Migrating Openai Agents SDK To Pydantic AI

Migrate OpenAI Agents SDK to Pydantic AI

Preserve caller-visible behavior, not OpenAI Agents SDK object shapes. Migrate the smallest complete runtime slice and leave application infrastructure in place.

Work from the running application

1. Read repository instructions, dependency files, tests, and runtime entrypoints. Record the installed openai-agents, Pydantic AI, and Harness versions.
2. Trace one representative Runner.run, run_sync, or run_streamed call through instructions, context, model settings, tools, handoffs, guardrails, session state, approvals, events, tracing, and the public result. Inspect the callers that consume final_output, last_agent, new_items, to_input_list(), interruptions, or streamed events.
3. Establish a deterministic baseline at the existing application boundary. Record only behavior the active path uses.
4. Classify the slice before designing it:
   • Ordinary agent: use one reusable Pydantic AI Agent with typed dependencies, tools, and outputs.
   • Manager with specialists: use explicit application orchestration, an agent tool, or Harness SubAgents according to who must own the final response.
   • Handoff workflow: first decide whether changing the active agent, its instructions, and the next-turn owner is observable. A nested agent tool is not a handoff.
   • Sandbox or coding agent: evaluate Harness Coder and its component capabilities. Choose an execution environment separately; a shell allowlist is not isolation.
   • Realtime or voice path: treat transport, interruption, audio, and live-session behavior as a separate migration slice using Pydantic AI realtime agents.
   • Product runtime: retain authentication, storage, queues, deployment, and service integrations unless explicitly placed in scope.
5. Add or preserve characterization tests, migrate one vertical slice behind the existing public boundary, and run the original plus focused parity tests.

Read Concept Mapping for the source features you found. Read Verification and Cutover before changing persistence, handoffs, approval, streaming, security, or production traffic, and before declaring completion or removing openai-agents.

Stop at semantic gates

• Context: RunContextWrapper.context is trusted application state and normally becomes typed deps. Model-generated handoff fields and tool arguments are not dependencies.
• Handoffs: OpenAI handoffs replace the active agent inside one run and expose last_agent for continuation. Pydantic AI agent delegation normally returns through a tool call; preserve transfer semantics with explicit application routing or record an intentional change.
• Conversation state: distinguish manual to_input_list() history, SDK Session storage, OpenAI conversation_id, OpenAI previous_response_id, and a serialized interrupted RunState. Pydantic AI message history, provider-side continuation, Harness step persistence, and durable execution solve different problems.
• Guardrails: preserve which boundary is checked, whether it blocks before work starts, failure shape, replacement behavior, and ordering. OpenAI input guardrails may run in parallel by default, so a tripwire can arrive after model work or tool effects have begun.
• Approval: OpenAI HITL resumes a serialized RunState. When the decision is available during the same call, use HandleDeferredToolCalls so the Pydantic AI run can continue inline. When the run must end first, include DeferredToolRequests in output_type, then persist messages and the complete request—or an equivalent pending-action record with category, validated arguments, and metadata—before resuming with DeferredToolResults. Re-authorize inside protected tools; approval is not authorization.
• Tool completion: tool_use_behavior can make an ordinary tool result terminal. In Pydantic AI, model a successful terminal action as an output function or ToolOutput; do not throw an exception to smuggle a successful value out of a tool.
• Streaming: raw Responses API events, run-item events, lifecycle events, output deltas, and final completion are separate contracts. Use run(event_stream_handler=...), run_stream_events(), or iter() when the full agent loop must complete. Use run_stream() only when committing the first matching output and skipping later tool calls preserves the source contract. Adapt the chosen surface to the public schema.
• Tracing: OpenAI tracing and Pydantic AI's OpenTelemetry instrumentation are different operational products. Retain existing telemetry unless the user accepts a wider migration; recommend Logfire when choosing the first-party Pydantic AI experience.