Framesleuth

Local video → structured context for coding agents, exposed over MCP.

Give Framesleuth any video — a bug recording, a feature demo, a design walkthrough, a Loom, a phone capture — and it understands it frame-by-frame (plus optional browser sidecars) and produces a structured Context Bundle. It is MCP-ready, so any MCP client — a VS Code agent, another coding agent, or a custom system — can drive the analysis and consume the result to fix a bug, add or change a feature, or build a whole new feature/app grounded in what the video actually shows.

Capture happens outside this repo: any video works, or a browser capture extension can record a session and post the video + sidecars to this agent's local API. This repo is the analysis agent only.

Everything runs locally. Nothing leaves your machine.

Quick start

Want to go from a video to a grounded change inside VS Code? Follow Use with VS Code & Claude (MCP) — connect the bundled MCP server and turn a recording into a fix, a feature, or a new build.

Fastest: one command with Docker

Everything — the model server, the models, and the API — comes up with a single command. No Python, no virtualenv, no manual model setup.

git clone https://github.com/santoshshinde2012/framesleuth.git
cd framesleuth
docker compose up            # or: ./scripts/dev_up.sh

Compose loads docker-compose.override.yml automatically; that override adds the Ollama server, model-pull job, and Ollama model volume. The first run automatically pulls the vision + coder models (qwen2.5vl and qwen2.5-coder:7b, ~11 GB total) into a Docker volume, then starts the backend on http://127.0.0.1:8010. Subsequent runs are instant. It's ready when the health check reports healthy:

curl -s http://127.0.0.1:8010/v1/healthz | python -m json.tool   # "status": "healthy"

That's the whole setup — run your first analysis (below), or connect the MCP server in your editor (VS Code & Claude).

docker compose logs -f                  # follow progress / model download
docker compose down --remove-orphans    # stop  (add -v to also delete model volumes)

The stack runs its own Ollama on the internal Docker network only (its port is not published), so it never clashes with a native Ollama you may already run on :11434 — the only host port is the API on :8010.

Already run Ollama natively (with the models)? The Docker stack's Ollama is separate and would re-download them. Skip Docker and use the direct path below instead — it reuses your existing Ollama and is faster (especially on macOS, where Docker can't use the GPU).

macOS / no GPU: Docker runs the models on CPU, so the vision model is slow. NVIDIA GPU on Linux: uncomment the deploy: block on the ollama service in docker-compose.override.yml for acceleration.

To run only the backend container against a native/external model server, use docker compose -f docker-compose.yml up. The base compose file defaults to native Ollama on http://host.docker.internal:11434; override VLM_URL and CODER_URL for another server.

Run your first analysis (curl)

Once the API reports healthy (either setup path), go from a video to a Context Bundle in three calls — analysis is async (submit → poll → read):

# 1. Submit any screen recording (mp4/webm). Returns 202 { job_id, ... }
JOB=$(curl -s -F "video=@bug.mp4" http://127.0.0.1:8010/v1/analyze \
  | python -c "import sys, json; print(json.load(sys.stdin)['job_id'])")

# 2. Poll until state is "done" (queued → running → done)
curl -s "http://127.0.0.1:8010/v1/jobs/$JOB" | python -m json.tool

# 3. Read the Context Bundle
curl -s "http://127.0.0.1:8010/v1/report/$JOB" | python -m json.tool

Optional form fields on step 1: -F intent="why does save hang?", -F skill=bug_report, -F action=fix (GET /v1/skills and /v1/actions list the choices). Prefer a UI? Import the Postman collection — it chains these calls for you.

Run it directly (no Docker — fastest on macOS, best for development)

Prerequisites: Python 3.11+, uv, 8 GB+ RAM, and a local model server. ffmpeg is not required (PyAV bundles its own; ffprobe, if present, is used opportunistically to detect an audio stream).

git clone https://github.com/santoshshinde2012/framesleuth.git
cd framesleuth

# 1. Models — native Ollama (uses the Mac GPU) is the quick path
ollama serve &                                  # skip if already running
ollama pull qwen2.5vl && ollama pull qwen2.5-coder:7b

# 2. Install
uv venv && source .venv/bin/activate
uv pip install -e ".[dev]"
python scripts/download_models.py               # optional: pre-warm ASR + check servers

# 3. Configure + start the API (binds 127.0.0.1:8010)
cp .env.example .env                            # already defaults to the Ollama path above
framesleuth-api                                 # or: uvicorn framesleuth.service.api:app --port 8010

# 4. Verify
curl -s http://127.0.0.1:11434/v1/models | grep -q qwen2.5vl && echo "VLM ready"
curl -s http://127.0.0.1:8010/v1/healthz | python -m json.tool   # status: healthy, vlm: ready

When /v1/healthz shows vlm: ready, recordings analyze with a real classification (analysis_quality.level = full/partial). With no vision model reachable, Framesleuth degrades gracefully — it still produces a valid Context Bundle from the browser sidecars (console errors, failed requests, clicks) and records what was thin in analysis_quality. Record with narration so the audio transcript (asr) stage contributes too.

