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Memoir

Memory server

First committed by zhangfengcdt

Published in zhangfengcdt/memoir. Publisher page

629 GitHub stars

What it is

Hierarchical Agent Memory with Git-Like Version Control
— the description by zhangfengcdt

AutoGineer overview, from the brain’s own files

Memoir is a memory system designed to replace opaque vector databases with a transparent, versioned, and cryptographically secure storage layer. By utilizing ProllyTrees, it enables hierarchical semantic paths instead of UUIDs, allowing for efficient O(log n) lookups. It addresses common AI memory issues like context contamination and token waste by providing branch, commit, merge, and rollback capabilities. Memoir integrates with various agents through plugins and an MCP server, supporting both keyword-based and LLM-powered semantic search.

How it works

Memory model: Not stated

AutoGineer overview, from the brain’s own files

Memoir organizes memory into a hierarchical structure using semantic paths, such as profile.professional.skills.python. It uses a ProllyTree-based store to manage data, allowing for Git-like operations such as branching and versioning. The system includes a classifier to categorize memories and a search engine for retrieval. It provides a CLI for manual interaction, hooks for automatic memory capture in supported agents, and an MCP server for integration with various AI development tools.

Folders
FolderRoleFiles
.githubagent-config10
docsdocs41
scriptsscripts9
srcsource-code216
teststests53
.agentsfolder1
.claude-pluginfolder1
benchmarksfolder10
packagingfolder3
pluginsfolder187
staticfolder2

The agent writes

Requirements

Runtimes: Python >=3.10

API keys (by name): ANTHROPIC_API_KEY, OPENAI_API_KEY

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Google Antigravity, Kiro, OpenCode, Amp, Roo Code, Cline, JetBrains Junie, Devin (ex-Windsurf)

Set it up

  1. This brain runs code automatically: 7 hooks (claude-code, codex), 2 scripts. Review Makefile, README.md, plugins/claude-code/hooks/hooks.json, plugins/codex/hooks/hooks.json before opening the folder in Claude Code. No step below turns a hook on for you.

    • Makefile: pip install -e .
    • README.md: curl -LsSf https://astral.sh/uv/install.sh | sh
    • plugins/claude-code/hooks/hooks.json: bash ${CLAUDE_PLUGIN_ROOT}/hooks/session-start.sh
    • plugins/claude-code/hooks/hooks.json: bash ${CLAUDE_PLUGIN_ROOT}/hooks/user-prompt-submit.sh
    • plugins/claude-code/hooks/hooks.json: bash ${CLAUDE_PLUGIN_ROOT}/hooks/stop.sh
    • plugins/claude-code/hooks/hooks.json: bash ${CLAUDE_PLUGIN_ROOT}/hooks/session-end.sh
    • plugins/codex/hooks/hooks.json: bash "${PLUGIN_ROOT}/hooks/session-start.sh"
    • plugins/codex/hooks/hooks.json: bash "${PLUGIN_ROOT}/hooks/user-prompt-submit.sh"
    • plugins/codex/hooks/hooks.json: bash "${PLUGIN_ROOT}/hooks/stop.sh"
  2. Not scanned by AutoGineerThe author’s README also describes installing it as a Claude Code plugin marketplace. Read its install section: AutoGineer did not scan that path. https://github.com/zhangfengcdt/memoir/blob/7fd1b3ef704fc554596571d5b9c8b73d7f2b0c5d/README.md#install-a-plugin-for-your-agent
  3. Not scanned by AutoGineerIts dependencies are installed by pip or uv when you run it and were not scanned by AutoGineer (7 declared). https://pypi.org/project/memoir-ai/

  4. First committed by zhangfengcdt. Published in zhangfengcdt/memoir. Licence: Apache License 2.0.

Usage

AutoGineer overview, from the brain’s own files

  • CLI-based memory management
  • Automatic context injection for coding agents
  • Version-controlled memory branching
  • Semantic search across stored facts
  • Cross-machine memory synchronization
“memoir remember "I prefer using Python for data analysis" -p preferences.coding.languages”
Stores the fact under the specified semantic path for later retrieval.
“memoir recall "what are my coding preferences?"”
Performs a semantic search to retrieve the relevant stored memory.

Documentation by zhangfengcdt

Memoir

<div align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://www.memoir-ai.dev/images/memoir.png"> <source media="(prefers-color-scheme: light)" srcset="https://www.memoir-ai.dev/images/memoir-light.png"> <img src="https://www.memoir-ai.dev/images/memoir.png" alt="Memoir Logo" width="200" height="200"> </picture>

Git for AI Memory

Hierarchical Memory with Git-Like Version Control

</div>

Image: Python Image: License Image: Status Image: Website Image: Docs

<p align="center"> <img src="docs/_static/memoir-demo.gif" alt="Memoir demo" width="900"> </p>

Memoir is a high-performance semantic memory system for AI agents that brings Git-like version control to AI memory management. It replaces opaque vector databases with transparent, versioned, cryptographically secure memory storage using hierarchical semantic paths.

