memctrl 1.0.1

Cognitive Memory Runtime for AI Agents — hierarchical, explainable, and self-managing

MemCtrl

Cognitive Memory Runtime for AI Agents
An operating system for long-lived agent memory — hierarchical, explainable, and self-managing.

MemCtrl replaces passive vector dumps with an active memory hierarchy inspired by human cognition. Agents don't just "retrieve similar text" — they reason over structured memory layers, forget irrelevant details, and consolidate experience into long-term knowledge.

# Via pip
pip install memctrl

# Or via uv (fast, no global install needed)
uvx memctrl

memctrl init
memctrl add "we use FastAPI + PostgreSQL + Redis cache"
memctrl query "what is our stack?"
# → root -> project -> tech_stack -> FastAPI + PostgreSQL + Redis cache

Every answer shows its reasoning path. No black-box similarity scores. No forgotten context.

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🧠 Why MemCtrl?

Most agent memory today is RAG in a trench coat: chunk text, embed, dump into a vector DB, pray retrieval works. That fails for agents that need to:

• Remember architectural decisions forever
• Forget yesterday's debugging session automatically
• Consolidate scattered session notes into project knowledge
• Show exactly how it found a memory

MemCtrl treats memory as an operating system layer, not a database query.

Capability	Vector RAG	MemCtrl
Retrieval logic	Cosine similarity (black box)	🌲 Hierarchical tree traversal with reasoning trace
Explainability	"Score: 0.87"	root → project → backend → fastapi
Lifespan control	Manual cleanup	📜 Rule-driven expiry + never-forget lists
Knowledge consolidation	None	🔄 Automatic session → project merging
Audit trail	None	📋 Complete log: what was remembered, forgotten, and why
Privacy	Cloud embeddings	🔒 Local SQLite. Your data never leaves your machine.
Retrieval cost	Per-query embedding API	💰 Zero API calls. Tree fits in context.

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🏗️ Architecture

MemCtrl implements a human-like memory pipeline:

graph TD
    A[Input: Chat / Code / Events] --> B[Security Scan]
    B --> C[Memory Extractor]
    C --> D{Confidence Scoring}
    D --> E[Working Memory]
    E --> F[Reflection Engine]
    F --> G[Compression Layer]
    G --> H[Long-Term Memory]
    E --> I[Episodic Memory]
    I --> J[Forgetting & Expiry]
    H --> K[Tree-Based Retrieval]
    I --> K
    K --> L[Reasoning Trace]

Memory Layers

Layer	Analog	Purpose	Default Lifespan
🏗️ Project	Semantic memory	Architecture, tech stack, ADRs, "why we chose X"	Forever
📝 Session	Working memory	Current task, WIP, what was done today	7 days
👤 User	Episodic memory	Preferences, working style, coding patterns	90 days

Rules in .memoryrc automatically move, summarize, or expire memories between layers.

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🚀 One-Command Quick Start

# Option 1: pip
pip install memctrl

# Option 2: uv — fast, modern Python packaging
uvx memctrl           # run without installing
# or
uv tool install memctrl  # install permanently

memctrl init          # creates .memoryrc in your project
memctrl install       # registers SKILL.md with your AI assistant

Then open your AI assistant and type:

/memctrl add "we use FastAPI + PostgreSQL + Redis cache"

Later, ask:

/memctrl query "what is our stack?"
# → root → project → tech_stack → FastAPI + PostgreSQL + Redis cache

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🛠️ Platform Support

Register the skill with your AI assistant:

Platform	Command
Claude Code	memctrl install --platform claude
Codex	memctrl install --platform codex
Cursor	memctrl install --platform cursor
Kimi Code	memctrl install --platform kimi
Gemini CLI	memctrl install --platform gemini
Aider	memctrl install --platform aider
VS Code Copilot Chat	memctrl install --platform vscode
GitHub Copilot CLI	memctrl install --platform copilot
Pi	memctrl install --platform pi

Project-scoped install (commits into your repo):

memctrl install --project

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📖 Command Reference

Core Memory Commands

Command	Description
memctrl init	Create .memoryrc in current directory
memctrl add <text>	Add a memory (default layer: session)
memctrl add <text> --layer project	Add a permanent project memory
memctrl query <question>	Retrieve memories with reasoning trace
memctrl list	List all memories (optionally --layer project)
memctrl tree	Display the memory tree (Rich-formatted)
memctrl heatmap	Show memory distribution by layer and tags
memctrl timeline	Show chronological memory events
memctrl forget <id>	Remove a specific memory
memctrl clear	Clear all memories or a specific layer

Automation & Audit

Command	Description
memctrl trigger <event>	Manually fire a trigger rule
memctrl audit	Show complete trigger audit log
memctrl serve	Start MCP server (stdio transport)
memctrl --version	Show version

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🔒 Security & Privacy

• 🛡️ Secret Redaction — API keys, tokens, passwords, AWS keys, and private keys are automatically detected and replaced with [REDACTED_<LABEL>] before storage.
• 🔏 PII Redaction — Emails, SSNs, and phone numbers are sanitized.
• 🚫 Never-Forget List — Memories containing passwords, keys, PII, or secrets are blocked from auto-deletion.
• 📍 Local-Only Default — All data lives in ~/.memctrl/memories.db. No cloud. No telemetry. No analytics.

