Gmail messages
Feed type: Email / GoogleEmail
Setup fields
Connect Gmail to Graphlit so email threads and attachments become searchable business context, then let agents draft or send approved follow-up emails from a connected account.
source: gmail
target: graphlit
Ingest + deliver
1 ingest surface • 2 delivery surfaces
Google OAuth
Connected account boundary
Graphlit API
createFeed + queryContents
MCP-native
Agent context and delivery flows
Ingest
Graphlit reads the selected Gmail surfaces, converts the source material into searchable content, and keeps enough source metadata for grounded answers, retrieval, and downstream agent workflows.
Feed type: Email / GoogleEmail
Setup fields
Deliver
Delivery pages show the full path from context to action: Graphlit retrieves context from Gmail, agents reason over that context, and approved outputs are delivered through MCP tools and connected accounts.
Delivery path: Graphlit MCP + Gmail
Delivery path: Graphlit MCP + Gmail
Prepare
The integration is not just a connector. Graphlit turns Gmail into content that can be searched, cited, summarized, enriched with observations, and supplied to agents through the Graphlit API and MCP.
{
"source": "Gmail",
"content": "Gmail messages",
"extracted": [
"entities",
"observations",
"facts",
"summaries"
],
"citations": [
"Gmail source references"
],
"agentContext": {
"searchable": true,
"groundedAnswers": true,
"deliveryEnabled": true
}
}Use cases
01
Build a Gmail knowledge base from messages and threads and labels and folders and search it alongside the rest of your Graphlit content.
02
Ask grounded questions over Gmail context without forcing users to leave the tools where the work originally happened.
03
Extract people, organizations, projects, events, facts, and decisions from Gmail content so agents can reason over durable context.
04
Let approved agents send approved follow-up email after retrieving the source context that justifies the delivery.
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Start building
Create a free Graphlit project, connect Gmail, and turn real operational context into retrieval, knowledge graphs, and MCP-native agent workflows.