Project page: memoir-ai.dev — full overview, demos, and roadmap.

Why agents need versioned memory

AI memory is a Global Variable anti-pattern. Every production agent hits the same three walls: context contamination, token rent, and memory drift. Memoir brings version control to your agent's mind.

Your agent doesn't respect your git state. Context contamination happens every time you git checkout. Without branch-aware memory, your agent tries to apply experimental refactor patterns to stable production hotfixes.

You're paying "token rent" on a flat file. Using CLAUDE.md or MEMORY.md as a global store is a cache-killer. Every minor memory update invalidates your entire prefix cache, forcing you to pay full price to re-process your entire conversation.

Your agent's memory is code without version control. Today's AI memory — CLAUDE.md, vector stores, scratchpads — is treated like an append-only blob. One bad session poisons every future retrieval. Without memoir blame or memoir checkout, there's no way to audit who taught the agent a rule or revert a hallucination without wiping the whole store.

Key Features

  • Git-like Versioning — Branch, commit, merge, and rollback memories with cryptographic integrity.

  • Semantic Paths — Replace UUID keys with meaningful paths like profile.professional.skills.python.

  • O(log n) Lookups — Fast hierarchical search instead of expensive vector operations.

  • Memory Aggregation — Automatic consolidation of related memories at semantic locations.

  • Clean Architecture — Proper separation of storage, classification, and search layers.

  • Multiple Search Engines — Choose between fast keyword-based or intelligent LLM-powered search.

<!-- ## Multi-agent sessions <p align="center"> <img src="docs/_static/memoir-multi-agent.gif" alt="Multiple agent sessions working with Memoir" width="900"> <br> <em>Multiple agent sessions working with Memoir.</em> </p> -->

Install from PyPI

pip install memoir-ai

The distribution name on PyPI is memoir-ai. The Python import is import memoir and the CLI is memoir.

Install a plugin for your agent

Memoir ships as a plugin for several coding and personal-assistant agents. Expand the one you use:

<details> <summary><b>Claude Code</b></summary>

Inside a Claude Code session, run:

/plugin marketplace add zhangfengcdt/memoir
/plugin install memoir@memoir

No manual pip install needed if you have uv on PATH — the plugin auto-resolves to uvx --from memoir-ai memoir when the bare memoir binary isn't installed. Don't have uv? Install it once with the standard one-liner:

curl -LsSf https://astral.sh/uv/install.sh | sh

That's enough — the plugin handles the rest. It registers hooks for session start, user-prompt-submit, and stop, so your project gets automatic context injection and auto-captured memories. See the Claude Code plugin guide for the full slash-command and hook reference.

</details>

<details> <summary><b>Codex</b></summary>

Memoir's Codex plugin is distributed from this repository's Codex marketplace. In Codex, run /plugins, add the memoir marketplace from zhangfengcdt/memoir, restart Codex if prompted, then choose Memoir Plugins and install memoir.

You can also register the marketplace from the CLI:

codex plugin marketplace add zhangfengcdt/memoir

While developing from a local checkout, use codex plugin marketplace add /absolute/path/to/memoir instead. The repo marketplace lives at .agents/plugins/marketplace.json and points Codex at ./plugins/codex, relative to the repository root.

No manual pip install needed if you have uv on PATH — the Codex plugin resolves the Memoir CLI the same way the Claude Code plugin does: memoir on PATH first, then uvx --from memoir-ai==<pinned> memoir, then uv tool run --from memoir-ai==<pinned> memoir.

Enable Codex hooks with [features].hooks = true in ~/.codex/config.toml or pass --enable hooks for a smoke run. The Codex plugin ships lifecycle hooks; memory-recall, memoir-onboard, memoir-remember, memoir-status, and memoir-ui skills; Codex-specific transcript parsing; and local marketplace metadata. See the Codex plugin guide for setup, limitations, and the real Codex smoke-test flow.

</details>

<details> <summary><b>Hermes</b></summary>

Memoir is a memory provider for Hermes, the Nous Research personal-assistant agent. Install the plugin into your Hermes home and activate it:

hermes plugins install zhangfengcdt/memoir/plugins/hermes   # or: cp -r plugins/hermes ~/.hermes/plugins/memoir
hermes memory setup                                         # choose "memoir"

Hermes then auto-captures durable facts (people, schedule, preferences, standing instructions) each turn and exposes memoir_recall / memoir_remember / memoir_forget / memoir_sync / memoir_status tools. Capture/classification run on your host-selected model via direct provider APIs (never the claude CLI). See the Hermes plugin guide for install, configuration, model selection, and proxy routing.

