Memgraph ZeroMemGQLUse CasesAgentic Access

Agentic Data Access

AI agents need to reason over organizational data to answer questions, make decisions, and take action. The challenge is that this data is scattered across relational databases, graph databases, data lakes, and SaaS tools—each with its own query language, connection protocol, and access model. Teaching agents to navigate this fragmentation is brittle and error-prone.

MemGQL solves this by exposing all connected data sources as a single, unified graph. Agents use standard GQL to explore relationships, traverse connections, and retrieve data—without knowing where each dataset lives or how to access it.

The Problem

Modern AI agents face a few critical barriers when accessing organizational data:

  1. Fragmented APIs: Each system exposes a different interface—SQL for Postgres, Cypher for Memgraph, REST for SaaS tools. Agents must learn multiple query languages and connection patterns.

  2. Context Loss: When data is split across systems, agents lose the ability to follow relationships. A customer ID in PostgreSQL may reference the same entity as a node in Memgraph, but the agent has no way to know this without explicit integration code.

  3. Discovery Friction: Agents cannot explore data autonomously. They need hardcoded schemas, pre-defined mappings, and human-written prompts to know what questions to ask and where to look.

The Solution

MemGQL provides a federated graph layer that abstracts all backend systems behind a single GQL endpoint. Agents connect to one URL and query everything.

Autonomous discovery

Agents don’t need hardcoded schemas or hand-written USE clauses. A single SHOW SCHEMA returns the unified index of every label, relationship type, and property across all connected backends — the agent’s map of what’s queryable and where the entities connect.

From there, USE-free routing lets the agent write a plain GQL query and let the engine pick the backend from the labels and properties it referenced:

-- The agent doesn't need to know `name` lives in PostgreSQL or that
-- FRIEND_OF lives in Memgraph — both route automatically.
MATCH (p:Person {id: 1})-[:FRIEND_OF]->(f:Person) RETURN f.name;

Routing is strict: if a query is ambiguous or matches nothing, MemGQL returns an actionable error that names the candidate sources and points back at SHOW SCHEMA — so an agent can self-correct instead of failing silently.

Enterprise ContextFeatures