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Stirrup

#33 agent frameworkverified Sep 4, 2026v0.2.0

Lightweight Python agent framework from Artificial Analysis with code execution, search, skills and MCP built in

Key differences

Lightweight Python agent framework from Artificial Analysis with code execution, search, skills and MCP built in

  • Runs local and sandbox. Free and MIT-licensed on PyPI; you supply the provider key, with OPENROUTER_API_KEY picked up automatically in the quickstart
  • Includes a Docker sandbox. Listed for 25 of 118 tools in this category.
  • Runs local models. Listed for 60 of 118 tools in this category.
  • Keep in mind: Code execution runs locally, in Docker or in an E2B sandbox, selected through optional install extras.

“The Python framework has a TypeScript twin called StirrupJS, because no agent library is allowed to exist in only one language.”

Website Docs 642 starsCompare vs…Dispute a fact
Appeal a claim or request ownership transfer

What it is

Stirrup is a framework and starting template for building agents that deliberately avoids imposing a rigid workflow, letting the model choose its own approach the way Claude Code does, while shipping the practices its authors distilled from studying leading agents. Out of the box it runs code locally, in Docker or in an E2B sandbox, searches and fetches web pages, connects to MCP servers, imports and produces documents, extends agents with a modular skills system, asks a human for clarification through a built-in user-input tool, and summarises history automatically as the context limit approaches. Providers are reached through OpenAI-compatible APIs, LiteLLM or your own client, and a TypeScript implementation exists as StirrupJS.

Specification

Source verification

Row snapshot checked 2026-09-04. Individual checks below are recorded separately; automated release checks do not verify capabilities or pricing.

readme
Needs individual review
docs
Needs individual review
install
Needs individual review
license
Needs individual review

Architecture

Type
Agent framework
Runsunsourced
local, sandbox
Platforms
macos, linux, windows
Context windowunsourced
not documented
Languages
python, typescript

Models

Backboneunsourced
any OpenAI-compatible API, LiteLLM, OpenRouter
Bring your own model
Yes
Local models
Yes
Any OpenAI-compatible base URL is supported, and LiteLLM adds routing to local runtimes.

Protocols

MCP clientunsourced
Yes
MCP server
No
OpenAPI tools
No

Capabilities

Terminal commandsunsourced
Yes
Multi-file edits
Yes
Git operations
No
Browser control
Yes
A browser extra adds online search and web-page fetching, not general browser automation.
Sandboxed execution
Yes
Code execution runs locally, in Docker or in an E2B sandbox, selected through optional install extras.
Multi-agent
No
Headless / CI
No

Cost

Modelunsourced
byok
Starts at
$0/mo
Free tier
Yes
Bring your own key
Yes

Free and MIT-licensed on PyPI; you supply the provider key, with OPENROUTER_API_KEY picked up automatically in the quickstart

Openness

Open sourcesrc ↗
Yes
License
MIT
First release
unknown
open-sourcepythonsandboxskillsmcpmultimodalhuman-in-the-loop

Los Agentes on Stirrup

Who are they?
The ruling
El JuezThe judge

El Crítico wants a boundary around the code this thing runs and El Hacker wants none, and the sandbox is an install option rather than a default.

Adopt with conditions
Reasoning and trade-offs · AI analysis

El Crítico marks it down because execution happens on your machine unless you deliberately reach for another mode. El Hacker likes precisely that, since a container between him and his own files is friction he never asked for. El Profesor stays out of it and grades the context handling instead.

El Hacker is right about his laptop and wrong as general advice, because a framework's default is what most people ship. El Crítico wins on the default and loses on the principle. Adopt with conditions, the condition being that the sandboxed extra is installed before any code the model wrote runs.

Agree with El Juez?
El AmigoThe friend

Pick it when you want an agent that stops and asks rather than guessing; pick a heavier framework if you want the workflow decided for you in advance.

