Turning Meeting Transcripts into an AI Knowledge Graph with tl;dv & Cognee

Engineering and product teams spend dozens of hours every week in meetings. Crucial technical decisions get debated, architectural tradeoffs get resolved, and action items get assigned.

Then the call ends—and all that vital context gets buried in a 45-minute recording or lost inside scattered notes.

To make meeting intelligence permanently accessible to AI agents and developers, we built the tl;dv Connector for Cognee. Here is how we turned raw meeting recordings from tl;dv into an interconnected entity graph.

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Why Vector Search Isn't Enough for Meeting Notes

If you store meeting transcripts as plain vector chunks, multi-speaker reasoning falls apart. Consider this query:

"Who volunteered to lead the vector engine migration in Tuesday's sync, and what blockers did they raise?"

Vector search finds chunks containing words like "vector" or "blockers", but it lacks entity grounding. A Knowledge Graph, however, extracts explicit relationships:

(Alice:Engineer) -[:VOLUNTEERED_FOR]-> (VectorMigration:Project)
(VectorMigration) -[:HAS_BLOCKER]-> (KuzuStorageDriver:Issue)

flowchart LR
    TLDV[tl;dv API] -->|Meetings + Transcripts + Notes| Client[TLDVClient]
    Client -->|Format Markdown Document| DLT[DLT Pipeline]
    DLT -->|DOCUMENT_SOURCE_ATTR| Cognee[Cognee Cognify]
    Cognee -->|Extract Entities & Dialogue Nodes| Graph[(Knowledge Graph + Vector Store)]

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Connector Architecture

1. Multi-Endpoint Synthesis

tl;dv splits data across multiple REST endpoints:

• /meetings: Lists metadata (meeting title, organizer, participant list, timestamps, duration).
• /meetings/{id}/transcript: Returns speaker-labeled dialogue turns with exact timestamps.
• /meetings/{id}/notes: Contains AI-generated executive overviews and action item checklists.

Our connector fetches and synthesizes these into a single unified Markdown document per meeting:

# Meeting: Q4 Distributed Vector Engine Planning
- **Organizer:** Alice Product Lead
- **Participants:** Alice Product Lead, Bob Tech Lead, Charlie SRE
- **Date:** 2026-10-01T14:00:00Z
- **Duration:** 3600 seconds

### Executive Summary
Reviewed roadmap milestones for the distributed graph indexing engine and agreed on partition strategy.

### Action Items
- Bob to finalize vector engine performance benchmarks
- Charlie to configure replica autoscaling in Kubernetes

### Full Transcript
Alice Product Lead: Let's review the Q4 milestones.
Bob Tech Lead: The distributed vector engine is on track, but we need to scale the partition handler.
Charlie SRE: I will deploy the new replica pool before Friday.

2. Cognee Graph Ingestion Contract

By annotating the dlt resource with DOCUMENT_SOURCE_ATTR = "tldv_document", Cognee treats each meeting as a document node, running its NLP extraction pipeline over the executive takeaways and dialogue.

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Quickstart: Ingesting Meetings with Python

import asyncio
import cognee
from cognee_community_connector_tldv import tldv_source

async def main():
    # 1. Initialize connector
    source = tldv_source(
        api_key="YOUR_TLDV_API_KEY",
        limit=50,
        include_transcripts=True,
        include_notes=True,
    )

    # 2. Add and Cognify
    await cognee.add(source, dataset_name="engineering_meetings")
    await cognee.cognify(dataset_name="engineering_meetings")

    # 3. Query meeting intelligence
    results = await cognee.search(
        "What action items were assigned to Charlie regarding replicas?",
        dataset_name="engineering_meetings"
    )
    print(results)

if __name__ == "__main__":
    asyncio.run(main())

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Offline Unit Testing

To ensure reliability in CI and local testing without live API tokens, the connector includes a 100% offline mocked test suite:

uv run pytest packages/connector/tldv/tests/test_tldv.py -v

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Links & Contributions

• Pull Request: topoteretes/cognee-community#236
• Monorepo: github.com/topoteretes/cognee-community