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Letta (formerly MemGPT) is a framework for creating LLM services with memory.
Supercharge your command line with LLMs and get shell scripting assistance in Fish. 💪
Autonomous coding agent right in your IDE, capable of creating/editing files, executing commands, using the browser, and more with your permission every step of the way.
library & platform to build, distribute, monetize ai apps that have the full context (like rewind, granola, etc.), open source, 100% local, developer friendly. 24/7 screen, mic, keyboard recording …
Agent S: an open agentic framework that uses computers like a human
A bibliography and survey of the papers surrounding o1
RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding.
Fast and memory-efficient exact attention
This repository provides an advanced Retrieval-Augmented Generation (RAG) solution for complex question answering. It uses sophisticated graph based algorithm to handle the tasks.
The interactive graphing library for Python ✨ This project now includes Plotly Express!
Take a modern Python codebase to the next level of performance.
grep for words with similar meaning to the query
Python PDF parser for scientific publications: content and figures
DSPy: The framework for programming—not prompting—language models
A Comprehensive Benchmark to Evaluate LLMs as Agents (ICLR'24)
This repo provides the server side code for llmsherpa API to connect. It includes parsers for various file formats.
[TMLR 2024] Efficient Large Language Models: A Survey
prompt2model - Generate Deployable Models from Natural Language Instructions
Dify is an open-source LLM app development platform. Dify's intuitive interface combines AI workflow, RAG pipeline, agent capabilities, model management, observability features and more, letting yo…
An intuitive LLM prompting framework for multifunctional agents, by explicitly constructing a complex "thought process" from simple natural language prompts.
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
The source code of our work "Prepacking: A Simple Method for Fast Prefilling and Increased Throughput in Large Language Models"
Multipack distributed sampler for fast padding-free training of LLMs
AI driven development in your terminal. Designed for large, real-world tasks.
[NeurIPS 2023] Reflexion: Language Agents with Verbal Reinforcement Learning