Stirrup: Our new open source framework for building agents

It's lightweight, flexible and extensible and incorporates best-practices from leading agents like Claude Code

Stirrup differs from other agent frameworks by avoiding the rigidity that can degrade output quality. Stirrup lets models drive their own workflow, like Claude Code, while still giving developers structure and building in essential features like context management, MCP support and code execution. We use Stirrup at Artificial Analysis as part of our agentic benchmarks, including as part of our GDPval-AA evaluation being released later today. Just `pip install stirrup` to start building your own agents today!

Key advantages:

• Works with the model, not against it: Stirrup steps aside and lets the model decide how to solve multi-step tasks, as opposed to existing frameworks which impose strict patterns that limit performance.

• Best practices built in: We studied leading agent systems (e.g. Claude Code) to extract practical patterns around context handling, tool design, and workflow stability, and embedded those directly into the framework.

• Fully customizable: Use Stirrup as a package or as a starting template to build your own fully customized agents.

Feature highlights:

• Essential tools ready to use: Ships with pre-built tools such as online search and browsing, code execution (local, docker, or using an e2b sandbox), MCP client and document IO

• Flexible tool layer: A Generic Tool interface makes it simple to define and extend custom tools

• Context management: Automatic summarization to stay within context limits while preserving task fidelity

• Provider flexibility: Built in support for OpenAI compatible APIs (including Open Router) and LiteLLM, or bring your own client

• Multimodal support: Process images, video, and audio with automatic format handling

Example code for setting up a Stirrup agent

Stirrup agents can be easily set up in just a few lines of code

Stirrup includes built in logging to help you observe and debug agents

Start using Stirrup today via `pip install stirrup` or explore the repo

For detailed documentation, visit https://stirrup.artificialanalysis.ai/

To find the package on PyPI, visit https://pypi.org/project/stirrup/