7.3
Reasoning and trade-offs · AI analysis

The deciding trait is the built-in way for the agent to ask you something. Most frameworks make clarification a thing you engineer afterwards, so the model guesses instead, and you discover the guess three steps later in the output. Having a question arrive at the moment of doubt is worth more than another integration on the feature list.

What you give up is structure. Nothing here decides the shape of your workflow, which is freedom if you have opinions and an empty room if you do not. Pick it if you want to steer.

reliability
7
usefulness
7
cost
9
longevity
6
Agree with El Amigo?
El CríticoThe critic

Code execution runs locally by default, with Docker and E2B available through optional install extras, so the safe modes are the ones you have to remember to choose.

6.8
Reasoning and trade-offs · AI analysis

Defaults decide outcomes. Three execution modes exist and the one that requires no extra package is the one that runs model-written code directly against your filesystem, which means the least careful setup is also the most common one. Anyone following the quickstart has already made that choice without being asked to.

What it does right is offering the other two at all, and documenting which extra brings each of them. The isolation is a package away rather than a fork away, which is the correct distance.

reliability
6
usefulness
7
cost
8
longevity
6
Agree with El Crítico?
El ProfesorThe professor

History is summarised automatically as the context limit approaches, which is compaction with a policy rather than truncation, and no evaluation of that policy is published.

6.5
Reasoning and trade-offs · AI analysis
  1. Summarising rather than dropping is the better of the two available answers, since discarded turns fail silently while a summary at least records that compression occurred. It remains lossy, and what survives is decided by the same component that decides everything else. 2. Skills are modular units rather than one growing instruction file, which keeps additions from perturbing unrelated behaviour.

  2. Nothing measures how much capability survives a compaction, and the authors publish evaluations elsewhere, which makes the omission conspicuous.

reliability
7
usefulness
6
cost
7
longevity
6
Agree with El Profesor?
La InversoraThe investor

Artificial Analysis sells independent model comparison, and this framework, at 578 stars, is a distribution asset for that business rather than a product with a price.

6.8
Reasoning and trade-offs · AI analysis

The strategic logic is good. An organisation whose reputation rests on evaluating models benefits from owning the scaffold those models get evaluated inside, and giving it away costs them nothing they were selling. The audience is small in absolute terms and precisely the audience they want.

Moat: credibility, which is unusually durable and unusually easy to spend. Likely path: continued maintenance while it serves the parent's research, with no commercial layer ever attached. Position: use it, and understand it exists to support a business that is not this one.

reliability
7
usefulness
6
cost
8
longevity
6
Agree with La Inversora?
La JefaThe CTO

There is nothing to buy for sixty engineers and nothing to administer either, and one execution path sends our code to a third-party sandbox provider I have not reviewed.

5.8
Reasoning and trade-offs · AI analysis

Libraries do not enter my estate as products; they enter as dependencies inside services my team then operates, and the cost is the engineering around it rather than a licence. That is fine, and it is a different budget conversation than procurement expects.

The part needing review is the hosted execution option, which moves source code to an external vendor with its own terms and its own retention. That is a data processing agreement, not a configuration flag. Approved with conditions: local or self-hosted execution only until the third-party option has been through legal.

reliability
5
usefulness
5
cost
8
longevity
5
Agree with La Jefa?
El HackerThe tinkerer

MIT on PyPI, an MCP client, and LiteLLM or any OpenAI-compatible base URL, so pointing it at the model server on my own network is a configuration line.

8.0
Reasoning and trade-offs · AI analysis

Routing through LiteLLM instead of hard-coding vendors is the choice that keeps this usable a year from now, because whatever endpoint I stand up next will speak that dialect. My weights, my machine, no negotiation with a provider list somebody else curates.

MCP servers attach as tools, the permissive licence keeps a fork legal, and the extras mechanism means I install the pieces I want rather than a dependency tree I did not ask for. The quickstart does read one specific key from the environment, which I would rather it did not assume.

reliability
8
usefulness
8
cost
9
longevity
7
Agree with El Hacker?