Something not working? Run the setup doctor — it works with a plain python3 even when your virtualenv is broken, and prints a one-line fix for each problem (stale/missing venv, framesleuth-api not on PATH, ffmpeg/render prerequisites, backend or model server not reachable, wrong VLM_URL):

python3 scripts/doctor.py

Common gotcha: command not found: framesleuth-api or a uv pip install error about a missing interpreter means your active venv was deleted/moved. Fix it from the framesleuth directory: deactivate; unset VIRTUAL_ENV; uv venv && source .venv/bin/activate && uv pip install -e ".[dev]".

Stop

# Stop the backend: Ctrl+C in its terminal, or
pkill -f framesleuth-api

# Stop Ollama (optional — leaving it running keeps the model warm)
pkill -f "ollama serve"              # macOS app users: quit Ollama from the menu bar

Related MCP server: popcorn

Architecture

Any video (mp4/webm) + optional sidecars
    ↓
Local Analysis Service (pipeline)
    ├─ Preprocess (PyAV: duration/fps/dims)
    ├─ Transcript (faster-whisper)
    ├─ Keyframes (visual-delta change scoring)
    ├─ Understanding (local vision model — Qwen2.5-VL by default)
    ├─ Fusion + Classification
    ├─ Extraction → Context Bundle
    ├─ Summarize (skill/system-prompt-driven)
    └─ Grounding (workspace search)
    ↓
Context Bundle
    ↓
MCP server + local HTTP API
    └─ consumed by any MCP client (VS Code agent, other agents, capture extension)

Features

• Frame-by-frame understanding using a local vision model (Qwen2.5-VL by default; engine-agnostic)

• Adaptive keyframe selection — coverage-binned, visual-salience-ranked (AKS-style), with a build-aware budget for feature/design videos and perceptual-hash dedup that drops near-identical frames so the VLM budget is spent on distinct content

• Bug and build — a feature class plus a structured build context (screens, UI components, a screen-to-screen user flow, design notes, and where to implement) so an agent can implement, not just diagnose

• Error detection and extraction from console, OCR, and UI state

• Corpus-aware grounding — error symbols or feature/UI nouns → ranked file:line (definitions preferred, distinctive symbols weighted via IDF + whole-word match), respecting .gitignore and bounded for large repos

• Trust signals — per-field confidence (with cross-modal corroboration — agreeing signals reinforce each other) and a task-aware actionability (ready/thin/insufficient) alongside the pipeline quality level

• Redaction-first design — secrets (passwords, tokens, keys) and PII (emails, Luhn-valid card numbers, SSNs/phones, cloud keys) are scrubbed from OCR, captions, the transcript, and the raw sidecar streams before any of it reaches a model or is persisted (bundle and the sibling timeline.json / sidecars.json / transcript.json)

• Observability — per-stage timings on every bundle (stage_timings) and live on GET /v1/jobs/{id}, so you can see where analysis time went

• Job lifecycle & delivery — cooperative cancellation (DELETE /v1/jobs/{id}, checked between frames), a hard per-job timeout (JOB_TIMEOUT_S), crash recovery (orphaned jobs are failed on restart, not left as zombies), SSE progress with explicit terminal events (GET /v1/jobs/{id}/events), a completion webhook (WEBHOOK_URL), real queue depth in /healthz, and TTL retention cleanup (BUNDLE_TTL_DAYS) swept at startup and periodically (RETENTION_SWEEP_INTERVAL_S)

• Interaction overlay — a click/cursor sidecar with coordinates draws a marker on the matching keyframe, so the model sees where the user acted

• Cleaner transcripts — faster-whisper voice-activity filtering (ASR_VAD_FILTER) drops silence before decoding; detected/forced language is recorded

• OCR backstop (optional ocr extra) — a sparse VLM OCR on an error frame gets a second, independent Tesseract reading; a no-op without the extra

• No data leaves your machine — fully local, no telemetry or cloud APIs

• Engine-agnostic — swap Ollama, llama.cpp, or vLLM via config only

• Works on any video — not just bug recordings. A general video (a demo, a walkthrough, a talk, a phone/real-world clip) yields a faithful summary + a timeline of key moments (summary, key_moments[]) instead of being forced into a bug shape; the bug-only fields (severity, expected/actual, repro steps) stay null rather than carrying fabricated placeholders

• Structured output — canonical Context Bundle with evidence citations

• Configurable response — pick a summary skill and an action mode (fix/implement/design/summarize/explain/triage/test/report/reproduce, auto-picked from the classification), plus a machine-readable suggested_actions menu and on-demand artifact renderers (markdown / GitHub issue / test plan)

• Eval harness — model-free classification / grounding / citation / faithfulness suites (python scripts/eval_harness.py --behavioral) run in CI (GitHub Actions: ruff, black, mypy --strict, pytest with coverage, then the eval harness) on every push and PR; the faithfulness suite proves every emitted key moment and step cites real, resolvable evidence (no fabrication)

• Resilient — handles no-audio videos, weak local models, low-confidence cases

• HTML → video (frame-by-frame) — turn a self-contained HTML animation (CSS/JS/canvas) into MP4, GIF, or WebM via the render_html_video MCP tool or POST /v1/render-html. Captures the animation frame-by-frame under a paused virtual clock and encodes a color-correct H.264 MP4 (yuv420p+bt709, near-lossless) — full color, no dropped frames, no quality loss (up to 4K, 5–60 fps). Included by default in the Docker image (headless Chromium + ffmpeg). For the direct (non-Docker) path, add the render extra (see below); returns 503 with an actionable message when unavailable.

