Comprehensive Guide to AI Agentic Frameworks in 2025
As of April 2025, the landscape of AI agentic frameworks has evolved significantly, offering diverse solutions for building autonomous agents that can perceive, reason, and act dynamically. These frameworks provide the architecture and tools necessary for creating AI systems capable of handling complex tasks with minimal human intervention.
Table of Major AI Agentic Frameworks
| Framework | Type | Category | License | GitHub Stars | Key Specialization |
|---|---|---|---|---|---|
| Amazon Bedrock | Commercial | General-purpose | Proprietary | N/A | Multi-agent collaboration with enterprise support |
| Fine | Commercial | Specialized | Proprietary | N/A | Code review and implementation |
| Microsoft AutoGen | Open Source | Multi-agent | MIT | 25K+ | Agent orchestration and conversation |
| LangGraph | Open Source | Workflow | Apache 2.0 | 15K+ | Graph-based workflows and decision trees |
| LangChain | Open Source | LLM Integration | MIT | 70K+ | Modular LLM application building |
| CrewAI | Open Source | Multi-agent | MIT | 12K+ | Role-based agent collaboration |
| Agno (Phidata) | Open Source | Multi-modal | MIT | 8K+ | Memory and knowledge management |
| OpenManus | Open Source | General-purpose | MIT | 5K+ | Alternative to Manus AI |
| Open Interpreter | Open Source | Specialized | MIT | 51K+ | Code execution |
| Vanna | Open Source | Specialized | MIT | 10K+ | SQL generation |
| Sweep | Open Source | Specialized | MIT | 7K+ | Coding tasks |
| PR-Agent | Open Source | Specialized | MIT | 5K+ | Pull request automation |
| Devon | Open Source | Specialized | MIT | 3K+ | Alternative to Devin |
| Jarvis | Open Source | Multi-agent | MIT | 23K+ | Multi-model collaboration |
| Baby AGI | Open Source | Multi-agent | MIT | 19K+ | Task-driven autonomous agents |
| DevOpsGPT | Open Source | Specialized | MIT | 6K+ | DevOps workflows |
| AgentGPT | Open Source | Platform | MIT | 31K+ | Browser-based agent deployment |
| Superagent | Open Source | Platform | MIT | 5K+ | Custom agent building |
| Project Alice | Open Source | Platform | MIT | N/A | Unified agent platform |
| LlamaIndex | Open Source | Data Integration | MIT | 25K+ | Data source connection |
| Botpress | Open Source | Conversational | Apache 2.0 | 19K+ | Conversational agents |
| Eliza | Open Source | General-purpose | MIT | N/A | General agent framework |
| SmolAgents | Open Source | Specialized | Apache 2.0 | N/A | Lightweight agent implementation |
| Composio | Open Source | Multi-agent | MIT | N/A | Emerging framework |
Commercial Agentic Frameworks
Amazon Bedrock and Bedrock Agents
Amazon Bedrock provides a robust framework for deploying and managing AI agents, with capabilities recently expanded to include multi-agent collaboration. This system enables specialized agents to work under a supervisor agent’s coordination, addressing complex workflows that require various skills.
Key features include:
- Multi-agent collaboration with supervisor-based architecture
- Task specialization with domain-specific expertise
- Memory retention across interactions
- Code interpretation for dynamic generation and execution
- Retrieval augmented generation (RAG) capabilities
Other Commercial Offerings
Fine serves as a virtual team member for developers, offering automated code reviews and implementations directly in GitHub. Its contextual understanding can answer questions about codebases and execute code changes based on specified tasks.
Leading Open-Source Frameworks
Microsoft AutoGen
AutoGen is Microsoft’s framework for building and orchestrating multiple AI agents that can converse with each other to solve tasks.
Key features include:
- Asynchronous messaging between agents
- Modular and extensible design with pluggable components
- Robust debugging and observability tools
- Scalable and distributed deployment capabilities
In its latest version (v0.4), AutoGen introduced significant improvements including an asynchronous, event-driven architecture that enables more flexible collaboration patterns and stronger observability.
