What is Graphlit?

The semantic memory platform for AI agents

Give your AI agents persistent memory and data infrastructure. One API for ingestion, extraction, storage, and retrieval - works standalone or integrates with frameworks like Mastra, Agno, and Vercel AI SDK via MCP.

What is Graphlit?

Graphlit is the data infrastructure layer for AI agents - providing persistent semantic memory, 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.

Semantic memory vs traditional RAG:

Feature	Traditional RAG	Graphlit Semantic Memory
Memory	Stateless (forgets between sessions)	Persistent semantic memory
Understanding	Text chunk retrieval	Entities + relationships + context
Recall	Keyword/similarity matching	Graph traversal + semantic search
Knowledge	Document vectors only	Knowledge graph + vectors
Infrastructure	7+ services to integrate	Complete platform (one API) + MCP integration
Processing	Manual pipeline setup	Automatic extraction workflows
Personalization	None (treats all users the same)	Per-user knowledge graphs
Citations	Basic text snippets	Entity-linked, contextualized

Think of it this way: RAG is like searching through filing cabinets. Semantic memory is like having a knowledgeable assistant who remembers everything.

Why Developers Choose Graphlit

The Problem

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)

• Storage (S3, Azure Blob)

• Search (Elasticsearch)

• 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.

The Graphlit Solution

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();

One API. No assembly required.

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Complete Platform Features

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

Capability	Memory-Only Platforms	Graphlit
Data Feeds	Manual ingestion	30+ feeds (Slack, Gmail, GitHub, S3, RSS, etc.) - OAuth, API keys, or public
Automatic Sync	Manual upload	Continuous polling (30 sec to hours, configurable per feed)
Audio Processing	Basic or not available	Transcription + speaker diarization (Speaker #1, #2, etc.) via Deepgram, AssemblyAI
Video Processing	Not available	Audio extraction + transcription (available) + frame analysis (coming soon)
Document Processing	Text extraction	Vision OCR + layout preservation (handles complex tables, diagrams)
Web Capabilities	Not available	Web crawling, screenshots, search integration (Tavily, Exa)
Workflows	Fixed pipeline	Customizable multi-stage pipelines (preparation + extraction stages)
Publishing	Retrieval only	Audio generation, summaries, Markdown export (TTS, content transformation)
Knowledge Graph	Vectors only (some have basic graphs)	Schema.org entities + relationships with temporal context
Search Types	Vector similarity	Hybrid: vector + graph + keyword
Advanced Filtering	Basic metadata filters	Geo-spatial, image similarity, entity-based, temporal, boolean (AND/OR)
Production Features	Basic user scoping	Per-user isolation (userId parameter), collections, specifications

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

With Graphlit:

// 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

With Memory-Only Platform:

// 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

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

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What Can You Build?

AI Agents with Memory

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

Quick Start (TypeScript)

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:

1. Sign up (30 seconds)

2. Create project (1 minute)

3. Get credentials (1 minute)

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

5. Quickstart: Your First Agent (7 minutes)

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.)

1. Say Hello to Graphlit

Verify your credentials work:

import { Graphlit } from 'graphlit-client';

const graphlit = new Graphlit();

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

main();

Run: npx tsx hello.ts

2. Ingest & Search

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({
    filter: {
      search: 'transformer innovations',
      searchType: SearchTypes.Hybrid,
    },
  });

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

main();

3. RAG Conversation

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();

Ready for more? → Quickstart: Add streaming and tool calling

Production Ready

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)

• Proof: Zine runs on Graphlit in production

Zine case study →

Connect Your Data

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

Next Steps

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

Then explore:

• AI Agents Tutorial - Multi-agent patterns (15 min)

• Knowledge Graph - Entity extraction (20 min)

Need help?

• Discord Community - Active developer community

• Ask Graphlit - AI code assistant

• 60+ Examples - Working code

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