> For the complete documentation index, see [llms.txt](https://docs.graphlit.dev/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.graphlit.dev/getting-started/overview.md).

# Platform Overview

Graphlit is the context layer for AI agents. We give developers complete infrastructure to build production AI applications with organizational knowledge — entities, relationships, and temporal state.

## Quick Reference

| Concept                | Description                                                                                        | Learn More                                                |
| ---------------------- | -------------------------------------------------------------------------------------------------- | --------------------------------------------------------- |
| **Feeds**              | **Automatic sync from 30+ sources** (Slack, Gmail, GitHub, S3, RSS, etc.) - Unique to Graphlit     | [Feeds](/platform/feeds.md)                               |
| **Content**            | Documents, audio transcription, video analysis, web crawling                                       | [Ingestion Guide](/api-guides/use-cases/content.md)       |
| **Advanced Filtering** | **Production-grade queries**: geo-spatial, image similarity, entity-based, temporal, boolean logic | [See Examples](#production-grade-metadata-filtering)      |
| **Workflows**          | **Custom extraction pipelines** with vision models, OCR, entity extraction - Unique depth          | [Workflows](/platform/key-concepts.md#workflows)          |
| **Conversations**      | RAG with streaming, citations, and tool calling                                                    | [AI Agents Tutorial](/tutorials/ai-agents.md)             |
| **Knowledge Graph**    | Schema.org entities + relationships + temporal context                                             | [Knowledge Graph Tutorial](/tutorials/knowledge-graph.md) |
| **Specifications**     | Reusable model configurations (GPT-4, Claude, Gemini, Deepseek, etc.)                              | [AI Models](/platform/models.md)                          |
| **Collections**        | Flexible content grouping (virtual folders, topics, projects, users/teams)                         | [Key Concepts](/platform/key-concepts.md#collections)     |

***

{% hint style="success" %}
**Start here roadmap:**

1. [Sign up for Graphlit](/account-setup-one-time/signup.md)
2. [Create your first project](/account-setup-one-time/create-project.md)
3. [Copy credentials and run `hello.ts`](/account-setup-one-time/credentials.md#verify-your-setup)
4. Continue to [Quickstart: Your First Agent](/getting-started/quickstart.md)
   {% endhint %}

## What is a Context Layer?

A context layer gives AI agents the ability to understand entities, relationships, and temporal state - not just retrieve similar documents.

**The key difference**: RAG retrieves text chunks by similarity. A context layer knows "Alice from Acme Corp mentioned pricing on Oct 15" and can answer "What did Alice say about pricing?"

[Deep dive: Semantic Memory architecture →](/platform/semantic-memory.md)

***

## What Makes Graphlit Different

Graphlit provides a complete data platform for production AI applications - from ingestion to processing to retrieval.

### 🔌 30+ Data Connectors

Connect to any data source with one API call. OAuth, API keys, or bot tokens - we handle authentication for you.

**Communication:** Slack, Microsoft Teams, Discord\
**Email:** Gmail, Outlook\
**Project Management:** Jira, Linear, GitHub Issues, Trello\
**Documents:** Google Drive, OneDrive, SharePoint, Dropbox, Box, Notion\
**Cloud Storage:** AWS S3, Azure Blob, Google Cloud Storage\
**Meetings:** Fireflies.ai, Fathom, Attio Meetings, Salesforce ECI\
**CRM & Contacts:** Salesforce CRM, Attio CRM, Google Contacts, Microsoft Contacts\
**Research:** Parallel Research, Parallel Entity Discovery\
**Social:** Twitter, Reddit, YouTube\
**Calendars:** Google Calendar, Outlook Calendar\
**Support:** Zendesk, Intercom\
**Web:** RSS feeds, web crawling, site maps

```typescript
import { Graphlit } from 'graphlit-client';
import { FeedTypes, FeedListingTypes } from 'graphlit-client/dist/generated/graphql-types';

const client = new Graphlit();

// Create Slack feed with bot token
const feed = await client.createFeed({
  name: 'Team Slack',
  type: FeedTypes.Slack,
  slack: {
    type: FeedListingTypes.Past,
    token: process.env.SLACK_BOT_TOKEN,  // Bot token from Slack app
    channel: 'general'  // Channel name or ID
  }
});
```

**What this means:** Connect any data source with a single API call. Authentication (OAuth, API keys, bot tokens), sync scheduling, data parsing, and indexing all handled automatically.

### 🎥 Multi-Format Processing

**Audio:** Automatic transcription with speaker diarization (Speaker #1, #2, etc.) via Deepgram, AssemblyAI\
**Video:** Audio extraction + transcription (available today), frame analysis coming soon (TwelveLabs, Azure Video Indexer)\
**Documents:** OCR with vision models, layout preservation\
**Web:** Crawling, screenshots, search integration (Tavily, Exa)\
**Email:** Parse and index with attachments\
**Code:** Repository indexing with GitHub connector

```typescript
import { Graphlit } from 'graphlit-client';

const client = new Graphlit();

// Transcribe audio with one call
const audio = await client.ingestUri(
  'https://example.com/meeting.mp3',
  'Team Meeting'
);
```

**Result:** Automatic transcription with speaker diarization (Speaker #1, Speaker #2, etc.), searchable transcript indexed for retrieval.

