Integrating AI into every workflow with our open-source, no-code platform, powered by the actor model for dynamic, graph-based solutions.
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Updated
Jun 3, 2024 - TypeScript
Integrating AI into every workflow with our open-source, no-code platform, powered by the actor model for dynamic, graph-based solutions.
A programming framework for agentic AI. Discord: https://aka.ms/autogen-dc. Roadmap: https://aka.ms/autogen-roadmap
The Open Source DevOps Assistant - solve problems twice as fast with an AI teammate
The all-in-one LLM developer platform: prompt management, evaluation, human feedback, and deployment all in one place.
[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
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.
Stitch simplifies and scales LLM application deployment, reducing infrastructure complexity and costs.
A framework for writing Unstract Tools/Apps
Harness LLMs with Multi-Agent Programming
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. 🚀💻📊
No-code multi-agent framework to build LLM Agents, workflows and applications with your data
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.
Backend server for AGI OS - Awesome Gamer Insight Orchestrating System
AICI: Prompts as (Wasm) Programs
Unstract's interface to LLMs, Embeddings and VectorDBs.
GoalChain for goal-orientated LLM conversation flows
Official Repo for ICML 2024 paper "Executable Code Actions Elicit Better LLM Agents" by Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, Heng Ji.
Geniusrise: Framework for building geniuses
ICLR 2024 论文和开源项目合集
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