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GitHub repo · AI & Machine LearningFree

Awesome AI Agents

Awesome AI Agents is a GitHub list from E2B that catalogues AI agent projects, frameworks and companies, split into open-source and closed-source sections, with a category, description and links for each. It is useful for exploring the agent landscape and finding prior art before building. Its product entries have not been substantially updated since late 2024, so treat it as a historical map and check newer sources for recent tools.

GitHub stars

30.1k

GitHub · Sept 2026

Forks

3.5k

GitHub · Sept 2026

Entries

About 215 detailed profiles

GitHub · Sept 2026

Last product added

September 2024

GitHub commit history · Sept 2026

Format

GitHub repo

Topic

AI & Machine Learning

Provider

E2B

Level

All

Access

Free

What is in the list

The README is divided into two big sections:

  • Open-source projects: frameworks and agents such as AutoGen, AutoGPT, AgentVerse, Aider and many research projects.
  • Closed-source projects and companies: commercial agent products across coding, research, customer support, data analysis, sales and more.

Each entry has a category, a short description, sometimes features and tech stack, and links to the project, docs or demo. The list contains roughly 215 detailed profiles, and there is also a web version and a visual map. It is maintained by E2B, a company that provides sandboxed code execution for AI agents.

The repository had about 30,100 stars and 3,500 forks on GitHub as of September 2026.

How current it is

The commit history shows that new products were last added in September 2024. Changes since then, including in 2026, update links and documentation rather than adding new agents. The agent field has moved quickly since then: coding agents, agent SDKs from model providers, the Model Context Protocol and conventions such as AGENTS.md all arrived or matured after most entries were written. Some listed projects have also been abandoned or renamed.

How to use it well

  • For background and history: it shows how the agent landscape developed through 2023 and 2024, which is useful context for understanding why current tools look the way they do.
  • For prior art: before building a new agent idea, search the list for similar projects. Even abandoned ones can show what was tried and where it stalled.
  • For research ideas: the open-source section links to many academic and experimental projects worth reading.
  • Always verify: open the project's own repository, check its last commit and stars, and look for a newer release before you depend on it.

Better sources for what is current

  • The documentation of the model provider or framework you are using (for example Anthropic's, OpenAI's or Google's agent SDK docs).
  • Anthropic's engineering articles such as Building Effective Agents, which explain design patterns that outlast any particular tool.
  • GitHub topic pages (such as ai-agents) sorted by recent activity.
  • Hugging Face and newsletters that track releases weekly.

Common questions

Is it still maintained? The repository receives occasional link updates, but the catalogue itself has not been refreshed with new products since late 2024.

Can I add a project? The README invites contributions through pull requests, but given the activity level, a response is not guaranteed.

Does inclusion mean a project is recommended? No. It is a catalogue, not a review. Judge each project on its own activity, documentation and licence.

Best for: developers exploring the history and breadth of AI agent projects, and anyone checking for prior art before building.

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