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Category
AI Agents
Rank
Pricing
Open Source
Type
AGENT
Use case
Coding · Security & Identity
Interfaces
CLI · Web
Builder
swe-agent
Latest release
v1.1.0
Date

About

SWE-agent lets a language model like GPT-4o or Claude autonomously use tools to fix real GitHub issues, solve custom coding tasks, or find cybersecurity vulnerabilities via its EnIGMA mode. It is a research project from Princeton and Stanford that achieves state-of-the-art results on SWE-bench and is fully configurable through a single YAML file.

What it does

SWE-agent wraps a large language model in a command-line loop that can read a codebase, run shell commands, edit files, and submit a patch, all aimed at closing a real bug report or coding task on its own. Every step the agent is allowed to take, and how it is prompted, comes from a YAML configuration rather than hardcoded logic, so the same harness can be pointed at ordinary software fixes or, in a separate mode, at security capture-the-flag puzzles.

Why it's ranked here

This is the project that established the agent-computer interface pattern much of today's coding-agent tooling still uses, from a Princeton and Stanford research team with a peer-reviewed paper behind it. That track record is the case for it, not current momentum: the maintainers themselves now point newcomers toward a smaller successor project and describe this one as superseded. Worth knowing for the ideas and the benchmark work; anyone choosing what to run today should read the maintainers' own recommendation before picking this over the newer one.

What's good

The configuration system is genuinely deep: agent prompts, allowed tools, model choice, and the execution interface are all controlled from YAML files that can be layered and merged rather than edited in code. Command-line usage covers single runs, batch runs, and trajectory replay for debugging, and there are two separate inspectors, a terminal one and a browser-based one, for stepping back through what the agent actually did. Model access goes through a provider-agnostic layer rather than being tied to one vendor's API.

Tradeoffs

The headline caveat is in the project's own documentation: it has been superseded by a simpler successor the maintainers now recommend by default, so anyone adopting this version is opting into the older, more complex codebase over the one actively being improved. The offensive-security mode is currently unsupported on the main line and requires checking out an old release tag to use. This is an academic research codebase first, not a polished product, so expect surface area built for experimentation rather than turnkey deployment.

How to use it well

Reach for this if you want to study or extend the agent-computer interface approach itself, reproduce benchmark results, or need the offensive-security capture-the-flag mode and are willing to pin an older release for it. If you just want a working coding agent to point at a repository today, read the maintainers' own recommendation first: they steer new users toward the newer, simpler successor project instead. Treat the YAML configuration layer as the main lever for adapting it to a new task.

Technical notes+

The package entry point is defined in pyproject.toml as project.scripts sweagent = sweagent.run.run:main, and sweagent/__main__.py just imports and calls that same main function, so the installed sweagent CLI and python -m sweagent are equivalent. sweagent/__init__.py enforces Python 3.11+ at import time, resolves CONFIG_DIR and TOOLS_DIR relative to the package location (overridable via the SWE_AGENT_CONFIG_DIR and SWE_AGENT_TOOLS_DIR environment variables), and calls impose_rex_lower_bound(), which raises if the separately installed swe-rex execution-backend package is below its minimum supported version. docs/usage/cli.md documents the run, run-batch, run-replay, inspect, inspector, quick-stats, merge-preds, traj-to-demo, and remove-unfinished subcommands. docs/config/index.md describes YAML configs as mergeable via repeated --config flags, resolved against SWE_AGENT_CONFIG_ROOT, and documents a multimodal profile (default_mm_with_images.yaml) that raises max_observation_length and adds image_tools and web_browser tool bundles plus an image_parsing history processor. pyproject.toml lists dependencies including litellm, GitPython, ghapi, flask/flask-cors/flask-socketio (backing the web inspector), textual (backing the terminal inspector), and swe-rex, and its [tool.pytest.ini_options] block points testpaths at a tests directory.

Observed

License
MIT licensed, per both README.md and the pyproject.toml license field.
Packaging
Requires Python 3.11 or newer; packaged as a pip-installable Python project with a sweagent CLI entry point.
CLI commands
CLI subcommands include run, run-batch, run-replay, inspect (terminal), inspector (web-based), quick-stats, merge-preds, traj-to-demo, and remove-unfinished.
Configuration
Agent behavior, prompts, tool access, and model choice are all set through external YAML config files rather than code, and multiple config files can be merged.
Dependencies
Depends on litellm for model access, making the LLM backend swappable rather than fixed to one provider.
Dependencies
Depends on a separate swe-rex package for executing commands in the target environment, with a minimum required version enforced at startup.
Interfaces
Ships a Flask/Flask-SocketIO-based web inspector alongside a Textual-based terminal inspector.
Operating modes
Has two operating modes: a general coding-task/bug-fix mode and a separate offensive-security (capture-the-flag) mode; the latter is documented as unsupported on the current main line and requiring an older release.
Project status
The README states development focus has shifted to a separate, simpler successor project that the maintainers describe as having superseded this one and recommend using instead.

Read from README.md, pyproject.toml, sweagent/__init__.py, sweagent/__main__.py, docs/usage/cli.md, docs/config/index.md.

What it can do

  • Autonomously resolve GitHub issues using an LLM-driven agent

    GitHub issue → Code fix/patch

  • Find cybersecurity vulnerabilities via EnIGMA mode

    CTF challenge or codebase → Identified vulnerability

  • Configure agent behavior through a single YAML file

    YAML configuration file → Configured agent

Tags

swe-agentswe-benchcoding-agentautonomous-agentllmcybersecurityctfresearch

Tech Stack

Python

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