### Development Installation

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/installation.md

Clones the repository and installs dependencies in editable mode with pre-commit hooks.

```bash
# Clone the repository
git clone https://github.com/zhangfengcdt/memoir.git
cd memoir

# Install in development mode with all dependencies
pip install -e ".[dev,docs]"

# Install pre-commit hooks
make pre-commit
```

--------------------------------

### Run Versioning Control Examples

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/examples.md

Execute the provided example scripts to observe versioning control behavior.

```bash
python examples/versioning_control_example.py
python examples/versioning_control.py
```

--------------------------------

### Initialize development environment

Source: https://github.com/zhangfengcdt/memoir/blob/main/CLAUDE.md

Activate the virtual environment and install dependencies for the first time.

```bash
source venv/bin/activate    # or `python -m venv venv && source venv/bin/activate`
make install-dev            # first time only
```

--------------------------------

### Install for development

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/quickstart.md

Install the package in editable mode with development dependencies.

```bash
pip install -e ".[dev]"
```

--------------------------------

### Verify Installation

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/installation.md

Checks the installed version and imports core components to ensure the environment is set up correctly.

```python
import memoir
print(f"Memoir version: {memoir.__version__}")

# Test basic components
from memoir import ProllyTreeStore, SemanticClassifier
print("Installation successful!")
```

--------------------------------

### Install Memoir via uv

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/installation.md

Installs Memoir as a persistent tool or runs it ephemerally without installation.

```bash
# Persistent install (recommended for daily use)
uv tool install memoir-ai

# OR — try without installing (one-shot, ephemeral venv)
uvx --from memoir-ai memoir --help
```

--------------------------------

### Install Memoir CLI

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/quickstart.md

Options for installing the Memoir CLI tool using uv or pip.

```bash
# 1. Try without installing (one-shot, ephemeral venv managed by uv)
uvx --from memoir-ai memoir --help

# 2. Persistent install with uv — faster than pip, isolates the CLI
uv tool install memoir-ai

# 3. Universal fallback (only if you can't or don't want to use uv)
pip install memoir-ai
```

--------------------------------

### Install Memoir Watch

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/watch.md

Install the required dependencies for the watch feature using pip.

```bash
pip install 'memoir-ai[watch]'
```

--------------------------------

### Complete Memoir Setup and Usage

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/basic_usage.md

Demonstrates the full lifecycle of initializing Memoir components, storing semantic memories, and performing intelligent searches.

```python
import asyncio
import os
import tempfile
from langchain_openai import ChatOpenAI

from memoir import ProllyTreeMemoryStoreManager
from memoir.classifier.intelligent import IntelligentClassifier
from memoir.search.intelligent import IntelligentSearchEngine
from memoir.taxonomy.taxonomy import TaxonomyVersion

async def main():
    # 1. Set up LLM
    llm = ChatOpenAI(
        model="gpt-4o-mini",
        temperature=0,
        max_tokens=500
    )

    # 2. Create components in dependency order

    # Step 1: Storage layer (pure storage, no business logic)
    from memoir.store.prolly_adapter import ProllyTreeStore

    prolly_store = ProllyTreeStore(
        path="/tmp/memory_store",
        enable_versioning=True,
        cache_size=10000
    )

    # Step 2: Classification layer (depends on LLM)
    classifier = IntelligentClassifier(
        llm=llm,
        taxonomy_version=TaxonomyVersion.GENERAL,
        confidence_thresholds={
            "high": 0.8,   # High confidence - store immediately
            "medium": 0.5, # Medium confidence - good memories
            "low": 0.0     # Low threshold - reject below this
        }
    )

    # Step 3: Search engine (depends on LLM + store)
    search_engine = IntelligentSearchEngine(
        llm=llm,
        store=prolly_store
    )

    # Step 4: Memory manager (orchestrates all components)
    memory_manager = ProllyTreeMemoryStoreManager(
        prolly_store=prolly_store,
        classifier=classifier,
        search_engine=search_engine,
        enable_versioning=True
    )

    # 3. Store memories with semantic classification
    user_id = "user123"

    memories_to_store = [
        "My name is Sarah Johnson and I'm 32 years old.",
        "I work as a senior software engineer at TechCorp in San Francisco.",
        "I prefer dark mode in all my development environments.",
        "My primary programming language is Python, but I also use JavaScript.",
        "I drink coffee every morning, specifically a double espresso with oat milk.",
        "I have 8 years of experience in machine learning and data science.",
        "My favorite IDE is VS Code with the Monokai Pro theme.",
        "I graduated from Stanford University with a Computer Science degree in 2014.",
    ]

    for memory_text in memories_to_store:
        semantic_key = await memory_manager.store_memory(
            content=memory_text,
            namespace=user_id,
            metadata={"source": "demo"},
            auto_classify=True
        )
        print(f"Stored: {memory_text[:40]}...")
        print(f"Path: {semantic_key}")

    # 4. Search for memories
    queries = [
        "What is the user's name and age?",
        "Where does the user work and what is their role?",
        "What are the user's IDE and theme preferences?",
        "What does the user drink in the morning?",
    ]

    for query in queries:
        results = await memory_manager.search_memories(
            query=query,
            namespace=user_id,
            limit=5
        )

        print(f"\nQuery: '{query}'")
        if results:
            best_result = results[0]
            print(f"Found: {best_result.content}")
            print(f"Path: {best_result.id}")
        else:
            print("No matches found")

if __name__ == "__main__":
    asyncio.run(main())
```

--------------------------------

### Install and Configure Memoir Plugin

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/openclaw.md

Install the plugin from a remote repository or a local path, and manually set the memory slot.

```bash
openclaw plugins install https://github.com/zhangfengcdt/openclaw-memoir

# …or link a local checkout while developing
openclaw plugins install --link /path/to/openclaw-memoir
```

```bash
openclaw config set plugins.slots.memory memory-memoir
```

--------------------------------

### Install Memoir via pip

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/installation.md

Standard installation method using the pip package manager.

```bash
pip install memoir-ai
```

--------------------------------

### Execute Context Branching Example

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/examples/context_branching.md

Run the provided Python script to initiate the context branching demonstration.

```bash
python examples/context_branching.py
```

--------------------------------

### Execute Debugging Example

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/examples/production_debugging.md

Command to run the production debugging demonstration script.

```bash
python examples/production_debugging.py
```

--------------------------------

### Install uv

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/installation.md

Installs the uv package manager for faster CLI installation and environment isolation.

