Merge branch 'pydantic:main' into feature/1783-minimal-mcp-resources-support

Author: fennb

.github/workflows/after-ci.yml

@@ -45,6 +45,10 @@ jobs:
       name: deploy-docs-preview
 
     steps:
+      - run: echo "$GITHUB_EVENT_JSON"
+        env:
+          GITHUB_EVENT_JSON: ${{ toJSON(github.event) }}
+
       - uses: actions/checkout@v4
 
       - uses: actions/setup-node@v4
@@ -57,7 +61,8 @@ jobs:
           enable-cache: true
           cache-suffix: deploy-docs-preview
 
-      - uses: dawidd6/action-download-artifact@v6
+      - id: download-artifact
+        uses: dawidd6/action-download-artifact@v6
         with:
           workflow: ci.yml
           name: site
@@ -69,6 +74,7 @@ jobs:
 
       - uses: cloudflare/wrangler-action@v3
         id: deploy
+        if: steps.download-artifact.outputs.found_artifact == 'true'
         with:
           apiToken: ${{ secrets.CLOUDFLARE_API_TOKEN }}
           environment: previews
@@ -78,12 +84,9 @@ jobs:
             --var GIT_COMMIT_SHA:${{ github.event.workflow_run.head_sha }}
             --var GIT_BRANCH:${{ github.event.workflow_run.head_branch }}
 
-      - run: echo "$GITHUB_EVENT_JSON"
-        env:
-          GITHUB_EVENT_JSON: ${{ toJSON(github.event) }}
-
       - name: Set preview URL
         run: uv run --no-project --with httpx .github/set_docs_pr_preview_url.py
+        if: steps.deploy.outcome == 'success'
         env:
           DEPLOY_OUTPUT: ${{ steps.deploy.outputs.command-output }}
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/ci.yml

@@ -473,7 +473,12 @@ jobs:
           import os
           import tweepy
 
-          client = tweepy.Client(os.getenv("TWITTER_ACCESS_TOKEN"))
+          client = tweepy.Client(
+              access_token=os.getenv("TWITTER_ACCESS_TOKEN"),
+              access_token_secret=os.getenv("TWITTER_ACCESS_TOKEN_SECRET"),
+              consumer_key=os.getenv("TWITTER_CONSUMER_KEY"),
+              consumer_secret=os.getenv("TWITTER_CONSUMER_SECRET"),
+          )
           version = os.getenv("VERSION").strip('"')
           tweet = os.getenv("TWEET").format(version=version)
           client.create_tweet(text=tweet)
@@ -483,4 +488,7 @@ jobs:
             Pydantic AI version {version} is out! 🎉
 
             https://github.com/pydantic/pydantic-ai/releases/tag/v{version}
+          TWITTER_CONSUMER_KEY: ${{ secrets.TWITTER_CONSUMER_KEY }}
+          TWITTER_CONSUMER_SECRET: ${{ secrets.TWITTER_CONSUMER_SECRET }}
           TWITTER_ACCESS_TOKEN: ${{ secrets.TWITTER_ACCESS_TOKEN }}
+          TWITTER_ACCESS_TOKEN_SECRET: ${{ secrets.TWITTER_ACCESS_TOKEN_SECRET }}

docs-site/wrangler.toml

@@ -4,6 +4,7 @@ compatibility_date = "2025-06-17"
 routes = ["ai.pydantic.dev/*"]
 main = "src/index.ts"
 workers_dev = false
+preview_urls = true
 compatibility_flags = [ "nodejs_compat_v2" ]
 
 [vars]

docs/changelog.md

@@ -1,7 +1,6 @@
 # Upgrade Guide
 
-In September 2025, Pydantic AI reached V1, which means we're committed to API stability: we will not introduce changes that break your code until V2 (if we do, you can shout at us as it's definitely a mistake).
-Once we release V2, in April 2026 at the earliest, we'll continue to provide security fixes for V1 for another 6 months minimum, so you have time to upgrade your applications.
+In September 2025, Pydantic AI reached V1, which means we're committed to API stability: we will not introduce changes that break your code until V2. For more information, review our [Version Policy](version-policy.md).
 
