Hermes StreamMemory: Local Visual Memory for Open Agents With FFmpeg, Frame Hashes, and Replayable Evidence

This is a submission for the Hermes Agent Challenge

What I Built

I built Hermes StreamMemory, a Hermes Agent application-layer concept using my ARC-StreamMemory project as the visual memory spine.

The idea is simple:

Hermes Agent should not only reason over text. It should be able to inspect visual sessions, screen recordings, videos, screenshots, robotics feeds, DAW sessions, UI states, and generated frame memories as replayable evidence.

Most agents can read a prompt.

Some agents can inspect a file.

But real project work often happens visually:

• a terminal session
• a GitHub workflow
• a DAW/plugin test
• a browser task
• a game/emulator session
• a robotics camera feed
• a UI bug
• a screen recording
• a visual build process

ARC-StreamMemory turns those visual sources into local-first AI-readable memory modules.

Hermes Agent becomes the agentic reasoning layer.

ARC-StreamMemory becomes the visual evidence layer.

Together, the goal is:

Let Hermes Agent understand what happened visually, while ARC-StreamMemory preserves the frames, hashes, timeline, digest, receipts, and replay path.

Demo

The runtime flow looks like this:

Visual source
  ↓
Video / screenshot / screen recording / camera feed
  ↓
FFmpeg or snapshot ingest
  ↓
Chosen AI frame-speed schedule
  ↓
Frame extraction
  ↓
Frame hashes
  ↓
Event timeline
  ↓
AI digest
  ↓
Module attachment JSON
  ↓
Receipt / bundle manifest
  ↓
Hermes Agent can inspect, summarize, reason, or act

Instead of giving the agent one image or a giant video file, ARC-StreamMemory creates a structured memory object.

Example session structure:

session/
  frames/
  memory/
    frame_index.json
    event_timeline.jsonl
    ai_digest.md
    ai_digest.json
    module_attachment.json
    memory_spine.json
    seed_spine.json
    session_summary.md
  receipts/
    arc_receipts.jsonl
  omnibinary/
    chunk_map.json
  arcrar/
    bundle_manifest.json
  reports/
    validation_report.json

Example visual memory record:

{
  "session_id": "streammemory-demo-001",
  "source_type": "screen_recording",
  "frame_policy": "1fps_ai_inspection",
  "frames_indexed": 120,
  "hashing": "sha256_per_frame",
  "ai_digest": true,
  "module_attachment": true,
  "replayable": true,
  "agent_ready": true
}

Example Hermes Agent use:

User:
Review this recorded workflow and tell me where the build failed.

Hermes Agent:
1. Reads the ARC-StreamMemory module attachment.
2. Opens the AI digest.
3. Checks the event timeline.
4. Jumps to the relevant frame range.
5. References the frame hashes.
6. Produces a summary with evidence pointers.

That changes visual work from:

Watch this whole video and guess what happened.

into:

Inspect this indexed visual memory bundle and cite the evidence.

Code

Core repository:

• ARC-StreamMemory — local-first visual second brain for AI-readable video, screen, snapshot, robotics, and source-spine memory.

Related ARC / agent infrastructure:

• ARC-Core — authority layer, receipts, event truth, and source governance.
• omnibinary-runtime — binary-addressable memory spine and chunk-ledger direction.
• Arc-RAR — portable archive / restore bundle direction.
• ARC-Neuron LLMBuilder — local AI memory, governed build loop, and module attachment use case.
• arc-language-module — language graph and routing foundation for future model/language memory work.
• TizWildin Entertainment HUB — public hub for the broader software, AI, automation, and audio ecosystem.
• FreeEQ8 — audio/plugin UI testing target for visual memory sessions.

The Hermes Agent challenge build focuses on this local visual-memory pattern:

Hermes Agent
  ↓
ARC-StreamMemory module attachment
  ↓
AI digest
  ↓
frame index
  ↓
event timeline
  ↓
frame hashes
  ↓
receipt / bundle manifest
  ↓
agent-readable visual memory

My Tech Stack

• Hermes Agent
• ARC-StreamMemory
• Python
• FFmpeg
• screenshot / video / frame ingest
• frame sampling policies
• SHA-256 frame hashing
• event timelines
• JSON / JSONL memory indexes
• Markdown + JSON AI digests
• module attachment JSON
• seeded source-spine metadata
• local HTML viewer
• ARC-Core-style receipts
• OmniBinary-style chunk maps
• Arc-RAR-style bundle manifests

The core pattern is:

Visual input
  ↓
frame sampling
  ↓
frame hashing
  ↓
timeline indexing
  ↓
AI digest
  ↓
module attachment
  ↓
Hermes Agent reasoning

How I Used Hermes Agent

Hermes Agent is the reasoning and action layer that benefits from ARC-StreamMemory.