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

--------------------------------

### Install Hermes Plugin

Source: https://github.com/zhangfengcdt/memoir/blob/main/README.md

Commands to install and configure the Memoir plugin for the Hermes agent.

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

--------------------------------

### Install Memoir Dependencies

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/examples.md

Install the required langchain-openai and memoir-ai packages via pip.

```bash
pip install langchain-openai memoir-ai
```

--------------------------------

### Install OpenClaw Plugin

Source: https://github.com/zhangfengcdt/memoir/blob/main/README.md

Commands to install the Memoir plugin into OpenClaw and configure gateway access.

```bash
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
```

--------------------------------

### Setup LoCoMo Benchmark Environment

Source: https://github.com/zhangfengcdt/memoir/blob/main/benchmarks/locomo/README.md

Clone the benchmark repository and configure the required environment variables.

```bash
git clone https://github.com/xjtuleeyf/Locomo-Plus.git /tmp/locomo-bench/Locomo-Plus
source venv/bin/activate
export OPENAI_API_KEY=...      # or: set -a && . .env.local && set +a
```

--------------------------------

### Smoke-test installation

Source: https://github.com/zhangfengcdt/memoir/blob/main/RELEASING.md

Commands to verify the package installation from TestPyPI in a clean virtual environment.

```bash
python -m venv /tmp/memoir-test
/tmp/memoir-test/bin/pip install \
  -i https://test.pypi.org/simple/ \
  --extra-index-url https://pypi.org/simple/ \
  memoir-ai==X.Y.Z
/tmp/memoir-test/bin/memoir --help
/tmp/memoir-test/bin/python -c "import memoir; print(memoir.__version__)"
```

--------------------------------

### Install the plugin via Hermes CLI

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/hermes.md

Install the plugin directly from the repository using the Hermes command-line interface.

```bash
hermes plugins install zhangfengcdt/memoir/plugins/hermes
```

--------------------------------

### Handle Namespaces

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/theory/search.md

Examples of defining and converting between string and tuple namespace formats.

```python
# String format
namespace = "user:alice"

# Tuple format
namespace = ("user", "alice")

# Conversion
namespace_tuple = tuple(namespace.split(":"))
```

--------------------------------

### Execute Memoir Examples

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/examples.md

Run the provided Python scripts to demonstrate core Memoir capabilities.

```bash
# Fast demos (no LLM required)
python examples/context_branching.py
python examples/memory_state_debugging.py
python examples/reproducible_testing.py
python examples/production_debugging.py
python examples/versioning_control_example.py
python examples/versioning_control.py
```

--------------------------------

### Install and Run Memoir MCP

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/mcp.md

Commands to install the package via pip or run it on-demand using uvx.

```bash
pip install "memoir-ai[mcp]"        # or: pipx install "memoir-ai[mcp]"
```

```bash
MEMOIR_STORE=~/.memoir/mcp memoir-mcp
```

```bash
uvx --from "memoir-ai[mcp]" memoir-mcp
```

--------------------------------

### Run Reproducible Testing Example

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/examples/reproducible_testing.md

Execute the provided Python script to initiate the reproducible testing demonstration.

```bash
python examples/reproducible_testing.py
```

--------------------------------

### Install Optional Dependencies

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/installation.md

Installs specific extras for terminal UI, memory storage, or all features.

```bash
pip install 'memoir-ai[tui]'         # Terminal UI (textual-based)
pip install 'memoir-ai[langmem]'     # ProllyTreeMemoryStoreManager (LangMem drop-in)
pip install 'memoir-ai[all]'         # tui + langmem + litellm
```

--------------------------------

### Launch the Memoir UI

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/ui.md

Commands to start the UI server with various configuration options like semantic search and custom ports.

```bash
# Connect to an existing store
memoir ui /path/to/store

# Enable semantic search in the UI's command input
memoir ui /path/to/store --usellm

# Custom port (default: 8080)
memoir ui /path/to/store --port 8081
```

--------------------------------

### Install Memoir via pipx

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/installation.md

Installs the Memoir CLI using pipx for isolated environment management.

```bash
pipx install memoir-ai
```

--------------------------------

### Install the plugin by manual copy

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/hermes.md

Manually copy the plugin directory from a local checkout to the Hermes plugins folder.

```bash
cp -r plugins/hermes ~/.hermes/plugins/memoir
rm -rf ~/.hermes/plugins/memoir/tests ~/.hermes/plugins/memoir/__pycache__
```

--------------------------------

### Launch the memoir UI

Source: https://github.com/zhangfengcdt/memoir/blob/main/plugins/claude-code/commands/ui.md

Executes the control script to start or reuse the memoir web server.

```bash
bash "${CLAUDE_PLUGIN_ROOT}/scripts/memoir-ui-ctl.sh" start "<STORE_PATH>"
```

--------------------------------

### Profile Summary Generation Examples

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/theory/memento.md

Examples of structured and narrative summary formats for user profile data.

```text
=== USER PROFILE SUMMARY ===

Personal Information:
  Identity:
    - Name: John Smith
    - Age: 25
    - Gender Identity: Male

Professional Profile:
  Current:
    - Company Name: Google
    - Position Title: Software Engineer
```

```text
John Smith is a 25-year-old software engineer currently working at Google.
His professional journey reflects a strong technical background...
```