### ⚙️ Custom Workflows

**Most content doesn't need workflows** - Graphlit's intelligent defaults handle PDFs, audio, web pages automatically.

**When you need workflows:** Entity extraction for knowledge graphs.

```typescript
import { Graphlit } from 'graphlit-client';
import { EntityExtractionServiceTypes, ObservableTypes } from 'graphlit-client/dist/generated/graphql-types';

const client = new Graphlit();

// Build knowledge graph from your documents
const workflow = await client.createWorkflow({
  name: 'Extract Entities',
  extraction: {
    jobs: [{
      connector: {
        type: EntityExtractionServiceTypes.ModelText,  // Extract entities with LLM
        extractedTypes: [
          ObservableTypes.Person,           // People mentioned
          ObservableTypes.Organization,     // Companies, teams
          ObservableTypes.Label             // Topics, themes, tags
        ]
      }
    }]
  }
});
```

**Result:** Content is automatically prepared (PDFs, audio, web pages), then entities are extracted. Search by person ("all mentions of Alice"), organization ("documents about Acme Corp"), or label/topic.

**For complex PDFs only:** Add preparation stage with vision models. See [Workflows documentation](/platform/workflows.md) for advanced options.

### 🔄 Automatic Sync

Continuous polling from all connected sources (30 seconds to hours, configurable). After feed creation, data flows automatically - no manual polling, no webhooks to manage, no API rate limits to handle.

### 🎨 Publishing Capabilities

**Audio Generation:** Text-to-speech with ElevenLabs\
**Summaries:** Automatic content summarization\
**Markdown Export:** Structured content extraction\
**Citations:** Entity-linked, contextualized references

```typescript
import { Graphlit } from 'graphlit-client';
import { ContentPublishingFormats, ContentPublishingServiceTypes, FileTypes } from 'graphlit-client/dist/generated/graphql-types';

const client = new Graphlit();

// Publish markdown summaries of all document content
const published = await client.publishContents(
  "Create a concise summary",
  { type: ContentPublishingServiceTypes.Text, format: ContentPublishingFormats.Markdown },
  undefined,  // summaryPrompt
  undefined,  // summarySpecification
  undefined,  // publishSpecification
  undefined,  // name
  { fileTypes: [FileTypes.Document] }  // filter: only documents
);
```

**What this enables:** Transform and republish content. Generate audio versions, create summaries, export structured data. Your knowledge base becomes a content creation engine.

### 🔍 Production-Grade Metadata Filtering

Graphlit provides advanced filtering that would take weeks to build yourself - geo-spatial, image similarity, entity-based, temporal, and complex boolean queries all in one API:

**Search by Location** (geo-spatial queries)

```typescript
import { Graphlit } from 'graphlit-client';

const client = new Graphlit();

// Find all content within 10km of San Francisco
const results = await client.queryContents({
  search: 'restaurant reviews',
  location: { latitude: 37.7749, longitude: -122.4194, distance: 10000 }
});
```

**Search by Image** (visual similarity)

```typescript
import { Graphlit } from 'graphlit-client';
import fs from 'fs';

const client = new Graphlit();

// Find similar images
const imageBuffer = fs.readFileSync('./reference-image.jpg');
const base64Image = imageBuffer.toString('base64');

const similar = await client.queryContents({
  imageData: base64Image,
  imageMimeType: 'image/jpeg',
  numberSimilar: 20
});
```

**Search by Entity** (extracted people, orgs, places)

```typescript
import { Graphlit } from 'graphlit-client';

const client = new Graphlit();

// Find all content mentioning specific people or organizations
const mentions = await client.queryContents({
  search: 'product launch',
  observations: [
    { observable: { name: 'Kirk Marple' }},
    { observable: { name: 'Graphlit' }}
  ]
});
```

**Complex Boolean Queries** (AND/OR logic)

```typescript
import { Graphlit } from 'graphlit-client';

const client = new Graphlit();

// Find content created last 7 days mentioning "deal closure"
const results = await client.queryContents({
  search: 'deal closure',
  createdInLast: 'P7D'  // ISO 8601 duration: 7 days
});
```

**What this means:** Filter by location (find content near you), by visual similarity (find images like this one), by entities (all mentions of a person/company), by time (last 24 hours, date ranges), or combine filters. This level of filtering is typically only found in enterprise search systems.

