# Einsia/OpenChronicle

Repository: https://github.com/Einsia/OpenChronicle
Canonical: https://ross.abutalabs.com/products/openchronicle
Language: Python
License: MIT
License Family: permissive
Last push: 2026-05-09T13:17:39+00:00

## Health v2 (maintenance only)
Score: 50/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 81, release rhythm 35, longevity 9
- inputs: {"age_days": 134, "days_push": 116, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, young
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2670, forks 219 (observed 2026-08-28T04:07:09.411594+00:00)

## What it is
OpenChronicle is a local-first, open-source memory layer for tool-capable LLM agents, running on macOS. It captures structured context from the accessibility tree (with screenshots as a secondary signal) and turns it into persistent Markdown and SQLite memory that any model-agnostic agent, especially MCP clients, can query.

## Use cases
- give my AI agent persistent memory of what I'm working on
- open-source alternative to OpenAI Chronicle for agent memory
- store agent context locally as Markdown instead of the cloud
- let an MCP client remember my apps, decisions, and projects
- build a memory layer for tool-using LLM agents with Ollama or LM Studio
- capture working context from my Mac's accessibility tree for an agent

## When to choose
- you want local-first, inspectable agent memory on macOS with no cloud dependency
- you need a model-agnostic memory layer that works with any tool-capable agent or MCP client
- you prefer cheap, structured AX-tree context over screenshot-heavy vision pipelines
- you want memory stored in readable Markdown and SQLite you can hack on

## When to avoid
- you need Windows or Linux support - it is macOS only
- you need a production-stable system - it is early alpha (v0.1.0)
- you rely primarily on visual/screenshot-based context rather than app and text context
- you need a hosted, multi-device synced memory solution

## Facets
- artifact type: application
- maturity: experimental
- function: agent-framework, mcp, rag, nlp, llm-inference, database, markdown
- domain: artificial-intelligence, large-language-models, privacy, self-hosted, developer-tools
- platform: python, self-hosted
- tags: agent-memory, local-first, accessibility-tree, screen-context, model-agnostic, mcp-server, openai-chronicle-alternative, early-alpha, ai-agents, macos, desktop

## Member repositories
- Einsia/OpenChronicle (main) score 50

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:09.411594+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:18:09.885983+00:00, confidence not recorded.
  - readme: https://github.com/Einsia/OpenChronicle (fetched 2026-08-28T04:07:09.411594+00:00, sha 39c7e7f110b9)
- Data as of 2026-08-30T08:39:29.467469+00:00.