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Tutorial 040: Sync Pydantic AI Agent

This tutorial demonstrates how to build a synchronous Pydantic AI agent on AgentEx with:

  • Tool calling (Pydantic AI handles the tool loop internally)
  • Streaming token output (including token-by-token tool-call argument streaming)

Key Concepts

Sync ACP

The sync ACP model uses HTTP request/response for communication. The @acp.on_message_send handler receives a message and yields streaming events back to the client.

Pydantic AI Integration

  • Agent: A single pydantic_ai.Agent that owns the model and tools. No graph required — Pydantic AI runs its own tool-call loop until the model is done.
  • @agent.tool_plain: Registers a Python function as a tool. Pydantic AI infers the schema from type hints and docstring.
  • agent.run_stream_events(...): Yields AgentStreamEvents (PartStartEvent / PartDeltaEvent / PartEndEvent / FunctionToolResultEvent) as the model produces them.

Streaming

The agent streams tokens and tool-call arguments as they're generated using convert_pydantic_ai_to_agentex_events(), which adapts Pydantic AI's stream into AgentEx TaskMessageUpdate events. Notably, tool-call arguments stream as ToolRequestDelta tokens rather than arriving as a single complete payload — a richer experience than what OpenAI Agents SDK currently exposes.

Files

File Description
project/acp.py ACP server and message handler
project/agent.py Pydantic AI agent + tool registration
project/tools.py Tool definitions (weather example)
tests/test_agent.py Integration tests
manifest.yaml Agent configuration

Running Locally

# From this directory
agentex agents run

Running Tests

pytest tests/test_agent.py -v

Notes

  • Multi-turn conversation memory is not wired in this tutorial. Pydantic AI does not ship a checkpointer like LangGraph; to add memory, load prior messages via adk.messages.list(task_id=...) and pass them to agent.run_stream_events(..., message_history=...).
  • Reasoning/thinking tokens are not exercised here because gpt-4o-mini does not emit ThinkingParts. Swap to a reasoning-capable model (e.g. openai:o1-mini via Pydantic AI's appropriate provider) if you want to test that branch end-to-end.