-gated to Python 3.10+, so
+      # on 3.9 it resolves to nothing and the bridge tests skip themselves via
+      # `pytest.importorskip('pydantic_ai')` in `tests/test_pydantic_ai.py`.
       - name: Install dependencies
-        run: uv sync --frozen
-
-      - name: Run linters
-        run: scripts/check
+        run: uv sync --frozen --extra pydantic-ai
 
       - name: Run tests
         run: scripts/test
@@ -33,7 +55,7 @@ jobs:
   # https://github.com/marketplace/actions/alls-green#why used for branch protection checks
   check:
     if: always()
-    needs: [test]
+    needs: [lint, test]
     runs-on: ubuntu-latest
     steps:
       - name: Decide whether the needed jobs succeeded or failed
diff --git a/pyproject.toml b/pyproject.toml
index 19ce3c8..c241662 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -56,8 +56,10 @@ pydantic-ai = ["pydantic-ai-slim>=1.92; python_version >= '3.10'"]
 
 [dependency-groups]
 dev = [
+    "anyio",
     "asgi-lifespan",
     "coverage",
+    "dirty-equals",
     "httpx",
     "inline-snapshot",
     "pytest",
diff --git a/tests/assets/kiwi.jpg b/tests/assets/kiwi.jpg
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diff --git a/tests/test_pydantic_ai.py b/tests/test_pydantic_ai.py
new file mode 100644
index 0000000..d8df329
--- /dev/null
+++ b/tests/test_pydantic_ai.py
@@ -0,0 +1,1060 @@
+"""Integration tests for the Pydantic AI bridge submodule (`fasta2a.pydantic_ai`).
+
+Ported from `tests/test_a2a.py` in the [pydantic-ai
+repo](https://github.com/pydantic/pydantic-ai), where these tests originally exercised
+`Agent.to_a2a()`. With the bridge code now hosted upstream here, this file owns the
+A2A integration test surface for the Pydantic AI workflow.
+
+Skipped on Python < 3.10 because `pydantic-ai-slim` requires 3.10+.
+"""
+
+# `fasta2a.pydantic_ai._bridge` raises ImportError when pydantic-ai is missing, so pyright sees an
+# Unknown branch on every call from this file. Silencing two Unknown-related rules at the file level
+# rather than scattering per-line ignores across 11 test bodies.
+# pyright: reportUnknownVariableType=false, reportUnknownArgumentType=false
+
+from __future__ import annotations as _annotations
+
+import base64
+import sys
+import uuid
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import TYPE_CHECKING, Any
+
+import anyio
+import httpx
+import pytest
+from asgi_lifespan import LifespanManager
+from inline_snapshot import snapshot
+
+pytest.importorskip('pydantic_ai', reason='pydantic-ai-slim required (Python 3.10+)')
+
+# `dirty_equals` matchers (`IsStr`, `IsDatetime`, `IsNow`) report `__eq__`-based equivalence but pyright
+# can't see they're meant to substitute for `str`/`datetime` in equality contexts. Mirror the stubs trick
+# used in `pydantic-ai/tests/conftest.py` to satisfy strict typecheck.
+if TYPE_CHECKING:
+
+    def IsStr(*args: Any, **kwargs: Any) -> str: ...
+    def IsDatetime(*args: Any, **kwargs: Any) -> datetime: ...
+    def IsNow(*args: Any, **kwargs: Any) -> datetime: ...
+else:
+    from dirty_equals import IsDatetime, IsNow, IsStr
+
+from pydantic import BaseModel
+from pydantic_ai import (
+    Agent,
+    BinaryContent,
+    ModelMessage,
+    ModelRequest,
+    ModelResponse,
+    TextPart as PydanticAITextPart,
+    ThinkingPart,
+    ToolCallPart,
+    ToolReturnPart,
+    UserPromptPart,
+)
+from pydantic_ai.models.function import AgentInfo, FunctionModel
+from pydantic_ai.usage import RequestUsage
+
+from fasta2a.applications import FastA2A
+from fasta2a.client import A2AClient
+from fasta2a.pydantic_ai import agent_to_a2a
+from fasta2a.schema import DataPart, FilePart, Message, TextPart
+from fasta2a.storage import InMemoryStorage
+
+pytestmark = [
+    pytest.mark.skipif(sys.version_info < (3, 10), reason='pydantic-ai-slim requires 3.10+'),
+    pytest.mark.anyio,
+]
+
+
+@pytest.fixture(scope='session')
+def image_content() -> BinaryContent:
+    return BinaryContent(
+        data=(Path(__file__).parent / 'assets' / 'kiwi.jpg').read_bytes(),
+        media_type='image/jpeg',
+    )
+
+
+def return_string(_: list[ModelMessage], info: AgentInfo) -> ModelResponse:
+    assert info.output_tools is not None
+    args_json = '{"response": ["foo", "bar"]}'
+    return ModelResponse(parts=[ToolCallPart(info.output_tools[0].name, args_json)])
+
+
+model = FunctionModel(return_string)
+
+
+class UserProfile(BaseModel):
+    name: str
+    age: int
+    email: str
+
+
+def return_pydantic_model(_: list[ModelMessage], info: AgentInfo) -> ModelResponse:
+    assert info.output_tools is not None
+    args_json = '{"name": "John Doe", "age": 30, "email": "john@example.com"}'
+    return ModelResponse(parts=[ToolCallPart(info.output_tools[0].name, args_json)])
+
+
+pydantic_model = FunctionModel(return_pydantic_model)
+
+
+async def test_pydantic_model_output():
+    """Pydantic model outputs carry metadata including JSON schema."""
+    agent = Agent(model=pydantic_model, output_type=UserProfile)
+    app: FastA2A = agent_to_a2a(agent)
+
+    async with LifespanManager(app):
+        transport = httpx.ASGITransport(app)
+        async with httpx.AsyncClient(transport=transport) as http_client:
+            a2a_client = A2AClient(http_client=http_client)
+
+            message = Message(
+                role='user',
+                parts=[TextPart(text='Get user profile', kind='text')],
+                kind='message',
+                message_id=str(uuid.uuid4()),
+            )
+            response = await a2a_client.send_message(message=message)
+            assert 'error' not in response
+            assert 'result' in response
+            result = response['result']
+            assert result['kind'] == 'task'
+
+            task_id = result['id']
+
+            while task := await a2a_client.get_task(task_id):  # pragma: no branch
+                if 'result' in task and task['result']['status']['state'] == 'completed':
+                    result = task['result']
+                    break
+                await anyio.sleep(0.1)
+
+            assert 'artifacts' in result
+            assert len(result['artifacts']) == 1
+            artifact = result['artifacts'][0]
+
+            assert artifact['parts'][0]['kind'] == 'data'
+            assert artifact['parts'][0]['data'] == {
+                'result': {'name': 'John Doe', 'age': 30, 'email': 'john@example.com'}
+            }
+
+            metadata = artifact['parts'][0].get('metadata')
+            assert metadata is not None
+
+            assert metadata['json_schema'] == snapshot(
+                {
+                    'properties': {
+                        'name': {'title': 'Name', 'type': 'string'},
+                        'age': {'title': 'Age', 'type': 'integer'},
+                        'email': {'title': 'Email', 'type': 'string'},
+                    },
+                    'required': ['name', 'age', 'email'],
+                    'title': 'UserProfile',
+                    'type': 'object',
+                }
+            )
+
+            assert result.get('history') == snapshot(
+                [
+                    {
+                        'role': 'user',
+                        'parts': [{'kind': 'text', 'text': 'Get user profile'}],
+                        'kind': 'message',
+                        'message_id': IsStr(),
+                        'context_id': IsStr(),
+                        'task_id': IsStr(),
+                    }
+                ]
+            )
+
+
+async def test_runtime_error_without_lifespan():
+    agent = Agent(model=model, output_type=tuple[str, str])
+    app: FastA2A = agent_to_a2a(agent)
+
+    transport = httpx.ASGITransport(app)
+    async with httpx.AsyncClient(transport=transport) as http_client:
+        a2a_client = A2AClient(http_client=http_client)
+
+        message = Message(
+            role='user',
+            parts=[TextPart(text='Hello, world!', kind='text')],
+            kind='message',
+            message_id=str(uuid.uuid4()),
+        )
+
+        with pytest.raises(RuntimeError, match='TaskManager was not properly initialized.'):
+            await a2a_client.send_message(message=message)
+
+
+async def test_simple():
+    agent = Agent(model=model, output_type=tuple[str, str])
+    app: FastA2A = agent_to_a2a(agent)
+
+    async with LifespanManager(app):
+        transport = httpx.ASGITransport(app)
+        async with httpx.AsyncClient(transport=transport) as http_client:
+            a2a_client = A2AClient(http_client=http_client)
+
+            message = Message(
+                role='user',
+                parts=[TextPart(text='Hello, world!', kind='text')],
+                kind='message',
+                message_id=str(uuid.uuid4()),
+            )
+            response = await a2a_client.send_message(message=message)
+            assert 'error' not in response
+            assert 'result' in response
+            result = response['result']
+            assert result['kind'] == 'task'
+            assert result == snapshot(
+                {
+                    'id': IsStr(),
+                    'context_id': IsStr(),
+                    'kind': 'task',
+                    'status': {'state': 'submitted', 'timestamp': IsDatetime(iso_string=True)},
+                    'history': [
+                        {
+                            'role': 'user',
+                            'parts': [{'kind': 'text', 'text': 'Hello, world!'}],
+                            'kind': 'message',
+                            'message_id': IsStr(),
+                            'context_id': IsStr(),
+                            'task_id': IsStr(),
+                        }
+                    ],
+                }
+            )
+
+            task_id = result['id']
+
+            while task := await a2a_client.get_task(task_id):  # pragma: no branch
+                if 'result' in task and task['result']['status']['state'] == 'completed':
+                    break
+                await anyio.sleep(0.1)
+
+            assert task == snapshot(
+                {
+                    'jsonrpc': '2.0',
+                    'id': None,
+                    'result': {
+                        'id': IsStr(),
+                        'context_id': IsStr(),
+                        'kind': 'task',
+                        'status': {'state': 'completed', 'timestamp': IsDatetime(iso_string=True)},
+                        'history': [
+                            {
+                                'role': 'user',
+                                'parts': [{'kind': 'text', 'text': 'Hello, world!'}],
+                                'kind': 'message',
+                                'message_id': IsStr(),
+                                'context_id': IsStr(),
+                                'task_id': IsStr(),
+                            }
+                        ],
+                        'artifacts': [
+                            {
+                                'artifact_id': IsStr(),
+                                'name': 'result',
+                                'parts': [
+                                    {
+                                        'metadata': {'json_schema': {'items': {}, 'type': 'array'}},
+                                        'kind': 'data',
+                                        'data': {'result': ['foo', 'bar']},
+                                    }
+                                ],
+                            }
+                        ],
+                    },
+                }
+            )
+
+
+async def test_file_message_with_file():
+    agent = Agent(model=model, output_type=tuple[str, str])
+    app: FastA2A = agent_to_a2a(agent)
+
+    async with LifespanManager(app):
+        transport = httpx.ASGITransport(app)
+        async with httpx.AsyncClient(transport=transport) as http_client:
+            a2a_client = A2AClient(http_client=http_client)
+
+            message = Message(
+                role='user',
+                parts=[
+                    FilePart(
+                        kind='file',
+                        file={'uri': 'https://example.com/file.txt', 'mime_type': 'text/plain'},
+                    )
+                ],
+                kind='message',
+                message_id=str(uuid.uuid4()),
+            )
+            response = await a2a_client.send_message(message=message)
+            assert 'error' not in response
+            assert 'result' in response
+            result = response['result']
+            assert result['kind'] == 'task'
+            assert result == snapshot(
+                {
+                    'id': IsStr(),
+                    'context_id': IsStr(),
+                    'kind': 'task',
+                    'status': {'state': 'submitted', 'timestamp': IsDatetime(iso_string=True)},
+                    'history': [
+                        {
+                            'role': 'user',
+                            'parts': [
+                                {
+                                    'kind': 'file',
+                                    'file': {'mime_type': 'text/plain', 'uri': 'https://example.com/file.txt'},
+                                }
+                            ],
+                            'kind': 'message',
+                            'message_id': IsStr(),
+                            'context_id': IsStr(),
+                            'task_id': IsStr(),
+                        }
+                    ],
+                }
+            )
+
+            task_id = result['id']
+
+            while task := await a2a_client.get_task(task_id):  # pragma: no branch
+                if 'result' in task and task['result']['status']['state'] == 'completed':
+                    break
+                await anyio.sleep(0.1)
+            assert task == snapshot(
+                {
+                    'jsonrpc': '2.0',
+                    'id': None,
+                    'result': {
+                        'id': IsStr(),
+                        'context_id': IsStr(),
+                        'kind': 'task',
+                        'status': {'state': 'completed', 'timestamp': IsDatetime(iso_string=True)},
+                        'history': [
+                            {
+                                'role': 'user',
+                                'parts': [
+                                    {
+                                        'kind': 'file',
+                                        'file': {'mime_type': 'text/plain', 'uri': 'https://example.com/file.txt'},
+                                    }
+                                ],
+                                'kind': 'message',
+                                'message_id': IsStr(),
+                                'context_id': IsStr(),
+                                'task_id': IsStr(),
+                            }
+                        ],
+                        'artifacts': [
+                            {
