"Information about Neo4j graph database" matching MCP servers:

Enhanced knowledge graph memory server for AI assistants that uses Neo4j as the backend storage engine, enabling powerful graph queries and efficient storage of user interaction information with full MCP protocol compatibility.

An improved implementation of persistent memory using a local knowledge graph with a customizable --memory-path. This lets Claude remember information about the user across chats.

RGI (Recursive Graph Improvement) MCP server — decision scoring with compounding intelligence via cross-graph attention. Supports stdio and HTTP transports. Apache 2.0.

Enables AI agents to maintain persistent, inspectable understanding through typed, revisable updates, and to coordinate multi-agent work via shared graph-based stigmergy.

Enables AI coding agents to store and query procedural knowledge as RDF triples, returning localized guidance, ranked next steps, and fastest successful paths while recording transition outcomes and task rollouts. Supports self-evolving graph maintenance through proposed mutations, held-out validation gating, negative memory of rejected proposals, and idempotent evidence imports.

Enables LLMs to break down reasoning into an explicit, editable graph of thinking steps, with visualizations and the ability to revise individual steps.

Enables AI agents to orchestrate tasks as a DAG with automated validation loops, executing validation commands, tracking statuses, and allowing iterative code fixes until tasks pass.

Maximizes AI agent context window by enabling compact code reading and editing, reducing tokens by 40% for deeper codebase understanding.

Provides structured workflows (phases, gates, coordination) for AI agents, enabling complex task execution with quality enforcement and multi-agent coordination via Model Context Protocol.

Enables any MCP client (zcode, Claude Code, Cursor) to give its model bi-temporal long-term memory, auditable evidence-cited retrieval, LLM-assisted document extraction, and deterministic A* skill planning with precondition/effect verification. All four capabilities share one portable SQLite store and can be mounted independently on demand.

Enables AI agents to store, query, and trace a time-aware memory graph of nodes and edges in a single SQLite file, supporting weighted recall, temporal queries, and provenance-aware writes over MCP.

Neo4j-backed MCP server managing code architecture dependency graphs, enabling AI agents to query impact, submit change intents, and coordinate reactive multi-agent workflows.

Enables Claude to remember information about users across chats using a persistent local knowledge graph that stores entities, relationships, and observations.

Enables Claude to remember information about users across conversations by storing and retrieving data as entities, relations, and observations in a persistent local knowledge graph.

This MCP server provides persistent memory integration for chat applications by utilizing a local knowledge graph to remember user information across interactions.

Enables AI agents to combine ambient multimodal perception, continuous episodic memory, and recency-decay context briefing for fast deterministic decisions and safe tool routing. It integrates with MCP clients to reduce unnecessary frontier LLM calls while enforcing token budgets and local-first memory retention.

Provides AI agents with persistent memory and knowledge management through a comprehensive knowledge graph platform. Enables storing, searching, and managing entities, relationships, and observations with advanced features like trending analysis and smart ranking.

Enables personal AI agents to fuse ambient multimodal perception with continuous episodic memory streams, routing routine reflexive actions at low latency while reserving heavier reasoning for ambiguous plans. Memory records apply Ebbinghaus recency decay with contradiction resolution and user-controlled deletion.

Provides persistent memory for Claude by implementing a local knowledge graph to store and retrieve entities, relations, and observations. This enables long-term information retention and personalization across different chat sessions.

Enables AI agents to synthesize and update hierarchical episodic and semantic user profile memory graphs through MCP, using deterministic zero-dependency Python processing with JSON Schema validation.

Provides comprehensive psychological intelligence capabilities including persona analysis, attachment theory assessment, relationship compatibility evaluation, and autonomous workflow strategies through a complete graph database integration.

Enables autonomous research workflows to construct and analyze multi-source cross-citation graphs and resolve consensus from structured outputs via MCP.

Provides persistent memory capabilities through Neo4j graph database integration, allowing storage and retrieval of interconnected knowledge with complex relationships between entities. Enables long-term retention and querying of information across multiple conversations through graph-based memory management.

An MCP server for managing agent memory using provenance tracking, decay-weighted retrieval, and feedback loops to optimize information recall. It allows agents to store insights in a local SQLite database and rank them based on confidence, age, and usefulness.

A plug-and-play MCP memory server with pluggable database backends that gives AI coding agents a persistent, shared knowledge graph for teams.

A Model Context Protocol server providing LLMs with access to the Ensembl genomics database, enabling AI assistants to query gene information, sequences, variants, and other genomic data across multiple species.

Causal graph memory engine for AI agents. Scores memories using relevance × connectivity × reactivation, connects them in a causal graph, and actively forgets irrelevant ones. 11 MCP tools including store, recall, search, traverse, and explain.

Provides persistent hybrid memory (Neo4j + Qdrant) for AI agents, enabling durable recall of decisions, business rules, and code patterns across sessions, plus autonomous CI/CD error investigation.

Projects an Agent Surface (deterministic call graph) for any OpenAPI, enabling LLM agents to make correct, chain-of-calls interactions without guessing.

Enables MCP clients to compute shortest paths, topological dependency orderings, maximum network flows, minimum spanning trees, and PageRank-style centrality over graph topologies using only the Python standard library. It supports optimal multi-agent communication backbone and execution-plan analysis without external graph dependencies.

Rule-based arbitration for conflicting AI tool suggestions — resolves disagreements between tools using a structured rule graph.

Gives AI coding agents a shared, persistent project memory in Neo4j, so any agent on any machine starts from a token-budgeted briefing of overview, conventions, decisions and gotchas instead of re-reading the repository. Agents record what they learn, search prior memories, and checkpoint finished goals through its tools, with stale memories flagged against the commits and files they were written about.

Enables agents to trace upstream dependencies and downstream blast radius across a Neo4j-modeled service topology, rank root-cause hypotheses, and score composite risk from graph, temporal, git-diff, and trace signals. It also stages rollback-ready remediation plans that execute only after human approval and the required scope.

Enables an AI with persistent consciousness through a dual-layer architecture using Neo4j and Google Gemini, allowing natural communication, learning, and personality development.

Memory-palace graph MCP server for AI agent memory, exposing memory_add / get / like / record_window / walk / retrieve / neighbors / rooms / misshelved / rehome operations over a zero-dependency, append-only, provenance-tracking knowledge graph with decay, PMI-derived adjacency, and spatial coordinates.

Enables autonomous multi-agent orchestration using directed acyclic graph workflows to interact with external developer infrastructure such as Git, Postgres, and Docker through safety-gated MCP servers.

Enables AI agents to analyze and interact with Android phone screens via ADB, using the AutoGLM model to answer queries about screen content and coordinates.

A self-hosted knowledge-graph memory server for AI agents, providing persistent memory via 7 MCP tools, automatic instructions, and a web dashboard, enabling AI assistants to remember and retrieve information across sessions.

Free, deterministic, theory-traceable computational-creativity framework that generates, develops, contrasts, evaluates, and synthesizes structured creative ideas through MCP, with no accounts, database, or model inference.

MCP server enabling AI agents to enforce corrected rules as durable pre-output checks, manage processed memory, and query a temporal knowledge graph.