--------------------------------

### Run Memory State Debugging Example

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/examples/memory_debugging.md

Execute the provided Python script to demonstrate memory state tracking and debugging.

```bash
python examples/memory_state_debugging.py
```

--------------------------------

### Install and Uninstall Plugin Hooks

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/codex.md

Use the provided script to install or uninstall Memoir's bundled hooks into the user hooks file.

```bash
PLUGIN_ROOT=$(find "${CODEX_HOME:-$HOME/.codex}/plugins/cache" \
  -path '*/memoir/memoir/*/.codex-plugin/plugin.json' -print -quit \
  | sed 's#/.codex-plugin/plugin.json$##')
bash "$PLUGIN_ROOT/scripts/install-codex-hooks.sh"
```

```bash
bash "$PLUGIN_ROOT/scripts/install-codex-hooks.sh" uninstall
```

--------------------------------

### Start Memoir MCP Server with HTTP

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/mcp.md

Run the server using Streamable HTTP to allow remote connections from web-based tiers.

```bash
memoir-mcp --http --host 0.0.0.0 --port 8000     # serves at /mcp
```

--------------------------------

### Visualize the directory structure

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/openclaw.md

An example of the directory layout for an agent named 'main' and a second agent named 'research'.

```text
~/.openclaw/memoir/main            # agent "main"        (store)
  ├── profile-main                 # scope=profile       (your facts)
  ├── default                      # shared across scopes
  └── taxonomy                     # built-in
~/.openclaw/memoir/research        # a second agent  →   separate store
```

--------------------------------

### Activate the memoir provider

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/hermes.md

Use the interactive setup command to configure Hermes to use Memoir as the active memory provider.

```bash
hermes memory setup        # interactive picker — choose "memoir"
```

--------------------------------

### Install Claude Code Plugin

Source: https://github.com/zhangfengcdt/memoir/blob/main/README.md

Commands to add and install the Memoir plugin within a Claude Code session.

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

--------------------------------

### Semantic Path Classification Example

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/basic_usage.md

Demonstrates how input text is mapped to a hierarchical semantic path for storage.

```python
# Input: "My name is Sarah Johnson"
# Classification: profile.identity.name.full
# Storage: Aggregated with other name-related memories
```

--------------------------------

### Construct LLM Path Selection Prompt

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/theory/search.md

Generates a prompt containing query and path context to guide the LLM in selecting relevant memory paths.

```python
prompt = f"""Given this search query: "{query}"

Please select the most relevant memory paths from:

- profile.personal.identity (5 memories): John Smith, age 28...
- preferences.technology.programming (3 memories): Loves Python...
- context.conversation.history (10 memories): Discussed AI...

Instructions:

- Select 1-3 paths most relevant to query
- Return ONLY path names, one per line
- If no paths relevant, return "NONE"
"""
```

--------------------------------

### Initialize Development Environment

Source: https://github.com/zhangfengcdt/memoir/blob/main/CONTRIBUTING.md

Commands to clone the repository and set up the virtual environment with dependencies.

```bash
git clone https://github.com/zhangfengcdt/memoir.git
cd memoir
python -m venv venv && source venv/bin/activate
make install-dev
```

--------------------------------

### Execute /memoir commands

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/hermes.md

Examples of slash commands available for inspecting memory state directly within the chat session.

```text
/memoir status
/memoir summarize --depth 3
/memoir recall "travel plans"
/memoir branch
/memoir blame profile.personal.identity
```

--------------------------------

### UI control script output format

Source: https://github.com/zhangfengcdt/memoir/blob/main/plugins/claude-code/commands/ui.md

Example of the single-line JSON document printed to stdout by the UI control script.

```json
{"pid": 12345, "port": 62891, "url": "http://localhost:62891/?store=…&readonly=1&usellm=0", "store": "/abs/path", "started": "...", "log": "/tmp/...", "reused": false}
```

--------------------------------

### Classification Bug Fix Example

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/faq.md

A complete walkthrough of identifying a conflict, creating a fix branch, applying a rule, and validating the result.

```python
# Scenario: Agent incorrectly classifies "I love Python programming"

# 1. Instant time-travel to bug occurrence
memory.branch.checkout("classification_bug_2024_01_15_14_30")

# 2. Claude Code analyzes: "I see the issue!"
bug_analysis = await memory.recall("programming AND personal")
# Finds conflicting memories:
# - "programming is hobby" (personal.hobbies.programming)
# - "Python for work" (profile.professional.skills.python)

# 3. Create fix branch and test solution
memory.branch.create("fix_programming_context")

# 4. Claude Code applies nuanced fix:
await memory.remember(
    "Context: 'I love X programming' indicates professional skill when discussing career",
    namespace="classification.rules.programming_context"
)

# 5. Test fix immediately on same memory state
test_result = agent.classify("I love Python programming")
assert test_result.path == "profile.professional.skills.python"  # Fixed!

# 6. Validate doesn't break other classifications
# 7. Merge fix to main - total time: minutes instead of hours!
```

--------------------------------

### Initialize Memory System Components

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/quickstart.md

Sets up the storage layer, classifier, search engine, and the main memory manager.

```python
from memoir.store.prolly_adapter import ProllyTreeStore
from memoir.classifier.intelligent import IntelligentClassifier
from memoir.search.intelligent import IntelligentSearchEngine
from memoir import ProllyTreeMemoryStoreManager

# Create storage layer
store = ProllyTreeStore(
    path="./memory_store",
    enable_versioning=True
)

# Create intelligent classifier
classifier = IntelligentClassifier(
    llm=llm,
    confidence_thresholds={
        "high": 0.8,
        "medium": 0.5,
        "low": 0.0
    }
)

# Create search engine
search_engine = IntelligentSearchEngine(
    llm=llm,
    store=store
)

# Assemble memory manager
memory_manager = ProllyTreeMemoryStoreManager(
    prolly_store=store,
    classifier=classifier,
    search_engine=search_engine
)
```

