What Is Graphlit and What Does It Do?

Updated

Graphlit is a context layer for AI agents. It connects the tools a team already uses — Slack, GitHub, Jira, and others — and keeps that content synchronized in real time, so an agent can retrieve relevant, current context through a single API instead of you building and maintaining separate integrations. It is aimed at teams building AI agents or AI-powered features that need grounded, up-to-date information from internal systems, and it is positioned as "one API, zero ops, semantic search built-in."

The problem it addresses

AI agents are only as useful as the context they can reach. A model that knows nothing about your open pull requests, recent incident threads, or sprint tickets will give generic answers. The usual fix is to wire up each source yourself: authenticate against Slack, poll GitHub, parse Jira, store the results, keep them fresh, and add a search layer on top. That work is repetitive and ongoing — every new source adds another connector to maintain.

Graphlit's approach is to own that layer. You point it at your sources, it syncs them continuously, and your agent queries one API for the relevant context.

Core capabilities

  • Real-time sync across sources. Content from Slack, GitHub, Jira, and other connected systems is kept current rather than snapshotted once. When something changes upstream, the context layer reflects it.
  • One API. Instead of integrating each source separately, your agent talks to a single interface. This reduces the number of moving parts in your stack.
  • Zero ops. The synchronization and retrieval infrastructure is managed for you, so you are not running ingestion pipelines or a vector store yourself.
  • Semantic search built in. Retrieval is meaning-based, not just keyword matching, which matters when an agent needs to find the right ticket or thread from a natural-language question.

Who it is for

Graphlit fits teams that are building agents or AI features and want them grounded in company knowledge without building a retrieval pipeline from scratch. Typical situations include:

  • An internal assistant that answers questions about engineering work by drawing on GitHub and Jira.
  • A support or ops agent that needs the latest discussion from Slack channels.
  • A product that surfaces relevant context from multiple internal tools inside one conversational interface.

If your agent only needs a single, static data source, a full context layer may be more than you need. The value shows up when context is spread across several tools and has to stay fresh.

How to think about adopting it

The practical question is whether the sources you care about are ones Graphlit already connects, and whether real-time freshness matters for your use case. Because the sync and search are managed, the integration effort shifts from "build connectors and a retrieval stack" to "connect sources and call an API." What you give up is some control over how ingestion and indexing work internally — a reasonable trade if you would rather not operate that infrastructure.

For current source coverage, API specifics, and any access or pricing terms, check Graphlit's own documentation, since those details change and are not fixed by the general description above.