AI Agent for MCP Memory Server
Integrate FlowHunt with the mcp-rag-local Memory Server to enable advanced semantic storage and retrieval of text data. Unlock powerful knowledge management by leveraging Ollama for text embeddings and ChromaDB for high-performance vector similarity search. Automatically memorize documents, PDFs, and conversational inputs for instant, relevant recall that goes beyond simple keyword matching.
Effortless Semantic Memorization
Store and retrieve information based on semantic meaning, not just keywords. Instantly memorize single texts, multiple entries, or entire PDF documents—making enterprise knowledge truly accessible and actionable.
- Semantic Memory Storage.
- Store and retrieve text passages based on meaning using cutting-edge embeddings.
- PDF & Bulk Memorization.
- Effortlessly memorize contents of PDF files and large text bodies in chunks.
- Conversational Knowledge Upload.
- Interactively chunk and memorize large texts via natural language conversation with the AI.
- Instant Similarity Search.
- Retrieve the most relevant knowledge snippets for any query in real time.
Powerful Vector Database Integration
Seamlessly manage, inspect, and search stored knowledge with the built-in ChromaDB vector database and admin GUI. Gain granular control for enterprise-scale memory management.
- ChromaDB Admin GUI.
- Browse, search, and manage your vector memory database from an intuitive web interface.
- Easy Setup & Configuration.
- Streamlined deployment with Docker Compose and simple config for rapid integration.
Natural Language Knowledge Recall
Ask questions in plain English and the AI agent returns the most relevant stored knowledge, complete with context and relevance scoring. Make enterprise memory conversational and user-friendly.
- Conversational Retrieval.
- Query the memory server and get context-rich answers, not just raw data.
- Relevance-Based Output.
- Receive results ranked by semantic relevance, ensuring you always get the best match.
MCP INTEGRATION
Available Memory Server (mcp-rag-local) MCP Integration Tools
The following tools are available as part of the Memory Server (mcp-rag-local) MCP integration:
- memorize_text
Store a single text passage for future semantic retrieval based on meaning.
- memorize_multiple_texts
Store several text passages at once, enabling batch memory storage for efficient retrieval.
- memorize_pdf_file
Extracts text from a PDF file, chunks it, and stores all segments for later semantic retrieval.
- retrieve_similar_texts
Find and return the most relevant stored texts for a given query using semantic similarity search.
Connect Your MCP Memory Server Integration with FlowHunt AI
Connect your MCP Memory Server Integration to a FlowHunt AI Agent. Book a personalized demo or try FlowHunt free today!
What is mcp-local-rag
mcp-local-rag is an open-source Model Context Protocol (MCP) server developed by Nikhil Kapila and available on LobeHub. It is designed to perform local Retrieval-Augmented Generation (RAG) searches on user input queries without requiring external data files or APIs. Instead, mcp-local-rag executes live web searches, extracts relevant context, and returns it to Large Language Models (LLMs), like Claude, in real-time. This enables LLMs to answer questions using up-to-date information from the web, even if that information is not included in their training data. The server is easy to install using Docker or the uvx command and supports integration with various MCP-compatible clients, making it ideal for users who want privacy, control, and fresh knowledge directly from their local environment.
Capabilities
What we can do with mcp-local-rag
mcp-local-rag empowers users and developers to perform web-based retrieval-augmented generation locally. It allows AI models to dynamically fetch, extract, and use the latest information from the internet, ensuring that responses are always current and relevant. Integration is seamless with major MCP clients, and the service prioritizes privacy by avoiding third-party APIs.
- Live Web Search
- Perform real-time searches on the internet for up-to-date information.
- Context Extraction
- Automatically extract relevant context from search results to enrich AI responses.
- Private & Local
- Run everything locally, ensuring your data and queries remain private—no external APIs needed.
- Seamless Client Integration
- Compatible with popular MCP clients like Claude Desktop, Cursor, and Goose.
- Easy Installation
- Deploy quickly using Docker or the uvx command with minimal configuration.
How AI Agents Benefit from mcp-local-rag
AI agents using mcp-local-rag gain the ability to access and utilize fresh, real-world information by performing live web searches and extracting context on demand. This dramatically extends their knowledge base beyond static training data, enabling them to answer time-sensitive or novel questions accurately. By running locally, mcp-local-rag also ensures greater privacy, control, and reliability for AI-powered workflows.