 ## Breaking Changes
 

docs/version-policy.md

@@ -0,0 +1,29 @@
+## Version Policy
+
+We will not intentionally make breaking changes in minor releases of V1. V2 will be released in April 2026 at the earliest, 6 months after the release of V1 in September 2025.
+
+Once we release V2, we'll continue to provide security fixes for V1 for another 6 months minimum, so you have time to upgrade your applications.
+
+Functionality marked as deprecated will not be removed until V2.
+
+Of course, some apparently safe changes and bug fixes will inevitably break some users' code — obligatory link to [xkcd](https://xkcd.com/1172/).
+
+The following changes will **NOT** be considered breaking changes, and may occur in minor releases:
+
+* Bug fixes that may result in existing code breaking, provided that such code was relying on undocumented features/constructs/assumptions.
+* Adding new [message parts][pydantic_ai.messages], [stream events][pydantic_ai.messages.AgentStreamEvent], or optional fields on existing message (part) and event types. Always code defensively when consuming message parts or event streams, and use the [`ModelMessagesTypeAdapter`][pydantic_ai.messages.ModelMessagesTypeAdapter] to (de)serialize message histories.
+* Changing OpenTelemetry span attributes. Because different [observability platforms](logfire.md#using-opentelemetry) support different versions of the [OpenTelemetry Semantic Conventions for Generative AI systems](https://opentelemetry.io/docs/specs/semconv/gen-ai/), Pydantic AI lets you configure the [instrumentation version](logfire.md#configuring-data-format), but the default version may change in a minor release. Span attributes for [Pydantic Evals](evals.md) may also change as we iterate on Evals support in [Pydantic Logfire](https://logfire.pydantic.dev/docs/guides/web-ui/evals/).
+* Changing how `__repr__` behaves, even of public classes.
+
+In all cases we will aim to minimize churn and do so only when justified by the increase of quality of Pydantic AI for users.
+
+## Beta Features
+
+At Pydantic, we like to move quickly and innovate! To that end, minor releases may introduce beta features (indicated by a `beta` module) that are active works in progress. While in its beta phase, a feature's API and behaviors may not be stable, and it's very possible that changes made to the feature will not be backward-compatible. We aim to move beta features out of beta within a few months after initial release, once users have had a chance to provide feedback and test the feature in production.
+
+## Support for Python versions
+
+Pydantic will drop support for a Python version when the following conditions are met:
+
+* The Python version has reached its [expected end of life](https://devguide.python.org/versions/).
+* less than 5% of downloads of the most recent minor release are using that version.

mkdocs.yml

@@ -14,7 +14,6 @@ nav:
   - install.md
   - help.md
   - troubleshooting.md
-  - changelog.md
 
   - Documentation:
       - Core Concepts:
@@ -191,6 +190,8 @@ nav:
 
   - Project:
       - contributing.md
+      - changelog.md
+      - version-policy.md
 
 extra:
   # hide the "Made with Material for MkDocs" message
@@ -333,7 +334,6 @@ plugins:
           - install.md
           - help.md
           - troubleshooting.md
-          - changelog.md
         Concepts documentation:
           - a2a.md
           - ag-ui.md
@@ -365,11 +365,15 @@ plugins:
           - durable_execution/*.md
         MCP:
           - mcp/*.md
+        UI Event Streams:
+          - ui/*.md
         Optional:
           - testing.md
           - cli.md
           - logfire.md
           - contributing.md
+          - changelog.md
+          - version-policy.md
         Examples:
           - examples/*.md
 

pydantic_ai_slim/pydantic_ai/durable_exec/temporal/_function_toolset.py

@@ -1,57 +1,22 @@
 from __future__ import annotations
 
 from collections.abc import Callable
-from dataclasses import dataclass
-from typing import Annotated, Any, Literal
+from typing import Any, Literal
 
