Overview
A livestream in Tessact is a Library asset that receives a live video signal. Point any RTMP encoder (OBS, Streamlabs, vMix, a hardware encoder, or ffmpeg) at the stream’s ingest URL, and Tessact captures the session in recorded chunks while it is still running. You can watch the live feed, drop timecoded comments, cut clips in the Editor, and let AI indexing make the content searchable before the stream even ends.
A live session playing on the asset page while recording is in progress
- Stream from anywhere: Tessact provisions dedicated RTMP ingest details per stream; any RTMP-compatible tool can push to them.
- Recorded in chunks: the session is captured as segments of a duration you choose, so recorded media appears while the stream is live.
- A growing asset: the livestream behaves like any other Library asset. Comment on it, share it, open it in the Editor, and search it.
- AI-ready in real time: each finished chunk is analysed, so transcripts, detections, and AI Search results build up during the session.
Create a livestream
Open the Library and choose New livestream

The Library create menu with New livestream
Name the stream and pick a chunk size

Configuring the livestream name and chunk size
Wait for the stream receiver
Copy the RTMP URL and Stream Key

Connect your streaming software: RTMP URL and Stream Key

Stream Details is always available from the asset page
Connect your streaming software
Any RTMP-compatible encoder works. In OBS Studio:- Open Settings → Stream and set Service to Custom.
- Paste the RTMP URL (Primary) into Server and the Stream Key (Primary) into Stream Key, then select OK.
- Select Start Streaming when you are ready to send the signal.

OBS Studio configured with the Tessact RTMP server and stream key
Run the live session
Start the live session

Starting the live session from the Library card
Wait for the signal

The live session is ready and waiting for the encoder signal
Start streaming from your encoder

Signal Detected: the live feed is being received
Start recording

Recording in progress: chunks are captured as the stream continues
Watch, comment, and share
While the session runs, the asset page plays the live feed with the standard Tessact player. The Comments panel works exactly as it does for uploaded videos: comments are timecoded, support#tags and @mentions, and notify collaborators. Use Share to give teammates access, and the Library card shows the live state at a glance (a pulsing red indicator with the current status).
Edit while it is still live
You can cut a livestream in the Editor before the stream ends:- In the Library, create a New video draft and open it.
- In the Editor, open Library, select the livestream, and choose Add to Timeline.
- The ready chunks appear as one grouped clip with filmstrips and waveforms, ready to trim and arrange.

Importing a livestream into a video draft while it is live

The growing livestream layer: Update live stream appends newly recorded chunks
End the session and archive
When the broadcast is over, choose End live session (also available as End Stream on the Library card menu). Tessact captures the trailing partial chunk, then stitches every recorded chunk into a single MP4. The Library card shows Finalizing Stream while this happens and settles on MP4 Ready, at which point the asset page plays the full recording like any other video. The recording stays in your Library; ending a stream tears down the live infrastructure, not the captured media.
After ending: Finalizing Stream, then MP4 Ready
Status reference
AI features on live content
Because the session is captured in chunks, Tessact’s AI can work on the stream while it is still running:- Search during the stream: each finished chunk is indexed, so scenes, shots, and dialogue from earlier in the session surface in AI Search before the stream ends.
- Transcripts and detections: the asset’s Metadata panel fills with transcriptions, keywords, and detected people as chunks complete.
- Faster with shorter chunks: a 1 to 5 minute chunk size makes live AI results appear sooner; longer chunks favour deeper per-segment context.