AI agents are quickly becoming a critical building block for developers, researchers, and startups. Whether you're automating research, building a task-based assistant, or deploying autonomous multi-agent systems, the open-source ecosystem is exploding with new frameworks designed to help you build faster and smarter.
In this post, we compare 7 of the most popular open-source AI agent projects on GitHub in 2025. These frameworks vary in maturity, use case, and architecture—but each offers powerful capabilities for building next-gen AI systems.
1. Owl by camel-ai
Owl is an open-source project from the team behind CAMEL, designed to build robust multi-agent AI systems. It's inspired by autonomous agents that can collaborate, reason, and self-improve.
Key Features:
- Modular agent architecture
- Supports hierarchical reasoning and tool use
- Integration with LLMs and custom memory stores
Use Case Fit:
- Multi-agent systems
- Task decomposition
- Research workflows
2. LangManus
LangManus focuses on building intelligent language agents that can reason, plan, and execute complex tasks using code and tools.
Key Features:
- Code-first agent design
- Supports Python functions and external APIs
- Emphasis on tool-augmented LLM behavior
Use Case Fit:
- Developer tools
- Data workflows and pipelines
- Automation of structured tasks
3. crewAI
CrewAI allows you to define and run teams of AI agents (“crews”) that can work together with role-based responsibilities.
Key Features:
- Role-based multi-agent collaboration
- Task orchestration via “Crew” definitions
- Built-in memory and context-sharing support
Use Case Fit:
- Customer service agents
- Collaborative writing and content generation
- AI-as-a-service apps
4. OpenManus
OpenManus is a lightweight and flexible agent framework that supports plug-and-play environments for experimentation and research.
Key Features:
- Custom task templates
- Modular agent environment wrappers
- Clean abstraction for memory, actions, and planning
Use Case Fit:
- Academic research
- Agent experimentation
- Flexible prototyping
5. DeepClaude by Asterisk
DeepClaude is an open research agent that focuses on deep document understanding and summarization using LLMs.
Key Features:
- Semantic search + summarization pipeline
- Handles PDF, HTML, and unstructured text
- Supports Claude, GPT, and open-source models
Use Case Fit:
- Competitive intelligence
- Legal and technical doc review
- Research automation
6. deep-research
This project is built to automate deep research tasks using autonomous agent loops. It focuses on recursive research, multi-step planning, and long-context memory.
Key Features:
- Looping agent workflows
- Memory persistence and context awareness
- Markdown + PDF report generation
Use Case Fit:
- Market research
- Investment analysis
- LLM research pipelines
7. open-deep-research
A fork and extension of the deep-research approach, this project emphasizes modularity, extensibility, and faster task runtime.
Key Features:
- Task queue management
- Modular tool integration (search, summarization, QA)
- CLI + API support for automation
Use Case Fit:
- Developer-first research agents
- AI-enhanced analyst workflows
- Background research bots
Final Thoughts: Which AI Agent Project Should You Choose?
If you're building:
- A collaborative multi-agent app → Try crewAI or Owl
- A research bot with deep doc parsing → Go with DeepClaude or deep-research
- A developer tool or agent-as-a-service → Explore LangManus or open-deep-research
These open-source projects give you the building blocks to create autonomous, LLM-powered systems tailored to your use case. As the agent ecosystem continues to evolve, we’ll keep updating TrustedBy.ai with the most trusted tools being used in the wild.