The point is not to make Hermes Agent store every visual detail inside hidden memory.

The point is to give Hermes Agent an external visual memory object that it can inspect, cite, and reason over.

In this pattern:

• Hermes Agent receives a user goal.
• ARC-StreamMemory provides a structured visual memory module.
• Hermes Agent reads the digest and timeline.
• Hermes Agent follows frame/event pointers.
• Hermes Agent summarizes what happened.
• Hermes Agent can decide what action should happen next.
• ARC-style receipts preserve the evidence path.

This is useful because real work is not always text-first.

A developer might ask:

What happened during this failed build recording?

A plugin developer might ask:

Did the UI glitch during this DAW test?

A robotics developer might ask:

Where did the navigation feed show the robot drifting?

A creator might ask:

Which frames show the best moment from this recorded session?

Hermes Agent can reason over the structured output instead of being handed a raw video with no memory spine.

Why This Matters

AI agents need better memory boundaries.

Text logs are not enough.

A lot of human work happens through screens, videos, tools, interfaces, editors, timelines, terminals, DAWs, games, dashboards, cameras, and visual states.

Without visual memory, an agent misses the actual work surface.

ARC-StreamMemory makes visual sessions more agent-readable by converting them into:

• frame indexes
• sampled image evidence
• event timelines
• AI digests
• hash-verified frames
• module attachments
• local viewer paths
• replayable bundle manifests

That gives Hermes Agent a visual evidence trail.

Instead of only asking:

What did the user say?

the system can ask:

What did the user see?
What changed on screen?
Which frame proves it?
Can the event be replayed?
Can the memory be attached to another AI module?

That is the difference between a chat transcript and a visual second brain.

Visual Memory Use Cases

1. Developer Workflow Memory

Record a debugging session, terminal run, GitHub PR flow, or build failure.

ARC-StreamMemory indexes the frames.

Hermes Agent reviews the digest and identifies what happened.

2. Audio Plugin Testing

Capture DAW/plugin sessions, analyzer movement, UI glitches, pluginval runs, or visual regressions.

Hermes Agent can inspect the visual record and summarize the test.

3. Robotics Camera Memory

Use FFmpeg-backed video ingest or future ARC-FusionCapture integration to turn camera feeds into memory bundles.

Hermes Agent can reason over navigation events, sensor-synced moments, and visual timelines.

4. Game / Emulator Session Replay

Capture game states, emulator footage, UI states, or test runs.

Hermes Agent can inspect the indexed frame memory instead of relying only on a written description.

5. Research and Reproducibility

Use hashes, seeded spines, receipts, and module attachments to make visual sessions easier to cite, restore, and verify.

Current Status

This is an experimental Hermes Agent challenge submission focused on visual memory for local agents.

ARC-StreamMemory already focuses on:

• snapshot folder ingest
• regular FFmpeg video ingest
• AI frame-speed policies
• frame hashing
• seeded source-spine metadata
• AI digest generation
• module attachment JSON
• ARC-Core-style receipt export direction
• OmniBinary-style chunk-map direction
• Arc-RAR-style bundle manifest direction
• local HTML viewer
• validation and bundle export direction

Remaining future integration gates include:

• live native screen capture
• real OCR engine hookup
• native OmniBinary persistence
• native Arc-RAR packaging
• live ARC-Core API sync
• production robotics sensor bus integration

This is not presented as a finished production visual AGI system.

It is a practical local-first direction for making Hermes Agent workflows more visually aware, inspectable, replayable, and evidence-backed.

Future Roadmap

Next steps:

• Add a Hermes Agent demo that reads an ARC-StreamMemory module attachment
• Add visual question-answering over session digests
• Add frame citation output
• Add OCR-backed event timelines
• Add “what changed?” comparison between frames
• Add replay/export bundles for agent sessions
• Add DAW/plugin validation session examples
• Add robotics camera memory examples
• Add ARC-Core registration for visual memory receipts
• Add OmniBinary persistence for large visual payloads
• Add Arc-RAR packaging for portable visual memory bundles

Closing Thought

Open agents should not only read text.

They should be able to inspect the visual work surface.

That is the core idea of this Hermes Agent experiment:

Hermes Agent for reasoning.

ARC-StreamMemory for sight.

FFmpeg for frame intake.

Hashes for proof.

Digests for understanding.

Module attachments for AI memory.

Replay bundles for continuity.