Your agent
Claude · Cursor · your own LLM app
↓ MCP Server · natural language → tools
Video Datalake — memory
index · search · read any moment
Why the Datalake is the agent’s memory
The Datalake turns raw video into queryable derived content — embeddings, captions, transcripts, entities, safety events. Instead of re-watching a video every turn, the agent asks the lake in natural language and gets the exact moments back — persistent, searchable memory that survives across sessions.What a Video Agent can do
Build & query a library
Ingest video → index → “find where the forklift enters the aisle” → read the caption and clip.
upload_video → wait_for_operation → search_moments → get_moment.Watch & alert on live
Attach a live stream and fire when something matters — a goal, a safety event.
open_stream + list_events.Investigate what happened
Combine moment search, the face library, and safety events to answer “who was in the store at 2pm and what did they do?”.
Summarize & extract
Pull titles, summaries, speakers, and transcripts from any moment for reports or downstream models.
get_moment with the fields you need.Build one
1
Connect the MCP Server
Add the MCP Server to your agent (Claude Code, Codex, Cursor, or your own client) — sign in via the browser and the Datalake tools appear.
2
Give it a goal
Prompt in natural language. The agent picks and chains tools — e.g.
search_moments → get_moment, or upload_video → wait_for_operation → search_moments.3
Let the Datalake remember
Anything you
upload_video stays indexed and searchable, so later turns (and later sessions) can search_moments without re-processing.Example
“Ingest yesterday’s store-cam footage, then tell me every time someone entered the stockroom and clip those moments.”The agent runs:
upload_video → wait_for_operation → search_moments (query: “person enters the stockroom”) → get_moment (with clip) for each hit — and returns the clips.
Bring your own framework
Not using MCP? Every capability is a plain REST call — point any agent framework at the Datalake API directly (curl / Python). The Datalake-as-memory pattern is the same: index once, search and read forever.