BabyAGI API, Libraries & Extensions

Top Builders

Explore the top contributors showcasing the highest number of app submissions within our community.

BabyAGI API, Libraries & Plugins

BabyAGI is a Python project that demonstrates an AI-powered task management system that uses OpenAI and Pinecone APIs to create, prioritize, and execute tasks. The system creates tasks based on the result of previous tasks and a predefined objective, and uses the LLMs capabilities to create new tasks based on the objective. It runs in an infinite loop that pulls tasks from a task list, sends them to the execution agent, enriches the results using Pinecone, and creates new tasks based on the objective and the result of the previous task.

General
Relese date	April 2, 2023
Repository	https://github.com/yoheinakajima/babyagi/tree/main
Type	Autonomous Agent

Start building with BabyAGI

We have collected the best BabyAGI libraries and resources to help you get started to build with BabyAGI today. To see what others are building with BabyAGI, check out the community built BabyAGI Use Cases and Applications.

BabyAGI Extensions

BabyAGI allows extended capabilites through extensions. Learn more by browsing the official documentation

BabyAGI Tutorials

BabyAGI tutorial: How to use BabyAGI to solve tasks?

👉 Discover more BabyAGI Tutorials on lablab.ai

BabyAGI Resources

Kickstart your development with BabyAGI and include it in your next project.

LangChain implementation of BabyAGI

Edit on GitHub

BabyAGI AI technology page Hackathon projects

Discover innovative solutions crafted with BabyAGI AI technology page, developed by our community members during our engaging hackathons.

RAYS Studio: Decentralized Federated OSINT Network

RAYS Studio is a revolutionary decentralized framework for open-source intelligence (OSINT) AI agents, built to solve the computational bottlenecks and privacy issues inherent in centralized LLM training. Standard agent architectures suffer from catastrophic forgetting when exposed to diverse localized datasets, while central servers struggle with massive training compute requirements. To solve this, RAYS Studio implements Federated Orthogonal Gradient Routing (FOGR) combined with Spectrally Bounded Zero-Gated Adapters (SB-ZGA). Local client devices execute complex OSINT workflows using RAYS-CORE, generating highly contextual, private task logs. When local fine-tuning is triggered, each edge client trains a low-rank adapter on its own hardware (utilizing GPU/MPS acceleration). We apply Singular Value Decomposition (SVD) constraints directly to the adapter weights, zeroing out dominant singular values to force the learning process into strict mathematical orthogonality. These localized, orthogonal adapter updates are securely submitted to the RAYS Studio daemon. The central daemon aggregates the weights, merges them with the base model, recompiles an optimized GGUF format, and hot-swaps the model instantly on a running llama.cpp server without downtime. By structuring learning spaces orthogonally, different clients can fine-tune on disparate OSINT targets simultaneously without erasing previously learned intelligence. The result is a highly scalable, privacy-first AI swarm capable of collective intelligence gathering across thousands of edge nodes.

RE-EVOLVE ON HGI

RE-EVOLVE ON HGI explores a shift from isolated AI inference to coordinated intelligence. Instead of relying on a single model, the platform orchestrates missions across planning, memory, governance, adaptive provider routing, and runtime execution through a modular architecture. Built using a specification-driven development process, the project combines multi-agent coordination, persistent memory concepts, runtime observability, and developer-focused tooling into a unified Intelligence Operating System. The repository includes production deployments, comprehensive engineering documentation, architecture diagrams, traceability matrices, runtime validation reports, and a complete judge guide. It is designed as an open engineering project that investigates how orchestration can complement heterogeneous AI compute environments while remaining transparent, extensible, and maintainable.

GhostWriter OCR: AI-Powered

GhostWriter OCR: Next-Gen AI Document Intelligence GhostWriter OCR is an advanced, agentic document processing platform designed to bridge the gap between messy, handwritten physical records and structured digital intelligence. While traditional OCR (Optical Character Recognition) simply converts images to text, GhostWriter goes a step further by interpreting intent, context, and compliance in real-time. Built to leverage the high-compute capabilities of AMD hardware, this project targets "high-stakes" industries like healthcare, legal services, and logistics, where handwritten forms remain a primary bottleneck. Key Features & Innovation Intent-Aware Extraction: GhostWriter doesn’t just read words; it understands fields. It can distinguish between a signature, a date of birth, and a medical prescription, mapping them to structured JSON/Database formats automatically. Agentic Verification: If the AI detects a smudge, a missing signature, or contradictory information (e.g., a "Date of Birth" that is in the future), an autonomous agent proactively flags the error. It can even generate a "Human-in-the-loop" notification to ask the user for clarification before the data is committed. Edge-Optimized Privacy: By utilizing AMD ROCm and local GPU acceleration, GhostWriter processes sensitive documents locally. This ensures that private data—like medical records or legal contracts—never needs to leave the local environment for cloud processing. Dynamic Document Reconstruction: Using a MERN-based dashboard, users can see a side-by-side comparison of the original scan and the digitized version, with "confidence scores" highlighted for every field. Technical Excellence GhostWriter is engineered for speed. By offloading the heavy lifting of Multi-modal Large Language Models onto AMD’s Developer Cloud infrastructure, we achieve near-instantaneous processing of multi-page documents. The frontend is a sleek, minimalist React interface, while the backend utilizes Node.js and

AgentReady

AI agents are like helpful robots that read your messages and do things for you, sending emails, writing code, even moving money. They're getting really popular. But there's a problem: they're easy to trick. OWASP, the people who write safety rules for the internet, just published a list of 10 ways to trick AI agents. December 2025. Brand new. And nobody had built a public tool to check if your agent could be tricked. So I built one. It uses two AI robots that run side by side on a single AMD MI300X GPU. The small robot pretends to be a sneaky attacker. It tries about 46 different tricks per agent, fake memos, fake calendar invites, fake messages that look like they came from another robot, slow-burn manipulation over multiple turns. The big robot reads each conversation and decides if the trick worked. Then I pointed it at every famous open-source AI agent on GitHub: BabyAGI, Cline, AutoGPT, Aider, AutoGen, CrewAI, and more. All 13 got tricked at least somewhat. Zero made it to the safe zone. But it doesn't stop there. For every trick that worked, the big robot writes new safety rules to block it. Then we run the same tricks again on the patched agent and watch the score go up. Same tricks, better defense proof, not vibes. Finally we open a real pull request on the agent's GitHub so the maintainer can merge the fix in one click. Why AMD MI300X? The big robot needs about 40 GB of GPU memory. The small robot needs about 16. Both running at once needs around 75 GB. The MI300X has 192 GB, plenty. The biggest single Nvidia H100 has only 80 GB, not enough.