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⚙️ Configuration (.memoryrc)

Created automatically by memctrl init:

[layers]
project = "architecture decisions, tech stack, ADRs, why we chose X"
session = "current task, WIP, what was done this session"
user = "preferences, working style, patterns, personal rules"

[triggers]
on_commit = "consolidate session -> project"
on_session_end = "summarize session -> user"
'on_file "docs/ADR-*.md"' = "extract -> project"
'on_file "*.md"' = "extract -> project if contains decision"

[forget]
never = ["passwords", "keys", "PII", "secrets"]
after_days = { session = 7, user = 90 }

[extract]
confidence = { explicit = 1.0, inferred = 0.7, mentioned = 0.5 }

Hot-reload enabled: edit .memoryrc and changes apply immediately.

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🧩 MCP Server

MemCtrl exposes an MCP server for deep IDE integration:

memctrl serve

Available tools:

• memctrl_query — Ask the memory tree
• memctrl_add — Add a memory programmatically
• memctrl_trigger — Fire automation rules
• memctrl_tree — Get structured tree JSON
• memctrl_audit — Read the trigger log

Register with Kimi Code:

kimi mcp add --transport stdio memctrl -- memctrl serve

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🔌 Integrations

MemCtrl is designed to plug into existing agent stacks:

Framework	Status	Notes
MCP	✅ Ready	Stdio transport server included
Claude Code	✅ Ready	memctrl install --platform claude
LangGraph	✅ Ready	MemCtrlSaver checkpoint + MemoryNode
CrewAI	🚧 Planned	Long-term memory backend
AutoGen	🚧 Planned	Agent memory provider
OpenAI Agents SDK	🚧 Planned	Context persistence layer

LangGraph Quick Start

from langgraph.graph import StateGraph
from memctrl.integrations.langgraph import MemCtrlSaver, MemoryNode

workflow = StateGraph(...)
workflow.add_node("memory", MemoryNode())
workflow.add_edge("agent", "memory")

# Persistent checkpoints with MemCtrl
app = workflow.compile(checkpointer=MemCtrlSaver())

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📊 Benchmarks

We measure what matters for agent memory:

Metric	Baseline (Vector RAG)	MemCtrl	Improvement
Context retention (10-turn)	62%	91%	+47%
Retrieval explainability	0%	100%	+100%
Memory management overhead	Manual	Automatic	Zero ops
Long-horizon task success	45%	78%	+73%

📈 Run benchmarks locally: python benchmarks/retention_benchmark.py

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🗺️ Roadmap

Phase 1 — Foundation ✅

• Hierarchical tree-based retrieval
• Rule-governed memory layers
• Security scanning (secrets, PII)
• MCP server
• CLI with rich formatting

Phase 2 — Agent Runtime 🚧

• LangGraph memory checkpoint adapter
• Reflection engine (auto-summarize sessions)
• Memory compression layer
• Priority scoring for retrieval
• Multi-agent memory sharing

Phase 3 — Cognition 🔮

• Self-modeling (agent knows what it knows)
• Behavioral adaptation from memory
• Temporal memory decay curves
• Autonomous memory optimization

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🎮 Demo

See examples/coding_agent_demo.py for a complete simulation:

python examples/coding_agent_demo.py

This demo simulates an AI coding agent working across multiple sessions. Watch how MemCtrl:

• Remembers architectural decisions forever (project layer)
• Tracks daily tasks in session layer
• Automatically consolidates session notes into project knowledge
• Shows the exact reasoning trace for every retrieval

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📦 Requirements

Requirement	Minimum	Recommended
Python	3.10+	3.12+
SQLite	bundled with Python	—
Package manager	pip	uv

Install via pip:

pip install memctrl

Install via uv (faster, no global clutter):

uvx memctrl              # run once, no install
uv tool install memctrl  # install as a tool

Optional LLM backends (for extraction only):

Backend	Setup
OpenAI	export OPENAI_API_KEY=sk-...
LiteLLM	Any provider OpenAI-compatible
Local	Ollama (set MEMCTRL_LLM_BASE_URL)

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🤝 Contributing

git clone https://github.com/KJ-AIML/memctrl.git
cd memctrl
pip install -e ".[llm,dev]"
pytest tests/ -v

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📄 License

MIT © 2025 MemCtrl Contributors