+                                'artifact_id': IsStr(),
+                                'name': 'result',
+                                'parts': [
+                                    {
+                                        'metadata': {'json_schema': {'items': {}, 'type': 'array'}},
+                                        'kind': 'data',
+                                        'data': {'result': ['foo', 'bar']},
+                                    }
+                                ],
+                            }
+                        ],
+                    },
+                }
+            )
+
+
+async def test_file_message_with_file_content(image_content: BinaryContent):
+    agent = Agent(model=model, output_type=tuple[str, str])
+    app: FastA2A = agent_to_a2a(agent)
+
+    async with LifespanManager(app):
+        transport = httpx.ASGITransport(app)
+        async with httpx.AsyncClient(transport=transport) as http_client:
+            a2a_client = A2AClient(http_client=http_client)
+
+            base64_image = base64.b64encode(image_content.data).decode('utf-8')
+            message = Message(
+                role='user',
+                parts=[
+                    FilePart(file={'bytes': base64_image, 'mime_type': image_content.media_type}, kind='file'),
+                ],
+                kind='message',
+                message_id=str(uuid.uuid4()),
+            )
+            response = await a2a_client.send_message(message=message)
+            assert 'error' not in response
+            assert 'result' in response
+            result = response['result']
+            assert result['kind'] == 'task'
+            assert result == snapshot(
+                {
+                    'id': IsStr(),
+                    'context_id': IsStr(),
+                    'kind': 'task',
+                    'status': {'state': 'submitted', 'timestamp': IsDatetime(iso_string=True)},
+                    'history': [
+                        {
+                            'role': 'user',
+                            'parts': [
+                                {'kind': 'file', 'file': {'bytes': base64_image, 'mime_type': image_content.media_type}}
+                            ],
+                            'kind': 'message',
+                            'message_id': IsStr(),
+                            'context_id': IsStr(),
+                            'task_id': IsStr(),
+                        }
+                    ],
+                }
+            )
+
+            task_id = result['id']
+
+            while task := await a2a_client.get_task(task_id):  # pragma: no branch
+                if 'result' in task and task['result']['status']['state'] == 'completed':
+                    break
+                await anyio.sleep(0.1)
+            assert task == snapshot(
+                {
+                    'jsonrpc': '2.0',
+                    'id': None,
+                    'result': {
+                        'id': IsStr(),
+                        'context_id': IsStr(),
+                        'kind': 'task',
+                        'status': {'state': 'completed', 'timestamp': IsDatetime(iso_string=True)},
+                        'history': [
+                            {
+                                'role': 'user',
+                                'parts': [
+                                    {
+                                        'kind': 'file',
+                                        'file': {'bytes': base64_image, 'mime_type': image_content.media_type},
+                                    }
+                                ],
+                                'kind': 'message',
+                                'message_id': IsStr(),
+                                'context_id': IsStr(),
+                                'task_id': IsStr(),
+                            }
+                        ],
+                        'artifacts': [
+                            {
+                                'artifact_id': IsStr(),
+                                'name': 'result',
+                                'parts': [
+                                    {
+                                        'metadata': {'json_schema': {'items': {}, 'type': 'array'}},
+                                        'kind': 'data',
+                                        'data': {'result': ['foo', 'bar']},
+                                    }
+                                ],
+                            }
+                        ],
+                    },
+                }
+            )
+
+
+async def test_file_message_with_data():
+    agent = Agent(model=model, output_type=tuple[str, str])
+    app: FastA2A = agent_to_a2a(agent)
+
+    async with LifespanManager(app):
+        transport = httpx.ASGITransport(app)
+        async with httpx.AsyncClient(transport=transport) as http_client:
+            a2a_client = A2AClient(http_client=http_client)
+
+            message = Message(
+                role='user',
+                parts=[DataPart(kind='data', data={'foo': 'bar'})],
+                kind='message',
+                message_id=str(uuid.uuid4()),
+            )
+            response = await a2a_client.send_message(message=message)
+            assert 'error' not in response
+            assert 'result' in response
+            result = response['result']
+            assert result['kind'] == 'task'
+            assert result == snapshot(
+                {
+                    'id': IsStr(),
+                    'context_id': IsStr(),
+                    'kind': 'task',
+                    'status': {'state': 'submitted', 'timestamp': IsDatetime(iso_string=True)},
+                    'history': [
+                        {
+                            'role': 'user',
+                            'parts': [{'kind': 'data', 'data': {'foo': 'bar'}}],
+                            'kind': 'message',
+                            'message_id': IsStr(),
+                            'context_id': IsStr(),
+                            'task_id': IsStr(),
+                        }
+                    ],
+                }
+            )
+
+            task_id = result['id']
+
+            while task := await a2a_client.get_task(task_id):  # pragma: no branch
+                if 'result' in task and task['result']['status']['state'] == 'failed':
+                    break
+                await anyio.sleep(0.1)
+            assert task == snapshot(
+                {
+                    'jsonrpc': '2.0',
+                    'id': None,
+                    'result': {
+                        'id': IsStr(),
+                        'context_id': IsStr(),
+                        'kind': 'task',
+                        'status': {'state': 'failed', 'timestamp': IsDatetime(iso_string=True)},
+                        'history': [
+                            {
+                                'role': 'user',
+                                'parts': [{'kind': 'data', 'data': {'foo': 'bar'}}],
+                                'kind': 'message',
+                                'message_id': IsStr(),
+                                'context_id': IsStr(),
+                                'task_id': IsStr(),
+                            }
+                        ],
+                    },
+                }
+            )
+
+
+async def test_error_handling():
+    """Errors during task execution properly update task state to 'failed'."""
+
+    def raise_error(_: list[ModelMessage], info: AgentInfo) -> ModelResponse:
+        raise RuntimeError('Test error during agent execution')
+
+    error_model = FunctionModel(raise_error)
+    agent = Agent(model=error_model, output_type=str)
+    app: FastA2A = agent_to_a2a(agent)
+
+    async with LifespanManager(app):
+        transport = httpx.ASGITransport(app)
+        async with httpx.AsyncClient(transport=transport) as http_client:
+            a2a_client = A2AClient(http_client=http_client)
+
+            message = Message(
+                role='user',
+                parts=[TextPart(text='Hello, world!', kind='text')],
+                kind='message',
+                message_id=str(uuid.uuid4()),
+            )
+            response = await a2a_client.send_message(message=message)
+            assert 'error' not in response
+            assert 'result' in response
+            result = response['result']
+            assert result['kind'] == 'task'
+
+            task_id = result['id']
+
+            max_attempts = 50  # 5 seconds total
+            for _ in range(max_attempts):
+                task = await a2a_client.get_task(task_id)
+                if 'result' in task and task['result']['status']['state'] == 'failed':
+                    break
+                await anyio.sleep(0.1)
+            else:  # pragma: no cover
+                raise AssertionError(f'Task did not fail within {max_attempts * 0.1} seconds')
+
+            assert 'result' in task
+            assert task['result']['status']['state'] == 'failed'
+
+
+async def test_multiple_tasks_same_context():
+    """Multiple tasks share the same context_id with accumulated history."""
+
+    messages_received: list[list[ModelMessage]] = []
+
+    def track_messages(messages: list[ModelMessage], info: AgentInfo) -> ModelResponse:
+        messages_received.append(messages.copy())
+        assert info.output_tools is not None
+        args_json = '{"response": ["foo", "bar"]}'
+        return ModelResponse(parts=[ToolCallPart(info.output_tools[0].name, args_json)])
+
+    tracking_model = FunctionModel(track_messages)
+    agent = Agent(model=tracking_model, output_type=tuple[str, str])
+    app: FastA2A = agent_to_a2a(agent)
+
+    async with LifespanManager(app):
+        transport = httpx.ASGITransport(app)
+        async with httpx.AsyncClient(transport=transport) as http_client:
+            a2a_client = A2AClient(http_client=http_client)
+
+            message1 = Message(
+                role='user',
+                parts=[TextPart(text='First message', kind='text')],
+                kind='message',
+                message_id=str(uuid.uuid4()),
+            )
+            response1 = await a2a_client.send_message(message=message1)
+            assert 'error' not in response1
+            assert 'result' in response1
+            result1 = response1['result']
+            assert result1['kind'] == 'task'
+
+            task1_id = result1['id']
+            context_id = result1['context_id']
+
+            while task1 := await a2a_client.get_task(task1_id):  # pragma: no branch
+                if 'result' in task1 and task1['result']['status']['state'] == 'completed':
+                    result1 = task1['result']
+                    break
+                await anyio.sleep(0.1)
+
+            assert len(messages_received) == 1
+            first_run_history = messages_received[0]
+            assert first_run_history == snapshot(
+                [
+                    ModelRequest(
+                        parts=[UserPromptPart(content='First message', timestamp=IsDatetime())],
+                        timestamp=IsNow(tz=timezone.utc),
+                        run_id=IsStr(),
+                        conversation_id=IsStr(),
+                    )
+                ]
+            )
+
+            message2 = Message(
+                role='user',
+                parts=[TextPart(text='Second message', kind='text')],
+                kind='message',
+                context_id=context_id,
+                message_id=str(uuid.uuid4()),
+            )
+            response2 = await a2a_client.send_message(message=message2)
+            assert 'error' not in response2
+            assert 'result' in response2
+            result2 = response2['result']
+            assert result2['kind'] == 'task'
+
+            task2_id = result2['id']
+            assert task2_id != task1_id
+            assert result2['context_id'] == context_id
+
+            while task2 := await a2a_client.get_task(task2_id):  # pragma: no branch
+                if 'result' in task2 and task2['result']['status']['state'] == 'completed':
+                    break
+                await anyio.sleep(0.1)
+
+            assert len(messages_received) == 2
+            second_run_history = messages_received[1]
+            assert second_run_history[0] == first_run_history[0]
+
+            assert second_run_history == snapshot(
+                [
+                    ModelRequest(
+                        parts=[UserPromptPart(content='First message', timestamp=IsDatetime())],
+                        timestamp=IsNow(tz=timezone.utc),
+                        run_id=IsStr(),
+                        conversation_id=IsStr(),
+                    ),
+                    ModelResponse(
+                        parts=[
+                            ToolCallPart(
+                                tool_name='final_result', args='{"response": ["foo", "bar"]}', tool_call_id=IsStr()
+                            )
+                        ],
+                        usage=RequestUsage(input_tokens=52, output_tokens=7),
+                        model_name='function:track_messages:',
+                        timestamp=IsDatetime(),
+                        run_id=IsStr(),
+                        conversation_id=IsStr(),
+                    ),
+                    ModelRequest(
+                        parts=[
+                            ToolReturnPart(
+                                tool_name='final_result',
+                                content='Final result processed.',
+                                tool_call_id=IsStr(),
+                                timestamp=IsDatetime(),
+                            ),
+                            UserPromptPart(content='Second message', timestamp=IsDatetime()),
+                        ],
+                        timestamp=IsNow(tz=timezone.utc),
+                        run_id=IsStr(),
+                        conversation_id=IsStr(),
+                    ),
+                ]
+            )
+
+
+async def test_thinking_response():
+    """ModelResponse messages with ThinkingPart are properly serialized to A2A."""
+
+    def return_thinking_response(_: list[ModelMessage], info: AgentInfo) -> ModelResponse:
+        assert info.output_tools is not None
+        return ModelResponse(
+            parts=[
+                ThinkingPart(content='Let me think about this...', id='thinking_1'),
+                PydanticAITextPart(content="Here's my response"),
+            ]
+        )
+
+    thinking_model = FunctionModel(return_thinking_response)
+    agent = Agent(model=thinking_model, output_type=str)
+    app: FastA2A = agent_to_a2a(agent)
+
+    async with LifespanManager(app):
+        transport = httpx.ASGITransport(app)
+        async with httpx.AsyncClient(transport=transport) as http_client:
+            a2a_client = A2AClient(http_client=http_client)
+
+            message = Message(