--------------------------------

### Current Workflow Pain Points

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/faq.md

An example of the manual, time-consuming process required to debug classification agents without Memoir.

```text
# Current workflow when Claude Code finds a classification bug:
1. Agent classifies "I love Python programming" → wrong path: "personal.hobbies.python"
2. Claude Code identifies the issue
3. Must rebuild ALL memories from scratch (hours of work)
4. Or write complex test cases that may miss edge cases
5. Hard to reproduce exact memory state that caused the bug
6. Risk breaking other correctly working classifications
```

--------------------------------

### Perform direct memory lookups

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/cli.md

Use the get command for fast, exact key retrieval. Missing keys return a found: false status, making it suitable for batch operations.

```bash
# Single lookup
memoir get preferences.coding.style

# Batched lookup in one call
memoir get preferences.coding.style profile.professional.skills

# Scope to a namespace, JSON output
memoir --json get preferences.coding.style -n default
```

--------------------------------

### Initialize ProllyTreeStore with Versioning

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/examples/memory_debugging.md

Sets up the store in a temporary directory with versioning enabled and a defined cache size.

```python
import asyncio
import os
import tempfile
import time
from datetime import datetime
from memoir.store.prolly_adapter import ProllyTreeStore

# Initialize store with versioning
temp_dir = tempfile.mkdtemp()
prolly_path = os.path.join(temp_dir, "memory_store")

prolly_store = ProllyTreeStore(
    path=prolly_path,
    enable_versioning=True,
    cache_size=10000,
)

# Track commit history with timestamps
commit_history = []
```

--------------------------------

### Build Documentation Locally

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/installation.md

Commands for building, serving, and cleaning the documentation site.

```bash
# Build the static site into ./site
make docs

# Start the live-reload dev server
make docs-live

# Clean the build output
make docs-clean
```

--------------------------------

### Initialize Production Memory Store

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/examples/production_debugging.md

Sets up a ProllyTreeStore with versioning enabled and populates it with initial user memories.

```python
import asyncio
import os
import tempfile
import time
from datetime import datetime, timedelta
from memoir.store.prolly_adapter import ProllyTreeStore

# Initialize production memory store
temp_dir = tempfile.mkdtemp()
prolly_path = os.path.join(temp_dir, "memory_store")

prolly_store = ProllyTreeStore(
    path=prolly_path,
    enable_versioning=True,
    cache_size=10000,
)

namespace = "production_user"

# Simulate 6 months of accumulated memories
base_memories = [
    ("User prefers dark theme for all interfaces", "preferences.ui.theme"),
    ("User typically works 9-5 PST timezone", "profile.schedule.work_hours"),
    ("User has accessibility needs for high contrast", "preferences.accessibility.contrast"),
    # ... 144 total memories accumulated over 6 months
]

for content, path in base_memories:
    await prolly_store.store_memory_async(namespace, content, path)

# Create checkpoint at 145 memories
initial_checkpoint = f"checkpoint_{int(time.time())}"
prolly_store.create_time_snapshot(initial_checkpoint)
```

--------------------------------

### Initialize Plugin Environment Variables

Source: https://github.com/zhangfengcdt/memoir/blob/main/plugins/codex/skills/memoir-onboard/SKILL.md

Sets up the PLUGIN_ROOT, STORE_PATH, and MEMOIR variables required for consistent CLI execution.

```bash
PLUGIN_ROOT="${PLUGIN_ROOT:-}"
if [ -z "$PLUGIN_ROOT" ]; then
  PLUGIN_ROOT=$(find "${CODEX_HOME:-$HOME/.codex}/plugins" -path '*/.codex-plugin/plugin.json' -print 2>/dev/null \
    | while IFS= read -r manifest; do
        python3 - "$manifest" <<'PY'
import json, sys
from pathlib import Path
path = Path(sys.argv[1])
try:
    data = json.loads(path.read_text())
except Exception:
    raise SystemExit(0)
if data.get("name") == "memoir":
    print(path.parent.parent)
PY
      done | head -n 1)
fi
STORE_PATH="${MEMOIR_STORE:-$(bash "${PLUGIN_ROOT}/scripts/derive-store-path.sh")}"
MEMOIR="${PLUGIN_ROOT}/scripts/memoir-cli.sh"
```

--------------------------------

### Initialize and Use Memoir Memory Manager

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/index.md

Demonstrates setting up the memory store, classifier, and search engine, followed by storing and searching for memories.

```python
from memoir import ProllyTreeMemoryStoreManager
from memoir.classifier.intelligent import IntelligentClassifier
from memoir.search.intelligent import IntelligentSearchEngine

# Initialize components with dependency injection
store = ProllyTreeStore(path="./memory_store")
classifier = IntelligentClassifier(llm=llm)
search_engine = IntelligentSearchEngine(llm=llm, store=store)

# Create memory manager
memory_manager = ProllyTreeMemoryStoreManager(
    prolly_store=store,
    classifier=classifier,
    search_engine=search_engine
)

# Store memories with automatic classification
await memory_manager.store_memory(
    content="I work as a senior software engineer at TechCorp",
    namespace="user123",
    auto_classify=True
)

# Search with intelligent path selection
results = await memory_manager.search_memories(
    query="What is the user's job?",
    namespace="user123"
)
```

--------------------------------

### Initialize IntelligentSearchEngine

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/architecture.md

Configures the search engine using an LLM and the existing storage instance.

```python
# Intelligent LLM-powered search
from memoir.search.intelligent import IntelligentSearchEngine
search_engine = IntelligentSearchEngine(llm=llm, store=store)
```

