Report Abuse

Basic Information

AutoAgent is an open source framework for building, deploying and running LLM-based agents and multi-agent systems using natural language instead of code. The project emphasizes fully automated agent creation and self-developing behavior, providing an out-of-the-box multi-agent user mode that aims to match Deep Research style assistants and achieves competitive scores on the GAIA benchmark. The repository includes CLI and Docker-based deployment options, support for many LLM providers via environment API keys, evaluation scripts for benchmarks, and tools for agent profiling, tool creation, workflow composition and self-managing vector storage. It is designed for users who want to generate, test and reproduce agent behaviors and research results with minimal engineering effort. Documentation, community channels and a paper are provided to support adoption.

Links

Categorization

App Details

Features
AutoAgent provides zero-code agent and workflow creation through natural language editors with automated profiling and tool generation. It includes a native self-managing vector database for agentic RAG tasks and evaluation scripts for GAIA and MultiHopRAG. The framework supports many LLM providers and models including OpenAI, Anthropic, Gemini, HuggingFace, Groq, DeepSeek and Mistral, and offers both function-calling and ReAct interaction modes. Deployment is supported via a CLI command set and Docker containerization with automatic image handling. The system can auto-clone a repository mirror to enable self-updating agents, accepts file uploads, and exposes commands and environment variables for model selection, debug mode and API base configuration. The project also publishes a research paper and benchmark results.
Use Cases
AutoAgent reduces development overhead by letting non-programmers and researchers create and iterate on LLM agents using natural language prompts and interactive editors. Researchers can reproduce benchmark experiments with provided scripts and compare agents on GAIA and RAG tasks. Operators can run a ready multi-agent assistant in a container or CLI with configurable models and API keys, enabling cost-effective alternatives to commercial research assistants. The native vector store and built-in RAG support simplify retrieval workflows for knowledge-heavy tasks. The framework"s extensibility, automatic repository mirroring and tool creation features help teams prototype complex agent workflows, integrate third-party tools and evaluate multi-agent strategies without building orchestration infrastructure from scratch.

Please fill the required fields*