+                role='user',
+                parts=[TextPart(text='Hello, world!', kind='text')],
+                kind='message',
+                message_id=str(uuid.uuid4()),
+            )
+            response = await a2a_client.send_message(message=message)
+            assert 'error' not in response
+            assert 'result' in response
+            result = response['result']
+            assert result['kind'] == 'task'
+
+            task_id = result['id']
+
+            while task := await a2a_client.get_task(task_id):  # pragma: no branch
+                if 'result' in task and task['result']['status']['state'] == 'completed':
+                    result = task['result']
+                    break
+                await anyio.sleep(0.1)
+
+            assert result == snapshot(
+                {
+                    'id': IsStr(),
+                    'context_id': IsStr(),
+                    'kind': 'task',
+                    'status': {'state': 'completed', 'timestamp': IsDatetime(iso_string=True)},
+                    'history': [
+                        {
+                            'role': 'user',
+                            'parts': [{'kind': 'text', 'text': 'Hello, world!'}],
+                            'kind': 'message',
+                            'message_id': IsStr(),
+                            'context_id': IsStr(),
+                            'task_id': IsStr(),
+                        },
+                        {
+                            'role': 'agent',
+                            'parts': [
+                                {
+                                    'metadata': {'type': 'thinking', 'thinking_id': 'thinking_1', 'signature': None},
+                                    'kind': 'text',
+                                    'text': 'Let me think about this...',
+                                },
+                                {'kind': 'text', 'text': "Here's my response"},
+                            ],
+                            'kind': 'message',
+                            'message_id': IsStr(),
+                            'context_id': IsStr(),
+                            'task_id': IsStr(),
+                        },
+                    ],
+                    'artifacts': [
+                        {
+                            'artifact_id': IsStr(),
+                            'name': 'result',
+                            'parts': [{'kind': 'text', 'text': "Here's my response"}],
+                        }
+                    ],
+                }
+            )
+
+
+async def test_multiple_messages():
+    agent = Agent(model=model, output_type=tuple[str, str])
+    storage = InMemoryStorage()
+    app: FastA2A = agent_to_a2a(agent, storage=storage)
+
+    async with LifespanManager(app):
+        transport = httpx.ASGITransport(app)
+        async with httpx.AsyncClient(transport=transport) as http_client:
+            a2a_client = A2AClient(http_client=http_client)
+
+            message = Message(
+                role='user',
+                parts=[TextPart(text='Hello, world!', kind='text')],
+                kind='message',
+                message_id=str(uuid.uuid4()),
+            )
+            response = await a2a_client.send_message(message=message)
+            assert response == snapshot(
+                {
+                    'jsonrpc': '2.0',
+                    'id': IsStr(),
+                    'result': {
+                        'id': IsStr(),
+                        'context_id': IsStr(),
+                        'kind': 'task',
+                        'status': {'state': 'submitted', 'timestamp': IsDatetime(iso_string=True)},
+                        'history': [
+                            {
+                                'role': 'user',
+                                'parts': [{'kind': 'text', 'text': 'Hello, world!'}],
+                                'kind': 'message',
+                                'message_id': IsStr(),
+                                'context_id': IsStr(),
+                                'task_id': IsStr(),
+                            }
+                        ],
+                    },
+                }
+            )
+
+            # Splice an agent message into history before the worker picks up the task.
+            assert 'result' in response
+            result = response['result']
+            assert result['kind'] == 'task'
+            task_id = result['id']
+            task = storage.tasks[task_id]
+            assert 'history' in task
+            task['history'].append(
+                Message(
+                    role='agent',
+                    parts=[TextPart(text='Whats up?', kind='text')],
+                    kind='message',
+                    message_id=str(uuid.uuid4()),
+                )
+            )
+
+            response = await a2a_client.get_task(task_id)
+            assert response == snapshot(
+                {
+                    'jsonrpc': '2.0',
+                    'id': None,
+                    'result': {
+                        'id': IsStr(),
+                        'context_id': IsStr(),
+                        'kind': 'task',
+                        'status': {'state': 'submitted', 'timestamp': IsDatetime(iso_string=True)},
+                        'history': [
+                            {
+                                'role': 'user',
+                                'parts': [{'kind': 'text', 'text': 'Hello, world!'}],
+                                'kind': 'message',
+                                'message_id': IsStr(),
+                                'context_id': IsStr(),
+                                'task_id': IsStr(),
+                            },
+                            {
+                                'role': 'agent',
+                                'parts': [{'kind': 'text', 'text': 'Whats up?'}],
+                                'kind': 'message',
+                                'message_id': IsStr(),
+                            },
+                        ],
+                    },
+                }
+            )
+
+            while task := await a2a_client.get_task(task_id):  # pragma: no branch
+                if 'result' in task and task['result']['status']['state'] == 'completed':
+                    break
+                await anyio.sleep(0.1)
+
+            assert task == snapshot(
+                {
+                    'jsonrpc': '2.0',
+                    'id': None,
+                    'result': {
+                        'id': IsStr(),
+                        'context_id': IsStr(),
+                        'kind': 'task',
+                        'status': {'state': 'completed', 'timestamp': IsDatetime(iso_string=True)},
+                        'history': [
+                            {
+                                'role': 'user',
+                                'parts': [{'kind': 'text', 'text': 'Hello, world!'}],
+                                'kind': 'message',
+                                'message_id': IsStr(),
+                                'context_id': IsStr(),
+                                'task_id': IsStr(),
+                            },
+                            {
+                                'role': 'agent',
+                                'parts': [{'kind': 'text', 'text': 'Whats up?'}],
+                                'kind': 'message',
+                                'message_id': IsStr(),
+                            },
+                        ],
+                        'artifacts': [
+                            {
+                                'artifact_id': IsStr(),
+                                'name': 'result',
+                                'parts': [
+                                    {
+                                        'metadata': {'json_schema': {'items': {}, 'type': 'array'}},
+                                        'kind': 'data',
+                                        'data': {'result': ['foo', 'bar']},
+                                    }
+                                ],
+                            }
+                        ],
+                    },
+                }
+            )
+
+
+async def test_multiple_send_task_messages():
+    agent = Agent(model=model, output_type=tuple[str, str])
+    storage = InMemoryStorage()
+    app: FastA2A = agent_to_a2a(agent, storage=storage)
+
+    async with LifespanManager(app):
+        transport = httpx.ASGITransport(app)
+        async with httpx.AsyncClient(transport=transport) as http_client:
+            a2a_client = A2AClient(http_client=http_client)
+
+            message = Message(
+                role='user',
+                parts=[TextPart(text='Hello, world!', kind='text')],
+                kind='message',
+                message_id=str(uuid.uuid4()),
+            )
+            response = await a2a_client.send_message(message=message)
+            assert response == snapshot(
+                {
+                    'jsonrpc': '2.0',
+                    'id': IsStr(),
+                    'result': {
+                        'id': IsStr(),
+                        'context_id': IsStr(),
+                        'kind': 'task',
+                        'status': {'state': 'submitted', 'timestamp': IsDatetime(iso_string=True)},
+                        'history': [
+                            {
+                                'role': 'user',
+                                'parts': [{'kind': 'text', 'text': 'Hello, world!'}],
+                                'kind': 'message',
+                                'message_id': IsStr(),
+                                'context_id': IsStr(),
+                                'task_id': IsStr(),
+                            }
+                        ],
+                    },
+                }
+            )
+            assert 'result' in response
+            result = response['result']
+            assert result['kind'] == 'task'
+            task_id = result['id']
+            context_id = result['context_id']
+
+            while task := await a2a_client.get_task(task_id):  # pragma: no branch
+                if 'result' in task and task['result']['status']['state'] == 'completed':
+                    result = task['result']
+                    break
+                await anyio.sleep(0.1)
+
+            assert result == snapshot(
+                {
+                    'id': IsStr(),
+                    'context_id': IsStr(),
+                    'kind': 'task',
+                    'status': {'state': 'completed', 'timestamp': IsDatetime(iso_string=True)},
+                    'history': [
+                        {
+                            'role': 'user',
+                            'parts': [{'kind': 'text', 'text': 'Hello, world!'}],
+                            'kind': 'message',
+                            'message_id': IsStr(),
+                            'context_id': IsStr(),
+                            'task_id': IsStr(),
+                        }
+                    ],
+                    'artifacts': [
+                        {
+                            'artifact_id': IsStr(),
+                            'name': 'result',
+                            'parts': [
+                                {
+                                    'metadata': {'json_schema': {'items': {}, 'type': 'array'}},
+                                    'kind': 'data',
+                                    'data': {'result': ['foo', 'bar']},
+                                }
+                            ],
+                        }
+                    ],
+                }
+            )
+
+            message = Message(
+                role='user',
+                parts=[TextPart(text='Did you get my first message?', kind='text')],
+                kind='message',
+                message_id=str(uuid.uuid4()),
+                context_id=context_id,
+            )
+            response = await a2a_client.send_message(message=message)
+            assert response == snapshot(
+                {
+                    'jsonrpc': '2.0',
+                    'id': IsStr(),
+                    'result': {
+                        'id': IsStr(),
+                        'context_id': IsStr(),
+                        'kind': 'task',
+                        'status': {'state': 'submitted', 'timestamp': IsDatetime(iso_string=True)},
+                        'history': [
+                            {
+                                'role': 'user',
+                                'parts': [{'kind': 'text', 'text': 'Did you get my first message?'}],
+                                'kind': 'message',
+                                'message_id': IsStr(),
+                                'context_id': IsStr(),
+                                'task_id': IsStr(),
+                            }
+                        ],
+                    },
+                }
+            )
+
+            while task := await a2a_client.get_task(task_id):  # pragma: no branch
+                if 'result' in task and task['result']['status']['state'] == 'completed':
+                    result = task['result']
+                    break
+                await anyio.sleep(0.1)  # pragma: lax no cover
+
+            assert result == snapshot(
+                {
+                    'id': IsStr(),
+                    'context_id': IsStr(),
+                    'kind': 'task',
+                    'status': {'state': 'completed', 'timestamp': IsDatetime(iso_string=True)},
+                    'history': [
+                        {
+                            'role': 'user',
+                            'parts': [{'kind': 'text', 'text': 'Hello, world!'}],
+                            'kind': 'message',
+                            'message_id': IsStr(),
+                            'context_id': IsStr(),
+                            'task_id': IsStr(),
+                        }
+                    ],
+                    'artifacts': [
+                        {
+                            'artifact_id': IsStr(),
+                            'name': 'result',
+                            'parts': [
+                                {
+                                    'metadata': {'json_schema': {'items': {}, 'type': 'array'}},
+                                    'kind': 'data',
+                                    'data': {'result': ['foo', 'bar']},
+                                }
+                            ],
+                        }
+                    ],
+                }
+            )
diff --git a/uv.lock b/uv.lock
index 0557720..071eaab 100644
--- a/uv.lock
+++ b/uv.lock
@@ -402,6 +402,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/07/6c/aa3f2f849e01cb6a001cd8554a88d4c77c5c1a31c95bdf1cf9301e6d9ef4/defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61", size = 25604, upload-time = "2021-03-08T10:59:24.45Z" },
 ]
 
