"Créer des systèmes de prompt pour les modèles de langage (LLM)" matching MCP servers:

Provides stateful prompt optimization using research-backed techniques like APE and OPRO, learning from historical performance data via a vector database. It enables users to automatically refine prompts, retrieve high-performing examples, and track performance analytics through iterative feedback.

A long-term memory MCP server for AI agents that stores memories (facts, decisions, etc.) in a single SQLite database with hybrid search and full edit history, ensuring consistency across sessions.

Kollektiv

A CLI tool and MCP server that turns markdown documentation into a searchable, queryable knowledge base.

Multi-modal RAG engine for AI assistants. Stores conversation history, conclusions, diffs, error traces, and other development artifacts in LanceDB with vector search, multi-factor scoring, and an LLM-driven consolidation pipeline.

Exposes a Neo4j knowledge-graph index over personal markdown notes to LLM agents via MCP, enabling semantic search, tag navigation, and relationship discovery.

Enables LLM hosts to retrieve live, relevant documentation excerpts from official library docs sites via a search-and-RAG tool, avoiding reliance on training data.

Local-first, source-traceable memory for AI agents — no LLM at ingest, $0 per message, zero data egress. Gives Claude Code, Cursor, and any MCP client one shared persistent memory with semantic recall, belief revision, selective forgetting, and a provenance guard that blocks acting on stale or unconfirmed memories.

A local-first knowledge base for LLM coding agents that indexes repository documentation, concept ontology, and build targets into Qdrant and exposes retrieval as MCP tools (search, get, list sources, reindex).

Enables agents to run hybrid dense and BM25 search over a local folder of Markdown files, read and write notes, and trigger reindexing as the folder changes. It also injects the most relevant sections into each prompt automatically and runs entirely locally with a bundled embedding model.

Enables ingestion and semantic search over text documents using PostgreSQL + pgvector and OpenAI-compatible embeddings, allowing any LLM agent to retrieve relevant chunks for grounded answers.

An MCP server that enables RAG-powered AI chat integration for websites by crawling content, building local vector stores, and generating embeddable chat widgets. It simplifies the setup of local chat servers with support for various LLM and embedding providers.

A personal memory system that provides AI assistants with long-term memory capabilities through semantic search and vector storage. It enables Claude Code to store, retrieve, and manage personal context and project preferences using flexible LLM backends.

Self-hosted mem0 MCP server for Claude Code. Run a complete memory server against self-hosted Qdrant + Neo4j + Ollama while using Claude as the main LLM.

Persistent AI memory server with 3-layer hybrid search (vector + FTS5 + keyword), confidence scoring via Reciprocal Rank Fusion, episodic/profile memory, and 16 tools. Zero LLM dependency. Works standalone with Claude Desktop and Claude Code. MIT licensed.

A TypeScript-based MCP server that provides project-specific knowledge graph memory for LLM agents to store and retrieve entities, relations, and observations. It features disk-persistent storage and supports cross-project knowledge sharing to enhance agent long-term memory.

A server that provides access to Baidu Cloud Vector Database functionality through the Model Context Protocol, enabling LLM applications to perform vector searches and database operations via natural language.

Provides LLM clients with a persistent, scalable knowledge graph memory system that supports semantic retrieval, contextual recall, and temporal awareness.

A flexible memory system for AI applications that supports multiple LLM providers and can be used either as an MCP server or as a direct library integration, enabling autonomous memory management without explicit commands.

Exposes infocepo.com infrastructure (LLM, ChromaDB, S3, Docker registry, service discovery) as MCP tools for AI agents.