v0.2.0 — Easy setup, background capture, and English-first docs

What changed

AI-Memory-Book now has an English primary README, a Chinese quickstart, and an English/Chinese local reader. Conversation titles and evidence keep their original language.

• Isolated Python installation on macOS, Linux, and Windows; no Git checkout is needed for the pinned release installer.
• One setup command for 12 MCP configuration profiles, with dry runs, backups, repeatable updates, and explicit conflict handling.
• Opt-in incremental capture for Claude Code, Codex, Gemini CLI, and legacy Python Kimi; current Kimi Code and other supported hosts can use MCP recall.
• Current-user background service support through systemd, LaunchAgents, and Windows logon tasks. Configuration changes reload in the running worker.
• A non-root Docker image and Compose reader with a persistent volume and host-loopback publication.
• Shared per-user data paths, migration guidance, diagnostics, and service lifecycle commands.

Verification

The final main commit e7c60c4 passed all nine CI jobs: Python 3.10/3.12/3.13 core checks, 158 tests with MCP/client extras, real browser interactions, Linux/macOS/Windows installer and recovery checks, and Docker MCP/reader/persistence checks.

Verified CI run · Validation details

A bounded local capture sample used 300 synthetic files: eight unchanged scans had a 36.95 ms median and zero reimports; changing one file updated one session and skipped 299. This warm-cache sample does not measure resident CPU or battery use.

Install and connect

See the English README, deployment guide, and client compatibility.

After installation:

memory-book setup --client claude-code --capture --background
memory-book doctor

Upgrade and limits

Re-run client setup and service installation after upgrading so their absolute Python paths use the new environment. Existing v0.1 .memory-book/memory.db files are preserved and are not moved automatically.

This remains experimental. Client schemas and synthetic adapters were tested; every vendor application and native login/logout service lifecycle was not exercised. MCP integration does not guarantee automatic context injection. DeepSeek models can use a compatible MCP host; this release does not claim a verified standalone DeepSeek Harness profile.

Historical v0.1 retrieval reports retain their original version and limitations. No competitor performance claim is made.