A programming framework for agentic AI. Discord: https://aka.ms/autogen-dc. Roadmap: https://aka.ms/autogen-roadmap
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Updated
May 20, 2024 - Jupyter Notebook
A programming framework for agentic AI. Discord: https://aka.ms/autogen-dc. Roadmap: https://aka.ms/autogen-roadmap
Integrating AI into every workflow with our open-source, no-code platform, powered by the actor model for dynamic, graph-based solutions.
The all-in-one LLM developer platform: prompt management, evaluation, human feedback, and deployment all in one place.
No-code multi-agent framework to build LLM Agents, workflows and applications with your data
Langtrace 🔍 is an open-source, Open Telemetry based end-to-end observability tool for LLM applications, providing real-time tracing, evaluations and metrics for popular LLMs, LLM frameworks, vectorDBs and more.. Integrate using Typescript, Python. 🚀💻📊
Harness LLMs with Multi-Agent Programming
Design, conduct and analyze results of AI-powered surveys and experiments. Simulate social science and market research with large numbers of AI agents and LLMs.
ICLR 2024 论文和开源项目合集
∇ Valyu LLManager simplifies and scales LLM application deployment, reducing infrastructure complexity and costs.
[AI Agent Application Development Framework] - 🚀 Build AI agent native application in very few code 💬 Easy to interact with AI agent in code using structure data and chained-calls syntax 🧩 Enhance AI Agent using plugins instead of rebuild a whole new agent
AICI: Prompts as (Wasm) Programs
The llama-cpp-agent framework is a tool designed for easy interaction with Large Language Models (LLMs). Allowing users to chat with LLM models, execute structured function calls and get structured output. Works also with models not fine-tuned to JSON output and function calls.
Build reliable and simple to customise AI Agent workflows using any Agent - batteries included.
A framework for writing Unstract Tools/Apps
Unstract's interface to LLMs, Embeddings and VectorDBs.
Geniusrise: Framework for building geniuses
LLMS can mutually compete with and enlighten each other to form a stronger group,Multi agents can be SFTed by other agents' positive samples and guided by self experiences with rl. This shows how human evolved
npm like package ecosystem for Prompts 🤖
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