"Tools and strategies for managing company knowledge" matching MCP servers:

Provides 10 structured reasoning strategies (Chain of Thought, ReAct, Tree of Thoughts, etc.) for complex problem-solving with session persistence, branching, and tool integration capabilities.

Secure, local-first collaboration layer for AI agent teams, enabling authenticated agent-to-agent communication, shared memory with provenance, and scoped service execution through MCP.

Enables AI agents to query and manage company knowledge from a Supabase GraphRAG store, including accounts, meetings, people, org hierarchy, product docs, and call logs.

Local RAG system for Claude Code with hybrid search (semantic + BM25), cross-encoder reranking, markdown-aware chunking, and 12 MCP tools. Zero external servers, pure ONNX in-process.

Provides centralized knowledge management for projects, allowing users to store, search, and maintain project-specific knowledge that persists across sessions.

Enables cross-session memory and recall for AI coding assistants through a git-synced knowledge base, hybrid semantic and TF-IDF search across tool transcripts, and automatic distillation of insights with secrets scrubbing.

Local knowledge engine for codebases with hybrid search, knowledge graph, and interaction tracking, enabling Claude Code to search and interact with project knowledge locally.

Long-term memory for AI agents. Compiles conversations into a structured knowledge base with Claim/Evidence model, source provenance, append-only timeline, and contradiction detection. Multi-path retrieval (Exact + BM25 + Graph + weighted RRF + reranker) — 96.6% R@5 on LongMemEval-S, zero vector dependencies.

A knowledge base MCP server that aggregates team knowledge from multiple sources into Postgres. It provides hybrid search (full-text + vector + RRF) via MCP tools, and enables direct recording of decisions, learnings, and pitfalls.

A local knowledge graph MCP server that provides AI agents with permanent, structured memory about codebases, enabling semantic search, blast radius analysis, and convention enforcement.

An MCP server that acts as a knowledge engine for software projects, delivering relevant context at the start of a task and accumulating knowledge at its end through tools like start_task, context, finish_task, remember, and search, with a file-based source of truth and optional semantic retrieval via Graphiti/Neo4j.

Provides a scalable knowledge graph implementation for Model Context Protocol using Elasticsearch, enabling AI models to store and query information with advanced search capabilities, memory-like behavior, and multi-zone architecture.

Enables building and managing knowledge workspaces with tools for ingesting content from text, clipboard, or URLs and searching organized knowledge bases. Supports multiple configurable workspaces with separate inbox and knowledge directories for structured information management.

An MCP server that enables semantic search over a private knowledge base via pgvector, with GitHub OAuth authentication and a RAG pipeline.

An MCP server that acts as an AI knowledge and skills hub, automatically discovering, indexing, and serving skill libraries to any MCP client with full-text search, rich metadata, and hot reload.

语义搜索知识库，支持文档获取和索引刷新，使AI Agent能够检索团队知识。

Provides MCP tools for workspace initialization, health checks, context recall, and durable memory capture, enabling project-specific memory and shared company knowledge with citations.

Enables AI coding tools to turn a list of links into a verified, browsable knowledge base with grounded summaries via MCP tools for update, validation, and enrichment.

MCP server for managing an OKF markdown knowledge base, enabling AI agents to query, read, write, and auto-sync architectural decisions and rules via Git.

An MCP server that enables AI agents to query specialized, domain-specific knowledge bases built using the LightRAG framework for enhanced retrieval-augmented generation. It allows for managing and searching knowledge graphs and vector embeddings to provide accurate, context-aware information during an AI assistant's reasoning process.