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.
An MCP server that records agent execution metrics and exposes a Context Window Explorer to visualize exactly what entered the model's context window across sessions, tokens, and tool calls.
MCP-native persistent memory layer for AI agents across Claude Code, Cursor, VS Code, and OpenClaw. Powered by hybrid vector search, BM25, and cross-encoder reranking with a published 73.1% LoCoMo benchmark accuracy.
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.
Captures AI agent runs and turns them into tamper-evident execution records showing tool use, timing, failures, recoveries, and human interventions. Records can be inspected, exported, and verified offline.