Terminal.skills
Skills/open-swe
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open-swe

Build asynchronous coding agents using LangChain's Open SWE framework — agents that plan, code, test, and iterate on software engineering tasks. Use when: building coding bots, automating issue resolution, creating SWE agents that work on repos asynchronously.

#swe-agent#coding-agent#langchain#async#automation
terminal-skillsv1.0.0
Works with:claude-codeopenai-codexgemini-clicursor
Source

Usage

$
✓ Installed open-swe v1.0.0

Getting Started

  1. Install the skill using the command above
  2. Open your AI coding agent (Claude Code, Codex, Gemini CLI, or Cursor)
  3. Reference the skill in your prompt
  4. The AI will use the skill's capabilities automatically

Example Prompts

  • "Review the open pull requests and summarize what needs attention"
  • "Generate a changelog from the last 20 commits on the main branch"

Information

Version
1.0.0
Author
terminal-skills
Category
Development
License
MIT

Use Cases

→Build an Async SWE Coding Bot

Documentation

Overview

Open SWE (by LangChain) is an open-source framework for building asynchronous software engineering agents that can autonomously plan, code, test, and submit pull requests. Unlike synchronous coding assistants, Open SWE agents work in the background — pick up a GitHub issue, work on it for minutes to hours, and deliver a ready-to-review PR.

GitHub Issue (labeled "ai-fix")
    ↓ webhook
Open SWE Agent
    ├── Planner: analyze issue, explore codebase, create plan
    ├── Coder: implement changes following plan
    ├── Tester: run tests, fix failures
    └── Reviewer: self-review before PR
    ↓
Pull Request with description + test results

Instructions

When a user asks to build an async coding agent, automate issue resolution, or create SWE bots:

  1. Install Open SWE — pip install open-swe langgraph langchain-anthropic
  2. Configure the agent — Instantiate SWEAgent with an LLM, repo path, and tools
  3. Connect to GitHub — Use GitHubIntegration to listen for labeled issues
  4. Decompose complex tasks — Use TaskPlanner for multi-step issues
  5. Enable test loops — Set run_tests=True so the agent iterates until tests pass

Basic Agent Setup

python
from open_swe import SWEAgent
from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(model="claude-sonnet-4-20250514")
agent = SWEAgent(llm=llm, repo_path="/path/to/repo", tools=["bash", "file_editor", "search"])

result = await agent.solve(
    issue="Fix the login timeout bug - sessions expire after 5 minutes instead of 30",
)
print(result.patch)       # unified diff
print(result.explanation) # what was changed and why

GitHub Integration

python
from open_swe.integrations import GitHubIntegration

github = GitHubIntegration(token=os.environ["GITHUB_TOKEN"], repo="owner/repo")

@github.on_issue(labels=["ai-fix"])
async def handle_issue(issue):
    agent = SWEAgent(llm=llm, repo_path=github.clone())
    result = await agent.solve(issue=issue.body)
    if result.success:
        pr = await github.create_pr(
            title=f"Fix: {issue.title}",
            body=f"Resolves #{issue.number}\n\n{result.explanation}",
            branch=f"ai-fix/{issue.number}",
            patch=result.patch,
        )
        await issue.comment(f"PR created: {pr.url}")
    else:
        await issue.comment(f"Could not resolve automatically:\n{result.error}")

Examples

Example 1: Decompose a Complex Feature into Subtasks

python
from open_swe.planner import TaskPlanner

planner = TaskPlanner(llm=llm)
tasks = await planner.decompose(
    issue="Add user avatar upload with S3 storage and image resizing",
    codebase_context=agent.explore_codebase(),
)
# Returns: [
#   "Add S3 upload utility in lib/storage.ts",
#   "Create avatar resize middleware using sharp",
#   "Add PUT /api/users/:id/avatar endpoint",
#   "Write tests for upload and resize",
#   "Update user profile component to show avatar",
# ]

for task in tasks:
    result = await agent.solve(issue=task)
    agent.apply_patch(result.patch)

Example 2: Process Multiple Issues in Parallel with Test Loops

python
import asyncio
from open_swe import SWEAgent

async def process_issues(issues: list[str]):
    tasks = []
    for issue in issues:
        agent = SWEAgent(llm=llm, repo_path=clone_repo())
        tasks.append(agent.solve(issue=issue, max_iterations=5, run_tests=True, test_command="pytest"))
    return await asyncio.gather(*tasks)

results = asyncio.run(process_issues([
    "Fix SQL injection in search endpoint",
    "Add rate limiting to API",
    "Update deprecated dependencies",
    "Add input validation to signup form",
    "Fix timezone bug in event scheduler",
]))

for r in results:
    for i, attempt in enumerate(r.iterations):
        print(f"Attempt {i+1}: {'PASS' if attempt.tests_passed else 'FAIL'}")

Guidelines

  • Explore first — The agent should read relevant files before coding
  • Plan before code — Create an implementation plan, get approval for large changes
  • Test after change — Run tests after every modification to catch regressions early
  • Self-review — Check own code for issues before submitting a PR
  • Incremental — Apply changes file by file, testing between each step