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swe agent

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Basic Information

This repository provides an AI-powered software engineering agent that automates code implementation by combining research-driven planning and atomic implementation workflows. It is built with LangGraph to orchestrate multiple agents and uses a two-stage design: an Architect agent that analyzes a target codebase, generates hypotheses and a structured implementation plan, and a Developer agent that executes the plan by producing precise diffs and file modifications. The project includes typed Pydantic state models to manage orchestrator state, agent-specific state, and shared entities such as ImplementationPlan, ImplementationTask, and AtomicTask. Tool integrations include tree-sitter for code parsing, code search utilities, and file operations to operate on a workspace repository. The repo targets early adopters and contributors and is explicitly marked as alpha with experimental features.

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515
App URL
https://github.com/langtalks/swe-agent
Github Repository
https://github.com/langtalks/swe-agent/blob/main/README.md

App Details

Features
The README documents core features such as intelligent code planning where an AI architect analyzes requirements and the codebase to create detailed, atomic implementation plans. Automated code generation is handled by a developer agent that applies diffs and modifies files step-by-step. The system uses a multi-agent LangGraph workflow separating planning and execution. Codebase understanding is enhanced with tree-sitter and semantic search tools. State management relies on Pydantic models for type safety, traceability, and resumability. Additional features include hypothesis-driven research pipelines, atomic task execution for safer changes, tooling for code search and codemap analysis, and test integration examples. The project also lists roadmap items like testing agents, error fixer agents, and GitHub MCP integration.
Use Cases
This project helps teams and contributors automate repetitive and complex software engineering tasks by turning high-level requests into validated implementation plans and applying incremental, verifiable code changes. It can accelerate feature development, automate bug fixes, assist with refactoring while preserving functionality, and generate or update documentation and tests. The typed state and message history improve traceability, allow workflows to be resumed, and support modular agent responsibilities. Integrations with Anthropic for reasoning, LangChain and LangGraph for orchestration, and tree-sitter for parsing make the agent practical for code analysis and edits. The repo includes quickstart instructions, developer tooling, and tests to onboard contributors and run agents against a workspace repository.

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