Open-source context retrieval layer for AI agents

Self-hosted, open-source agent skill registry for enterprises. Publish & version skill packages, govern with RBAC and audit logs, deploy on-premise with Docker or Kubernetes.

Local persistent memory store for LLM applications including claude desktop, github copilot, codex, antigravity, etc.

14-stage Fusion Pipeline for LLM token compression — reversible compression, AST-aware code analysis, intelligent content routing. Zero LLM inference cost. MIT licensed.

AI Infrastructure Engineer Learning Track - Production ML infrastructure curriculum (2-4 years experience)

Graph-Native Infrastructure for Context and Accountable AI Systems

UniRL is a Framework for Unified Multimodal Model Reinforcement Learning

Governed shared memory for AI agent fleets — multi-agent, multi-tenant, MCP-native. Trust tiers, keystone policies, audit trails, knowledge graph, self-improving retrieval. Apache 2.0.

🛡️The governance runtime for AI agents. Intercept actions, enforce guard policies, require approvals, and produce audit-ready decision trails.

Plug-and-play memory for LLMs in 3 lines of code. Add persistent, intelligent, human-like memory and recall to any model in minutes.

Local-first AI conversation memory hub to capture, search, summarize, and export chats across major AI platforms. 本地优先的 AI 对话记忆与知识中台。

Give AI coding agents the context they need to ship production-quality software.

Open-source protocol suite standardizing LLM, Vector, Graph, and Embedding infrastructure across LangChain, LlamaIndex, AutoGen, CrewAI, Semantic Kernel, and MCP. 3,330+ conformance tests. One protocol. Any framework. Any provider.

Deploy intelligence. Open-source infrastructure for AI agents in production.

Unified AI Gateway for 30+ LLMs (OpenAI, Anthropic, Bedrock, Azure etc) with Caching, Guardrails, A/B test & cost controls. Go-native Fastest & Scalable AI Gateway LiteLLM & Kong AI Gateway alternative.

AI Infrastructure Junior Engineer Learning Track - Comprehensive curriculum for entry-level ML infrastructure engineers (0-2 years experience)

Grov automatically captures the context from your private AI sessions and syncs it to a shared team memory. It auto injects relevant memories across developers and future sessions to save tokens and time spent on tasks.

The free, open companion to the original Grokking the System Design Interview course by DesignGurus.io.

Plug-and-play homelab dashboard in one container — GPU, local-AI VRAM, Docker, systemd, host health. Built-in read-only MCP server so AI agents can explore it too.

A local workbench for agents doing continuous work across sessions, commands, human input, and external events.