-from pydantic import ConfigDict, Discriminator, with_config
 from temporalio import activity, workflow
 from temporalio.workflow import ActivityConfig
-from typing_extensions import assert_never
 
 from pydantic_ai import FunctionToolset, ToolsetTool
-from pydantic_ai.exceptions import ApprovalRequired, CallDeferred, ModelRetry, UserError
+from pydantic_ai.exceptions import UserError
 from pydantic_ai.tools import AgentDepsT, RunContext
 from pydantic_ai.toolsets.function import FunctionToolsetTool
 
 from ._run_context import TemporalRunContext
-from ._toolset import TemporalWrapperToolset
-
-
-@dataclass
-@with_config(ConfigDict(arbitrary_types_allowed=True))
-class _CallToolParams:
-    name: str
-    tool_args: dict[str, Any]
-    serialized_run_context: Any
-
-
-@dataclass
-class _ApprovalRequired:
-    kind: Literal['approval_required'] = 'approval_required'
-
-
-@dataclass
-class _CallDeferred:
-    kind: Literal['call_deferred'] = 'call_deferred'
-
-
-@dataclass
-class _ModelRetry:
-    message: str
-    kind: Literal['model_retry'] = 'model_retry'
-
-
-@dataclass
-class _ToolReturn:
-    result: Any
-    kind: Literal['tool_return'] = 'tool_return'
-
-
-_CallToolResult = Annotated[
-    _ApprovalRequired | _CallDeferred | _ModelRetry | _ToolReturn,
-    Discriminator('kind'),
-]
+from ._toolset import (
+    CallToolParams,
+    CallToolResult,
+    TemporalWrapperToolset,
+)
 
 
 class TemporalFunctionToolset(TemporalWrapperToolset[AgentDepsT]):
@@ -70,7 +35,7 @@ def __init__(
         self.tool_activity_config = tool_activity_config
         self.run_context_type = run_context_type
 
-        async def call_tool_activity(params: _CallToolParams, deps: AgentDepsT) -> _CallToolResult:
+        async def call_tool_activity(params: CallToolParams, deps: AgentDepsT) -> CallToolResult:
             name = params.name
             ctx = self.run_context_type.deserialize_run_context(params.serialized_run_context, deps=deps)
             try:
@@ -84,15 +49,7 @@ async def call_tool_activity(params: _CallToolParams, deps: AgentDepsT) -> _Call
             # The tool args will already have been validated into their proper types in the `ToolManager`,
             # but `execute_activity` would have turned them into simple Python types again, so we need to re-validate them.
             args_dict = tool.args_validator.validate_python(params.tool_args)
-            try:
-                result = await self.wrapped.call_tool(name, args_dict, ctx, tool)
-                return _ToolReturn(result=result)
-            except ApprovalRequired:
-                return _ApprovalRequired()
-            except CallDeferred:
-                return _CallDeferred()
-            except ModelRetry as e:
-                return _ModelRetry(message=e.message)
+            return await self._wrap_call_tool_result(self.wrapped.call_tool(name, args_dict, ctx, tool))
 
         # Set type hint explicitly so that Temporal can take care of serialization and deserialization
         call_tool_activity.__annotations__['deps'] = deps_type
@@ -123,25 +80,18 @@ async def call_tool(
 
         tool_activity_config = self.activity_config | tool_activity_config
         serialized_run_context = self.run_context_type.serialize_run_context(ctx)
-        result = await workflow.execute_activity(  # pyright: ignore[reportUnknownMemberType]
-            activity=self.call_tool_activity,
-            args=[
-                _CallToolParams(
-                    name=name,
-                    tool_args=tool_args,
-                    serialized_run_context=serialized_run_context,
-                ),
-                ctx.deps,
-            ],
-            **tool_activity_config,
+        return self._unwrap_call_tool_result(
+            await workflow.execute_activity(  # pyright: ignore[reportUnknownMemberType]
+                activity=self.call_tool_activity,
+                args=[
+                    CallToolParams(
+                        name=name,
+                        tool_args=tool_args,
+                        serialized_run_context=serialized_run_context,
+                        tool_def=None,
+                    ),
+                    ctx.deps,
+                ],
+                **tool_activity_config,
+            )
         )
-        if isinstance(result, _ApprovalRequired):
-            raise ApprovalRequired()
-        elif isinstance(result, _CallDeferred):
-            raise CallDeferred()
-        elif isinstance(result, _ModelRetry):
-            raise ModelRetry(result.message)
-        elif isinstance(result, _ToolReturn):
-            return result.result
-        else:
-            assert_never(result)

