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🔮 AI Agents
Welcome to our list of AI autonomous agents. Your favorite one is missing? Add them via pull request. ❤️
AgentGPT
Here is a short summary
[Website](https://agentgpt.reworkd.ai/)
[GitHub](https://github.com/reworkd/AgentGPT)
Stack:
- Frontend: NextJS + Typescript
- Backend: FastAPI + Python
- DB: MySQL through docker with the option of running SQLite locally
- GitHub
AI Legion
Here is a short summary
- [**AI Legion**](https://gpt3demo.com/apps/ai-legion)
- [GitHub](https://github.com/eumemic/ai-legion)
AIrplane
- AIrplane - Developer infrastructure for internal tools
Aomni
- [**Aomni**](Aomni.com)BabyAGI
- BabyAGI
- Default model is OpenAI GPT3-turbo
- Paper: Task-driven Autonomous Agent Utilizing GPT-4, Pinecone, and LangChain for Diverse Applications
BabyCatAGI
- BabyCatAGI
- BabyCatAGI is a mod of BabyBeeAGI, which is a mod of OG BabyAGI. BabyCatAGI is at 300 lines of code.
BitBuilder
Bloop
Butternut AI
Camel
- Camel - 🐫 CAMEL: Communicative Agents for “Mind” Exploration of Large Scale Language Model Society
Cognosys
- Cognosys - Friendly UI for building AI agents
Databerry
Factory
Fixie
Generative Agents
- Generative Agents - "Paper presenting an architecture that extends a language model to store and synthesize the agent's experiences, enabling dynamic behavior planning in an interactive sandbox environment with generative agents"
GitWit
Grit
HayStack Agent
Hex Magic
Heymoon.ai
- Heymoon.ai - Personal assistant for life: to keep you on top of your calendar, tasks and information. Was at Llama event demo
Hyperwrite
- Hyperwrite - Your personal AI writing assistant
Jarvis
LangChain
LastMile AI
Loop GPT
- Loop GPT
- Languages: Python
- Default model: GPT-3.5-turbo (also possible with GPT-4)
- Modular Auto-GPT Framework
- Plug N Play" API - Extensible and modular "Pythonic" framework, not just a command line tool
- Easy to add new features, integrations and custom agent capabilities, all from python code, no nasty config files!
- Minimal prompt overhead - Every token counts. We are continuously working on getting the best results with the least possible number of tokens.
- Human in the Loop - Ability to "course correct" agents who go astray via human feedback.
- Full state serialization - can save the complete state of an agent, including memory and the states of its tools to a file or python object. No external databases or vector stores required (but they are still supported)!