"Information about Automata Labs Team" matching MCP servers:

An MCP server for team documentation and knowledge bases, enabling semantic search over documentation files using local embeddings.

Provides a local-first memory layer for coding agents, storing principles, rules, and corrections on-device and surfacing them at the moments they apply so agents act on the intended outcomes without sending data to the cloud.

An MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.

Enables AI coding assistants to query and store a team's historical development experience through semantic search, so lessons and decisions persist across conversations.

Provides local-first, zero-LLM memory retrieval for AI agents like Claude Code and Codex, enabling verbatim recall of stored notes and context through tools such as fidelis_recall and fidelis_orient.

Durable memory MCP server for AI agents. Temporal knowledge graph with hybrid semantic + keyword retrieval, LLM-powered fact extraction, conflict detection, and consolidation. Backed by SQLite or PostgreSQL. 11 MCP tools, 6 LLM provider backends. Part of JamJet.

Provides LLM access to Exasol database via MCP tools, including metadata reading and data queries, with an optional on-premise text-to-SQL (Governed SQL) translation.

Persistent memory for AI coding agents: local-first semantic search, dependency graphs, git history.

Connects AI agents to persistent topological memory on the Volumetric Lattice Network, enabling sub-5ms associative recall and storage of facts, documents, and code.

Durable hybrid memory for AI agents. Combines vector search, BM25, temporal retrieval, and optional Memgraph knowledge graph via reciprocal rank fusion. 6 MCP tools: health, search, answer, feedback, graph_query, graph_add_triple. Self-hosted with Qdrant backend.

Semantic search across Claude Code conversations. Hybrid vector + keyword search, fully local, background indexing.

Enables local semantic search over documents and code for Claude Code and Claude Desktop, running entirely offline with local embeddings and vector storage.

An implementation of the Model Context Protocol (MCP) server for SingleStore that enables natural language interaction with SingleStore databases through compatible LLM clients like Claude Desktop and Cursor.

Shared fleet memory for AI agents — what one agent learns, the whole fleet knows. Nightly self-correction: stale facts rewritten in place, duplicates merged. Recall pushed into every prompt in supported coding agents. Self-hosted.

Enables AI agents and MCP clients to start each session with a pre-assembled context pack of the project's Markdown notes, decisions, rules, Git and tracker state, and to write progress and decisions back as dated logs. Context is gathered locally by lookups and queries rather than model calls, so different tools like Claude Code, Codex, and Antigravity share the same memory without spending tokens on retrieval.

AI-powered code generator for the Arbitrum ecosystem with 19 MCP tools. Generates Stylus smart contracts (Rust/WASM), SDK bridging code, full-stack dApps, and Orbit chain deployments using RAG-based retrieval over Arbitrum documentation.

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

A server that provides DuckDuckGo search capabilities (text, image, news, video search and AI chat) through the Model Context Protocol.

A production-grade MCP server for integrating RAG into AI agents, supporting multiple vector databases with enterprise security and dynamic tool selection.

Enables interaction with the RAGFlow API to manage knowledge base datasets and conduct AI-driven chat sessions. Users can list datasets, create chat assistants, and retrieve or query information from specialized knowledge bases.