# BabyAGI — Task-Driven Autonomous Agent Framework

> Lightweight autonomous agent that creates, prioritizes, and executes tasks using LLMs in a continuous loop.

## Install

Save as a script file and run:

# BabyAGI — Task-Driven Autonomous Agent Framework

## Quick Use
```bash
git clone https://github.com/yoheinakajima/babyagi && cd babyagi
pip install -r requirements.txt
cp .env.example .env  # set OPENAI_API_KEY and OBJECTIVE
python babyagi.py
```

## Introduction
BabyAGI is a minimalist autonomous agent that runs a continuous loop of task creation, prioritization, and execution. It was one of the earliest demonstrations of LLM-powered autonomous agents, showing how a simple loop of three LLM calls can produce goal-directed behavior from a single objective.

## What BabyAGI Does
- Takes a high-level objective and breaks it into actionable tasks
- Executes tasks one at a time using LLM calls and optional tool integrations
- Generates new tasks based on execution results and the remaining task list
- Re-prioritizes the task queue after each cycle to stay on track
- Stores task results in a vector database for context retrieval in future steps

## Architecture Overview
BabyAGI runs three core functions in a loop: an execution agent that completes the current task, a task creation agent that generates new tasks from results, and a prioritization agent that reorders the queue. It uses Pinecone or Chroma as a vector store to maintain memory of completed work. The entire system fits in a single Python file.

## Self-Hosting & Configuration
- Clone the repository and install the minimal Python dependencies
- Set OPENAI_API_KEY and your OBJECTIVE in the .env file
- Configure the vector store backend (Pinecone, Chroma, or Weaviate)
- Adjust the INITIAL_TASK to set the starting point for the agent loop
- Set a TABLE_NAME to namespace results in the vector store

## Key Features
- Minimal codebase that fits in a single file for easy understanding
- Continuous task loop with creation, execution, and prioritization
- Vector store memory for context-aware task execution
- Configurable objectives and initial tasks
- Foundational pattern that influenced many later agent frameworks

## Comparison with Similar Tools
- **AutoGPT** — more complex with file system and web access; BabyAGI is deliberately minimal
- **MetaGPT** — role-based software development agents; BabyAGI is a general task runner
- **CrewAI** — multi-agent teams with role assignment; BabyAGI uses a single agent loop
- **LangChain Agents** — tool-using agents with chains; BabyAGI focuses on the task planning loop
- **SuperAGI** — production agent platform with GUI; BabyAGI is a lightweight reference implementation

## FAQ
**Q: Is BabyAGI still maintained?**
A: The original repository is archived as a reference. The concept lives on in frameworks it inspired.

**Q: Can it use models other than OpenAI?**
A: Yes. Any OpenAI-compatible API endpoint works, including local model servers.

**Q: Does it run indefinitely?**
A: It loops until the task queue is empty or you stop it manually.

**Q: What vector databases are supported?**
A: Pinecone, Chroma, and Weaviate, with Chroma as the default local option.

## Sources
- https://github.com/yoheinakajima/babyagi
- https://yoheinakajima.com/birth-of-babyagi/

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Source: https://tokrepo.com/en/workflows/f3ff673a-3e8b-11f1-9bc6-00163e2b0d79
Author: Script Depot