u/Dapper_Ad599

I was trying to pull context out of some local video files for an agent workflow, and the state of things drove me nuts. You either send gigabytes of video to a third-party cloud API (expensive, slow, privacy nightmare), or you write a fragile web of FFmpeg commands, Whisper wrappers, and frame samplers that breaks the moment a video codec looks at it funny.

So I got fed up and hacked together saccade.

It’s a local-first Python library that handles transcripts, frame extraction, and timestamped search/querying entirely on your own machine (CPU or GPU), with no external cloud dependencies.

from saccade import Saccade

video = Saccade("path/to/video.mp4")

# Search for specific moments with timestamps
results = video.search("where do they discuss the database setup")

# Or query it directly
answer = video.ask("Summarize the key takeaways from the second half.")

If you're working on local video pipelines and want something that just runs locally without calling home, the repo is on the comments below:

Curious how others are handling video context in local setups, or if you run into any weird edge cases testing it out.

TL;DR: built an agent runtime that stores state as a belief graph with dependencies (Jon Doyle's TMS) instead of chat logs. correct one fact and everything downstream auto-updates without context rot or full re-runs. (repo link in the comments below)

Hey guys,

Every agent framework ive used so far handles state pretty much the same way, just appending messages to a long chat log. The problem is long running agents rot super fast. If a tool returns bad data at step 3, that error just sits in context forever. And if a key fact changes mid run, you either have to wipe the whole context or re run everything from step 1.

I’ve been hacking on an open source project called Corollary to try a different approach. Instead of a message transcript, it stores state as a belief base backed by a truth maintenance system (basically an old concept from jon doyle back in 1979).

how it works under the hood:

• every belief or conclusion tracks what it depends on (its justifications)
• if you retract or update a single base fact, it automatically retracts and re derives anything downstream that relied on it
• independent conclusions arent touched, so you get a clean diff of what changed instead of re running llm calls
• the LLM only sees currently valid ("IN") beliefs, so old retracted facts cant leak back in through a transcript

its still super pre-alpha so I'd love to get some feedback, pushback or edge cases you think this pattern will hit.

(repo link in the comments below)

how are you guys handling context rot in long running agents currently?