</details>

<details> <summary><b>OpenClaw</b> (community)</summary>

Memoir is a memory plugin for OpenClaw, the personal-assistant gateway. It lives in its own repo, zhangfengcdt/openclaw-memoir. Install it into the memory slot, allow conversation access, and restart the gateway:

openclaw plugins install https://github.com/zhangfengcdt/openclaw-memoir   # or: --link /path/to/openclaw-memoir
openclaw config set plugins.entries.memory-memoir.hooks.allowConversationAccess true
systemctl --user restart openclaw-gateway

OpenClaw then auto-captures durable facts each turn (remembered across your sessions by default) and exposes memoir_recall / memoir_remember / memoir_forget / memoir_status tools plus an in-chat /memoir command. Set a capture model + provider key in config — plugins.entries.memory-memoir.config.{model,apiKey} (OpenClaw keeps its own keys in a secret store; use a temperature=0-capable model like anthropic/claude-haiku-4-5). See the OpenClaw plugin guide for configuration, scoping, model selection, and tool-profile notes.

</details>

<details> <summary><b>OpenCode</b> (community)</summary>

opencode-memoir is a community-maintained plugin that brings Memoir's long-term memory workflows to OpenCode through its native plugin system. Follow the install instructions in that repository.

</details>

<details> <summary><b>Any MCP host</b> (Claude Desktop, Cursor, Cline, Windsurf, VS Code, Zed, …)</summary>

Memoir ships an MCP server (memoir-mcp) on the official Model Context Protocol SDK, so any MCP-compatible host gets git-versioned memory — including the memoir_branches / memoir_checkout / memoir_commits versioning tools no other memory server has. No install needed if you have uv; add to your host's mcpServers config:

{
  "mcpServers": {
    "memoir": {
      "command": "uvx",
      "args": ["--from", "memoir-ai[mcp]", "memoir-mcp"],
      "env": { "MEMOIR_STORE": "~/.memoir/mcp" }
    }
  }
}

Recall is LLM-free by default (keyless), with opt-in single-call or multi-level tiered LLM modes; the store auto-creates on first use. See the MCP server guide for per-host config (VS Code/Zed/Continue/LibreChat differ slightly), recall modes + LLM setup, remote HTTP for ChatGPT/Claude.ai connectors, and the one-click Claude Desktop .mcpb.

</details>

Quick look

Memoir's CLI defaults to Anthropic claude-haiku-4-5 as of v0.1.6 — set your key first:

export ANTHROPIC_API_KEY="sk-..."

Note that if you do not set any model api keys, it will try to use the claude -p command to authenticate via claude code's subscription.

Then create a store and round-trip a memory:

# 1. Create a memoir store
memoir new my-memoir-store
cd my-memoir-store

# 2. Store with an explicit path (offline, no LLM call)
memoir remember "Sarah prefers tabs and 2-space indents" -p preferences.coding.style

# 3. Store with auto-classification (LLM picks the path; needs API key)
memoir remember "I work in Pacific time"

# 4. Read back by path (offline)
memoir get preferences.coding.style

# 5. Semantic search (LLM-backed)
memoir recall "what does Sarah prefer?"

# 6. Watch a file by indexing it on the fly
memoir watch add ~/papers/transformer.pdf -n research

# 7. Search the indexed content
memoir search "transformer attention mechanism"

# 8. Open the visual explorer (auto-opens in your browser)
memoir ui

Curious what the UI looks like before installing? Browse the UI Gallery.

Prefer a different model? memoir recall "..." --model gpt-4o-mini (needs OPENAI_API_KEY), or set MEMOIR_LLM_MODEL in your shell. Resolution order: --model flag → MEMOIR_LLM_MODEL → claude-haiku-4-5.

See the full Quickstart for the Python API and version-control workflow.

Documentation

Full docs live at zhangfengcdt.github.io/memoir:

  • Quickstart — five-minute tour of the core loop.

  • CLI Reference — every command, flag, and exit code.

  • UI — the visual explorer (Tree / Graph / Timeline / Places + /stats).

  • Claude Code — plugin install, slash commands, hooks, lifecycle.

  • Codex — plugin install, hooks, skills, transcript parsing, smoke-test evidence.

  • Architecture — taxonomy, classifier, store, search.

  • API Reference — Python SDK.

  • Examples — context branching, memory debugging, reproducible testing.

Contributing

Memoir is alpha and contributions are very welcome — especially from people building coding agents, since that's the audience we're optimizing for. Good first paths in:

  • Pick an issue from the issue tracker or open one describing a gap.

  • Fork the repo, branch off main, and run make ci before opening a PR (lint, tests, docs build must be green).

  • Bug reports with a minimal reproducer and benchmark / taxonomy proposals for coding-agent use cases are particularly appreciated.

License

Apache License 2.0 — see LICENSE.

docs/architecture.md

Architecture Overview

Memoir implements a clean layered architecture with proper separation of concerns and dependency injection patterns. This design enables high performance, maintainability, and flexibility.