+[[package]]
+name = "dirty-equals"
+version = "0.11"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/30/1d/c5913ac9d6615515a00f4bdc71356d302437cb74ff2e9aaccd3c14493b78/dirty_equals-0.11.tar.gz", hash = "sha256:f4ac74ee88f2d11e2fa0f65eb30ee4f07105c5f86f4dc92b09eb1138775027c3", size = 128067, upload-time = "2025-11-17T01:51:24.451Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/bb/8d/dbff05239043271dbeace563a7686212a3dd517864a35623fe4d4a64ca19/dirty_equals-0.11-py3-none-any.whl", hash = "sha256:b1d7093273fc2f9be12f443a8ead954ef6daaf6746fd42ef3a5616433ee85286", size = 28051, upload-time = "2025-11-17T01:51:22.849Z" },
+]
+
 [[package]]
 name = "eval-type-backport"
 version = "0.2.2"
@@ -453,8 +462,10 @@ pydantic-ai = [
 
 [package.dev-dependencies]
 dev = [
+    { name = "anyio" },
     { name = "asgi-lifespan" },
     { name = "coverage" },
+    { name = "dirty-equals" },
     { name = "httpx" },
     { name = "inline-snapshot" },
     { name = "pyright" },
@@ -481,8 +492,10 @@ provides-extras = ["logfire", "pydantic-ai"]
 
