LLMCompiler alternatives for AI agents.

Pocket Flow: Codebase to Tutorial

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.

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.

A programming framework for agentic AI

Demystify AI agents by building them yourself. Local LLMs, no black boxes, real understanding of function calling, memory, and ReAct patterns.

MaiSaka, an LLM-based intelligent agent, is a digital lifeform devoted to understanding you and interacting in the style of a real human. She does not pursue perfection, nor does she seek efficiency; instead, she values warmth, authenticity, and genuine connection.

The RL Bridge for LLM-based Agent Applications. Made Simple & Flexible.

Harness LLMs with Multi-Agent Programming

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.

A Comprehensive Benchmark to Evaluate LLMs as Agents (ICLR'24)

[ICLR'25] OpenRCA: Can Large Language Models Locate the Root Cause of Software Failures?

The LLM Anti-Framework

A generative speech model for daily dialogue.

Automated Penetration Testing Agentic Framework Powered by Large Language Models

"DeepCode: Open Agentic Coding (Paper2Code & Text2Web & Text2Backend)"

Odyssey: Empowering Minecraft Agents with Open-World Skills