Graphiti MCP Server

Overview

Graphiti is a specialized framework that turns continuous streams of user interactions, enterprise data, and external feeds into a temporally‑aware knowledge graph. Instead of treating information as static blobs that must be re‑indexed after every change, Graphiti incrementally updates the graph, preserving a full audit trail of how facts evolve over time. This makes it possible for AI agents to ask “What did the user say last week?” or “How has the relationship between product X and customer Y changed over the past month?” without recomputing the entire structure.

For developers building AI assistants, Graphiti solves a core pain point: memory that scales with context. Traditional retrieval‑augmented generation (RAG) pipelines rely on dense vector indexes that are expensive to refresh and lack fine‑grained temporal control. Graphiti’s graph model, on the other hand, stores facts as triples (subject–predicate–object) with timestamps and versioning. This enables precise, time‑bound queries and state‑based reasoning that are essential for task automation, compliance tracking, and personalized interactions.

Key capabilities include:

• Incremental ingestion of structured data (CSV, JSON) and unstructured text (documents, chat logs) without full re‑builds.
• Multi‑modal querying: semantic search, keyword filtering, and graph traversal all in a single API.
• Temporal navigation: retrieve the graph as it existed at any point, or compute deltas between snapshots.
• Rich analytics: compute centrality scores, detect emerging entities, and surface trend alerts.
• Integration hooks: expose the graph through MCP endpoints (resources, tools, prompts) so that Claude, Cursor, or custom agents can call into it directly.

Real‑world scenarios where Graphiti shines include:

• Customer support agents that must remember a client’s purchase history, preferences, and recent complaints across multiple interactions.
• Enterprise workflow automation where tasks depend on the latest status of orders, tickets, or inventory levels.
• Compliance monitoring that tracks changes to regulatory documents and flags deviations over time.
• Personalized recommendation engines that adapt to evolving user interests without re‑training large language models.

When integrated into an AI workflow, Graphiti acts as a dynamic memory layer. An MCP client can issue a prompt that queries the graph for context, receive structured JSON back, and then feed that into a language model to generate a response. Because the graph is continuously updated, the assistant always works with the most recent state, yet can also reference historical snapshots for audit or explainability purposes.

Graphiti’s standout advantage is its temporal fidelity combined with low‑latency access. By avoiding heavy recomputation and leveraging efficient graph traversal algorithms, developers can build agents that are both contextually aware and responsive—qualities that traditional RAG systems struggle to deliver at scale.