What is Graphlit?

The context layer for AI agents

Give your AI agents the context they need to work reliably. One API for ingestion, extraction, storage, and retrieval - organizational knowledge with entities, relationships, and temporal state.

Graphlit is the context layer for AI agents - providing organizational knowledge infrastructure, data ingestion from 30+ sources, and intelligent retrieval. Whether you're building with Mastra, Agno, Vercel AI SDK, or custom code, Graphlit handles the hard parts so you can focus on your agent's logic and UX.

🔌 Works with your framework: Use Graphlit's MCP server to give any MCP-enabled framework instant access to 30+ feeds, audio/video processing, semantic search, and knowledge graphs. Or use our TypeScript/Python/C# SDKs directly.

Context layer vs traditional RAG:

Stateless (forgets between sessions)

Persistent organizational memory

Entities + relationships + temporal state

Keyword/similarity matching

Graph traversal + semantic search

Knowledge graph + vectors

Complete platform (one API) + MCP integration

Automatic extraction workflows

None (treats all users the same)

Per-user knowledge graphs

Entity-linked with provenance

Think of it this way: RAG is like searching through filing cabinets. A context layer is like having a knowledgeable assistant who understands your organization.

Why Developers Choose Graphlit

Building data infrastructure for AI agents means integrating:

Vector database (Pinecone, Weaviate)
Document parsers (Unstructured, LlamaParse)
Entity extraction (spaCy, custom LLMs)
Embedding models (OpenAI, Cohere)
OAuth connectors for data sources (Slack, Gmail, etc.)
Sync infrastructure (polling, webhooks, rate limits)

Result: 3-20 months of integration work before building your actual agent application.

One API. No assembly required.

Complete Platform Features

Graphlit provides everything you need to build production AI applications - from data ingestion to advanced processing:

30+ feeds (Slack, Gmail, GitHub, S3, RSS, etc.) - OAuth, API keys, or public

Continuous polling (30 sec to hours, configurable per feed)

Transcription + speaker diarization (Speaker #1, #2, etc.) via Deepgram, AssemblyAI

Audio extraction + transcription (available) + frame analysis (coming soon)

Vision OCR + layout preservation (handles complex tables, diagrams)

Web crawling, screenshots, search integration (Tavily, Exa)

Customizable multi-stage pipelines (preparation + extraction stages)

Audio generation, summaries, Markdown export (TTS, content transformation)

Vectors only (some have basic graphs)

Schema.org entities + relationships with temporal context

Hybrid: vector + graph + keyword

Geo-spatial, image similarity, entity-based, temporal, boolean (AND/OR)

Per-user isolation (userId parameter), collections, specifications

Real example: Building a Slack assistant with Graphlit vs memory-only platforms:

With Graphlit:

With Memory-Only Platform:

The difference: Graphlit provides a complete platform - from data ingestion through processing to retrieval - so you can focus on building your application.

Customer support agents that remember every interaction and have full context from past conversations. → Build an agent in 7 minutes

Production SaaS Applications

Zine runs in production on Graphlit with growing user base and multi-source data sync. → See the architecture

Knowledge Extraction Systems

Automatically extract people, organizations, and relationships from any content. → Extract knowledge graphs

SDK availability: Python, TypeScript, and .NET SDKs available. All quickstart examples use TypeScript. Click "Convert to Python/.NET" links throughout for instant language conversion via Ask Graphlit.

Launch checklist:

✅ Verify setup: Run hello.ts (Step 1 below)

Key terms you'll use:

Content – anything you've ingested (files, web pages, emails)
Conversation – AI session that remembers prior messages and retrieved context
Specification – which LLM + settings to use (model, temperature, etc.)

Verify your credentials work:

Run: npx tsx hello.ts

Graphlit handles production scale out of the box:

Multi-tenant: Per-user data isolation within a single project (create users in Graphlit, scope SDK with userId)
Scale: Built to handle thousands of users and millions of documents per project
Automatic sync: Feed connectors poll on configurable schedules (30 seconds to hours)

Zine case study →

30+ feeds (Slack, Gmail, GitHub, S3, RSS, and more) with automatic sync - view all →

🚀 Start here: Quickstart: Your First Agent (7 minutes) Build a streaming agent with tool calling. Fastest way to see Graphlit in action.

Then explore:

Need help?

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import { Graphlit } from 'graphlit-client';

async function main() {
  const graphlit = new Graphlit();

  // Ingest a document
  const content = await graphlit.ingestUri(
    'https://arxiv.org/pdf/1706.03762.pdf',
    'Attention Paper',
    undefined,
    undefined,
    true  // Wait for processing
  );

  // Ask questions about it
  const conversation = await graphlit.createConversation({
    name: 'Q&A Session',
    filter: { contents: [{ id: content.ingestUri.id }] }
  });

  const answer = await graphlit.promptConversation(
    'What are the key innovations?',
    conversation.createConversation.id
  );

  console.log(answer.promptConversation.message?.message);
}

main();

// 1. Setup OAuth connector (one-time)
const feed = await graphlit.createFeed({
  name: 'Team Slack',
  type: FeedTypes.Slack,
  slack: { type: FeedListingTypes.Past }
});
// ✅ All messages automatically synced, indexed, and searchable

// 1. Build Slack OAuth integration yourself
// 2. Poll Slack API yourself
// 3. Handle rate limits yourself  
// 4. Parse messages yourself
// 5. Upload to memory platform
// 6. Repeat for every data source
// ❌ Weeks of integration work per connector

import { Graphlit } from 'graphlit-client';

const graphlit = new Graphlit();

async function main() {
  const project = await graphlit.getProject();
  console.log(`✅ Connected: ${project.project.name}`);
}

main();

import { Graphlit } from 'graphlit-client';
import { SearchTypes } from 'graphlit-client/dist/generated/graphql-types';

const graphlit = new Graphlit();

async function main() {
  // Ingest document
  const content = await graphlit.ingestUri(
    'https://arxiv.org/pdf/1706.03762.pdf',
    'Attention Paper',
    undefined,
    undefined,
    true, // Wait for processing
  );

  console.log(`✅ Document ready: ${content.ingestUri.id}`);

  // Hybrid search (vector + keyword)
  const results = await graphlit.queryContents({
    search: 'transformer innovations',
    searchType: SearchTypes.Hybrid,
  });

  console.log(`Found ${results.contents?.results?.length ?? 0} documents`);
}

main();

import { Graphlit } from 'graphlit-client';

const graphlit = new Graphlit();

async function main() {
  const content = await graphlit.ingestUri(
    'https://arxiv.org/pdf/1706.03762.pdf',
    'Attention Paper',
    undefined,
    undefined,
    true,
  );

  const conversation = await graphlit.createConversation({
    name: 'Q&A Session',
    filter: { contents: [{ id: content.ingestUri.id }] },
  });

  const answer = await graphlit.promptConversation(
    'What are the key innovations?',
    conversation.createConversation.id,
  );

  console.log(answer.promptConversation.message?.message);
}

main();