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@github-actions github-actions released this 12 Nov 13:53
v0.3.0
dd2f88b

v0.3.0 — Faster local memory, smaller MCP responses

AI-Memory-Book gives agents a shared local archive of original evidence. This
experimental release packages the verified retrieval, context-budget and MCP
improvements with a shorter setup path and concise English/Chinese documentation.

Highlights

  • Lightweight default: Python + SQLite memory writing and lexical retrieval,
    zero model calls, original source references and project/task/session scopes.
  • Faster identical-output recall: actual MCP pooled p95 falls 1.25%–9.50%
    in four balanced rounds; public-corpus ingestion time falls 46.45% and
    unchanged-session replay time 61.38%. The full non-timing payload is preserved.
  • Optional smaller references: memory-book mcp --source-format indexed
    preserves evidence and reconstructs every source field. At the documented
    1,015-question public / 4,000-character / compact / o200k_base setting,
    response-text tokens fall 19.63% and the first-call reference total 8.15%.
    The latter includes schema, initialization instructions and arguments.
  • Explicit quality/budget options: exact named-tokenizer context limits,
    local Q2 semantic fusion and multi-fact coverage; the default remains lexical.
  • Simpler deployment: installers now use the v0.3.0 universal wheel instead
    of downloading the repository's benchmark history. MCP/client extras are
    included; Git, Node.js and a separate database server are not needed.

Current comparison

On the same-host, 256-question HotpotQA component comparison with a common
1,024-token cl100k_base whole-page packer, Q2 complete evidence is 57.81%,
versus 48.83% for claude-mem 15.18.1 FTS and 53.91% for ai-memory v3.4.5
local hybrid. Book's default lexical component has 8.301 ms p95; Q2 has
22.523 ms, compared with 29.948 ms and 33.251 ms for those two
components. Interfaces differ, so these are not whole-product speed ratios.
The billion-context kernel leads this slice's completeness (61.72%),
evidence recall and search latency.

Full comparison, costs and scope ·
Default performance ·
Indexed references

Install and connect

Requires Python 3.10+ with SQLite FTS5.

macOS / Linux

curl -fsSLo install-memory-book.sh https://raw.githubusercontent.com/Afloat16/AI-Memory-Book/v0.3.0/scripts/install.sh
bash install-memory-book.sh

Windows PowerShell

Invoke-WebRequest https://raw.githubusercontent.com/Afloat16/AI-Memory-Book/v0.3.0/scripts/install.py -OutFile install-memory-book.py
py -3 .\install-memory-book.py

Then connect a client and explicitly enable supported transcript capture:

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

Use the exact launcher path printed by the installer if it is not on PATH, then
restart the client. Replace claude-code with your supported profile; omit
--capture for MCP access alone. Host permissions and model tool selection apply.

Upgrade: re-run the installer and the same setup command so client and
background-service paths use the new environment. The installer retains existing
memory and the previous environment. The source-preserving schema upgrade runs
when the new engine opens an older archive. Older v0.1 working-directory databases
are not moved automatically. Back up an existing database before upgrading:
schema 3 has no automatic downgrade, and older binaries require the pre-upgrade
database backup for rollback.

English quick start ·
中文快速开始与流程图 ·
Deployment and upgrades

Validation and remaining limits

The release requires all nine exact-commit CI jobs: Python 3.10/3.12/3.13,
actual MCP stdio, the reader browser, Linux/macOS/Windows installers and container
persistence. Published assets include wheel/source distributions, installers,
checksums and source/build provenance; the exact CI run is linked below. The repository retains original measurement
files, negative experiments and independent audit records.

Q2 adds local model/index costs and can lose evidence at small budgets: native
completeness at 512 o200k_base tokens / 8,000 characters is 42.19% lexical →
41.02% Q2. Sparse indexed references can add tokens; the displayed p95 changes
from 7.126 to 7.131 ms, with no format speedup claim. Reference-token counts
are not provider billing. Public/reused data are regression evidence, and the
current component scores are not final-answer accuracy or proof of cross-domain
transfer. Earlier generated-answer/workflow diagnostics retain their own source
and model settings. The rejected capped-novelty candidate is not included.

This remains an experimental, local single-user project. Client configuration
fixtures do not certify every vendor app; automatic invocation is not guaranteed.
The local archive is unencrypted, and recalled evidence enters the AI client's
model context.

Source commit: dd2f88be742ed330269e7dd5915b6f75089d7fef.

CI: https://github.com/Afloat16/AI-Memory-Book/actions/runs/43230570803

Release workflow: https://github.com/Afloat16/AI-Memory-Book/actions/runs/43230570862

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