Core Principles

  1. Git-like Versioning: Every memory change is tracked with cryptographic integrity

  2. Semantic Paths: Replace UUID keys with meaningful hierarchical paths

  3. Memory Aggregation: Group related memories at semantic locations

  4. Dependency Injection: Clean separation between storage, classification, and search

  5. Performance First: O(log n) lookups instead of expensive vector operations

System Architecture

flowchart TB
    MM["Memory Manager<br/>(Orchestration Layer)"]
    S[Storage Layer]
    C[Classification Layer]
    SE[Search Engine]
    PT[ProllyTree]
    TX[Taxonomy System]
    PS[Path Selection]
    MM --> S
    MM --> C
    MM --> SE
    S --> PT
    C --> TX
    SE --> PS

Layer Details

1. Storage Layer (memoir.store)

The storage layer provides pure data persistence without business logic:

  • ProllyTreeStore: Git-like versioned key-value storage

  • Memory Aggregation: Groups memories at semantic paths

  • Cryptographic Integrity: SHA-256 hashing for all operations

  • Efficient Queries: O(log n) prefix searches

from memoir.store.prolly_adapter import ProllyTreeStore

store = ProllyTreeStore(
    path="./memory_store",
    enable_versioning=True,
    cache_size=10000
)
2. Classification Layer (memoir.classifier)

Handles semantic classification of memories into hierarchical paths:

  • SemanticClassifier: Fast pattern-based classification (1-5ms)

  • IntelligentClassifier: LLM-powered with dynamic taxonomy expansion

  • Confidence Thresholds: Configurable acceptance criteria

  • Multi-stage Pipeline: Pattern matching → LLM → Expansion

from memoir.classifier.intelligent import IntelligentClassifier

classifier = IntelligentClassifier(
    llm=llm,
    confidence_thresholds={
        "high": 0.8,    # Auto-store
        "medium": 0.5,  # Review
        "low": 0.0      # Reject threshold
    }
)
3. Search Engine Layer (memoir.search)

Provides intelligent memory retrieval capabilities:

  • IntelligentSearchEngine: LLM-powered path selection

  • Multi-strategy: Breadth-first, depth-first, best-match

  • Relevance Scoring: Combined semantic and structural scoring

# Intelligent LLM-powered search
from memoir.search.intelligent import IntelligentSearchEngine
search_engine = IntelligentSearchEngine(llm=llm, store=store)
4. Memory Manager (memoir.core)

Orchestrates all components with proper dependency injection:

  • Dependency Injection: Clean separation of concerns

  • Transaction Management: Atomic operations

  • Version Control: Branching, merging, rollback

  • Performance Monitoring: Built-in metrics

from memoir.core.memory import ProllyTreeMemoryStoreManager

memory_manager = ProllyTreeMemoryStoreManager(
    prolly_store=store,        # Injected dependency
    classifier=classifier,      # Injected dependency
    search_engine=search_engine # Injected dependency
)

Data Flow

Storage Flow:

flowchart LR
    A["Memory Input<br/>I work at X"] --> B["Classification<br/>Classifier analysis"]
    B --> C["Path Selection<br/>profile.professional.occupation"]
    C --> D["Aggregation<br/>with similar memories"]
    D --> E["Storage<br/>ProllyTree"]

Retrieval Flow:

flowchart LR
    A["Query<br/>user job"] --> B["Path Selection<br/>profile.* paths"]
    B --> C["Storage Lookup<br/>Tree Search"]
    C --> D["Aggregation<br/>Collect memories"]
    D --> E["Results<br/>Ranked"]

Memory Aggregation

Memories are aggregated at semantic paths rather than stored individually:

Traditional Approach:

uuid-1234-5678 → "I work at TechCorp"
uuid-9876-5432 → "I'm a software engineer"
uuid-1111-2222 → "I've been coding for 5 years"

Memoir Approach:

profile.professional.occupation → {
  "memories": [
    {"content": "I work at TechCorp", "confidence": 0.95},
    {"content": "I'm a software engineer", "confidence": 0.87},
    {"content": "I've been coding for 5 years", "confidence": 0.82}
  ],
  "count": 3,
  "last_updated": "2024-01-15"
}

Taxonomy System

The taxonomy system provides hierarchical organization:

Fixed Taxonomy (memoir.taxonomy.semantic):

  • ~200 predefined paths

  • Fast pattern matching

  • Consistent organization

Dynamic Taxonomy (memoir.taxonomy.iterative):

  • LLM-driven expansion

  • Automatic growth

  • Context-aware paths

Example Paths:

profile.
├── identity.
│   ├── name.{first,last,full}
│   └── demographics.{age,location}
├── professional.
│   ├── occupation.{role,company}
│   └── skills.{technical,soft}
└── personal.
    ├── interests.{hobbies,sports}
    └── relationships.{family,friends}

Performance Characteristics

Search Performance:

  • Semantic Search: 0.1-1ms average latency

  • Intelligent Search: 100-500ms (includes LLM calls)

  • Traditional Vector Search: 150-750ms

Storage Performance:

  • Memory Classification: 1-5ms (pattern) / 100-500ms (LLM)

