The 10 most popular open source agent projects in 2025: AutoGPT, MetaGPT, AutoGen, etc.

Comprehensive Review of Popular Open Source AI Agents

I've divided open-source AI agents into three main categories: automatic agents, semi-automatic agents, and customized agents that support human intervention. Before we dive in, it's essential to note that these agent projects are currently experimental. They lack the maturity and community support typically seen with knowledge-based question-answer projects. Deployment issues often arise, and solutions might be scarce.

Below, I provide insights into popular agents based on my experiences from deployment and development over the past year.

1. Fully Automatic Agents

autoGPT

• Fully automated and lacks human intervention capability.
• Requires OpenAI API, users report it's more of an API cost driver rather than productive.
• Limited customization, primarily changing agent names.

loopGPT

• Enhanced version of autoGPT.
• Improved token optimization.
• Allows minimal human corrections.
• Retains model memory after interruptions.
• Recommended over autoGPT for basic experimentation.

babyAGI

• Breaks user instructions into distinct tasks executed sequentially by large models.
• Utilizes vector databases like Pinecone or Weaviate.
• Logical framework similar but distinct from Camel, suitable for simple applications.

2. Semi-Automatic Agents

Camel

• Superior deployment and customization compared to autoGPT and loopGPT.
• Introduces interactive role-play between multiple AI agents.
• Customizable roles and tasks with user-defined SOPs.
• Supports API integrations (web browsing, document processing, multimedia generation).
• Compatible with open-source models like Vicuna and Llama 2, no vector database required.

3. Domain-Specific Agents

chatDev

• Targets software development with pre-defined organizational roles.
• Effective for collaborative coding, testing, and documentation.

MetaGPT

• Converts high-level user requirements into detailed software development processes.
• Structured around roles like product managers, engineers, and architects.
• Strong community and mature deployment for software-oriented tasks.

4. Customized Agents

SuperAGI

• Highly mature with commercial-level applications.
• Supports customizable models, knowledge bases, and extensive tool integrations (Notion, Twitter, Slack).
• Advanced agent management and concurrent operations.
• Lacks native domestic model support, optimal for international tool integration.

autoGen

• Offers comprehensive customizability with models, knowledge bases, SOPs, and human intervention.
• Notably mature community.
• Deployment challenges noted, particularly related to OpenAI API compatibility.

AIwaves Waveform Intelligent Agents

• Extensive customization, including agent roles, dialogue interactions, and human correction capabilities.
• Offers examples for practical applications like IT support and e-commerce bots.
• Immature community with notable deployment hurdles.

Swarms

• Allows detailed customization of models, tools, SOP, and response cycles.
• Simplified multiple-agent management.
• Community still developing, worth monitoring for future improvements.

Summary and Recommendations

• Fully automatic agents (autoGPT, loopGPT, babyAGI) have limited practical value but are good for initial explorations.
• Semi-automatic agent Camel is versatile and relatively mature, ideal for intermediate deployments.
• Domain-specific agents (chatDev, MetaGPT) offer excellent structured processes for software development tasks.
• Customized agents (SuperAGI, autoGen, AIwaves, Swarms) provide the highest customization potential:
  • SuperAGI excels internationally.
  • autoGen offers extensive customization but has deployment complexities.
  • AIwaves and Swarms present high potential but require further community maturity.

Carefully consider your project's specific requirements and the agent's deployment maturity before selecting an AI agent solution.