pydantic_ai_slim/pydantic_ai/durable_exec/temporal/_mcp_server.py

@@ -11,11 +11,15 @@
 
 from pydantic_ai import ToolsetTool
 from pydantic_ai.exceptions import UserError
-from pydantic_ai.mcp import MCPServer, ToolResult
+from pydantic_ai.mcp import MCPServer
 from pydantic_ai.tools import AgentDepsT, RunContext, ToolDefinition
 
 from ._run_context import TemporalRunContext
-from ._toolset import TemporalWrapperToolset
+from ._toolset import (
+    CallToolParams,
+    CallToolResult,
+    TemporalWrapperToolset,
+)
 
 
 @dataclass
@@ -24,15 +28,6 @@ class _GetToolsParams:
     serialized_run_context: Any
 
 
-@dataclass
-@with_config(ConfigDict(arbitrary_types_allowed=True))
-class _CallToolParams:
-    name: str
-    tool_args: dict[str, Any]
-    serialized_run_context: Any
-    tool_def: ToolDefinition
-
-
 class TemporalMCPServer(TemporalWrapperToolset[AgentDepsT]):
     def __init__(
         self,
@@ -72,13 +67,16 @@ async def get_tools_activity(params: _GetToolsParams, deps: AgentDepsT) -> dict[
             get_tools_activity
         )
 
-        async def call_tool_activity(params: _CallToolParams, deps: AgentDepsT) -> ToolResult:
+        async def call_tool_activity(params: CallToolParams, deps: AgentDepsT) -> CallToolResult:
             run_context = self.run_context_type.deserialize_run_context(params.serialized_run_context, deps=deps)
-            return await self.wrapped.call_tool(
-                params.name,
-                params.tool_args,
-                run_context,
-                self.tool_for_tool_def(params.tool_def),
+            assert isinstance(params.tool_def, ToolDefinition)
+            return await self._wrap_call_tool_result(
+                self.wrapped.call_tool(
+                    params.name,
+                    params.tool_args,
+                    run_context,
+                    self.tool_for_tool_def(params.tool_def),
+                )
             )
 
         # Set type hint explicitly so that Temporal can take care of serialization and deserialization
@@ -125,22 +123,24 @@ async def call_tool(
         tool_args: dict[str, Any],
         ctx: RunContext[AgentDepsT],
         tool: ToolsetTool[AgentDepsT],
-    ) -> ToolResult:
+    ) -> CallToolResult:
         if not workflow.in_workflow():
             return await super().call_tool(name, tool_args, ctx, tool)
 
         tool_activity_config = self.activity_config | self.tool_activity_config.get(name, {})
         serialized_run_context = self.run_context_type.serialize_run_context(ctx)
-        return await workflow.execute_activity(  # pyright: ignore[reportUnknownMemberType]
-            activity=self.call_tool_activity,
-            args=[
-                _CallToolParams(
-                    name=name,
-                    tool_args=tool_args,
-                    serialized_run_context=serialized_run_context,
-                    tool_def=tool.tool_def,
-                ),
-                ctx.deps,
-            ],
-            **tool_activity_config,
+        return self._unwrap_call_tool_result(
+            await workflow.execute_activity(  # pyright: ignore[reportUnknownMemberType]
+                activity=self.call_tool_activity,
+                args=[
+                    CallToolParams(
+                        name=name,
+                        tool_args=tool_args,
+                        serialized_run_context=serialized_run_context,
+                        tool_def=tool.tool_def,
+                    ),
+                    ctx.deps,
+                ],
+                **tool_activity_config,
+            )
         )