  • Storage Operations: 20-30ms

  • Version Control Ops: 50-100ms

Scalability:

  • Memory Count: Tested up to 1M memories

  • Path Depth: Up to 8 levels deep

  • Concurrent Users: Horizontal scaling ready

Version Control

Git-like operations for memory management:

main branch
│
├─ commit: "Initial user profile"
│  └─ memories: profile.identity.*
│
├─ commit: "Added work info"
│  └─ memories: profile.professional.*
│
└─ branch: experiment
   ├─ commit: "Testing new classifier"
   └─ merge → main

Extensions and Plugins

Memento Collections (memoir.memento):

  • LocationMemento: Spatial/geographic memories

  • TimelineMemento: Temporal/chronological memories

  • ProfileMemento: Identity/personal memories

Custom Extensions:

  • Custom classifiers

  • Custom search engines

  • Custom taxonomy systems

  • Custom storage backends

This architecture enables Memoir to provide fast, reliable, and scalable semantic memory management while maintaining clean code organization and extensibility.

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Advantages

  • Provides Git-like version control for AI memory, allowing for branching, merging, and rolling back. evidence: README.md
  • Uses hierarchical semantic paths for O(log n) lookups, offering better performance than traditional vector databases. evidence: README.md

Limitations

  • Requires an API key for LLM-powered features like intelligent classification and semantic search. evidence: README.md
  • Some advanced features like document watching require additional dependencies or manual configuration. evidence: pyproject.toml

Security

Risk level: Medium risk

This brain runs

Git hooks (5)

  • .pre-commit-config.yaml: pre-commit hooks from https://github.com/pre-commit/pre-commit-hooks at v6.0.0 (contributor tooling: runs only after pre-commit install) · uses the network
  • .pre-commit-config.yaml: pre-commit hooks from https://github.com/pycqa/isort at 6.0.1 (contributor tooling: runs only after pre-commit install) · uses the network
  • .pre-commit-config.yaml: pre-commit hooks from https://github.com/astral-sh/ruff-pre-commit at v0.12.9 (contributor tooling: runs only after pre-commit install) · uses the network
  • .pre-commit-config.yaml: pre-commit hooks from https://github.com/PyCQA/bandit at 1.8.6 (contributor tooling: runs only after pre-commit install) · uses the network
  • .pre-commit-config.yaml: pytest

Agent instructions that run or fetch something (73)

