- Researchers benchmarking autonomous coding agent capabilities
- Teams studying how LLMs interact with real-world development environments
- Developers building autonomous issue-fixing pipelines for GitHub repositories
openagent @ swe-agent ~ $
cat README.md
SWE-agent
Autonomous coding agent that takes GitHub issues and fixes them using LLMs, achieving state-of-the-art results on SWE-bench.
$
pip install swe-agentInstalling SWE-agent...
SWE-agent ready
$
swe-agent --helpReading swe-agent configuration & environment...
●
Waiting for input... # core strengths
What makes SWE-agent different
◆
Autonomous issue fixing
SWE-agent takes a GitHub issue as input and autonomously produces a fix, from understanding the problem to writing the code and creating a pull request.
This is the end-to-end vision for coding agents: describe a problem and get a fix, with no human intervention required.◆
Agent-computer interface design
SWE-agent's ACI defines how the agent interacts with the development environment — file navigation, search, editing, and testing — optimized for LLM capabilities.
The ACI design influenced many subsequent coding agents and demonstrates that how an agent interacts with tools matters as much as the model itself.◆
SWE-bench benchmark leader
Achieves state-of-the-art results on the SWE-bench benchmark, which tests agents on real-world GitHub issues from popular Python repositories.
Benchmark results provide objective evidence of capability and help researchers compare different agent approaches. # quick start
Your first command
$
pip install swe-agent●
Ready. Run --help to explore. # use cases
How developers use SWE-agent
01
Autonomous bug fixing research
Use SWE-agent to study how well LLMs can understand bugs, navigate codebases, and produce correct fixes without human guidance.
02
Agent design experimentation
Experiment with different ACI configurations to understand how tool design affects agent performance on coding tasks.
03
Issue triage and fixing pipelines
Build pipelines that automatically attempt to fix incoming GitHub issues and submit PRs for human review.
# comparison
How SWE-agent compares
Choose SWE-agent for autonomous issue fixing vs interactive coding agents
SWE-agent is designed for autonomous operation: give it an issue and get a fix. Interactive tools like Aider or Claude Code are better for conversational coding sessions.
# faq
Questions
Q: What should I check before using SWE-agent?
Start with one safe workflow for SWE-agent. Inspect official setup instructions, required credentials, execution logs, approval points, and failure recovery before expanding from a sandbox task into production automation.
Q: Is SWE-agent open source?
SWE-agent is listed with MIT based on the official source links in this profile. Re-check the repository, model card, or docs before production use.
Q: Who should evaluate SWE-agent?
SWE-agent is most worth evaluating for researchers benchmarking autonomous coding agent capabilities.
Decision brief
JSON
Should you use SWE-agent?
- Developers who need interactive, conversational coding assistance
- Teams looking for a production-ready CI/CD integration for automated bug fixing
- Verified 2026-05-27
- License: MIT
- Repo: SWE-agent/SWE-agent
- Open-source signal
Deployment
cloud
Permission surface
shell/files
Decision signals
No extra signals recorded
Agent packet
Structured decision data for SWE-agent
This packet is the compact machine-readable view agents should use before following source links or taking action.
Capabilities
workflow orchestration
Constraints
open source
Deployment
cloud
Permission surface
shell/files
Recommended workflows
Coding agent workflow
Overview
What SWE-agent does
What it is
SWE-agent is an open agent resource to evaluate by action surface: what software it can operate, which tools or browser steps it touches, and how much supervision it needs before it can run real work.
Why it matters
The SWE-bench benchmark, which SWE-agent helped popularize, tests agents on real-world GitHub issues from popular Python repositories. SWE-agent's state-of-the-art results on this benchmark demonstrated that autonomous coding is viable, not just a research curiosity. Its ACI design also showed that tool interface design is a critical factor in agent performance.
How to evaluate it
Start with one safe workflow for SWE-agent. Inspect official setup instructions, required credentials, execution logs, approval points, and failure recovery before expanding from a sandbox task into production automation.