 [package.metadata.requires-dev]
 dev = [
+    { name = "anyio" },
     { name = "asgi-lifespan" },
     { name = "coverage" },
+    { name = "dirty-equals" },
     { name = "httpx" },
     { name = "inline-snapshot" },
     { name = "pyright" },

From b4a778b68f019ddb5d7213290653c459bdabd26c Mon Sep 17 00:00:00 2001
From: David Sanchez <64162682+dsfaccini@users.noreply.github.com>
Date: Thu, 21 May 2026 15:21:11 -0500
Subject: [PATCH 10/11] Conform pydantic-ai bridge to the A2A v1 schema

The bridge and its tests were ported against fasta2a's pre-v1 schema
(discriminated-union `Part` with a `kind` field, `Message` with `kind`).
PRs #46-#51 on main collapsed `Part` into a single flat TypedDict, dropped
the `kind` discriminators, and wrapped the `message/send` result in a
`{task: Task}` envelope.

- `_bridge.py`: emit/parse flat `Part` (`text`/`raw`/`url`/`data` detected by
  key presence instead of `kind`); drop `kind='message'`.
- `test_pydantic_ai.py`: construct flat `Part`/`Message`; unwrap the task from
  `message/send` responses via `task_from_response`; regenerate snapshots.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
---
 fasta2a/pydantic_ai/_bridge.py |  66 ++++-----
 tests/test_pydantic_ai.py      | 261 +++++++++++++--------------------
 2 files changed, 127 insertions(+), 200 deletions(-)

diff --git a/fasta2a/pydantic_ai/_bridge.py b/fasta2a/pydantic_ai/_bridge.py
index ff5b115..e7299d1 100644
--- a/fasta2a/pydantic_ai/_bridge.py
+++ b/fasta2a/pydantic_ai/_bridge.py
@@ -9,7 +9,6 @@
 from typing import Any, Generic, TypeVar
 
 from pydantic import TypeAdapter
-from typing_extensions import assert_never
 
 try:
     from pydantic_ai import (
@@ -46,13 +45,11 @@
 from fasta2a.schema import (
     AgentProvider,
     Artifact,
-    DataPart,
     Message,
     Part,
     Skill,
     TaskIdParams,
     TaskSendParams,
-    TextPart as A2ATextPart,
 )
 from fasta2a.storage import InMemoryStorage, Storage
 from fasta2a.worker import Worker
@@ -141,9 +138,7 @@ async def run_task(self, params: TaskSendParams) -> None:
                     continue
                 a2a_parts = self._response_parts_to_a2a(message.parts)
                 if a2a_parts:
-                    a2a_messages.append(
-                        Message(role='agent', parts=a2a_parts, kind='message', message_id=str(uuid.uuid4()))
-                    )
+                    a2a_messages.append(Message(role='agent', parts=a2a_parts, message_id=str(uuid.uuid4())))
 
             artifacts = self.build_artifacts(result.output)
         except Exception:
@@ -164,12 +159,12 @@ def build_artifacts(self, result: WorkerOutputT) -> list[Artifact]:
 
     def _convert_result_to_part(self, result: WorkerOutputT) -> Part:
         if isinstance(result, str):
-            return A2ATextPart(kind='text', text=result)
+            return Part(text=result)
         output_type = type(result)
         type_adapter = TypeAdapter(output_type)
         data = type_adapter.dump_python(result, mode='json')
         json_schema = type_adapter.json_schema(mode='serialization')
-        return DataPart(kind='data', data={'result': data}, metadata={'json_schema': json_schema})
+        return Part(data={'result': data}, metadata={'json_schema': json_schema})
 
     def build_message_history(self, history: list[Message]) -> list[ModelMessage]:
         model_messages: list[ModelMessage] = []
@@ -183,56 +178,53 @@ def build_message_history(self, history: list[Message]) -> list[ModelMessage]:
     def _request_parts_from_a2a(self, parts: list[Part]) -> list[ModelRequestPart]:
         model_parts: list[ModelRequestPart] = []
         for part in parts:
-            if part['kind'] == 'text':
+            if 'text' in part:
                 model_parts.append(UserPromptPart(content=part['text']))
-            elif part['kind'] == 'file':
-                file_content = part['file']
-                if 'bytes' in file_content:
-                    data = base64.b64decode(file_content['bytes'])
-                    mime_type = file_content.get('mime_type', 'application/octet-stream')
-                    content = BinaryContent(data=data, media_type=mime_type)
-                    model_parts.append(UserPromptPart(content=[content]))
-                else:
-                    url = file_content['uri']
-                    for url_cls in (DocumentUrl, AudioUrl, ImageUrl, VideoUrl):
-                        content = url_cls(url=url)
-                        try:
-                            content.media_type
-                        except ValueError:
-                            continue
-                        else:
-                            break
+            elif 'raw' in part:
+                data = base64.b64decode(part['raw'])
+                mime_type = part.get('media_type', 'application/octet-stream')
+                content = BinaryContent(data=data, media_type=mime_type)
+                model_parts.append(UserPromptPart(content=[content]))
+            elif 'url' in part:
+                url = part['url']
+                for url_cls in (DocumentUrl, AudioUrl, ImageUrl, VideoUrl):
+                    content = url_cls(url=url)
+                    try:
+                        content.media_type
+                    except ValueError:
+                        continue
                     else:
-                        raise ValueError(f'Unsupported file type: {url}')
-                    model_parts.append(UserPromptPart(content=[content]))
-            elif part['kind'] == 'data':
+                        break
+                else:
+                    raise ValueError(f'Unsupported file type: {url}')
+                model_parts.append(UserPromptPart(content=[content]))
+            elif 'data' in part:
                 raise NotImplementedError('Data parts are not supported yet.')
             else:
-                assert_never(part)
+                raise ValueError(f'Unsupported part: {part}')
         return model_parts
 
     def _response_parts_from_a2a(self, parts: list[Part]) -> list[ModelResponsePart]:
         model_parts: list[ModelResponsePart] = []
         for part in parts:
-            if part['kind'] == 'text':
+            if 'text' in part:
                 model_parts.append(TextPart(content=part['text']))
-            elif part['kind'] == 'file':
+            elif 'raw' in part or 'url' in part:
                 raise NotImplementedError('File parts are not supported yet.')
-            elif part['kind'] == 'data':
+            elif 'data' in part:
                 raise NotImplementedError('Data parts are not supported yet.')
             else:
-                assert_never(part)
+                raise ValueError(f'Unsupported part: {part}')
         return model_parts
 
     def _response_parts_to_a2a(self, parts: Sequence[ModelResponsePart]) -> list[Part]:
         a2a_parts: list[Part] = []
         for part in parts:
             if isinstance(part, TextPart):
-                a2a_parts.append(A2ATextPart(kind='text', text=part.content))
+                a2a_parts.append(Part(text=part.content))
             elif isinstance(part, ThinkingPart):
                 a2a_parts.append(
-                    A2ATextPart(
-                        kind='text',
+                    Part(
                         text=part.content,
                         metadata={'type': 'thinking', 'thinking_id': part.id, 'signature': part.signature},
                     )
diff --git a/tests/test_pydantic_ai.py b/tests/test_pydantic_ai.py
index d8df329..2f91b36 100644
--- a/tests/test_pydantic_ai.py
+++ b/tests/test_pydantic_ai.py
@@ -60,7 +60,7 @@ def IsNow(*args: Any, **kwargs: Any) -> datetime: ...
 from fasta2a.applications import FastA2A
 from fasta2a.client import A2AClient
 from fasta2a.pydantic_ai import agent_to_a2a
-from fasta2a.schema import DataPart, FilePart, Message, TextPart
+from fasta2a.schema import Message, Part, SendMessageResponse, Task
 from fasta2a.storage import InMemoryStorage
 
 pytestmark = [
@@ -69,6 +69,13 @@ def IsNow(*args: Any, **kwargs: Any) -> datetime: ...
 ]
 
