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:
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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.
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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.
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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.