The ultimate RAG for your monorepo. Query, understand, and edit multi-language codebases with the power of AI and knowledge graphs

Fast and Accurate Code Search for Agents. Uses 99% fewer tokens than grep+read

The canonical 12ui CLI, SDK, MCP server, and visual interface design skills

A structural code search engine for Al agents.

A code repository indexing tool to supercharge your LLM experience.

A powerful MCP toolkit for coding, providing semantic retrieval and editing capabilities - the IDE for your agent

Symbol-level code retrieval for AI agents: fetch the function you need, not the whole file. Runs locally. 96.2% fewer tokens per lookup than grep-and-read on our 15-lookup benchmark. Works with Claude Code, Cursor, Codex and any MCP client.

Agentic RAG for local and self-hosted document search: hybrid retrieval, reranking and multimodal RAG on embedded LanceDB, with Docling parsing and an MCP server

MCP server giving AI agents (Claude Code, Claude Desktop, Cursor, ChatGPT, Codex, OpenClaw) persistent long-term memory backed by your local Obsidian markdown vault. Hybrid retrieval (BM25 + ML embeddings + BGE reranker, RRF-fused), HNSW + int8 quantizati

Apple developer docs and WWDC transcripts with RAG retrieval and Jev relevance ranking.

Enables AI coding agents to maintain lossless, queryable long-term project memory through local SQLite storage, bi-temporal history, hybrid retrieval, and Git-backed Markdown mirrors, exposed via MCP tools and resources.

Enables retrieval and structural analysis of FDA regulations.gov dockets: searching dockets, pulling comments and attachments into a local cache, extracting sectioned guidance text, and computing section-aligned draft-versus-final guidance diffs. All results are deterministic and cache-first, with no model calls in the server.

Enables AI assistants to persist and recall local memory across sessions and projects through hybrid BM25/TF-IDF retrieval over a file-based vault. Facts are tracked with dual-clock timestamps and require no native dependencies.

Permission-aware retrieval for AI systems: policy-enforced access to organizational knowledge.

Encrypted, fully offline agentic memory. One click install, GUI w/ memory map, all OS and agents. Superior memory creation, storage and retrieval.

Caura (formerly MemClaw) — 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.

Context engineering for AI agents. ~80% fewer tokens. Fix tool overload. Skills and memory with in-process BM25 and semantic retrieval. Progressive Disclosure. No vector DB.

Page-cited retrieval for embedded docs, datasheets, MISRA, CMSIS, and RTOS references.

Session-level (not fragment) local memory for AI coding agents: passively captures Claude Code, Codex, ZCode and Qoder conversations into one SQLite store, with provenance-cited retrieval and a human-only right to be forgotten.

Stores what AI agents learn — thoughts, decisions, session events and repo facts — in PostgreSQL with pgvector and serves it back through versioned MCP tools with hybrid vector-plus-full-text retrieval and budgeted context packs. Enables multiple agents to share one scoped, role-isolated long-term memory, with deliberate promotion of distilled candidates into durable memories.

Enables MCP hosts to spawn, supervise, and collect results from parallel panels of pinned-model OpenCode v2 agents for delegated tasks, reviews, and cross-model comparisons. Supports per-member roles, models, permissions, follow-up prompts, cancellation, and persisted result retrieval.

Bridges a local Figma development plugin to a local MCP server, letting the client read and write real canvas layers through the Figma Plugin API without consuming official MCP or Dev Mode quota. Supports layer and auto-layout editing, component and variable inspection, design-to-code context retrieval, and PNG/JPG/SVG/PDF export.

Provides AI assistants offline fuzzy search, precise section retrieval, and section listing over bundled mobile-development documentation, enabling natural-language lookups of components, hooks, guides, and best practices.

Model Context Protocol (MCP) server that enables AI assistants to securely interact with Odoo ERP systems through standardized resources and tools for data retrieval and manipulation.