  • CLAUDE.md: `make ci` runs the full pipeline (lint, test, security, docs).
  • CLAUDE.md: tests use the fake gateway in `tests/fake_cloud.py` (real `git http-backend`, no network).
  • CLAUDE.md: When touching the mod run `claude plugin validate plugins/claude-code` and `claude plugin test plugins/claude-code`;
  • CLAUDE.md: Run with `pytest`.
  • CLAUDE.md: Deep structural details (layouts, refactor history, per-file line counts) intentionally omitted — use `rg` / `git log` when you need them.
  • CLAUDE.md: flipping it later (`git init`, or `rm -rf .git`) surfaces a warning at SessionStart but does not block writes.
  • CLAUDE.md: When changing a prompt, **run the harness against haiku before merging**.
  • CLAUDE.md: The harness has two modes: **gate** (deterministic shell-hook tests, no LLM, sub-second per case — pins the recall-trigger gate in `hooks/user-prompt-submit.sh`) and **LLM mode** (`run` / `case` / `adhoc` against `claude -p` for prompt templates).
  • CLAUDE.md: # Gate mode — no LLM cost, run on every commit.
  • CLAUDE.md: plugins/claude-code/tests/prompt-harness/runner.py run --prompt stop_capture --model haiku
  • CLAUDE.md: plugins/claude-code/tests/prompt-harness/runner.py run --prompt stop_capture --model sonnet
  • CLAUDE.md: Every run drops `system.txt` (the assembled prompt actually sent), `input.txt`, `output.txt`, `command.sh` (replayable), and `result.json` per case under `/tmp/memoir-prompt-tests/<UTC-timestamp>/`.
  • CLAUDE.md: Skill prompts (`skills/<name>/SKILL.md`) are evaluated by the orchestrating Claude rather than dispatched as system prompts to a model, so they are *not* covered by this harness — to test those, run the slash command in a real session and inspect behavior.
  • CLAUDE.md: **Build the docs locally:** `make docs` (mkdocs) → `site/index.html`, or `make docs-live` for auto-reload.
  • plugins/claude-code/skills/memoir-onboard/SKILL.md: bash -c 'source "${CLAUDE_PLUGIN_ROOT}/hooks/common.sh" >/dev/null 2>&1; in_git_repo && echo codebase:onboard || echo project:onboard'
  • plugins/claude-code/skills/memoir-onboard/SKILL.md: Store: !`bash -c 'if [ -n "${MEMOIR_STORE:-}" ];
  • plugins/claude-code/skills/memoir-onboard/SKILL.md: `$MEMOIR` is a wrapper that resolves the right invocation for this machine — `memoir` on PATH if installed, otherwise `uvx --from memoir-ai==<pin> memoir`, otherwise `uv tool run --from memoir-ai==<pin> memoir` (pin lives in `scripts/resolve-memoir-cli.sh`).
  • plugins/claude-code/skills/memoir-onboard/SKILL.md: Wrap **every** memoir call — both branches A and B, both reads and writes — in a subshell that cd's into the store first, and use `"$MEMOIR"` (NOT bare `memoir`) so the call works on machines without a global memoir install:
  • plugins/claude-code/skills/memoir-onboard/SKILL.md: `test.strategy` — test layout, how to run tests, what coverage emphasizes.
  • plugins/claude-code/skills/memoir-onboard/SKILL.md: bash -c 'source "${CLAUDE_PLUGIN_ROOT}/hooks/common.sh" >/dev/null 2>&1; concurrent_session_warning'
  • plugins/claude-code/skills/memoir-onboard/SKILL.md: If the command prints anything, stop and report: "Concurrent session detected — run /memoir:onboard after the other session finishes, or set a distinct MEMOIR_STORE."
  • plugins/claude-code/skills/memoir-onboard/SKILL.md: `git log --oneline -20` and `git log --stat -5` for recent change patterns (informs lessons / rules).
  • plugins/claude-code/skills/memoir-onboard/SKILL.md: Run `git log --stat <last_sha>..HEAD` to enumerate changed paths since the last onboarding pass.
  • plugins/claude-code/skills/memoir-onboard/SKILL.md: Run the confirmation check (`memoir status` → path == `$STORE_PATH`) before doing any writes.
  • plugins/claude-code/skills/memoir-onboard/SKILL.md: `python3 ${CLAUDE_PLUGIN_ROOT}/skills/memoir-onboard/extractors.py walk <root>` — JSON list of `{path, size, mtime_ns, kind}` plus `snapshot_hash`.
  • plugins/claude-code/skills/memoir-onboard/SKILL.md: `python3 ${CLAUDE_PLUGIN_ROOT}/skills/memoir-onboard/extractors.py extract <path>` — `key=value` blob for one file (with `kind=` first, `extractor.stdlib.fields=[…]` for provenance).
  • plugins/claude-code/skills/memoir-onboard/SKILL.md: `python3 ${CLAUDE_PLUGIN_ROOT}/skills/memoir-onboard/extractors.py snapshot-hash <root>` — just the hash.
  • plugins/claude-code/skills/memoir-onboard/SKILL.md: `python3 ${CLAUDE_PLUGIN_ROOT}/skills/memoir-onboard/extractors.py tree <root>` — pruned tree.
  • plugins/claude-code/skills/memoir-onboard/SKILL.md: `python3 ${CLAUDE_PLUGIN_ROOT}/skills/memoir-onboard/extractors.py shape <root>` — `{shape, overview}` JSON.
  • plugins/claude-code/skills/memoir-onboard/SKILL.md: bash -c 'source "${CLAUDE_PLUGIN_ROOT}/hooks/common.sh" >/dev/null 2>&1; concurrent_session_warning'
  • plugins/claude-code/skills/memoir-onboard/SKILL.md: For each file in the walk result, run `extractors.py extract <path>` and capture the structured blob.
  • plugins/claude-code/skills/memoir-onboard/SKILL.md: python3 -c "import json,sys; print('\n'.join(e['path'] for e in json.loads(sys.argv[1])['files']))" "$WALK_JSON" \
  • plugins/claude-code/skills/memoir-onboard/SKILL.md: **added** (in walk, not in store) → run `extract <path>`, write `files.<san>.meta` and `files.<san>.summary`.
  • plugins/claude-code/skills/memoir-onboard/SKILL.md: **modified** (same path, different `(size, mtime_ns)`) → re-run `extract <path>`, write both keys.
  • plugins/claude-code/skills/memoir-onboard/SKILL.md: If a cold run fails partway through, the `_meta.*` keys act as commit markers.
