Persome

The local-first Personal Model Runtime for macOS. Persome observes the apps you already use, turns cross-app activity into an inspectable model of a real person, and serves that model to Chat and MCP agents.

Actual /model screenshot produced by scripts/sample_demo.py: 4 synthetic Points, 2 Lines, 2 Faces, 1 Volume, and 1 Root. It contains no personal data.

Product job

Persome runs quietly on one Mac and does four jobs:

1. Collect focused macOS Accessibility (AX) context across apps, with an optional on-device OCR fallback for AX-poor surfaces.

2. Model observations into sourced facts, evolving relations, stable patterns, cross-domain structure, and one current Root.

3. Serve local memory and model tools over MCP, plus an optional terminal Chat that uses the same tools.

4. Give control back through receipts, time travel, correction, export, and deletion.

This is the Runtime, not a hosted account or a single assistant's private memory. One local model can be used by Claude Code, Codex, Cursor, or another trusted MCP client.

Related MCP server: spark-mcp

Five-minute sample demo

See the whole model without an API key, Accessibility permission, or access to your real ~/.persome data. This path requires Git and uv:

git clone https://github.com/Persome-ai/persome-core.git
cd persome-core
uv run python scripts/sample_demo.py

The script opens http://127.0.0.1:8743/model, serves MCP at http://127.0.0.1:8743/mcp, and deletes its temporary synthetic data when you press Ctrl-C. To inspect the exact search, receipt, and snapshot payloads:

PERSOME_LLM_MOCK=1 uv run python scripts/sample_demo.py --json

With the sample server still running, verify the actual MCP transport from a second terminal:

uv run python scripts/verify_sample_mcp.py

This sample path is deliberately separate from the real-data path below.

Quick start with your data

Requirements: macOS 13 or newer and Xcode Command Line Tools. The installer finds or installs uv, provisions Python 3.11-3.13, compiles the Swift AX helpers, generates the local screenshot-encryption key, and offers to register detected MCP clients.

git clone https://github.com/Persome-ai/persome-core.git
cd persome-core
bash install.sh

persome doctor
persome start
open http://127.0.0.1:8742/model

Grant Accessibility to the terminal or app that launches Persome in System Settings -> Privacy & Security -> Accessibility. This permission is required to read focused AX text and structure. Grant Screen Recording only when enabling OCR fallback or screenshot retention; it supplies pixels to the local OCR worker. Persome does not require Full Disk Access.

An LLM key is optional for collection and BM25 recall, but required for real semantic modeling. install.sh can save an Anthropic key or an Anthropic-compatible gateway key to the owner-only ~/.persome/env file. Nothing ships with a key.

# If the installer was run without a key:
printf 'ANTHROPIC_API_KEY=%s\n' 'your-provider-key' >> ~/.persome/env
chmod 600 ~/.persome/env
persome stop || true
persome start

Active work is reduced every five minutes by default. A first useful recall is therefore expected within ten minutes of valid capture plus a working semantic provider; persome status, persome model status, and the viewer explain sparse or degraded states instead of inventing geometry.

Proof points

Local-first

• Durable Markdown, SQLite/FTS5, model snapshots, and logs live under ~/.persome unless PERSOME_ROOT is set.

• AX is the default signal. Optional PP-OCRv6 runs locally in an isolated subprocess with bundled weights.

• The HTTP/MCP server binds to 127.0.0.1 by default, and there is no telemetry.

• Only configured semantic stages send derived text to the LLM or embedding endpoint you choose.

Cross-app

The Swift watcher reads the focused AX tree across native and browser apps. Persome normalizes focused element, visible text, window, application, URL, and time into one capture and session pipeline. OCR is a fallback, not a parallel cloud recorder.

Agent-ready

• Streamable HTTP MCP: http://127.0.0.1:8742/mcp

• stdio MCP: persome mcp

• Local Chat: persome chat

• Stable model contract: persome model export and GET /model/graph

• Evidence tools: search, read_receipt, verify_fact, and get_model_snapshot

Connect an MCP client

Start Persome first, then register the endpoint:

persome start
persome install claude-code
persome install codex

# Generate a stdio config that can be merged into Cursor's MCP config:
persome install mcp-json --filename persome-mcp.json

The canonical JSON shape is:

{
  "mcpServers": {
    "persome": {
      "command": "persome",
      "args": ["mcp"]
    }
  }
}

See MCP client setup and verification for HTTP configs, uninstall commands, tested client versions, and privacy boundaries.

Real MCP query with a cited answer

The following result is generated by the committed synthetic sample through the same search and read_receipt implementation exposed by MCP.

Tool: search
Input: {"query":"When does the user prefer focused writing?","top_k":2}

Top result:
  id:        20260701-0800-d4e5f6
  path:      project-work.md
  timestamp: 2026-07-01T08:00
  content:   The user reserves mornings for focused writing and review.