--------------------------------

### Initialize Memory Store with Versioning

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/examples/context_branching.md

Sets up the ProllyTreeStore with versioning enabled to support branching features.

```python
import asyncio
import os
import tempfile
import time
from memoir.store.prolly_adapter import ProllyTreeStore

# Create memory store with versioning enabled
temp_dir = tempfile.mkdtemp()
prolly_path = os.path.join(temp_dir, "memory_store")

prolly_store = ProllyTreeStore(
    path=prolly_path,
    enable_versioning=True,  # Enable Git-like features
    cache_size=10000,
)
```

--------------------------------

### Extract Location Events

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/theory/classifier.md

Example of capturing geographic references from content.

```python
Captures geographic references:

- "The support group in Los Angeles"
  → location: "Los Angeles", description: "support group attendance"
```

--------------------------------

### memoir get

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/theory/search.md

Performs a batched exact-path lookup for memory keys.

```APIDOC
## memoir get

### Description
Performs a batched exact-path lookup. If a key is missing, it returns 'found: false' instead of an error.

### Parameters
- **key** (string) - Required - One or more keys to retrieve.
- **-n** (string) - Optional - The namespace to search within.
```

--------------------------------

### Initialize ProllyTreeStore

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/architecture.md

Configures the storage layer with versioning and cache settings.

```python
from memoir.store.prolly_adapter import ProllyTreeStore

store = ProllyTreeStore(
    path="./memory_store",
    enable_versioning=True,
    cache_size=10000
)
```

--------------------------------

### Execute Cold Path Onboarding Extraction and Storage

Source: https://github.com/zhangfengcdt/memoir/blob/main/plugins/codex/skills/memoir-onboard/SKILL.md

Performs project analysis using extractors and stores the resulting data into the Memoir system. Ensure the environment variables like STORE_PATH and MEMOIR are correctly set before execution.

```bash
ROOT="$(pwd)"
WALK_JSON=$(python3 ${PLUGIN_ROOT}/skills/memoir-onboard/extractors.py walk "$ROOT")
SNAPSHOT_HASH=$(python3 -c "import json,sys; print(json.loads(sys.argv[1])['snapshot_hash'])" "$WALK_JSON")
SHAPE_JSON=$(python3 ${PLUGIN_ROOT}/skills/memoir-onboard/extractors.py shape "$ROOT")
SHAPE=$(python3 -c "import json,sys; print(json.loads(sys.argv[1])['shape'])" "$SHAPE_JSON")
OVERVIEW=$(python3 -c "import json,sys; print(json.loads(sys.argv[1])['overview'])" "$SHAPE_JSON")
TREE=$(python3 ${PLUGIN_ROOT}/skills/memoir-onboard/extractors.py tree "$ROOT")
FILE_COUNT=$(python3 -c "import json,sys; print(len(json.loads(sys.argv[1])['files']))" "$WALK_JSON")

# Every memoir write below is wrapped in `( cd "$STORE_PATH" && ... )` —
# without this the prollytree backend reads cwd for git ops and the writes
# silently land in memoir's connected default store. See "CRITICAL: how to
# invoke memoir from this skill" at the top of Branch B.
( cd "$STORE_PATH" && "$MEMOIR" -s "$STORE_PATH" remember "$OVERVIEW" -p summary.overview     -n project:onboard )
( cd "$STORE_PATH" && "$MEMOIR" -s "$STORE_PATH" remember "$SHAPE"    -p structure.shape      -n project:onboard )
( cd "$STORE_PATH" && "$MEMOIR" -s "$STORE_PATH" remember "$TREE"     -p structure.tree       -n project:onboard )

# For each entry in WALK_JSON.files, drive the loop with bash. Sanitize each
# relative path with `/` → `_` and `.` → `_` (matches extractors.sanitize_path).
python3 -c "import json,sys; print('\n'.join(e['path'] for e in json.loads(sys.argv[1])['files']))" "$WALK_JSON" \
  | while IFS= read -r rel; do
      sanitized=$(printf '%s' "$rel" | tr '/.' '__')
      meta_blob=$(python3 -c "import json,sys; files=json.loads(sys.argv[1])['files']; e=next(f for f in files if f['path']==sys.argv[2]); print('\n'.join(f'{k}={v}' for k,v in sorted(e.items())))" "$WALK_JSON" "$rel")
      summary_blob=$(python3 ${PLUGIN_ROOT}/skills/memoir-onboard/extractors.py extract "$ROOT/$rel")
      ( cd "$STORE_PATH" && "$MEMOIR" -s "$STORE_PATH" remember "$meta_blob"    -p "files.${sanitized}.meta"    -n project:onboard ) >/dev/null
      ( cd "$STORE_PATH" && "$MEMOIR" -s "$STORE_PATH" remember "$summary_blob" -p "files.${sanitized}.summary" -n project:onboard ) >/dev/null
    done
```

--------------------------------

### Generate Hierarchical Summary

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/theory/memento.md

Example output format for the hierarchical timeline summary.

```text
=== USER TIMELINE ===

2023:
  December:
    01: Promoted to senior engineer

  June:
    20: Attended AI conference | Met Dr. Smith as mentor

  March:
    15: Started new job at Google
    14: Last day at previous company
```

--------------------------------

### Extract Timeline Events

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/theory/classifier.md

Example of temporal event extraction with date calculation.

```python
Extracts temporal events with date calculation:

- "Yesterday was my first day" (session: 2023-03-15)
  → date: "20230314", description: "first day at new job"
```

--------------------------------

### Initialize LLM Client

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/quickstart.md

Configures the LLM client using environment variables and the get_llm helper.

```python
import os
from memoir.llm import get_llm

os.environ["ANTHROPIC_API_KEY"] = "your-api-key-here"

# memoir.llm.get_llm() routes through litellm (a default dep as of v0.1.7).
llm = get_llm(model="claude-haiku-4-5", temperature=0)
```

