GitHub projects

Instruct-tune LLaMA on consumer hardware

A build, packaging, and run system for ephemeral multi-container environments.

The Jupyter notebook is a web-based notebook environment for interactive computing.

🏕️ Reproducible development environment for AI/ML.

Moby is an open-source project created by Docker to enable and accelerate software containerization.

OS-agnostic, system-level binary package manager and ecosystem.

Run VS Code on any machine anywhere and access it in the browser.

Smart code context extractor for AI assistants with accurate token counting and budget management

An open-source alternative to GitHub Copilot server

Open Code LLMs for Code Understanding and Generation.

CodeGen is an open-source model for program synthesis. Trained on TPU-v4. Competitive with OpenAI Codex.

CodeGeeX: An Open Multilingual Code Generation Model (KDD 2023)

Deterministic Python orchestrator for 37 CLI coding agents (Claude Code, Codex CLI, Gemini CLI, GitHub Copilot CLI, Cursor, Aider, OpenHands, OpenCode, Goose, Qwen, Ollama, ...) running in parallel git worktrees. First-class MCP server, quality gates, cost tracking with budgets.

Weaviate is an open source vector search engine that stores both objects and vectors, allowing for combining vector search with structured filtering with the fault-tolerance and scalability of a cloud-native database, all accessible through GraphQL, REST, and various language clients.

A Python vector database you just need - no more, no less.

A distributed system for embedding-based vector retrieval

A Highly Scalable Distributed Vector Search Engine

Vector database plugin for Postgres, written in Rust, specifically designed for LLM.

Scalable, fast, and disk-friendly vector search in Postgres, the successor of pgvecto.rs .

The transactional alternative to Elasticsearch, built on Postgres.

Typed graph database where agents branch and merge like Git. S3-native, Rust, traversal + vector + BM25 in one runtime.

Tensor search for humans.

Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps!

A 10x faster, cheaper, and better vector database

AI Native database for embedding vectors

An easy to use Neural Search Engine. Index latent vectors along with JSON metadata and do efficient k-NN search.

An easy way to turn any app into searchable data for LLMs.

Open-source self-hostable end-to-end agent engineering and optimization platform unifying tracing, evals, simulations, datasets, gateway, and guardrails for LLM and AI agent applications.

Self-hosted multi-agent AI runtime with 23+ LLM providers, persistent memory, skills, schedules, sub-agent spawning, and MCP client + server support. Ships as desktop app, CLI, or Docker.

Open-source framework for orchestrating, experimenting and deploying production-grade ML solutions, with built-in langchain & llama index integrations.

Build and control your personal LLMs with fast and efficient fine-tuning.

Distributed semantic cache and stateful routing system that cuts LLM API costs by returning cached responses for semantically similar queries. Uses ANN vector search (cosine ≥ 0.8) and consistent hashing to pin requests to the same worker, achieving ~7× latency reduction on cache hits while scaling

Open-source testing infrastructure for LLM and agentic applications. Collaborative platform enabling teams to define quality metrics, run evaluations, and ship confidently with version control and peer review workflows built for AI engineering.

Universal index and routing layer for AI agents. Aggregates agent metadata from multiple registries (NANDA, MCP, Virtuals, OpenRouter, A2A, X402 Bazaar) across web2 and web3, normalizes profiles, and provides protocol translation between agent ecosystems.

Handle OpenAI Errors (overloaded OpenAI servers, rotated keys, or context window errors) for your production LLM Applications.

A lightweight Python library for prompt lifecycle management that helps you version control, track, experiment and debug with your LLM prompts with ease. Minimal setup, no servers, databases, or API keys required - works directly with your local filesystem, ideal for data scientists and engineers t

Open-source tool to simplify the process of creating and managing LLM workflows and prompts as a self-hosted solution.

A declarative, extensible, and composable approach for developing LLM prompts using Markdown and JSX.

Pezzo is the open-source LLMOps platform built for developers and teams. In just two lines of code, you can seamlessly troubleshoot your AI operations, collaborate and manage your prompts in one place, and instantly deploy changes to any environment.

OpenLIT is an OpenTelemetry-native GenAI and LLM Application Observability tool and provides OpenTelmetry Auto-instrumentation for monitoring LLMs, VectorDBs and Frameworks. It provides valuable insights into token & cost usage, user interaction, and performance related metrics.

Intuitive convenience tooling for lightning-fast, efficient development and ensuring quality in LLM-based applications

Open-source memory infrastructure for AI agents. Provides semantic (entities/facts), episodic (conversations), and procedural (learned behaviors) memory with auto-reflection. Python SDK, JS SDK, MCP server, and REST API.

CLI/TUI tool for managing localization files (.resx, JSON, Android, iOS) with LLM-powered translation via Ollama, validation, and code scanning for unused/missing keys.

LLMFlows is a framework for building simple, explicit, and transparent LLM applications such as chatbots, question-answering systems, and agents.

LLM App is a Python library that helps you build real-time LLM-enabled data pipelines with few lines of code.

Out-of-the-box LLM telemetry collection library that extracts features and profiles prompts, responses and metadata about how your LLM is performing over time to find problems at scale.

An effortless way to experiment and prototype LangChain flows with drag-and-drop components and a chat interface.

Open-source all-in-one platform for engineering AI products. Traces, Evals, Datasets, Labels.