A programming framework for agentic AI 🤖 PyPi: autogen-agentchat Discord: https://aka.ms/autogen-discord Office Hour: https://aka.ms/autogen-officehour
-
Updated
Feb 23, 2025 - Python
A programming framework for agentic AI 🤖 PyPi: autogen-agentchat Discord: https://aka.ms/autogen-discord Office Hour: https://aka.ms/autogen-officehour
Harness LLMs with Multi-Agent Programming
Automatable GenAI Scripting
The open-source LLMOps platform: prompt playground, prompt management, LLM evaluation, and LLM Observability all in one place.
AICI: Prompts as (Wasm) Programs
No-code multi-agent framework to build LLM Agents, workflows and applications with your data
[ICML 2024] LLMCompiler: An LLM Compiler for Parallel Function Calling
[GenAI Application Development Framework] 🚀 Build GenAI application quick and easy 💬 Easy to interact with GenAI agent in code using structure data and chained-calls syntax 🧩 Use Agently Workflow to manage complex GenAI working logic 🔀 Switch to any model without rewrite application code
Low code tool to rapidly build and coordinate multi-agent teams
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. 🚀💻📊
Your 24/7 On-Call AI Agent - Solve Alerts Faster with Automatic Correlations, Investigations, and More
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.
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.
Minimalist LLM Framework in 100 Lines. Enable LLMs to Program Themselves.
InternEvo is an open-sourced lightweight training framework aims to support model pre-training without the need for extensive dependencies.
Build, Improve Performance, and Productionize your LLM Application with an Integrated Framework
Integrating AI into every workflow with our open-source, no-code platform, powered by the actor model for dynamic, graph-based solutions.
FineTune LLMs in few lines of code (Text2Text, Text2Speech, Speech2Text)
Super-Efficient RLHF Training of LLMs with Parameter Reallocation
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.
Add a description, image, and links to the llm-framework topic page so that developers can more easily learn about it.
To associate your repository with the llm-framework topic, visit your repo's landing page and select "manage topics."