Tool: read_receipt
Input: {"entry_id":"20260701-0800-d4e5f6"}

A grounded client response can then say:

The user prefers mornings for focused writing and review. [project-work.md, 2026-07-01 08:00; receipt 20260701-0800-d4e5f6]

The receipt is resolvable, the superseded earlier statement remains available as history, and the answer does not rely on the model's unsupported memory.

Benchmark and verification status

This repository reports Runtime engineering evidence, not a paper-quality personalization benchmark.

The sample uses synthetic fixtures and cannot establish recall quality on a real person. No cross-user benchmark, next-action accuracy, latency percentile, or comparison win is claimed. The launch machine's three isolated source installs had an 11.896-second median with a warm uv cache; conditions and limitations are recorded in benchmark scope.

Why Persome

These projects solve adjacent but different jobs:

Persome is not a replacement for a full screen archive, a hosted vector memory, or a provider's preference feature. Choose it when the core requirement is a local, cross-app, auditable model that multiple agents can query.

How it works

flowchart LR
  AX[macOS AX watcher] --> S0[S0 debounce]
  OCR[Optional local OCR] --> S1[S1 normalized capture]
  S0 --> S1
  S1 --> BUF[Capture buffer]
  BUF --> TL[1-minute timeline]
  TL --> SES[Deterministic sessions]
  SES --> DELTA[5-minute memory delta]
  DELTA --> PL[Points and Lines]
  PL --> FV[Faces and Volumes]
  FV --> ROOT[Root]
  PL --> RET[BM25 and optional dense retrieval]
  FV --> MCP[MCP, Chat, export, viewer]
  ROOT --> MCP
  RET --> MCP

Every modeled object keeps source receipts and bitemporal history. A sparse store can truthfully contain Points and Lines without a Face, Volume, or Root. The viewer shows that incomplete state rather than fabricating one.

Read Runtime architecture, the model contract, and the detailed maintainer architecture.

Inspect, correct, export, and delete

# Inspect
persome status
persome model status
persome faces-report
persome contradictions
open http://127.0.0.1:8742/model

# Correct or revoke one memory while retaining its audit trail
persome correct --help
# Agents can also call MCP correct_memory.

# Export a redacted owner-only snapshot (0600)
persome model export

# Delete model memory, or all captures/timeline/model state
persome stop
persome clean memory
persome clean all

For a complete uninstall that preserves personal data by default:

bash uninstall.sh

# Explicitly remove the remaining data, config, env, exports, and logs:
bash uninstall.sh --delete-data --yes

Client registrations are removed separately and idempotently:

persome uninstall claude-code
persome uninstall codex
persome uninstall claude-desktop

See operations and data control for exact paths, backup advice, export sensitivity, reset behavior, and manual removal steps.

Privacy boundary

• Personal data remains local until a configured model stage or connected agent sends selected text to its own provider.

• MCP capture tools can return raw screen text, titles, URLs, and focused-field values. Connect only clients you trust.

• Screenshots are omitted from MCP by default and encrypted at rest when retention is enabled.

• persome model export is redacted by default; --raw is an explicit opt-out.

• There is no built-in remote account, sync service, telemetry, meeting audio capture, computer-use actuation, or filesystem profiler.

Read Security and privacy before using real personal data, and report vulnerabilities through SECURITY.md.

Platform support

Python 3.11-3.13 is supported by the installer. See operations and troubleshooting.

Persome and Personome

Persome is this open-source Runtime and project name. Personome is the research term for the learned model of one person: a dynamic state assembled from sourced observations, relations, stable patterns, and higher-level structure. The product name stays Persome in commands, packages, paths, APIs, and documentation.

Paper and architecture-note status

This repository ships the executable Runtime and an implementation-oriented architecture note. The architecture documents are not a peer-reviewed paper, and the Runtime's synthetic gates are not publication benchmarks. The paper, benchmark suite, data statements, and project publication will live as separate artifacts with independent licenses before release. See licensing boundaries and benchmark limitations.

Roadmap

The public roadmap is issue-driven:

• more tested MCP client integrations;

• richer first-run permission diagnostics;

• explicit import/export interoperability;

• Intel and future-macOS compatibility evidence;

• a separate, reproducible personal-model benchmark suite.

Browse starter issues or start a design question in Discussions.

Contributing and community

Read CONTRIBUTING.md, follow the Code of Conduct, and use SUPPORT.md to choose the right channel. Every commit requires DCO sign-off, and CI blocks known secrets, personal data, non-English source text, contract drift, lint failures, and offline regressions.

If an inspectable, user-owned personal model is useful to your agents, star the repository and share the MCP client or workflow you want Persome to support.

License

Runtime code is Apache-2.0. Paper, benchmark, project-note, third-party, and personal-data boundaries are explained in LICENSES.md. Required incorporated-work notices remain in NOTICE and THIRD_PARTY_NOTICES.