--------------------------------

### get <path>

Source: https://github.com/zhangfengcdt/memoir/blob/main/plugins/claude-code/skills/memory-recall/SKILL.md

Retrieves specific memory items based on provided paths.

```APIDOC
## CLI Command: get

### Description
Queries memory items by path. Batching multiple paths is supported.

### Usage
`$MEMOIR --json -s $STORE get <path> [<path>...] -n default`

### Response
Returns an array of items, each containing: `key`, `namespace`, `full_key`, `found`, and `value`.
```

--------------------------------

### GET /api/recall

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/theory/search.md

Retrieves memory data based on a query and a specified retrieval mode.

```APIDOC
## GET /api/recall

### Description
Retrieves memory information using a specified retrieval mode. The mode parameter is whitelisted to 'single' or 'tiered'.

### Method
GET

### Endpoint
/api/recall

### Parameters
#### Query Parameters
- **path** (string) - Required - The path context for the recall.
- **query** (string) - Required - The search query string.
- **mode** (string) - Required - The retrieval mode, either 'single' or 'tiered'.
```

--------------------------------

### summarize

Source: https://github.com/zhangfengcdt/memoir/blob/main/plugins/claude-code/skills/memory-recall/SKILL.md

Provides a summary of the memory store, typically used as a starting point for queries.

```APIDOC
## CLI Command: summarize

### Description
Performs a single-shot summary of the memory store. It returns a taxonomy listing and total memory count.

### Usage
`$MEMOIR --json -s $STORE summarize --depth 3 -n default`

### Response
Returns `total_memories` and `prefix_counts` (a listing of full L1.L2.L3 key paths).
```

--------------------------------

### Execute Cold Path Onboarding Data Extraction and Storage

Source: https://github.com/zhangfengcdt/memoir/blob/main/plugins/claude-code/skills/memoir-onboard/SKILL.md

Performs project directory analysis and stores the resulting metadata and summaries into the Memoir store. Ensure the environment variables CLAUDE_PLUGIN_ROOT, STORE_PATH, and MEMOIR are correctly set before execution.

```bash
ROOT="$(pwd)"
WALK_JSON=$(python3 ${CLAUDE_PLUGIN_ROOT}/skills/memoir-onboard/extractors.py walk "$ROOT")
SNAPSHOT_HASH=$(python3 -c "import json,sys; print(json.loads(sys.argv[1])['snapshot_hash'])" "$WALK_JSON")
SHAPE_JSON=$(python3 ${CLAUDE_PLUGIN_ROOT}/skills/memoir-onboard/extractors.py shape "$ROOT")
SHAPE=$(python3 -c "import json,sys; print(json.loads(sys.argv[1])['shape'])" "$SHAPE_JSON")
OVERVIEW=$(python3 -c "import json,sys; print(json.loads(sys.argv[1])['overview'])" "$SHAPE_JSON")
TREE=$(python3 ${CLAUDE_PLUGIN_ROOT}/skills/memoir-onboard/extractors.py tree "$ROOT")
FILE_COUNT=$(python3 -c "import json,sys; print(len(json.loads(sys.argv[1])['files']))" "$WALK_JSON")

# Every memoir write below is wrapped in `( cd "$STORE_PATH" && ... )` —
# without this the prollytree backend reads cwd for git ops and the writes
# silently land in memoir's connected default store. See "CRITICAL: how to
# invoke memoir from this skill" at the top of Branch B.
( cd "$STORE_PATH" && "$MEMOIR" -s "$STORE_PATH" remember "$OVERVIEW" -p summary.overview     -n project:onboard )
( cd "$STORE_PATH" && "$MEMOIR" -s "$STORE_PATH" remember "$SHAPE"    -p structure.shape      -n project:onboard )
( cd "$STORE_PATH" && "$MEMOIR" -s "$STORE_PATH" remember "$TREE"     -p structure.tree       -n project:onboard )

# For each entry in WALK_JSON.files, drive the loop with bash. Sanitize each
# relative path with `/` → `_` and `.` → `_` (matches extractors.sanitize_path).
python3 -c "import json,sys; print('\n'.join(e['path'] for e in json.loads(sys.argv[1])['files']))" "$WALK_JSON" \
  | while IFS= read -r rel; do
      sanitized=$(printf '%s' "$rel" | tr '/.' '__')
      meta_blob=$(python3 -c "import json,sys; files=json.loads(sys.argv[1])['files']; e=next(f for f in files if f['path']==sys.argv[2]); print('\n'.join(f'{k}={v}' for k,v in sorted(e.items())))" "$WALK_JSON" "$rel")
      summary_blob=$(python3 ${CLAUDE_PLUGIN_ROOT}/skills/memoir-onboard/extractors.py extract "$ROOT/$rel")
      ( cd "$STORE_PATH" && "$MEMOIR" -s "$STORE_PATH" remember "$meta_blob"    -p "files.${sanitized}.meta"    -n project:onboard ) >/dev/null
      ( cd "$STORE_PATH" && "$MEMOIR" -s "$STORE_PATH" remember "$summary_blob" -p "files.${sanitized}.summary" -n project:onboard ) >/dev/null
    done
```

--------------------------------

### Run the container interactively

Source: https://github.com/zhangfengcdt/memoir/blob/main/plugins/docker-tests/hermes/README.md

Starts the container in interactive mode for manual debugging and exploration.

```bash
docker run --rm -it -e ANTHROPIC_API_KEY="$ANTHROPIC_API_KEY" hermes-memoir-test bash
```

