These open-source AI agents enhance the autonomy of large language models (LLMs) by leveraging tool-use and decision-making capabilities. Some tools described as “AI agents” aren’t actually all that agentic; these systems (e.g., Devon PR-agent) are largely RL-based AI workflows, with LLMs organized through predefined code paths:
🧑💻 Coding agents:
- Open Interpreter – Runs code locally using LLMs (like ChatGPT on your machine).
- Vanna – Converts data questions into SQL code automatically.
- Devon – An open-source version of Devin (AI coding assistant).
- PR-Agent – Automates pull request generation and updates in codebases.
📋 General-purpose agents:
- Jarvis – Connects 20+ AI models for multitasking and collaboration.
- evoninja – Uses AI personas to handle specific business workflows.
🛠️ AI agent builders (build your own):
- LangGraph – Creates AI workflows across APIs and tools; best for RAG and custom pipelines.
- AutoGen – Multi-agent coordination for tasks like code gen; supports low-code.
- Camel – Creates multi-agent role-play systems; low-code friendly.
- CrewAI – Uses pre-built agent templates; no-code required.
Open-source AI agent examples
Open-source AI agents enable developers, engineers, and users to enhance the autonomy of large language models (LLMs) by leveraging tool-use, decision-making, and problem-solving capabilities, without vendor lock-in.
Open Interpreter
LLM-based AI
Vanna
LLM-based AI (RAG only)
Sweep
LLM-based AI
PR-Agent
RL-based AI
Devon
RL-based AI
| AI agent | Autonomy level | Specialization | # GitHub stars (k) |
|---|---|---|---|
| Open Interpreter | LLM-based AI | Coding | 51+ |
| Vanna | LLM-based AI (RAG only) | Coding (writing SQL) | 10+ |
| Sweep | LLM-based AI | Coding | 7+ |
| PR-Agent | RL-based AI | Coding (automated pull requests) | 5+ |
| Devon | RL-based AI | Coding | 3+ |
| ReactAgent | LLM-based AI | Coding | 1+ |
| Aide | LLM-based AI | Coding | 0.3 |
| Microsoft Jarvis | LLM-based AI | General purpose | 23+ |
| Baby AGI | LLM-based AI | General purpose | 19+ |
| DevOpsGPT | LLM-based AI | General purpose | 6+ |
| evoninja | LLM-based AI | General purpose | 1+ |
| Allice | LLM-based AI | General purpose | 0.7 |
| GPT researcher | LLM-based AI | Research | 13+ |
See the explanation for specializations.
Open-source AI agent builders
Open-source AI agent builders are best for complex, API-driven AI projects that require customization and coding. Some, like Crew AI Camel and AutoGen, also support low-code setups.
| AI agent | Specialization | Autonomy level | # GitHub stars (k) |
|---|---|---|---|
| LangGraph | NLP task automation | LLM-based AI | 11+ |
| AutoGen | Data and content automation | LLM-based AI | 43+ |
| CrewAI | Workflow automation | LLM-based AI | 30+ |
| Camel | Workflow automation | LLM-based AI | 5+ |
| AgentGPT | Workflow automation | LLM-based AI | 31+ |
| Superagent | Workflow automation | LLM-based AI | 5+ |
LangGraph is proprietary software, but it provides an open-source library for agent development.
Read more:
What is an AI agent?
Traditional AI defines agents as systems that interact with their surroundings. However, some researchers use the term “agentic” in the context of action-based LLM systems.
We agree with this viewpoint, hence, we do not provide a strict definition. Instead, we list the factors that cause an AI system to be considered more agentic:
- Environment and objectives:
- AI systems in complex environments, such as those with multiple tasks and unexpected changes, are agentic.
- AI systems that follow goals without being instructed are agentic.
- User interface and supervision: AI systems that can learn natural languages and systems that need less user supervision are agentic.
