🤖AutoAgent
"AutoAgent: Fully-Automated and Zero-Code LLM Agent Framework"
⭐ Popular: 10k stars, gaining about 49 a week
View on GitHub ↗repo profile
momentum
durability
since we covered it
why it's a big deal
- Lets non-technical users build and run LLM agents through natural-language dialogue, removing the need to write code.
- Aimed at researchers, developers, and non-coders who want a self-hosted alternative to proprietary deep-research assistants.
- Ships a User mode for deep-research tasks plus editors for single agents and multi-agent workflows, covering retrieval, analysis, and report generation.
under the hood
- Written almost entirely in Python and runs agents inside Docker containers for isolated, interactive execution.
- Uses LiteLLM to support multiple providers including Claude, OpenAI, DeepSeek, Gemini, and Groq, configured via environment variables.
- Generates tools, agents, and workflows through iterative controlled code generation, and includes GAIA and Agentic-RAG evaluation setups.
Radar summary, generated from the project's public sources
star history
- PR#11 5k 2025-06-25
- now 10k + 5k since first covered
curve is sampled from GitHub's star history, plus our own daily readings since we covered it; the dashed stretch is before we first covered it, the solid line since. figures at coverage are the numbers we printed then (approx.), current count is live.
understory
Better known than its recent output, coasting a little on attention.
- output, commits & releases
- clout, star velocity
output = commits & releases; clout = star velocity, both 0 to 100 monthly indices; the gap where output runs above clout is the understory. The understory →
covered in
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Fully-Automated, Zero-Code LLM Agent Framework
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