 
+def task_from_response(response: SendMessageResponse) -> Task:
+    """Unwrap the `Task` from a successful `message/send` response."""
+    assert 'result' in response
+    assert 'task' in response['result']
+    return response['result']['task']
+
+
 @pytest.fixture(scope='session')
 def image_content() -> BinaryContent:
     return BinaryContent(
@@ -113,15 +120,13 @@ async def test_pydantic_model_output():
 
             message = Message(
                 role='user',
-                parts=[TextPart(text='Get user profile', kind='text')],
-                kind='message',
+                parts=[Part(text='Get user profile')],
                 message_id=str(uuid.uuid4()),
             )
             response = await a2a_client.send_message(message=message)
             assert 'error' not in response
             assert 'result' in response
-            result = response['result']
-            assert result['kind'] == 'task'
+            result = task_from_response(response)
 
             task_id = result['id']
 
@@ -135,7 +140,7 @@ async def test_pydantic_model_output():
             assert len(result['artifacts']) == 1
             artifact = result['artifacts'][0]
 
-            assert artifact['parts'][0]['kind'] == 'data'
+            assert 'data' in artifact['parts'][0]
             assert artifact['parts'][0]['data'] == {
                 'result': {'name': 'John Doe', 'age': 30, 'email': 'john@example.com'}
             }
@@ -160,8 +165,7 @@ async def test_pydantic_model_output():
                 [
                     {
                         'role': 'user',
-                        'parts': [{'kind': 'text', 'text': 'Get user profile'}],
-                        'kind': 'message',
+                        'parts': [{'text': 'Get user profile'}],
                         'message_id': IsStr(),
                         'context_id': IsStr(),
                         'task_id': IsStr(),
@@ -180,8 +184,7 @@ async def test_runtime_error_without_lifespan():
 
         message = Message(
             role='user',
-            parts=[TextPart(text='Hello, world!', kind='text')],
-            kind='message',
+            parts=[Part(text='Hello, world!')],
             message_id=str(uuid.uuid4()),
         )
 
@@ -200,26 +203,22 @@ async def test_simple():
 
             message = Message(
                 role='user',
-                parts=[TextPart(text='Hello, world!', kind='text')],
-                kind='message',
+                parts=[Part(text='Hello, world!')],
                 message_id=str(uuid.uuid4()),
             )
             response = await a2a_client.send_message(message=message)
             assert 'error' not in response
             assert 'result' in response
-            result = response['result']
-            assert result['kind'] == 'task'
+            result = task_from_response(response)
             assert result == snapshot(
                 {
                     'id': IsStr(),
                     'context_id': IsStr(),
-                    'kind': 'task',
                     'status': {'state': 'submitted', 'timestamp': IsDatetime(iso_string=True)},
                     'history': [
                         {
                             'role': 'user',
-                            'parts': [{'kind': 'text', 'text': 'Hello, world!'}],
-                            'kind': 'message',
+                            'parts': [{'text': 'Hello, world!'}],
                             'message_id': IsStr(),
                             'context_id': IsStr(),
                             'task_id': IsStr(),
@@ -242,13 +241,11 @@ async def test_simple():
                     'result': {
                         'id': IsStr(),
                         'context_id': IsStr(),
-                        'kind': 'task',
                         'status': {'state': 'completed', 'timestamp': IsDatetime(iso_string=True)},
                         'history': [
                             {
                                 'role': 'user',
-                                'parts': [{'kind': 'text', 'text': 'Hello, world!'}],
-                                'kind': 'message',
+                                'parts': [{'text': 'Hello, world!'}],
                                 'message_id': IsStr(),
                                 'context_id': IsStr(),
                                 'task_id': IsStr(),
@@ -261,7 +258,6 @@ async def test_simple():
                                 'parts': [
                                     {
                                         'metadata': {'json_schema': {'items': {}, 'type': 'array'}},
-                                        'kind': 'data',
                                         'data': {'result': ['foo', 'bar']},
                                     }
                                 ],
@@ -284,35 +280,23 @@ async def test_file_message_with_file():
             message = Message(
                 role='user',
                 parts=[
-                    FilePart(
-                        kind='file',
-                        file={'uri': 'https://example.com/file.txt', 'mime_type': 'text/plain'},
-                    )
+                    Part(url='https://example.com/file.txt', media_type='text/plain'),
                 ],
-                kind='message',
                 message_id=str(uuid.uuid4()),
             )
             response = await a2a_client.send_message(message=message)
             assert 'error' not in response
             assert 'result' in response
-            result = response['result']
-            assert result['kind'] == 'task'
+            result = task_from_response(response)
             assert result == snapshot(
                 {
                     'id': IsStr(),
                     'context_id': IsStr(),
-                    'kind': 'task',
                     'status': {'state': 'submitted', 'timestamp': IsDatetime(iso_string=True)},
                     'history': [
                         {
                             'role': 'user',
-                            'parts': [
-                                {
-                                    'kind': 'file',
-                                    'file': {'mime_type': 'text/plain', 'uri': 'https://example.com/file.txt'},
-                                }
-                            ],
-                            'kind': 'message',
+                            'parts': [{'url': 'https://example.com/file.txt', 'media_type': 'text/plain'}],
                             'message_id': IsStr(),
                             'context_id': IsStr(),
                             'task_id': IsStr(),
@@ -334,18 +318,11 @@ async def test_file_message_with_file():
                     'result': {
                         'id': IsStr(),
                         'context_id': IsStr(),
-                        'kind': 'task',
                         'status': {'state': 'completed', 'timestamp': IsDatetime(iso_string=True)},
                         'history': [
                             {
                                 'role': 'user',
-                                'parts': [
-                                    {
-                                        'kind': 'file',
-                                        'file': {'mime_type': 'text/plain', 'uri': 'https://example.com/file.txt'},
-                                    }
-                                ],
-                                'kind': 'message',
+                                'parts': [{'url': 'https://example.com/file.txt', 'media_type': 'text/plain'}],
                                 'message_id': IsStr(),
                                 'context_id': IsStr(),
                                 'task_id': IsStr(),
@@ -358,7 +335,6 @@ async def test_file_message_with_file():
                                 'parts': [
                                     {
                                         'metadata': {'json_schema': {'items': {}, 'type': 'array'}},
-                                        'kind': 'data',
                                         'data': {'result': ['foo', 'bar']},
                                     }
                                 ],
@@ -382,29 +358,28 @@ async def test_file_message_with_file_content(image_content: BinaryContent):
             message = Message(
                 role='user',
                 parts=[
-                    FilePart(file={'bytes': base64_image, 'mime_type': image_content.media_type}, kind='file'),
+                    Part(raw=base64_image, media_type=image_content.media_type),
                 ],
-                kind='message',
                 message_id=str(uuid.uuid4()),
             )
             response = await a2a_client.send_message(message=message)
             assert 'error' not in response
             assert 'result' in response
-            result = response['result']
-            assert result['kind'] == 'task'
+            result = task_from_response(response)
             assert result == snapshot(
                 {
                     'id': IsStr(),
                     'context_id': IsStr(),
-                    'kind': 'task',
                     'status': {'state': 'submitted', 'timestamp': IsDatetime(iso_string=True)},
                     'history': [
                         {
                             'role': 'user',
                             'parts': [
-                                {'kind': 'file', 'file': {'bytes': base64_image, 'mime_type': image_content.media_type}}
+                                {
+                                    'raw': 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',
+                                    'media_type': 'image/jpeg',
+                                }
                             ],
-                            'kind': 'message',
                             'message_id': IsStr(),
                             'context_id': IsStr(),
                             'task_id': IsStr(),
@@ -426,18 +401,16 @@ async def test_file_message_with_file_content(image_content: BinaryContent):
                     'result': {
                         'id': IsStr(),
                         'context_id': IsStr(),
-                        'kind': 'task',
                         'status': {'state': 'completed', 'timestamp': IsDatetime(iso_string=True)},
                         'history': [
                             {
                                 'role': 'user',
                                 'parts': [
                                     {
-                                        'kind': 'file',
-                                        'file': {'bytes': base64_image, 'mime_type': image_content.media_type},
+                                        'raw': 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',
+                                        'media_type': 'image/jpeg',
                                     }
                                 ],
-                                'kind': 'message',
                                 'message_id': IsStr(),
                                 'context_id': IsStr(),
                                 'task_id': IsStr(),
@@ -450,7 +423,6 @@ async def test_file_message_with_file_content(image_content: BinaryContent):
                                 'parts': [
                                     {
                                         'metadata': {'json_schema': {'items': {}, 'type': 'array'}},
-                                        'kind': 'data',
                                         'data': {'result': ['foo', 'bar']},
                                     }
                                 ],
@@ -472,26 +444,22 @@ async def test_file_message_with_data():
 