  • plugins/claude-code/skills/memory-recall/SKILL.md: Run this preamble first and reuse `$STORE` and `$MEMOIR` for every memoir invocation below:
  • plugins/claude-code/skills/memory-recall/SKILL.md: `$MEMOIR` is a wrapper that resolves the right invocation for this machine — `memoir` on PATH if installed, otherwise `uvx --from memoir-ai==<pin> memoir`, otherwise `uv tool run --from memoir-ai==<pin> memoir` (pin lives in `scripts/resolve-memoir-cli.sh`).
  • plugins/claude-code/skills/memory-recall/SKILL.md: that way recall works on machines where memoir isn't `pip install`ed but `uv` is.
  • plugins/codex/skills/memoir-onboard/SKILL.md: bash -c 'source "${PLUGIN_ROOT}/hooks/common.sh" >/dev/null 2>&1; in_git_repo && echo codebase:onboard || echo project:onboard'
  • plugins/codex/skills/memoir-onboard/SKILL.md: Store: !`bash -c 'if [ -n "${MEMOIR_STORE:-}" ];
  • plugins/codex/skills/memoir-onboard/SKILL.md: python3 - "$manifest" <<'PY'
  • plugins/codex/skills/memoir-onboard/SKILL.md: `$MEMOIR` is a wrapper that resolves the right invocation for this machine — `memoir` on PATH if installed, otherwise `uvx --from memoir-ai==<pin> memoir`, otherwise `uv tool run --from memoir-ai==<pin> memoir` (pin lives in `scripts/resolve-memoir-cli.sh`).
  • plugins/codex/skills/memoir-onboard/SKILL.md: Wrap **every** memoir call — both branches A and B, both reads and writes — in a subshell that cd's into the store first, and use `"$MEMOIR"` (NOT bare `memoir`) so the call works on machines without a global memoir install:
  • plugins/codex/skills/memoir-onboard/SKILL.md: `test.strategy` — test layout, how to run tests, what coverage emphasizes.
  • plugins/codex/skills/memoir-onboard/SKILL.md: bash -c 'source "${PLUGIN_ROOT}/hooks/common.sh" >/dev/null 2>&1; concurrent_session_warning'
  • plugins/codex/skills/memoir-onboard/SKILL.md: If the command prints anything, stop and report: "Concurrent session detected - run memoir-onboard after the other session finishes, or set a distinct MEMOIR_STORE."
  • plugins/codex/skills/memoir-onboard/SKILL.md: `git log --oneline -20` and `git log --stat -5` for recent change patterns (informs lessons / rules).
  • plugins/codex/skills/memoir-onboard/SKILL.md: Run `git log --stat <last_sha>..HEAD` to enumerate changed paths since the last onboarding pass.
  • plugins/codex/skills/memoir-onboard/SKILL.md: Run the confirmation check (`memoir status` → path == `$STORE_PATH`) before doing any writes.
  • plugins/codex/skills/memoir-onboard/SKILL.md: `python3 ${PLUGIN_ROOT}/skills/memoir-onboard/extractors.py walk <root>` — JSON list of `{path, size, mtime_ns, kind}` plus `snapshot_hash`.
  • plugins/codex/skills/memoir-onboard/SKILL.md: `python3 ${PLUGIN_ROOT}/skills/memoir-onboard/extractors.py extract <path>` — `key=value` blob for one file (with `kind=` first, `extractor.stdlib.fields=[…]` for provenance).
  • plugins/codex/skills/memoir-onboard/SKILL.md: `python3 ${PLUGIN_ROOT}/skills/memoir-onboard/extractors.py snapshot-hash <root>` — just the hash.
  • plugins/codex/skills/memoir-onboard/SKILL.md: `python3 ${PLUGIN_ROOT}/skills/memoir-onboard/extractors.py tree <root>` — pruned tree.
  • plugins/codex/skills/memoir-onboard/SKILL.md: `python3 ${PLUGIN_ROOT}/skills/memoir-onboard/extractors.py shape <root>` — `{shape, overview}` JSON.
  • plugins/codex/skills/memoir-onboard/SKILL.md: bash -c 'source "${PLUGIN_ROOT}/hooks/common.sh" >/dev/null 2>&1; concurrent_session_warning'
  • plugins/codex/skills/memoir-onboard/SKILL.md: For each file in the walk result, run `extractors.py extract <path>` and capture the structured blob.
  • plugins/codex/skills/memoir-onboard/SKILL.md: python3 -c "import json,sys; print('\n'.join(e['path'] for e in json.loads(sys.argv[1])['files']))" "$WALK_JSON" \
  • plugins/codex/skills/memoir-onboard/SKILL.md: **added** (in walk, not in store) → run `extract <path>`, write `files.<san>.meta` and `files.<san>.summary`.
  • plugins/codex/skills/memoir-onboard/SKILL.md: **modified** (same path, different `(size, mtime_ns)`) → re-run `extract <path>`, write both keys.
  • plugins/codex/skills/memoir-onboard/SKILL.md: If a cold run fails partway through, the `_meta.*` keys act as commit markers.
  • plugins/codex/skills/memoir-onboard/SKILL.md: A subsequent forced memoir-onboard run will rewrite cleanly.
  • plugins/codex/skills/memoir-remember/SKILL.md: python3 - "$manifest" <<'PY'
  • plugins/codex/skills/memoir-remember/SKILL.md: bash "$PLUGIN_ROOT/scripts/ensure-store.sh" "$STORE" >/dev/null
  • plugins/codex/skills/memoir-status/SKILL.md: Run:
  • plugins/codex/skills/memoir-status/SKILL.md: python3 - "$manifest" <<'PY'
  • plugins/codex/skills/memoir-status/SKILL.md: bash "$PLUGIN_ROOT/scripts/status-cmd.sh"
  • plugins/codex/skills/memoir-ui/SKILL.md: Run:
  • plugins/codex/skills/memoir-ui/SKILL.md: python3 - "$manifest" <<'PY'
  • plugins/codex/skills/memoir-ui/SKILL.md: bash "$PLUGIN_ROOT/scripts/memoir-ui-ctl.sh" start "$STORE"
  • plugins/codex/skills/memory-recall/SKILL.md: Run this preamble first and reuse `$STORE` and `$MEMOIR` for every memoir invocation below:
  • plugins/codex/skills/memory-recall/SKILL.md: python3 - "$manifest" <<'PY'
  • plugins/codex/skills/memory-recall/SKILL.md: `$MEMOIR` is a wrapper that resolves the right invocation for this machine — `memoir` on PATH if installed, otherwise `uvx --from memoir-ai==<pin> memoir`, otherwise `uv tool run --from memoir-ai==<pin> memoir` (pin lives in `scripts/resolve-memoir-cli.sh`).
  • plugins/codex/skills/memory-recall/SKILL.md: that way recall works on machines where memoir isn't `pip install`ed but `uv` is.