Enable & troubleshoot HTML → video

Using Docker (docker compose up)? HTML→video already works — the image bakes in Playwright + Chromium + ffmpeg. (Build with --build-arg INSTALL_RENDER=false for a slimmer image without it.) The steps below are for the direct path.

Why is Playwright not in the core install? It's an optional [render] extra, not a core dependency, because it pulls a ~150 MB headless-Chromium browser the core video→bundle pipeline never needs — the standard way to ship a heavy, feature-specific dependency. (av, opencv, faster-whisper are core because the pipeline requires them.) Install the extra and you're done — the Chromium build downloads automatically on your first render, so there's no separate playwright install chromium step:

# In the same environment the server runs in:
uv pip install -e ".[render]"        # or ".[all]" = dev + render
# ffmpeg must be on PATH (brew install ffmpeg / apt-get install ffmpeg)

# Restart framesleuth-api, then verify (Chromium fetches itself on first render):
curl -s http://127.0.0.1:8010/v1/healthz | python -m json.tool
# → "render": {"playwright": true, "chromium": <true after first render>, "ffmpeg": true}

Set FRAMESLEUTH_AUTO_INSTALL_BROWSER=0 to disable the auto-download and run playwright install chromium yourself (e.g. in a locked-down environment).

Other optional extra — ocr. For the dedicated OCR backstop on error frames, uv pip install -e ".[ocr]" and put the tesseract binary on PATH (brew install tesseract / apt-get install tesseract-ocr). It's a no-op when absent — the VLM still does OCR; the backstop only adds a second reading. Use ".[all]" for dev + render + ocr.

If render.ready is false, the render.hint field tells you exactly what's missing. The most common cause of "Playwright is not installed" despite following the steps is that framesleuth-api is running from a different environment than the one you installed into (the render.python field shows which interpreter the server uses) — or the server simply wasn't restarted.

Project structure

framesleuth/
├── framesleuth/              # Main package
│   ├── config.py            # Typed config (pydantic-settings)
│   ├── schemas.py           # Data contracts (Context Bundle, enums)
│   ├── errors.py            # Exception taxonomy
│   ├── logging_config.py    # Structured JSON logging, job-id correlation
│   ├── prompts.py           # VLM / classify / summary / fix prompt templates
│   ├── skills.py            # Built-in summary skills (summary, bug_report, ...)
│   ├── actions.py           # Action modes (fix/explain/triage/...) + suggested-actions menu
│   ├── render.py            # Artifact renderers (markdown / GitHub issue / test plan)
│   ├── clients/             # VLM, coder HTTP clients (OpenAI-compatible)
│   ├── pipeline/            # preprocess, asr, scenes, understand, fusion, classify, bug_extract, redact, summarize, sidecars, grounding, html_render
│   ├── orchestrator/        # graph.py — linear async stage pipeline
│   ├── jobs/                # store.py — SQLite job state + bundle index
│   ├── service/             # FastAPI HTTP endpoints
│   └── mcp_server/          # framesleuth MCP server (VS Code + any MCP client)
├── tests/                   # pytest tests + fixtures
├── scripts/                 # doctor.py (setup check), download_models.py, dev_up.sh
├── postman/                 # HTTP API collection + environment
├── docs/                    # capabilities, use-with-vscode-and-claude, web-integration
└── pyproject.toml           # Dependencies and tool config

Development

Run tests

pytest tests/ -v --cov=framesleuth

Code quality

ruff check framesleuth tests
black --check framesleuth tests
mypy --strict framesleuth

Set up pre-commit hooks

pre-commit install

A short, focused set:

• Capabilities — the single reference: every input, output, skill, action, renderer, HTTP endpoint, and MCP tool

• Use with VS Code & Claude (MCP) — connect the framesleuth MCP server to Copilot, Claude Code, and Claude Desktop

• Web App Integration (end-to-end) — embed Framesleuth behind your own backend with an agent loop

• Postman Collection — exercise the HTTP API end-to-end (import or run headless with Newman)

• Runbook & Troubleshooting — setup, health checks, and common issues

License

Apache-2.0

Capture client

Bug capture lives outside this repo. Any screen recording works — drive the agent directly with your own video file. A browser capture extension can also record a session, collect browser sidecars (console errors, failed requests, clicks), and post the video + sidecars to this agent's local API. CORS is allowlisted (WEB_ORIGINS, default: the hosted demo site

• local dev) plus chrome-extension:// origins, and the agent answers Chrome's Private Network Access preflight — so both a capture extension and the "Try it" widget on framesleuth.com work against a locally running backend with no extra setup. The agent stays bound to loopback; CORS only controls which browser origins may read its responses.

Status: Backend + pipeline + MCP server completed. Questions? Open an issue or check runbook.md for common questions.