Facts
Known metadata and operating surface
These fields are separated from editorial interpretation so agents can reason over facts and missing checks.
Resource type agent
Category Agents
Maturity active
Difficulty Unknown
License MIT
Pricing open source
Verified 2026-05-27
Source confidence high
Risk level moderate
Fit matrix
Where SWE-agent fits in an agent stack
strong
Coding agent workflow
SWE-agent has multiple signals for coding agent workflow, including matching tags, capabilities, category, or positioning.
- Run a small repository change and inspect the diff, tests, and rollback path.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Connector or protocol layer
SWE-agent has at least one signal for connector or protocol layer, but should be checked against a real task before adoption.
- Connect one low-risk service, then inspect schemas, auth scope, errors, and logs.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Evaluation and observability
SWE-agent has at least one signal for evaluation and observability, but should be checked against a real task before adoption.
- Add one repeatable test case and confirm results can run again in review or CI.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Memory or RAG workflow
SWE-agent has at least one signal for memory or rag workflow, but should be checked against a real task before adoption.
- Create, update, retrieve, correct, and delete memory or retrieval objects with real data.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Reusable skill workflow
SWE-agent has at least one signal for reusable skill workflow, but should be checked against a real task before adoption.
- Run one skill end to end and check whether it produces evidence or structured output.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
weak
Browser automation
SWE-agent is not primarily positioned for browser automation in the current metadata.
- Run one non-sensitive website task and inspect clicks, waits, retries, and changed URLs.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
Inputs and outputs
What an agent should inspect
Likely inputs
- Repositories, files, issues, terminal output, and test results
- Prompts, messages, documents, images, or model inputs
- Official setup instructions and a small real workflow
Likely outputs
- Diffs, commits, explanations, test results, or review notes
- A decision on whether this resource fits the target workflow
Evidence
Sources, claims, and missing checks
Claims are marked separately from source links so future crawlers and reviewers can update them without rewriting the page.
GitHub github
Repository source for code, license, issues, releases, and implementation details.
Homepage homepageOfficial or project-controlled source for this resource profile.
Docs docsDocumentation source for setup, API shape, and operational behavior.
SWE-agent is listed as open source.
License metadata: MITSWE-agent has a recorded GitHub repository: SWE-agent/SWE-agent.
Resource facts and GitHub source link.SWE-agent supports these recorded deployment modes: cloud.
OpenAgent decision signal metadata.SWE-agent is tagged with workflow orchestration capabilities.
OpenAgent capability taxonomy.- Repository freshness has not been recorded.
Next action
How to start evaluating SWE-agent
Inspect repository
Check license, recent activity, issues, examples, and security-sensitive code paths.
Open sourceOpen Homepage
Start from the official source before adopting third-party instructions.
Open sourceRead setup docs
Use docs as the source of truth for installation and supported interfaces.
Open sourceInstall SWE-agent
Install via pip, then configure your GitHub token and model provider to start running autonomous issue fixes.
pip install swe-agent Compare
Alternatives and nearby resources
Use related resources to compare category fit, license, deployment model, and first-workflow behavior.
FAQ
Common questions about SWE-agent
What should I check before using SWE-agent?
Start with one safe workflow for SWE-agent. Inspect official setup instructions, required credentials, execution logs, approval points, and failure recovery before expanding from a sandbox task into production automation.
Is SWE-agent open source?
SWE-agent is listed with MIT based on the official source links in this profile. Re-check the repository, model card, or docs before production use.
Who should evaluate SWE-agent?
SWE-agent is most worth evaluating for researchers benchmarking autonomous coding agent capabilities.
Who should use SWE-agent?
Researchers studying autonomous coding agents, teams building automated bug-fixing pipelines, and developers interested in agent-computer interface design.
How does SWE-agent compare to other coding agents?
SWE-agent is unique in its focus on autonomous issue fixing and benchmark performance. Unlike interactive tools like Aider or Claude Code, it's designed to operate without human intervention.