--------------------------------

### Extract Profile Updates

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/theory/classifier.md

Examples of mapping definitive personal facts to specific profile paths.

```python
Detects definitive facts that replace previous information:

- "I'm 25 years old" → profile.personal.identity.age.current
- "I work at Google" → profile.professional.current.company.name
```

--------------------------------

### Initialize Store Path and CLI Wrapper

Source: https://github.com/zhangfengcdt/memoir/blob/main/plugins/claude-code/skills/memory-recall/SKILL.md

Sets the storage path and defines the CLI wrapper command to ensure consistent execution across different environments.

```bash
STORE="${MEMOIR_STORE:-$(bash "$CLAUDE_PLUGIN_ROOT/scripts/derive-store-path.sh")}"
MEMOIR="$CLAUDE_PLUGIN_ROOT/scripts/memoir-cli.sh"
```

--------------------------------

### Manage Claude Code Plugin

Source: https://github.com/zhangfengcdt/memoir/blob/main/CONTRIBUTING.md

Commands for installing, updating, and uninstalling the local Claude Code plugin.

```text
/plugin marketplace add /absolute/path/to/memoir
/plugin install memoir@memoir
```

```text
/plugin marketplace update memoir   # re-read the local marketplace
```

```text
/plugin uninstall memoir@memoir
/plugin marketplace remove memoir
```

--------------------------------

### Initialize Memoir Environment Variables

Source: https://github.com/zhangfengcdt/memoir/blob/main/plugins/codex/skills/memory-recall/SKILL.md

Configures the plugin root, store path, and CLI wrapper path. This preamble must be executed before any memoir commands to ensure correct environment resolution.

```bash
PLUGIN_ROOT="${PLUGIN_ROOT:-}"
if [ -z "$PLUGIN_ROOT" ]; then
  PLUGIN_ROOT=$(find "${CODEX_HOME:-$HOME/.codex}/plugins" -path '*/.codex-plugin/plugin.json' -print 2>/dev/null \
    | while IFS= read -r manifest; do
        python3 - "$manifest" <<'PY'
import json, sys
from pathlib import Path
path = Path(sys.argv[1])
try:
    data = json.loads(path.read_text())
except Exception:
    raise SystemExit(0)
if data.get("name") == "memoir":
    print(path.parent.parent)
PY
      done | head -n 1)
fi
STORE="${MEMOIR_STORE:-$(bash "$PLUGIN_ROOT/scripts/derive-store-path.sh")}"
MEMOIR="$PLUGIN_ROOT/scripts/memoir-cli.sh"
```

--------------------------------

### Initialize ProllyTreeMemoryStoreManager

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/architecture.md

Orchestrates the system components using dependency injection for the store, classifier, and search engine.

```python
from memoir.core.memory import ProllyTreeMemoryStoreManager

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

--------------------------------

### Configure External Onboarding Tools

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/claude_code.md

Define external tools for processing specific file types by creating a YAML configuration file in the user or project directory.

```yaml
# ~/.memoir/onboard-tools.yaml          (user-global)
# <project>/.memoir/onboard-tools.yaml  (project-local; merged after global)
audio:
  - name: whisper
    command: "whisper {path} --output-format json"
    timeout_s: 60
image:
  - name: claude-vision
    command: "vision-caption.sh {path}"
    timeout_s: 30
```

--------------------------------

### Validate and pack Claude Desktop extension

Source: https://github.com/zhangfengcdt/memoir/blob/main/packaging/mcp/README.md

Commands to validate the manifest and generate the .mcpb file for one-click installation.

```bash
cd packaging/mcp
npx @anthropic-ai/mcpb validate manifest.json
npx @anthropic-ai/mcpb pack .            # → memoir.mcpb (double-click to install)
```

--------------------------------

### Build Main Timeline

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/examples/context_branching.md

Populates the main memory timeline and creates a snapshot for future reference.

```python
namespace = "user123"

# Store user's actual career path
await prolly_store.store_memory_async(
    namespace,
    "I graduated with a computer science degree in 2020",
    "profile.education.degree"
)

await prolly_store.store_memory_async(
    namespace,
    "I chose to work at a startup doing backend development",
    "profile.career.current.startup"
)

await prolly_store.store_memory_async(
    namespace,
    "I've been promoted to senior engineer after 3 years",
    "profile.career.progression.senior"
)

# Create snapshot of main timeline
main_snapshot = f"main_timeline_{int(time.time())}"
prolly_store.create_time_snapshot(main_snapshot)
print(f"Main timeline snapshot: {main_snapshot}")
```

--------------------------------

### Fetch specific memory keys

Source: https://github.com/zhangfengcdt/memoir/blob/main/plugins/claude-code/skills/memory-recall/SKILL.md

Retrieve up to 7 exact keys using a single batched get command.

```bash
"$MEMOIR" --json -s "$STORE" get <path1> <path2> ... -n default
```

--------------------------------

### Manage and search memoir files

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/watch.md

Commands for initializing a store, watching directories or files, listing registered paths, scanning for changes, and performing semantic searches.

```bash
# Create a store (or reuse an existing one)
memoir new ~/.memoir/notes
export MEMOIR_STORE=~/.memoir/notes

# Point watch at a folder of docs
memoir watch add ~/Documents/notes
# → walks the folder, parses each file, classifies, indexes for search

# Or a single file
memoir watch add ~/papers/transformer.pdf -n research

# See what's registered
memoir watch list

# Re-scan (idempotent — only changed files are re-indexed)
memoir watch scan ~/Documents/notes

# Search the indexed content
memoir search "transformer attention mechanism"
memoir search "async patterns" -n research -k 10 --json

# Tear down
memoir watch remove ~/Documents/notes --purge
```

--------------------------------

### Visualize Storage Flow

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/architecture.md

Diagram illustrating the pipeline from memory input to ProllyTree storage.