Install scripts (2)

  • Makefile (make install): pip install -e .
  • README.md: curl -LsSf https://astral.sh/uv/install.sh | sh · uses the network

MCP servers (1)

  • README.md (memoir): uvx --from memoir-ai[mcp] memoir-mcp

Hooks (7)

  • plugins/claude-code/hooks/hooks.json (SessionStart): bash ${CLAUDE_PLUGIN_ROOT}/hooks/session-start.sh
  • plugins/claude-code/hooks/hooks.json (UserPromptSubmit): bash ${CLAUDE_PLUGIN_ROOT}/hooks/user-prompt-submit.sh
  • plugins/claude-code/hooks/hooks.json (Stop): bash ${CLAUDE_PLUGIN_ROOT}/hooks/stop.sh
  • plugins/claude-code/hooks/hooks.json (SessionEnd): bash ${CLAUDE_PLUGIN_ROOT}/hooks/session-end.sh
  • plugins/codex/hooks/hooks.json (SessionStart): bash "${PLUGIN_ROOT}/hooks/session-start.sh"
  • plugins/codex/hooks/hooks.json (UserPromptSubmit): bash "${PLUGIN_ROOT}/hooks/user-prompt-submit.sh"
  • plugins/codex/hooks/hooks.json (Stop): bash "${PLUGIN_ROOT}/hooks/stop.sh"
  • Medium risk: runs-git-hook-remote-exec (.pre-commit-config.yaml:2)
  • Medium risk: runs-git-hook-remote-exec (.pre-commit-config.yaml:23)
  • Medium risk: runs-git-hook-remote-exec (.pre-commit-config.yaml:30)
  • Medium risk: runs-git-hook-remote-exec (.pre-commit-config.yaml:40)
  • Medium risk: runs-install-script-remote-exec (README.md:86)
  • Medium risk: runs-mcp-server-unpinned-runner (README.md:153)
  • Low risk: network-instructions (Makefile:37)
  • Low risk: scripts-present (benchmarks/classifier.py)
  • Low risk: scripts-present (benchmarks/locomo/run.py)
  • Low risk: scripts-present (plugins/claude-code/hooks/session-end.sh)
  • Low risk: scripts-present (plugins/claude-code/hooks/session-start.sh)
  • Low risk: scripts-present (plugins/claude-code/hooks/stop.sh)
  • Low risk: scripts-present (plugins/claude-code/hooks/user-prompt-submit.sh)
  • Low risk: network-instructions (plugins/claude-code/skills/memory-recall/SKILL.md:20)
  • Low risk: scripts-present (plugins/codex/hooks/session-start.sh)
  • Low risk: scripts-present (plugins/codex/hooks/stop.sh)
  • Low risk: scripts-present (plugins/codex/hooks/user-prompt-submit.sh)
  • Low risk: network-instructions (plugins/codex/skills/memory-recall/SKILL.md:36)
  • Low risk: network-instructions (pyproject.toml:57)
  • Low risk: scripts-present (scripts/cc-smoke/run.sh)
  • Low risk: scripts-present (scripts/check_version_consistency.py)

Attribution and licence

First committed by
GitHub avatar of zhangfengcdtzhangfengcdt
Declared authors
  • zhangfengcdt (declared in pyproject.toml)
Publisher
Feng Zhang (Community)
Source
zhangfengcdt/memoir · commit 7fd1b3e
Licence
Apache License 2.0 (files served here)
  • ATTRIBUTION.md (written by AutoGineer)
Synced
Contributors
zhangfengcdt, rapidSpace, disafronov, KevFoxCodeCraft, Ottohere-Mourn

AutoGineer is not affiliated with or endorsed by zhangfengcdt.

Install from source

489 of its files are not served here, so there is no ZIP. zhangfengcdt/memoir at 7fd1b3e