Witness – a voice-anchored evidence recorder for AI coding agents

Voice-anchored causal evidence recorder for AI coding agents.

You experience the product and talk — “this button does nothing.” Witness welds every word to the causal evidence of that exact second: clicks, requests, console errors, DOM mutations, screenshots, full rrweb replay — then hands a structured evidence package to your AI coding agent.

Why

AI agents can't see what you see. Describing a visual bug costs ten minutes of screenshots and prompt crafting — and still loses the why. Voice is the cheapest annotation channel there is; it just never had a recorder.

Three streams run in parallel the moment wit start opens a managed browser:

• Your voice — continuous audio + live transcription. One take, many issues.

• Operations + causal bursts — every click captures its ~1.8s aftermath (requests, responses, console errors, DOM changes). A click with no aftermath is itself recorded as evidence.

• Page evolution — rrweb full-DOM replay + event-driven screenshots + periodic DOM snapshots.

Install

        npm i -g witness-recorder        # needs Node 18+ and system Edge/Chrome
wit start http://localhost:5173       # managed browser opens — talk & click
      

rec is an alias for wit — same binary, shorter to type.

No downloads, no API keys. System Edge/Chrome is preferred; bundled Chromium is the fallback.

During recording

events.jsonl streams to disk as you go — kill the process and you lose only the last few hundred milliseconds. wit recover rebuilds everything.

What the agent reads

Each spoken fragment lands on a timeline as a SAY block carrying the causal burst of that moment — dead clicks, mutations, errors — with linked shots and DOM snapshots.

CLI

        wit start <url> [--lang en-US] [--duration 60] [--no-rr] [--associate]
                [--shot-format jpeg|webp] [--dsf 1|2] [--keyframes]
wit list                          # sessions with url/duration/say count
wit open latest                   # open review.html
wit transcribe <dir>              # re-run cloud ASR
wit correct <dir>                 # re-run LLM text polish
wit associate <dir>               # LLM picks relevant evidence per SAY (ZHIPU_API_KEY)
wit recover <dir>                 # rebuild artifacts after a crash
wit segments <dir>                # regenerate speech-anchor index + bundles
wit keyframes <dir>               # render rrweb → per-segment keyframe jpgs
wit mcp [dir]                     # stdio MCP server over the evidence
wit config asr <engine>           # live ASR: auto|doubao-web|doubao-ime|sr|off
      

The evidence package — .witness/sessions/<ts>_<host>/

Agent-facing artifacts (PROMPT.md / SEGMENTS.md / session.md) are always generated in English — the most reliable corpus for LLMs; your words stay verbatim, never translated.

        SEGMENTS.md       # speech anchors: verbatim + suspects + shot/dom refs (agents read first)
segments/*.md   # per-segment bundle: verbatim frags, dead clicks, errors, linked shots
keyframes/*.jpg  # rrweb-rendered replay frames per segment (wit keyframes)
KEYFRAMES.md     # keyframe index
segments.json    # machine-readable segment index (what MCP serves)
session.md      # timeline archive: each SAY block carries its causal burst
PROMPT.md       # paste-ready handoff instructions + page vocabulary
review.html     # human view: speech cards + audio seek + filmstrip + embedded rrweb replay
events.jsonl    # complete raw stream, crash-safe
rrweb.jsonl     # full DOM event stream (--no-rr to disable)
dom/*.html      # DOM snapshots on nav/mark/30s
assoc.json      # optional LLM evidence-association suggestions
shots/*.jpg     # click/mark crops + periodic + spoken-segment frames (--shot-format webp for ~35% smaller)
bodies/*        # response bodies ≤64KB (.json for JSON, .txt otherwise)
session.json    # meta: url, duration, asr engine, counts
audio.webm / transcript.json
      

MCP — evidence as an API

wit mcp serves the session over stdio. Point any MCP-capable agent (Claude Code, Cursor, Devin) at it and it pulls exactly what it needs:

list_segments · segment_detail(n) · session_overview · list_says · session_md · events_around(t_ms, radius, kinds) · get_errors · get_shot · get_body · get_dom_state(t) · get_asr · set_asr

Principles

• Evidence is never rewritten — speech lands verbatim; merging/correction/association are display-layer suggestions.

• Evidence ≠ intent — behavior without a spoken anchor is never presented as a bug.

• Local-first — deterministic recording; live SAY subtitles stream through free cloud ASR (auto = doubao-web → doubao-ime → browser SR fallback — pick via wit config asr, --asr, WIT_ASR, or the get_asr/set_asr MCP tools); final transcript falls back to bcut (bilibili editor ASR, no key needed) when no OPENAI key is set. LLM layers are optional.

• Privacy engineered in — password fields masked across four channels, credential fields redacted in POST bodies, WIT_NO_BODIES=1 disables body capture, P freezes everything, .witness/ is auto-gitignored.

• Anonymous usage stats — the CLI sends one ping per command (command name, version, OS/arch, Node major; never URLs, paths or session data) to our own Cloudflare Worker — no third-party analytics, no cookies. Disable with WIT_TELEMETRY=0.

Development

        npm install && npm run build && npm link
npm test        # build + node:test suite (align / speech-run / bursts / prompts / review html)