Ingest Fathom into Graphlit

Connect Fathom to Graphlit so meeting recordings, transcripts, and summaries become searchable context for RAG, customer intelligence, and agent workflows.

Ingest

1 ingest surface

No OAuth required

URL, key, or query setup

Graphlit API

createFeed + queryContents

MCP-native

Agent context flows

Ingest

What gets ingested

Graphlit reads the selected Fathom surfaces, converts the source material into searchable content, and keeps enough source metadata for grounded answers, retrieval, and downstream agent workflows.

Fathom meetings

Feed type: Meeting / Fathom

Fathom
Meeting recordings
Transcripts and speaker turns
AI-generated summaries
Meeting metadata and participants

Setup fields

API Key *After DateMaximum Meetings

Prepare

What Graphlit prepares

The integration is not just a connector. Graphlit turns Fathom into content that can be searched, cited, summarized, enriched with observations, and supplied to agents through the Graphlit API and MCP.

{
  "source": "Fathom",
  "content": "Fathom meetings",
  "extracted": [
    "entities",
    "observations",
    "facts",
    "summaries"
  ],
  "citations": [
    "Fathom source references"
  ],
  "agentContext": {
    "searchable": true,
    "groundedAnswers": true,
    "deliveryEnabled": false
  }
}

Use cases

What you can build with Fathom

01

Build a Fathom knowledge base from meeting recordings and transcripts and speaker turns and search it alongside the rest of your Graphlit content.

02

Ask grounded questions over Fathom context without forcing users to leave the tools where the work originally happened.

03

Extract people, organizations, projects, events, facts, and decisions from Fathom content so agents can reason over durable context.

04

Combine Fathom with Slack, Notion, and HubSpot to build cross-tool RAG, research, and operational intelligence workflows.

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SDK:
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FAQ

Common questions

Start building

Start building with Fathom and Graphlit

Create a free Graphlit project, connect Fathom, and turn real operational context into retrieval, knowledge graphs, and MCP-native agent workflows.

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