### 🎬 True Multimodal Processing

Graphlit processes audio and video content - not just stores files, but actually extracts and indexes the content:

**Audio Files (MP3, WAV, M4A, etc.)**

```typescript
import { Graphlit } from 'graphlit-client';

const client = new Graphlit();

// Upload audio, get searchable transcript
const audio = await client.ingestUri(
  'https://example.com/podcast-episode.mp3',
  'Podcast Episode'
);
```

**Result:** Searchable transcript with speaker diarization (Speaker #1, Speaker #2, etc.).

**Video Files (MP4, MOV, etc.)**

```typescript
import { Graphlit } from 'graphlit-client';

const client = new Graphlit();

// Upload video, extract and transcribe audio
const video = await client.ingestUri(
  'https://example.com/product-demo.mp4',
  'Product Demo Video'
);
```

**Result:** Searchable transcript of audio track. Frame analysis coming soon (TwelveLabs, Azure Video Indexer).

**What this means:** Upload media files and immediately search their content. Meeting recordings become searchable transcripts with speaker identification (Speaker #1, #2, etc.). Product videos' audio becomes fully searchable. No separate transcription services needed.

***

## Why Graphlit?

Graphlit saves you 3-20 months of integration work and $160k-400k in Year 1 by providing complete data infrastructure for AI agents.

[See detailed TCO and competitive comparison →](/why-graphlit.md)

***

## AI Models

Graphlit supports 100+ LLMs including GPT-5, Claude 4.5 Sonnet, Gemini 2.5 Pro, Deepseek Reasoner, and more.

[Complete model reference and comparison →](/platform/models.md)

***

## Data Connectors

30+ feeds including Slack, Gmail, GitHub, Notion, Linear, Jira, Google Drive, OneDrive, S3, RSS, and more. Automatic sync with OAuth, API keys, or public sources.

[Browse all feeds and setup guides →](/platform/feeds.md)

***

## MCP-Native Integration

Bring Graphlit into Cursor, Windsurf, Claude Desktop, or VS Code - query your Slack, Gmail, Notion, and 30+ other sources directly from your IDE.

Install: `npx -y graphlit-mcp-server`

[Complete MCP setup guide →](/mcp-integration/mcp-integration.md)

***

## What You Can Build

**AI agents with memory** - Customer support, sales assistants, engineering agents with persistent context\
**Production SaaS apps** - Multi-tenant platforms ([Zine](https://www.zine.ai) runs on Graphlit with thousands of users)\
**Knowledge extraction** - Automatically extract entities, relationships, timelines from unstructured content

[See tutorials →](/getting-started/quickstart.md) | [Zine case study →](/examples/zine-case-study.md)

***

## Developer Experience

### 60+ Working Examples

Explore the sample gallery with working code you can run immediately:

* **Google Colab notebooks** - Run in browser, no setup
* **Next.js applications** - Deploy to Vercel
* **Streamlit apps** - Python UI examples
* **.NET console apps** - C# examples

[Browse sample gallery →](https://github.com/graphlit/graphlit-samples)

### Ask Graphlit

Get instant code examples and answers from our AI code assistant.

* Generate SDK code from descriptions
* Get best practices
* Find relevant examples
* Troubleshoot issues

[Use Ask Graphlit →](/resources/ask-graphlit.md)

***

## Security & Scale

### Enterprise Security

* **Encryption at rest** - All data encrypted using AES-256
* **Per-user data isolation** - Multi-tenant with user scoping
* **Project-level access** - Managed via Developer Portal
* **SOC 2** - Compliance coming soon
* **API authentication** - JWT-based secure access

### Built for Scale

* **Serverless architecture** - Auto-scaling infrastructure
* **Global deployment** - Low latency worldwide
* **Usage-based pricing** - Pay only for what you use
* **No infrastructure** - We handle operations

**Production proof:** Zine runs on Graphlit with thousands of users, automatic sync across 20+ data sources, and millions of documents.

***

## Pricing

**Free to get started** - No credit card required.

* **Free Tier**: 100 credits, 1GB storage, unlimited conversations
* **Hobby**: $49/month + usage
* **Starter**: $199/month + usage (10% off)
* **Growth**: $999/month + usage (20% off)

[View detailed pricing →](https://www.graphlit.com/#pricing)

***

## Need Help?

**Community & Support:**

* [**Discord**](https://discord.gg/ygFmfjy3Qx) - Community support, fastest response time
* [**Ask Graphlit**](/resources/ask-graphlit.md) - AI code assistant trained on Graphlit
* [**GitHub Issues**](https://github.com/graphlit/graphlit-client-python/issues) - Report bugs and feature requests
* **Email** - <support@graphlit.com> for direct support

***

## About Graphlit

We're building the context layer infrastructure for AI applications and agents.

**Our mission:** Give every developer the tools to build production AI apps and agents with organizational knowledge.

**Products:**

* **Graphlit** - Context layer for AI agents
* **Zine** - Team memory built on Graphlit ([zine.ai](https://www.zine.ai))

**Connect:** [Website](https://www.graphlit.com) • [Twitter](https://twitter.com/graphlit) • [LinkedIn](https://www.linkedin.com/company/graphlit) • [YouTube](https://www.youtube.com/@Graphlit) • [Blog](https://www.graphlit.com/blog)