- System design: Systems that use design patterns such as tool usage (e.g., web search, programming) or planning (e.g., reflection, subgoal breakdown) are agentic.
See a detailed explanation: Capabilities of AI agents.
Source: LangChain1
AI agents use cases
There have been attempts to create AI agents in several domains. These autonomous tools can serve as:
- Software and application developers
- Human-like gaming characters
- Content creators
- Insurance assistants
- Human resources (HR) assistants
- Customer service assistants
- Research assistants
- Computer users
- AI agent builders
Are these agents fully autonomous?
Not yet. Most open-source AI agents enhance LLM autonomy by enabling tool use, decision-making, and problem-solving, but they still require structured inputs and a human in the loop.
Examples like Devon and PR-Agent follow predefined logic or RL workflows rather than demonstrating full agentic behavior. Other AI agents still lack (Autonomous Learning + Generalization) capabilities.
Why use open-source AI agents?
AI agents have the potential to execute complex goals with minimal direct supervision—removing labor-intensive activities while allowing humans to focus on higher-level tasks.
Here are some key points why open-source AI agents are useful for businesses, developers, and researchers:
- Data ownership and privacy: Using open-source autonomous agents allows you to maintain control over your data. Since the AI tools will be hosted on your own servers, sensitive data does not need to be sent to third-party services. This improves privacy and security.
- Cost-effectiveness: Open-source AI agents are typically free, making them accessible to smaller companies, startups, and individual developers.
- Code transparency: Open-source AI agents provide full access to their source code, enabling developers to view how the agent functions, what data it processes, and the methods it uses to perform tasks.
- Multitenancy: Multiple contributors, or “tenants,” can collaborate on agent development. Developers can build on existing tools, introduce new features, and share improvements with the community.
Open-source AI agent specializations
1. Coding-focused AI agents
These agents help developers with coding, debugging, and code review. They can automate code suggestions, identify bugs, and optimize code for performance.
Features:
- Auto-completes code with suggestions based on context
- Detects and highlights potential bugs
- Refactors and optimizes code
2. General purpose
A general-purpose AI agent is a highly versatile, adaptive system that can perform several tasks across domains, often combining reasoning, memory, tools, and autonomy. Think of it like your always-on digital co-pilot—capable of switching roles based on context and goals.
Features:
- Productivity:
- Plan week, book meetings, summarize docs
- Write emails, generate reports
- Technical:
- Review code, fix bugs, and deploy apps
- Build scripts, run tests
- Business Ops:
- Follow up leads, update CRMs, fill forms
- Auto-send reports, trigger invoices
- Automated research and data analysis
3. Personal AI agents
Personal AI agents are designed to assist individuals by automating tasks.
Features:
- Summarize emails, draft replies, and unsubscribe from junk.
- Capture meeting notes, transcribe voice memos, and tag ideas.
- Schedule meetings, set reminders, and prioritize tasks.
4. Build-your-own AI agents
These tools allow users to leverage custom agent capabilities tailored to specific needs, from simple chatbots to complex automation systems. They offer frameworks for users to create agents without extensive coding knowledge.
Features:
- No-code/low-code agent platforms for building agents
- Integration with APIs and vector databases
- Custom workflows and automation processes
5. AI voice agents
AI voice agents can interact with users through voice commands, enabling hands-free operation and more natural communication. They are often used in smart devices, customer service, and personal assistants.
Features:
- Speech recognition and natural language processing
- Integration with smart home devices
- Speech synthesis for interacting with users
- Control smart devices, make calls
6. AI agents focused on research
These agents assist with tasks like literature reviews, data analysis, and hypothesis generation in academic or industrial research. These systems help researchers automate repetitive processes and uncover new insights from large datasets.
Features:
- Automates data collection and analysis
- Assists in literature review and citation management
- Natural language processing (NLP) for scientific papers analysis (see NLP use cases)
- Supports hypothesis testing with simulations
Further reading
External Links
- 1. Reflection Agents. LangChain Blog
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