             message = Message(
                 role='user',
-                parts=[DataPart(kind='data', data={'foo': 'bar'})],
-                kind='message',
+                parts=[Part(data={'foo': 'bar'})],
                 message_id=str(uuid.uuid4()),
             )
             response = await a2a_client.send_message(message=message)
             assert 'error' not in response
             assert 'result' in response
-            result = response['result']
-            assert result['kind'] == 'task'
+            result = task_from_response(response)
             assert result == snapshot(
                 {
                     'id': IsStr(),
                     'context_id': IsStr(),
-                    'kind': 'task',
                     'status': {'state': 'submitted', 'timestamp': IsDatetime(iso_string=True)},
                     'history': [
                         {
                             'role': 'user',
-                            'parts': [{'kind': 'data', 'data': {'foo': 'bar'}}],
-                            'kind': 'message',
+                            'parts': [{'data': {'foo': 'bar'}}],
                             'message_id': IsStr(),
                             'context_id': IsStr(),
                             'task_id': IsStr(),
@@ -513,13 +481,11 @@ async def test_file_message_with_data():
                     'result': {
                         'id': IsStr(),
                         'context_id': IsStr(),
-                        'kind': 'task',
                         'status': {'state': 'failed', 'timestamp': IsDatetime(iso_string=True)},
                         'history': [
                             {
                                 'role': 'user',
-                                'parts': [{'kind': 'data', 'data': {'foo': 'bar'}}],
-                                'kind': 'message',
+                                'parts': [{'data': {'foo': 'bar'}}],
                                 'message_id': IsStr(),
                                 'context_id': IsStr(),
                                 'task_id': IsStr(),
@@ -547,15 +513,13 @@ def raise_error(_: list[ModelMessage], info: AgentInfo) -> ModelResponse:
 
             message = Message(
                 role='user',
-                parts=[TextPart(text='Hello, world!', kind='text')],
-                kind='message',
+                parts=[Part(text='Hello, world!')],
                 message_id=str(uuid.uuid4()),
             )
             response = await a2a_client.send_message(message=message)
             assert 'error' not in response
             assert 'result' in response
-            result = response['result']
-            assert result['kind'] == 'task'
+            result = task_from_response(response)
 
             task_id = result['id']
 
@@ -594,15 +558,13 @@ def track_messages(messages: list[ModelMessage], info: AgentInfo) -> ModelRespon
 
             message1 = Message(
                 role='user',
-                parts=[TextPart(text='First message', kind='text')],
-                kind='message',
+                parts=[Part(text='First message')],
                 message_id=str(uuid.uuid4()),
             )
             response1 = await a2a_client.send_message(message=message1)
             assert 'error' not in response1
             assert 'result' in response1
-            result1 = response1['result']
-            assert result1['kind'] == 'task'
+            result1 = task_from_response(response1)
 
             task1_id = result1['id']
             context_id = result1['context_id']
@@ -628,16 +590,14 @@ def track_messages(messages: list[ModelMessage], info: AgentInfo) -> ModelRespon
 
             message2 = Message(
                 role='user',
-                parts=[TextPart(text='Second message', kind='text')],
-                kind='message',
+                parts=[Part(text='Second message')],
                 context_id=context_id,
                 message_id=str(uuid.uuid4()),
             )
             response2 = await a2a_client.send_message(message=message2)
             assert 'error' not in response2
             assert 'result' in response2
-            result2 = response2['result']
-            assert result2['kind'] == 'task'
+            result2 = task_from_response(response2)
 
             task2_id = result2['id']
             assert task2_id != task1_id
@@ -713,15 +673,13 @@ def return_thinking_response(_: list[ModelMessage], info: AgentInfo) -> ModelRes
 
             message = Message(
                 role='user',
-                parts=[TextPart(text='Hello, world!', kind='text')],
-                kind='message',
+                parts=[Part(text='Hello, world!')],
                 message_id=str(uuid.uuid4()),
             )
             response = await a2a_client.send_message(message=message)
             assert 'error' not in response
             assert 'result' in response
-            result = response['result']
-            assert result['kind'] == 'task'
+            result = task_from_response(response)
 
             task_id = result['id']
 
@@ -735,13 +693,11 @@ def return_thinking_response(_: list[ModelMessage], info: AgentInfo) -> ModelRes
                 {
                     'id': IsStr(),
                     'context_id': IsStr(),
-                    'kind': 'task',
                     'status': {'state': 'completed', 'timestamp': IsDatetime(iso_string=True)},
                     'history': [
                         {
                             'role': 'user',
-                            'parts': [{'kind': 'text', 'text': 'Hello, world!'}],
-                            'kind': 'message',
+                            'parts': [{'text': 'Hello, world!'}],
                             'message_id': IsStr(),
                             'context_id': IsStr(),
                             'task_id': IsStr(),
@@ -751,12 +707,10 @@ def return_thinking_response(_: list[ModelMessage], info: AgentInfo) -> ModelRes
                             'parts': [
                                 {
                                     'metadata': {'type': 'thinking', 'thinking_id': 'thinking_1', 'signature': None},
-                                    'kind': 'text',
                                     'text': 'Let me think about this...',
                                 },
-                                {'kind': 'text', 'text': "Here's my response"},
+                                {'text': "Here's my response"},
                             ],
-                            'kind': 'message',
                             'message_id': IsStr(),
                             'context_id': IsStr(),
                             'task_id': IsStr(),
@@ -766,7 +720,7 @@ def return_thinking_response(_: list[ModelMessage], info: AgentInfo) -> ModelRes
                         {
                             'artifact_id': IsStr(),
                             'name': 'result',
-                            'parts': [{'kind': 'text', 'text': "Here's my response"}],
+                            'parts': [{'text': "Here's my response"}],
                         }
                     ],
                 }
@@ -785,8 +739,7 @@ async def test_multiple_messages():
 
             message = Message(
                 role='user',
-                parts=[TextPart(text='Hello, world!', kind='text')],
-                kind='message',
+                parts=[Part(text='Hello, world!')],
                 message_id=str(uuid.uuid4()),
             )
             response = await a2a_client.send_message(message=message)
@@ -795,36 +748,34 @@ async def test_multiple_messages():
                     'jsonrpc': '2.0',
                     'id': IsStr(),
                     'result': {
-                        'id': IsStr(),
-                        'context_id': IsStr(),
-                        'kind': 'task',
-                        'status': {'state': 'submitted', 'timestamp': IsDatetime(iso_string=True)},
-                        'history': [
-                            {
-                                'role': 'user',
-                                'parts': [{'kind': 'text', 'text': 'Hello, world!'}],
-                                'kind': 'message',
-                                'message_id': IsStr(),
-                                'context_id': IsStr(),
-                                'task_id': IsStr(),
-                            }
-                        ],
+                        'task': {
+                            'id': IsStr(),
+                            'context_id': IsStr(),
+                            'status': {'state': 'submitted', 'timestamp': IsDatetime(iso_string=True)},
+                            'history': [
+                                {
+                                    'role': 'user',
+                                    'parts': [{'text': 'Hello, world!'}],
+                                    'message_id': IsStr(),
+                                    'context_id': IsStr(),
+                                    'task_id': IsStr(),
+                                }
+                            ],
+                        }
                     },
                 }
             )
 
             # Splice an agent message into history before the worker picks up the task.
             assert 'result' in response
-            result = response['result']
-            assert result['kind'] == 'task'
+            result = task_from_response(response)
             task_id = result['id']
             task = storage.tasks[task_id]
             assert 'history' in task
             task['history'].append(
                 Message(
                     role='agent',
-                    parts=[TextPart(text='Whats up?', kind='text')],
-                    kind='message',
+                    parts=[Part(text='Whats up?')],
                     message_id=str(uuid.uuid4()),
                 )
             )
@@ -837,21 +788,18 @@ async def test_multiple_messages():
                     'result': {
                         'id': IsStr(),
                         'context_id': IsStr(),
-                        'kind': 'task',
                         'status': {'state': 'submitted', 'timestamp': IsDatetime(iso_string=True)},
                         'history': [
                             {
                                 'role': 'user',
-                                'parts': [{'kind': 'text', 'text': 'Hello, world!'}],
-                                'kind': 'message',
+                                'parts': [{'text': 'Hello, world!'}],
                                 'message_id': IsStr(),
                                 'context_id': IsStr(),
                                 'task_id': IsStr(),
                             },
                             {
                                 'role': 'agent',
-                                'parts': [{'kind': 'text', 'text': 'Whats up?'}],
-                                'kind': 'message',
+                                'parts': [{'text': 'Whats up?'}],
                                 'message_id': IsStr(),
                             },
                         ],
@@ -871,21 +819,18 @@ async def test_multiple_messages():
                     'result': {
                         'id': IsStr(),
                         'context_id': IsStr(),
-                        'kind': 'task',
                         'status': {'state': 'completed', 'timestamp': IsDatetime(iso_string=True)},
                         'history': [
                             {
                                 'role': 'user',
-                                'parts': [{'kind': 'text', 'text': 'Hello, world!'}],
-                                'kind': 'message',
+                                'parts': [{'text': 'Hello, world!'}],
                                 'message_id': IsStr(),
                                 'context_id': IsStr(),
                                 'task_id': IsStr(),
                             },
                             {
                                 'role': 'agent',
-                                'parts': [{'kind': 'text', 'text': 'Whats up?'}],
-                                'kind': 'message',
+                                'parts': [{'text': 'Whats up?'}],
                                 'message_id': IsStr(),
                             },
                         ],
@@ -896,7 +841,6 @@ async def test_multiple_messages():
                                 'parts': [
                                     {
                                         'metadata': {'json_schema': {'items': {}, 'type': 'array'}},
-                                        'kind': 'data',
                                         'data': {'result': ['foo', 'bar']},
                                     }
                                 ],
@@ -919,8 +863,7 @@ async def test_multiple_send_task_messages():
 
