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This PR contains the following updates:

Package Change Age Confidence
openai-agents ==0.4.2==0.8.3 age confidence

Release Notes

openai/openai-agents-python (openai-agents)

v0.8.3

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v0.8.2

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v0.8.1

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v0.8.0

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Key Changes

Human-in-the-Loop (HITL)

The human-in-the-loop (HITL) flow enables your agents to pause execution until a person approves or rejects sensitive tool calls. Tools declare when they need approval, run results surface pending approvals as interruptions, and RunState lets you serialize and resume runs after decisions are made.

import asyncio
from agents import Agent, Runner, RunState, function_tool

@​function_tool
async def get_weather(city: str) -> str:
    return f"The weather in {city} is sunny"

# This function requires approval
@​function_tool(needs_approval=True)
async def get_temperature(city: str) -> str:
    return f"The temperature in {city} is 20° Celsius"

agent = Agent(
    name="Weather Assistant",
    instructions="You are a helpful weather assistant. Answer questions about weather and temperature using the available tools.",
    tools=[get_weather, get_temperature],
)

async def main():
    result = await Runner.run(agent, "What is the weather and temperature in Oakland?")
    has_interruptions = len(result.interruptions) > 0

    while has_interruptions:
        state = result.to_state()
        # Process each interruption
        for interruption in result.interruptions:
            print("\nTool call details:")
            print(f"  Agent: {interruption.agent.name}")
            print(f"  Tool: {interruption.name}")
            print(f"  Arguments: {interruption.arguments}")
            confirmed = await confirm("\nDo you approve this tool call?")
            if confirmed:
                print(f"✓ Approved: {interruption.name}")
                state.approve(interruption)
            else:
                print(f"✗ Rejected: {interruption.name}")
                state.reject(interruption)

        # Resume execution with the updated state
        print("\nResuming agent execution...")
        result = await Runner.run(agent, state)
        has_interruptions = len(result.interruptions) > 0

    print(result.final_output)

Refer to the document page and examples for more details.

Migration Guide

In this version, two runtime behavior changes may require migration work:

  • Function tools wrapping synchronous Python callables now execute on worker threads via asyncio.to_thread(...) instead of running on the event loop thread. If your tool logic depends on thread-local state or thread-affine resources, migrate to an async tool implementation or make thread affinity explicit in your tool code.
  • Local MCP tool failure handling is now configurable, and the default behavior can return model-visible error output instead of failing the whole run. If you rely on fail-fast semantics, set mcp_config={"failure_error_function": None}. Server-level failure_error_function values override the agent-level setting, so set failure_error_function=None on each local MCP server that has an explicit handler.

What's Changed

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Full Changelog: openai/openai-agents-python@v0.7.0...v0.8.0

v0.7.0

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Key Changes

Nested handoff behavior is now opt-in

The nested handoffs behavior were enabled by default in v0.6.0. Now, it is now disabled by default. To enable it again, you need to set the nest_handoff_history option to True.

from agents import Agent, MCPServerManager, RunConfig, Runner

agent = Agent(name="My agent", instructions="Be creative")
result = await Runner.run(
    agent,
    input="Hey, can you tell me something interesting about Japan?",
    run_config=RunConfig(nest_handoff_history=True),
)
MCPServerManager for multiple MCP server instances

Starting with this version, there is a new, convenient way to manage multiple MCP server instances. See #​2350 and examples/mcp/manager_example.

from contextlib import asynccontextmanager
from fastapi import FastAPI
from agents import Agent, Runner
from agents.mcp import MCPServerManager, MCPServerStreamableHttp

@​asynccontextmanager
async def lifespan(app: FastAPI):
    async with MCPServerManager(
        servers=[
            MCPServerStreamableHttp({"url": 'http://localhost:8001/mcp'}),
            MCPServerStreamableHttp({"url": 'http://localhost:8002/mcp'}),
        ],
        connect_in_parallel=True,
    ) as manager:
        app.state.mcp_manager = manager
        yield

app = FastAPI(lifespan=lifespan)

@​app.post("/agent")
async def run_agent(req) -> dict[str, object]:
    agent = Agent(
        name="Test Agent",
        instructions="Use the MCP tools when needed.",
        mcp_servers= app.state.mcp_manager.active_servers,
    )
    result = await Runner.run(starting_agent=agent, input=build_query(req))
    return {"output": result.final_output}
Other key changes
  • The default reasoning.effort for gpt-5.1/5.2 is now set to "none"; if you rely on the previous default "low" set by the default model configuration, please explicitly set it to "low" in your agent's model_settings.
  • When using a sessions store, session_input_callback used to be required to be provided. Now, it is optional and the default behavior is to append the new input to the session history.

What's Changed

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v0.6.9

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v0.6.7

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Experimental: Codex Tool Support

Starting with this version, we have added a new experimental Codex extension (agents.extensions.experimental.codex). This extension allows you to use Codex as a tool within your agents. Since this module is still experimental, its behavior and implementation details may change in future releases.

If you run an agent with codex_tool() on a host where Codex is installed, the agent will use Codex as a tool to answer the question. The tool simply runs the Codex CLI as a subprocess, so all existing Codex configuration, skills, and capabilities are available without any additional setup.

See the example code and #​2320 for more details.

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Full Changelog: openai/openai-agents-python@v0.6.6...v0.6.7

v0.6.6

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v0.6.0

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Key changes

Handoff behavior has a breaking change in this version. Specifically, the message history is now collapsed into a single message when handing off to a new agent. We have verified and evaluated this behavior and think it works better, but recommend testing with your agents before upgrading to v0.6.0 in production.

What's Changed

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@renovate renovate bot force-pushed the renovate/openai-agents-0.x branch from 8c213d3 to e35718d Compare November 11, 2025 01:11
@renovate renovate bot changed the title fix(deps): update dependency openai-agents to v0.5.0 fix(deps): update dependency openai-agents to v0.5.1 Nov 14, 2025
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@renovate renovate bot changed the title fix(deps): update dependency openai-agents to v0.6.0 fix(deps): update dependency openai-agents to v0.6.1 Nov 20, 2025
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