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Agentic AI
Updated on Apr 30, 2025

10+ Open-source AI Agents Based on GitHub Stars in 2025

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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

Specialization
Coding
# GitHub stars (k)
51+

Vanna

LLM-based AI (RAG only)

Specialization
Coding (writing SQL)
# GitHub stars (k)
10+

Sweep

LLM-based AI

Specialization
Coding
# GitHub stars (k)
7+

PR-Agent

RL-based AI

Specialization
Coding (automated pull requests)
# GitHub stars (k)
5+

Devon

RL-based AI

Specialization
Coding
# GitHub stars (k)
3+
Updated at 04-30-2025
AI agentAutonomy levelSpecialization# GitHub stars (k)
Open InterpreterLLM-based AICoding51+
VannaLLM-based AI (RAG only)Coding (writing SQL)10+
SweepLLM-based AICoding7+
PR-AgentRL-based AI Coding (automated pull requests)5+
DevonRL-based AI Coding3+
ReactAgentLLM-based AICoding1+
AideLLM-based AICoding0.3
Microsoft JarvisLLM-based AIGeneral purpose 23+
Baby AGILLM-based AIGeneral purpose19+
DevOpsGPTLLM-based AIGeneral purpose6+
evoninjaLLM-based AIGeneral purpose1+
AlliceLLM-based AIGeneral purpose0.7
GPT researcherLLM-based AIResearch13+

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.

Updated at 04-24-2025
AI agentSpecializationAutonomy level# GitHub stars (k)
LangGraphNLP task automationLLM-based AI 11+
AutoGenData and content automationLLM-based AI43+
CrewAIWorkflow automationLLM-based AI30+
CamelWorkflow automationLLM-based AI5+
AgentGPTWorkflow automationLLM-based AI31+
SuperagentWorkflow automationLLM-based AI5+

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:

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

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Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 55% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
Mert Palazoglu is an industry analyst at AIMultiple focused on customer service and network security with a few years of experience. He holds a bachelor's degree in management.

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