Agentic WeeklyGet the weekly
OpenAIv0.24.0Tested (smoke test)

OpenAI Agents SDK: what’s new in v0.24.0 and our verdict

Lightweight Python framework for multi-agent workflows, with tools, handoffs, sessions and tracing.

Official site

Release Ruler for OpenAI Agents SDK: marked at its Tested release date on the timeline of every AI model we track.

At a glance

Evidence
Tested (smoke test)
Released
Oct 15, 2026
Last verified
Oct 25, 2026
Pricing
Not verified
Best for
agent building

Evidence and ranking

Evidence behind this verdict

  1. Sources read2 cited sources, latest fetched Oct 25, 2026. (done)
  2. Facts re-checkedLast verified Oct 25, 2026. (done)
  3. Run by usSmoke test on Oct 25, 2026 (scope below). (done)

Passed Test log result: Passed. Read the log

Where we rank it (editorial)

Editorial picks, not benchmark scores. How we rank.

Pricing: We have not verified pricing for this tool; see the official site.

Our verdict

Pick it if you want a small, official framework for multi-agent flows on the OpenAI API; the update is incremental.

Good for

  • Python developers building agents on OpenAI models
  • Teams that want MCP tools plus approvals and sessions
  • Prototypers who want few abstractions

Skip if

  • You need a TypeScript-first framework
  • You want a framework tied to no model vendor

Tested. Tested 2026-10-25 (America/Chicago). Installed openai-agents 0.24.0 into a fresh Python 3.13 venv. Ran an Agent with one @function_tool through Runner.run using the Chat Completions model class against a local Ollama model (no OpenAI key). The run produced a tool call, a tool output and a final message ("9 words", which is correct). Not tested: OpenAI-hosted models, the default Responses API, tracing, MCP, approvals or sessions. Test environment: Linux x86_64 box with 8 CPUs and no GPU, no vendor account or API key. Agent tools were pointed at a local open-weight model (Qwen2.5-3B-Instruct, 4-bit, served by Ollama 0.40.11), so we tested the tool, not any vendor's hosted models. Extended 2026-10-25: OpenAI Agents SDK for JS (0.21.0) passed 2 of 5 (Node 20) and 1 of 5 (Node 24) at default sampling but 5 of 5 on both Node versions once temperature was pinned to 0. OpenAI Agents SDK (Python) 0.24.0 passed our one-tool task on a local 3B model: 3 of 3 trials; cold uv install 1.7 s. Full log: /tests/openai-agents-js. Full log: /tests/openai-agents-sdk.

Full test log with exact commands, versions and raw output: OpenAI Agents SDK for JavaScript 0.21.0: our test log, OpenAI Agents SDK (Python) 0.24.0: our test log.

Evidence
Tested (smoke test, 2026-10-25, local Qwen2.5-3B-Instruct, not vendor models). A smoke test is not a benchmark.
What we ran
Installed openai-agents 0.24.0 and ran an Agent with one function tool through Runner.run using the Chat Completions model class against a local model; it produced a tool call, a tool output and a correct final message (9 words). Extended: OpenAI Agents SDK for JS (0.21.0) passed 2 of 5 (Node 20) and 1 of 5 (Node 24) at default sampling but 5 of 5 on both Node versions once temperature was pinned to 0. OpenAI Agents SDK (Python) 0.24.0 passed our one-tool task on a local 3B model: 3 of 3 trials; cold uv install 1.7 s.
Model used
Qwen2.5-3B-Instruct (4-bit), a small local open-weight model served by Ollama 0.40.11; no vendor-hosted model was used
Environment
Linux x86_64 box, 8 CPUs, no GPU; no vendor account, login or API key
Not tested
OpenAI-hosted models, the default Responses API, tracing, MCP, approvals, sessions; Any vendor-hosted model: everything ran against one local 3B model; Multi-agent flows, memory, streaming and deployment; Anything beyond one tool and one question; five trials is a smoke test, not a benchmark; Any vendor-hosted model: everything ran on a local 3B model, so tool-call reliability with frontier models is not measured here; Multi-agent flows, memory, long conversations, streaming, tracing and cloud deployment; Anything beyond one tool and one question; three trials is a smoke test, not a benchmark.
Date
(America/Chicago)

Best for

What’s new

openai-agents-python v0.24.0 exposes MCP annotations on converted tools and adds run-wide tool exception formatting, alongside session-write and compaction fixes.

  • v0.24.0 (Oct 15): Expose MCP annotations on converted tools.
  • v0.24.0: Support run-wide tool exception formatting.
  • v0.24.0: Batch OpenAI conversation session writes within the API limit, and defer automatic compaction for pending approvals.
  • v0.24.0: Many dependency bumps, including temporalio and vercel-sandbox.

How to set it up: Install the SDK and run a first agent

Install steps come from the vendor's docs. We ran the install and a short real task on 2026-10-25; see the test notes above for what was and was not covered.

  1. Create a virtual environment and install

    The README shows pip, or uv if you prefer.

    python -m venv .venv
source .venv/bin/activate
pip install openai-agents

  2. Set your API key

    The SDK calls the OpenAI API, so export your key as an environment variable. Keep it out of source control.

    export OPENAI_API_KEY=your-key

  3. Copy the hello-world agent from the README

    The README has a short example that creates an agent and runs it. Start there before adding tools or handoffs.

Pros and cons

Pros

  • MIT license, official OpenAI project
  • Small surface area
  • MCP, sessions and tracing support

Cons

  • Releases are mostly dependency bumps and fixes
  • Works best with OpenAI models
  • We only ran it against a local model through the Chat Completions class, not OpenAI's hosted models

Alternatives and comparisons

  • LangGraph: Low-level framework for building long-running, stateful agents as graphs.
  • Google ADK: Open-source, code-first Python toolkit for building, evaluating and deploying agents.
  • CrewAI: Python framework for orchestrating role-playing, autonomous AI agents that work as a crew.
  • Langflow: MIT-licensed visual builder for AI agents and MCP servers with a very large GitHub following.

See all alternatives to OpenAI Agents SDK

OpenAI models we track

All models · Releases by month

FAQ

What is new in openai-agents-python 0.24.0?

MCP annotations are exposed on converted tools, run-wide tool exception formatting is supported, and session writes and compaction have fixes.

How do I install the OpenAI Agents SDK?

pip install openai-agents in a virtual environment, or uv add openai-agents.

Does it only work with OpenAI?

The README is built around the OpenAI API; we did not verify support for other providers.

What we have not verified

  • Support for non-OpenAI models

Sources

Last verified · Agentic Weekly

Part of the week of Oct 12, 2026 archive.

Latest news about OpenAI Agents SDK

  • Langflow: MIT-licensed visual builder for AI agents and MCP servers with a very large GitHub following.
  • Botpress: Customer-support AI agent platform with a free plan and conversation-based paid plans.
  • Gumloop: Agent builder for teams; plans start at $37 per month with 20k included credits.

How to cite this page

Agentic Weekly, "OpenAI Agents SDK v0.24.0: what's new, verdict & setup", last updated 2026-10-25, https://theagenticweekly.com/tools/openai-agents-sdk

Keep the Reported or Tested label when you quote a verdict. Plain-text version: https://theagenticweekly.com/tools/openai-agents-sdk.md. More for AI assistants: /ai, /tools.json, /llms.txt.