```mermaid
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"]
```

--------------------------------

### Manual Memory Capture with Explicit Policy

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/claude_code.md

Example of bypassing interactive prompts by providing an explicit merge policy via the CLI.

```bash
/memoir:remember "…" -p <path> --merge-policy replace
```

--------------------------------

### Basic CLI operations

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/quickstart.md

Commands for creating a store, remembering information, and recalling memories.

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

--------------------------------

### Creating Baseline State

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/examples/reproducible_testing.md

Initializes the ProllyTreeStore with versioning enabled and populates it with shared baseline memories before creating a snapshot.

```python
import asyncio
import os
import tempfile
import time
from memoir.store.prolly_adapter import ProllyTreeStore

# Initialize store with versioning
temp_dir = tempfile.mkdtemp()
prolly_path = os.path.join(temp_dir, "memory_store")

prolly_store = ProllyTreeStore(
    path=prolly_path,
    enable_versioning=True,
    cache_size=10000,
)

namespace = "test_user"

# Create shared baseline memories
await prolly_store.store_memory_async(
    namespace,
    "User is a software engineer with 5 years experience",
    "profile.professional.occupation.role"
)

await prolly_store.store_memory_async(
    namespace,
    "User knows Python and JavaScript proficiently",
    "profile.skills.programming.languages"
)

# Create baseline snapshot
baseline_snapshot = f"baseline_{int(time.time())}"
prolly_store.create_time_snapshot(baseline_snapshot)
```

--------------------------------

### Create a release branch

Source: https://github.com/zhangfengcdt/memoir/blob/main/RELEASING.md

Commands to initialize a new release branch from the main branch.

```bash
git checkout main
git pull
git checkout -b release/X.Y.Z
```

--------------------------------

### Determine Onboarding Procedure

Source: https://github.com/zhangfengcdt/memoir/blob/main/plugins/claude-code/skills/memoir-onboard/SKILL.md

Detects whether to use codebase:onboard or project:onboard based on the current directory's git status.

```bash
bash -c 'source "${CLAUDE_PLUGIN_ROOT}/hooks/common.sh" >/dev/null 2>&1; in_git_repo && echo codebase:onboard || echo project:onboard'
```

--------------------------------

### Configure MCP for Zed

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/mcp.md

Zed uses a context_servers key with a nested command structure in settings.json.

```json
{
  "context_servers": {
    "memoir": {
      "command": { "path": "uvx", "args": ["--from", "memoir-ai[mcp]", "memoir-mcp"] }
    }
  }
}
```

--------------------------------

### Configure search and monitor metrics

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/faq.md

Implement intelligent search engines and retrieve performance metrics for monitoring.

```python
# Use intelligent search for memory retrieval
from memoir.search.intelligent import IntelligentSearchEngine
search_engine = IntelligentSearchEngine(llm=llm, store=store)
```

```python
# Get performance metrics from the low-level manager
# (requires the `langmem` extra: pip install memoir-ai[langmem])
from memoir import ProllyTreeMemoryStoreManager

stats = memory_manager.get_performance_metrics()
print(f"Average search time: {stats['avg_search_ms']}ms")
print(f"Classification time: {stats['avg_classification_ms']}ms")
```

--------------------------------

### Add Marketplace via CLI

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/codex.md

Register the Memoir marketplace using the Codex CLI.

```bash
codex plugin marketplace add zhangfengcdt/memoir
```

```bash
codex plugin marketplace add /absolute/path/to/memoir
```

--------------------------------

### Create Alternative Branch

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/examples/context_branching.md

Initializes and switches to a new branch for hypothetical memory storage.

```python
# Create branch for hypothetical exploration
alternative_branch = f"alternative_path_{int(time.time())}"
prolly_store.tree.create_branch(alternative_branch)
prolly_store.tree.checkout(alternative_branch)

print(f"Created branch: {alternative_branch}")
print("Now exploring: What if I had joined Google instead?")
```

--------------------------------

### Configure MCP for VS Code (GitHub Copilot)

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/mcp.md

VS Code requires a top-level servers key in the .vscode/mcp.json file.

```json
{
  "servers": {
    "memoir": {
      "type": "stdio",
      "command": "uvx",
      "args": ["--from", "memoir-ai[mcp]", "memoir-mcp"],
      "env": { "MEMOIR_STORE": "${userHome}/.memoir/mcp" }
    }
  }
}
```

--------------------------------

### Run Quality Assurance Tasks

Source: https://github.com/zhangfengcdt/memoir/blob/main/CONTRIBUTING.md

Standard commands to format, lint, and test the codebase before committing.

```bash
make format    # black + isort + ruff --fix
make lint      # must pass
make test      # must pass
make ci        # full pipeline (lint, test, security, docs build)
```

--------------------------------

### Inspect Plugin Store

Source: https://github.com/zhangfengcdt/memoir/blob/main/docs/codex.md

Commands to resolve the store path and inspect plugin data using the CLI.

```bash
PLUGIN_ROOT=/path/to/memoir/plugins/codex
STORE=$("$PLUGIN_ROOT/scripts/derive-store-path.sh" /path/to/project)
"$PLUGIN_ROOT/scripts/memoir-cli.sh" -s "$STORE" status
"$PLUGIN_ROOT/scripts/memoir-cli.sh" --json -s "$STORE" summarize --keys "*" -n default
```

--------------------------------

### Initialize Memoir Environment Variables

Source: https://github.com/zhangfengcdt/memoir/blob/main/plugins/claude-code/skills/memoir-onboard/SKILL.md

Sets the required STORE_PATH and MEMOIR wrapper variables for consistent CLI invocation.

```bash
STORE_PATH="${MEMOIR_STORE:-$(bash "${CLAUDE_PLUGIN_ROOT}/scripts/derive-store-path.sh")}"
MEMOIR="${CLAUDE_PLUGIN_ROOT}/scripts/memoir-cli.sh"
```