mini-swe-agent
Mini SWE Agent - A simple AI software engineering agent
Decision gist · record as of 2026-08-14
Yes, if you need an AI agent for code tasks and want transparency and simplicity over feature richness. The low install friction, active maintenance, permissive MIT license, and zero known vulnerabilities make it a safe choice. The alpha status and reliance on external LLM APIs (not included) are minor caveats. Best suited for developers, researchers, and teams already comfortable with language model APIs.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Requires a configured language model API (via litellm, openrouter, portkey, or similar).
- Active maintenance with recent release (22 days old).
License · maintenance · safety
permissive license (permissive) — MIT License permits unrestricted use, modification, and distribution. No restrictions on commercial or proprietary use.
last release 2026-07-23 (22 days) · last repo commit 2026-08-10 · 6,509 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 15,501,428 downloads/mo, #1,184 on PyPI
Alternatives
Verify before relying
pip install mini-swe-agent
from mini_swe_agent import DefaultAgent, LitellmModel, LocalEnvironment
agent = DefaultAgent(
LitellmModel(model_name="gpt-4"),
LocalEnvironment(),
)
agent.run("Write a sudoku game")- Whether all 14 runtime dependencies are truly required for basic usage or if some are optional for specific features.
- Specific performance metrics on SWE-bench verified benchmark (description mentions >74% but exact version/conditions unclear).
- Stability and maturity level given 'Development Status :: 3 - Alpha' classifier despite adoption claims.
What it is and what it does
mini-swe-agent is a stripped-down AI coding agent that uses language models to solve software engineering tasks by executing bash commands in isolated environments. Unlike more complex agent frameworks, it has no custom tools beyond bash and maintains a linear message history, making it transparent for debugging and fine-tuning. It supports multiple deployment modes (local, Docker, Singularity, bubblewrap) and works with any model accessible through litellm, openrouter, or portkey.
The package is designed for developers who want a hackable baseline rather than a black-box system. It includes a CLI tool for interactive use, batch inference capabilities, and Python bindings for programmatic control. With 14 runtime dependencies (pyyaml, requests, jinja2, pydantic, litellm, tenacity, rich, python-dotenv, typer, platformdirs, textual, prompt_toolkit, datasets, openai), it provides a complete but minimal agent stack.
Use it for
- Automated code generation and bug fixing in software repositories using language model reasoning.
- Benchmarking language model coding capabilities on SWE-bench or similar evaluation frameworks.
- Building custom AI agents for software tasks by extending the simple agent scaffold with domain-specific prompts.
- Running AI-driven code analysis and refactoring workflows in isolated sandbox environments.
- Fine-tuning language models on agent trajectories without overfitting to complex agent infrastructure.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need an AI agent for code tasks and want transparency and simplicity over feature richness.
The low install friction, active maintenance, permissive MIT license, and zero known vulnerabilities make it a safe choice. The alpha status and reliance on external LLM APIs (not included) are minor caveats. Best suited for developers, researchers, and teams already comfortable with language model APIs.
Install
mini-swe-agent on PyPI
Before you install
Active maintenance with recent release (22 days old). Low install friction: pure Python wheel with 14 runtime dependencies including well-established packages like pydantic, requests, and litellm. No compiled dependencies required.
Requires Python 3.10 or later. Requires a configured language model API (via litellm, openrouter, portkey, or similar).
License in practice
MIT License permits unrestricted use, modification, and distribution. No restrictions on commercial or proprietary use.
Quickstart
pip install mini-swe-agent
from mini_swe_agent import DefaultAgent, LitellmModel, LocalEnvironment
agent = DefaultAgent(
LitellmModel(model_name="gpt-4"),
LocalEnvironment(),
)
agent.run("Write a sudoku game")Verify before relying
- Whether all 14 runtime dependencies are truly required for basic usage or if some are optional for specific features.
- Specific performance metrics on SWE-bench verified benchmark (description mentions >74% but exact version/conditions unclear).
- Stability and maturity level given 'Development Status :: 3 - Alpha' classifier despite adoption claims.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 14 packagespyyamlrequestsjinja2pydanticlitellmtenacityrichpython-dotenvtyperplatformdirstextualprompt_toolkitdatasetsopenai |
| Maintenance | Actively maintained 22 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 15,501,428 / month, #1,184 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10 |
Evidence: mini_swe_agent-2.4.6-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “llm bash executor”
- mini-swe-agentAn AI agent that solves software engineering tasks by running bash…
- deepagents-codeA terminal-based AI coding agent that works with any LLM supporting…
- jinaJina is a framework for building and deploying AI services that…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also swe-rex · swesmith · swebench · google-agents-cli · tom-swe · deepagents-code · qwen-agent · agent_governance_toolkit · terminal-bench · code-puppy