llm-framework

A programming framework for agentic AI

Pocket Flow: Codebase to Tutorial

Pocket Flow: 100-line LLM framework. Let Agents build Agents!

Harness LLMs with Multi-Agent Programming

The open-source LLMOps platform: prompt playground, prompt management, LLM evaluation, and LLM observability all in one place.

Automatable GenAI Scripting

No-code multi-agent framework to build LLM Agents, workflows and applications with your data

AICI: Prompts as (Wasm) Programs

[ICML 2024] LLMCompiler: An LLM Compiler for Parallel Function Calling

ContextGem: Effortless LLM extraction from documents

[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 Event-Driven Flow *TriggerFlow* to manage complex GenAI working logic 🔀 Switch to any model without rewrite application code

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.

Your 24/7 On-Call AI Agent - Solve Alerts Faster with Automatic Correlations, Investigations, and More

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. 🚀💻📊

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.

The TypeScript framework for agents & workflows with react-like components. Lightning fast dev loop. Easy to learn. Easy to extend.

历年ICLR论文和开源项目合集，包含ICLR2021、ICLR2022、ICLR2023、ICLR2024、ICLR2025.

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