Activity-frames – give your AI agent eyes on your day

Nocta released activity-frames, an open-source tool that records a user's screen locally and compiles it into structured activity frames for AI agents, enabling episodic memory without cloud processing or LLM inference. The tool provides agent-ready context blocks and MCP integration, aiming to give AI assistants awareness of a user's actual workday activities.

- Nocta uses activity-frames to watch how you work and brief you daily on what needs your attention. 100% local. Download the desktop app Episodic memory for AI agents. Your agent can read your code, search the web, and call APIs - but it has no idea what you have been doing for the last 8 hours. It starts every conversation blind. activity-frames gives your agent eyes. It records your screen locally, compiles what it sees into structured activity frames bounded episodes of what you actually did , and serves them to any agent over MCP. No cloud, no LLM in the pipeline, no guessing. pip install activity-frames aframes record start capturing local, audio off by default aframes context your last 2 hours, agent-ready Capture stores instants: thousands of snapshot rows a day, each one saying "at 22:53:05, Chrome showed linkedin.com/in/...". Useless to reason over. activity-frames compiles those instants into episodes: - id: f-0007 app: Google Chrome site: linkedin.com start: "20:24:04" end: "20:42:11" duration min: 18.0 pages: - {kind: people search, entity: "cto paris", count: 2} - {kind: profile, entity: najmuzzaman} - {kind: company, entity: nexdotai} input: {keys: 214, clicks: 31} evidence: {frame ids: "99871..100147"} And into a compact context block for any system prompt: USER ACTIVITY 2026-07-04, local time; measured from screen capture, no interpretation : coverage: 09:12-18:47, 342 active min, 11 apps away: 12:30-13:15 45m - 09:12-09:58 Cursor 46.2m : main.py - api - 10:01-10:44 Google Chrome/github.com 41.3m : pull request:acme/api 412; code:acme/api - 20:24-20:42 Google Chrome/linkedin.com 18.0m : people search:cto paris x2; profile:najmuzzaman; company:nexdotai Drop that into a prompt and your agent knows your day. A full day compiles in under a second and costs zero tokens. Agent memory today means conversation memory: what you told the model. Episodic memory is what you actually did - and the hard part is representing it without lying. activity-frames enforces a two-tier contract SPEC.md /nossa-y/activity-frames/blob/main/SPEC.md : Tier 1, measured this package : everything is derivable by deterministic code from capture data. Sessions, durations, typed page entities, input volume, coverage gaps. Same input, same output, every time. There are no intent labels - code cannot know that 2 profile views + a people search was "prospecting". That is your agent's job; it is an LLM. Tier 2, inferred optional extension : tools that add interpretation must namespace it, tag confidence high | medium | speculative , and link evidence. Facts and guesses can never silently mix. Every frame carries evidence pointers back to raw capture rows. Every document declares its blind spots. What the system did not see, it says it did not see. Claude Code claude mcp add activity-frames -- aframes mcp Any MCP client works: command aframes , args "mcp" . Four tools: get context , get activity , get day summary , get patterns repetitive-workflow detection: repeated clicks, URL loops, daily habits . python from activity frames import ActivityLog log = ActivityLog doc = log.day today, structured doc = log.recent hours=2 last 2 hours print log.context hours=2 paste-ready context block Local only. Capture, storage, and compilation all happen on your machine. Nothing is uploaded anywhere, ever. Read-only compilation. The compiler opens the capture database read-only. Content opt-in at the output. Compiled documents carry input counts by default; typed-text content appears only if you explicitly pass --include-text this also gates the repeated-text pattern detector . Be clear about the boundary: the capture database itself does store what the recorder sees, locally, so protect it like any sensitive file FileVault, permissions . Audio off by default. aframes record --audio to opt in. No LLM in the compile path. Compilation is plain code, so no language model, local or remote, is involved in producing memory. The capture engine does run on-device OCR to read what is on screen; that stays on your machine. You choose what leaves , when you paste a context block into an agent. Note that window titles and page entities originate from your screen and can contain third-party text; agents should treat them as data, not instructions. capture engine compiler this package your agent ------------------ --------------------------- ----------------- screen snapshots -- sessionize dwell, gaps, -- MCP tools / accessibility tree flicker merge context blocks / input events entity typing 20+ sites JSON, YAML, md local SQLite enrichment, patterns The default capture engine is screenpipe https://github.com/mediar-ai/screenpipe : aframes record provisions a pinned, MIT-licensed build v0.3.324 , verifies its published sha512 before first run, and manages it for you see ACKNOWLEDGMENTS.md /nossa-y/activity-frames/blob/main/ACKNOWLEDGMENTS.md . Already running your own recorder? Point $AFRAMES DB at any capture database with compatible frames / ui events / elements tables and skip aframes record entirely. aframes record start capture --stop / --status / --audio aframes today today's frames YAML aframes day 2026-07-03 -f json any day, JSON aframes context --hours 3 agent context block aframes apps per-app time ledger aframes patterns --days 7 repetitive workflow detection aframes mcp MCP stdio server YAML output uses PyYAML pip install "activity-frames yaml " ; without it the CLI falls back to JSON. v0.1. Developed and tested on macOS Apple Silicon ; Intel macOS and Linux x64 engine builds exist but are less exercised - reports welcome. Entity parsers cover LinkedIn, GitHub, Google Search/Docs/Gmail/Maps/Meet/Calendar , YouTube, X, Instagram, Reddit, Luma, Partiful, Product Hunt, Vercel, Supabase, Stripe, Discord, Notion, Figma, Stack Overflow, Calendly, ChatGPT/Claude, localhost; unknown sites fall back to a generic page reference - always total, never lossy. Issues and parser PRs welcome. Built by Nossa Iyamu https://github.com/nossa-y , maker of Nocta https://usenocta.app . MIT.