The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service.
Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
A server that provides data retrieval capabilities powered by Chroma embedding database, enabling AI models to create collections over generated data and user inputs, and retrieve that data using vector search, full text search, and metadata filtering.
Persistent, auditable memory for AI agents. Hybrid BM25 + vector recall
with 18 MCP tools, adaptive block metadata (A-MEM), intent-aware routing,
contradiction detection, and governance workflows. Zero external
dependencies. Drop-in memory for Claude Code and any MCP-compatible agent.
A modular RAG framework exposing knowledge retrieval tools via MCP, enabling AI assistants to perform hybrid search, reranking, and multimodal document queries with full observability and evaluation.
Tamper-evident audit logging for AI agents. Append-only, hash-chained, optionally Ed25519-signed log. The MCP server lets an agent keep and verify a record of what it actually did.
A local-first MCP server that gives AI coding agents runtime visibility and AI-managed debug logging. It replaces blind print() debugging by turning runtime execution into causal chains, allowing agents to instantly locate bugs by finding missing .success events in Python and TypeScript code. Single binary with MCP, CLI, and HTTP interfaces.