SWE-agent is a developer-focused AI agent framework that integrates with GitHub to autonomously diagnose and resolve code issues. It runs in Docker or GitHub Codespaces, uses your preferred language model, and allows you to configure tool bundles for tasks like linting, testing, and deployment. SWE-agent generates clear action trajectories, applies pull requests with fixes, and provides insights via its trajectory inspector, enabling teams to automate code review, bug fixing, and repository cleanup efficiently.
SWE-agent Core Features
Autonomous code issue detection and fixing
Integration with GitHub repositories
Support for GPT-4, Claude, and custom LMs
Configurable tool bundles
Docker and Codespaces deployment
Trajectory inspector for step-by-step output
SWE-agent Pro & Cons
The Cons
No explicit pricing information available
No mention of native mobile or desktop applications
May require technical expertise to install and customize
Limited information about user community or commercial support
The Pros
State-of-the-art performance on SWE-bench among open-source projects
Enables autonomous language model tool usage for diverse tasks
Highly configurable and fully documented with a simple YAML file
Free-flowing and generalizable design allowing maximum LM agency
Developed and maintained by leading researchers at Princeton and Stanford
Open-source and research-friendly, designed to be hackable
CAMEL-AI is an open-source LLM multi-agent framework enabling autonomous agents to collaborate using retrieval-augmented generation and tool integration.
CAMEL-AI is a Python-based framework that allows developers and researchers to build, configure, and run multiple autonomous AI agents powered by LLMs. It offers built-in support for retrieval-augmented generation (RAG), external tool usage, agent communication, memory and state management, and scheduling. With modular components and easy integration, teams can prototype complex multi-agent systems, automate workflows, and scale experiments across different LLM backends.
HMAS is an open-source Python framework that enables development of hierarchical multi-agent systems. It offers abstractions for defining agent hierarchies, inter-agent communication protocols, environment integration, and built-in training loops. Researchers and developers can use HMAS to prototype complex multi-agent interactions, train coordinated policies, and evaluate performance in simulated environments. Its modular design makes it easy to extend and customize agents, environments, and training strategies.