LangGraph and LangChain
LangGraph is designed for building complex AI workflows as graphs, where each node represents a specific task or function. It works well with LangChain, which focuses on integrating Large Language Models (LLMs) for modular development.
Key capabilities:
- Visual and structural mapping of workflows
- Transparency at each decision point
- Support for human-in-the-loop processes
- Specialized in dynamic decision-making
CrewAI
CrewAI focuses on orchestrating agent teams with defined roles and shared objectives. It’s particularly designed for scenarios requiring collaboration among agents.
Key features:
- Role-based agent collaboration with defined goals
- Production-ready framework for real-world applications
- Pythonic design using annotations to define agents, tools, and tasks
- Inter-agent communication through real-time message passing
Phidata (now Agno)
Phidata (rebranded as Agno) is a lightweight framework for building multi-modal agents that leverages both closed and open LLMs. It specializes in memory and knowledge management, making it suitable for building agents that need to maintain context across interactions.
OpenManus
OpenManus is an emerging open-source alternative to Manus AI, built on state-of-the-art LLMs like GPT-4o. Unlike invitation-only platforms, OpenManus is freely available and offers:
- Modular and extensible architecture
- Advanced autonomous capabilities for complex tasks
- Ability to generate and execute workflows
- Open-source community contributions
The platform can be applied to web development, research, data analysis, and task planning with minimal human intervention.
Specialized Frameworks
Coding-Focused Frameworks
Several frameworks specialize in coding tasks:
- Open Interpreter: Enables LLMs to run code on your computer (51k+ GitHub stars)
- Vanna: Transforms analytical questions into SQL code (10k+ GitHub stars)
- Sweep: Specializes in coding tasks (7k+ GitHub stars)
- PR-Agent: Handles automated pull requests (5k+ GitHub stars)
- Devon: An open-source alternative to Devin (3k+ GitHub stars)
Multi-Agent Systems
- Jarvis: Microsoft’s framework for connecting and collaborating with 20+ different AI models (23k+ GitHub stars)
- Baby AGI: Multi-agent system with 19k+ GitHub stars
- DevOpsGPT: Specialized for DevOps workflows (6k+ GitHub stars)
Framework Development Platforms
- AgentGPT: Allows users to configure and deploy autonomous AI agents in browsers (31k+ GitHub stars)
- Superagent: Build-your-own agent framework (5k+ GitHub stars)
- Project Alice: Combines Autogen (chat), Autogen Studio (UI), and Langchain (tasks) into a single platform
Emerging and Specialized Frameworks
- LlamaIndex: Specializes in connecting data sources like PDFs and databases to LLMs, ideal for knowledge retrieval applications
- Botpress: Offers pre-built modules for creating conversational agents
- Eliza: Listed among top frameworks for 2025
- SmolAgents: Developed by HuggingFace for lightweight agent implementations
- Composio: Emerging framework for multi-agent systems
Considerations for Selecting an Agentic Framework
When choosing an agentic framework, organizations should consider:
- Use case requirements: Different frameworks excel at different tasks – from conversation-based agents to workflow orchestration
- Integration capabilities: How well the framework integrates with existing systems and LLMs
- Scalability needs: Whether the framework can handle enterprise-scale deployments
- Customization options: The level of control and flexibility offered in agent design
- Development complexity: Learning curves and technical expertise required
- Open-source vs. commercial: Budget considerations and need for community support
- Security and compliance: Data handling practices and regulatory alignment
- Community activity: Regular updates and responsive maintenance
- Documentation quality: Comprehensive guides and examples for implementation
Conclusion
The AI agent framework landscape continues to evolve rapidly, with frameworks becoming increasingly specialized and sophisticated. While commercial offerings like AWS Bedrock provide enterprise-grade solutions with comprehensive support, open-source frameworks such as AutoGen, CrewAI, and OpenManus offer flexibility and community-driven innovation.
Organizations should evaluate these frameworks based on their specific use cases, technical requirements, and long-term AI strategy. As multi-agent systems become more prevalent, frameworks that facilitate collaboration and orchestration between specialized agents will likely see increased adoption across industries.