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Graphlit

Context layer for AI agents

Graphlit positions itself as the context layer for AI agents, supplying them with context from connected data sources. It maintains an open-source MIT-licensed MCP server in TypeScript.

The data framework for connecting agents to your data.

LlamaIndex provides ingestion, indexing and retrieval primitives that give agents grounded context over enterprise data — a foundational piece of the retrieval side of the harness stack.

4 pros · 3 cons

Stateful agents with memory, from the team behind MemGPT.

Letta (formerly MemGPT, out of UC Berkeley) builds an agent server with built-in memory, context management and state — letting agents persist, learn and be edited like files rather than living in a chat window.

$10M seed led by Felicis (2024)

4 pros · 3 cons

Exa

Search API purpose-built for AI agents.

Exa (formerly Metaphor) provides neural search and web-crawl APIs tuned for agent consumption — live, cited knowledge for agents instead of stale training data.

4 pros · 4 cons

Connections

Relations from the Harness Hub knowledge graph, as charted in the State of Harness reports.

Built on · 1

Sources

  1. Graphlit website
  2. Graphlit MCP server repo