             message = Message(
                 role='user',
-                parts=[TextPart(text='Hello, world!', kind='text')],
-                kind='message',
+                parts=[Part(text='Hello, world!')],
                 message_id=str(uuid.uuid4()),
             )
             response = await a2a_client.send_message(message=message)
@@ -929,26 +872,25 @@ async def test_multiple_send_task_messages():
                     'jsonrpc': '2.0',
                     'id': IsStr(),
                     'result': {
-                        'id': IsStr(),
-                        'context_id': IsStr(),
-                        'kind': 'task',
-                        'status': {'state': 'submitted', 'timestamp': IsDatetime(iso_string=True)},
-                        'history': [
-                            {
-                                'role': 'user',
-                                'parts': [{'kind': 'text', 'text': 'Hello, world!'}],
-                                'kind': 'message',
-                                'message_id': IsStr(),
-                                'context_id': IsStr(),
-                                'task_id': IsStr(),
-                            }
-                        ],
+                        'task': {
+                            'id': IsStr(),
+                            'context_id': IsStr(),
+                            'status': {'state': 'submitted', 'timestamp': IsDatetime(iso_string=True)},
+                            'history': [
+                                {
+                                    'role': 'user',
+                                    'parts': [{'text': 'Hello, world!'}],
+                                    'message_id': IsStr(),
+                                    'context_id': IsStr(),
+                                    'task_id': IsStr(),
+                                }
+                            ],
+                        }
                     },
                 }
             )
             assert 'result' in response
-            result = response['result']
-            assert result['kind'] == 'task'
+            result = task_from_response(response)
             task_id = result['id']
             context_id = result['context_id']
 
@@ -962,13 +904,11 @@ async def test_multiple_send_task_messages():
                 {
                     'id': IsStr(),
                     'context_id': IsStr(),
-                    'kind': 'task',
                     'status': {'state': 'completed', 'timestamp': IsDatetime(iso_string=True)},
                     'history': [
                         {
                             'role': 'user',
-                            'parts': [{'kind': 'text', 'text': 'Hello, world!'}],
-                            'kind': 'message',
+                            'parts': [{'text': 'Hello, world!'}],
                             'message_id': IsStr(),
                             'context_id': IsStr(),
                             'task_id': IsStr(),
@@ -981,7 +921,6 @@ async def test_multiple_send_task_messages():
                             'parts': [
                                 {
                                     'metadata': {'json_schema': {'items': {}, 'type': 'array'}},
-                                    'kind': 'data',
                                     'data': {'result': ['foo', 'bar']},
                                 }
                             ],
@@ -992,8 +931,7 @@ async def test_multiple_send_task_messages():
 
             message = Message(
                 role='user',
-                parts=[TextPart(text='Did you get my first message?', kind='text')],
-                kind='message',
+                parts=[Part(text='Did you get my first message?')],
                 message_id=str(uuid.uuid4()),
                 context_id=context_id,
             )
@@ -1003,20 +941,20 @@ async def test_multiple_send_task_messages():
                     'jsonrpc': '2.0',
                     'id': IsStr(),
                     'result': {
-                        'id': IsStr(),
-                        'context_id': IsStr(),
-                        'kind': 'task',
-                        'status': {'state': 'submitted', 'timestamp': IsDatetime(iso_string=True)},
-                        'history': [
-                            {
-                                'role': 'user',
-                                'parts': [{'kind': 'text', 'text': 'Did you get my first message?'}],
-                                'kind': 'message',
-                                'message_id': IsStr(),
-                                'context_id': IsStr(),
-                                'task_id': IsStr(),
-                            }
-                        ],
+                        'task': {
+                            'id': IsStr(),
+                            'context_id': IsStr(),
+                            'status': {'state': 'submitted', 'timestamp': IsDatetime(iso_string=True)},
+                            'history': [
+                                {
+                                    'role': 'user',
+                                    'parts': [{'text': 'Did you get my first message?'}],
+                                    'message_id': IsStr(),
+                                    'context_id': IsStr(),
+                                    'task_id': IsStr(),
+                                }
+                            ],
+                        }
                     },
                 }
             )
@@ -1031,13 +969,11 @@ async def test_multiple_send_task_messages():
                 {
                     'id': IsStr(),
                     'context_id': IsStr(),
-                    'kind': 'task',
                     'status': {'state': 'completed', 'timestamp': IsDatetime(iso_string=True)},
                     'history': [
                         {
                             'role': 'user',
-                            'parts': [{'kind': 'text', 'text': 'Hello, world!'}],
-                            'kind': 'message',
+                            'parts': [{'text': 'Hello, world!'}],
                             'message_id': IsStr(),
                             'context_id': IsStr(),
                             'task_id': IsStr(),
@@ -1050,7 +986,6 @@ async def test_multiple_send_task_messages():
                             'parts': [
                                 {
                                     'metadata': {'json_schema': {'items': {}, 'type': 'array'}},
-                                    'kind': 'data',
                                     'data': {'result': ['foo', 'bar']},
                                 }
                             ],

From ef49f879e67820febf0eb26aeb83fdc79a8f9e88 Mon Sep 17 00:00:00 2001
From: David Sanchez <64162682+dsfaccini@users.noreply.github.com>
Date: Thu, 21 May 2026 15:34:05 -0500
Subject: [PATCH 11/11] Shrink file-content test asset and matcher the base64
 in snapshots

The 98KB kiwi.jpg produced two ~131KB base64 literals in the
test_file_message_with_file_content snapshots, bloating the file to ~260KB.
The agent is a deterministic FunctionModel mock that never inspects the
image, so any bytes suffice.

- Replace kiwi.jpg with a 1x1 tiny.jpg (631 bytes).
- Snapshot the base64 file content as IsStr() instead of the literal.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
---
 tests/assets/kiwi.jpg     | Bin 98572 -> 0 bytes
 tests/assets/tiny.jpg     | Bin 0 -> 631 bytes
 tests/test_pydantic_ai.py |   6 +++---
 3 files changed, 3 insertions(+), 3 deletions(-)
 delete mode 100644 tests/assets/kiwi.jpg
 create mode 100644 tests/assets/tiny.jpg

diff --git a/tests/assets/kiwi.jpg b/tests/assets/kiwi.jpg
deleted file mode 100644
index c8bd7962fd408b2147e57e5839f617966b6bd7cd..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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diff --git a/tests/assets/tiny.jpg b/tests/assets/tiny.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ffcb7eeca4018e9c70082e6e2135228a622c601b
GIT binary patch
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HcmV?d00001

diff --git a/tests/test_pydantic_ai.py b/tests/test_pydantic_ai.py
index 2f91b36..b6abe7c 100644
--- a/tests/test_pydantic_ai.py
+++ b/tests/test_pydantic_ai.py
@@ -79,7 +79,7 @@ def task_from_response(response: SendMessageResponse) -> Task:
 @pytest.fixture(scope='session')
 def image_content() -> BinaryContent:
     return BinaryContent(
-        data=(Path(__file__).parent / 'assets' / 'kiwi.jpg').read_bytes(),
+        data=(Path(__file__).parent / 'assets' / 'tiny.jpg').read_bytes(),
         media_type='image/jpeg',
     )
 
@@ -376,7 +376,7 @@ async def test_file_message_with_file_content(image_content: BinaryContent):
                             'role': 'user',
                             'parts': [
                                 {
-                                    'raw': 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',
+                                    'raw': IsStr(),
                                     'media_type': 'image/jpeg',
                                 }
                             ],
@@ -407,7 +407,7 @@ async def test_file_message_with_file_content(image_content: BinaryContent):
                                 'role': 'user',
                                 'parts': [
                                     {
-                                        'raw': 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',
+                                        'raw': IsStr(),
                                         'media_type': 'image/jpeg',
                                     }
                                 ],