State Memory MCP MCP Server
io.github.putervision/state-memory-mcp
Deterministic, persistent SQLite graph for AI agents to track workflow state, decisions, and blockers with zero infrastructure.
What is the State Memory MCP MCP server?
State Memory MCP is a Model Context Protocol server that provides AI coding assistants with a structured, persistent SQLite graph for tracking workflow state—tasks, decisions, artifacts, plans, and blockers with their semantic relationships. It eliminates multi-step file scanning, reduces latency by 67–74%, and keeps all state local and private within your workspace.
State Memory MCP gives AI agents like Claude and Cursor a deterministic memory system for complex workflows. Instead of re-scanning files or losing context, agents can query a local SQLite database to retrieve unblocked tasks, trace dependencies, verify specs, and log decisions in milliseconds. It's designed for multi-turn coding sessions, spec-driven development, and parallel multi-agent coordination.
How to install State Memory MCP
Copy-paste configuration for popular MCP clients.
Tools & capabilities
Tools this server exposes to the agent.
manage_nodes— Node CRUD, FTS5/TF-IDF vector search, atomic batch mutations, observation notes, and thresholded fast decision logging for tasks, decisions, blockers, and other node types.manage_edges— Typed DAG links and multimodal visual state linking between nodes with cycle detection.manage_tasks— Topological dependency queue, blocker detection, task completion with artifacts, and auto-pruning.manage_sessions— Agent attribution, turn tracking, and context bootstrap for multi-agent coordination.manage_specs— PRD/RFC parsing, requirement-to-task decomposition, live acceptance criteria verification, and compliance scoring.get_analytics— Velocity metrics, burndown charts, token ROI, cognitive load, and critical path analysis.get_events— SHA-256 tamper-evident append-only event ledger for audit trails.run_diagnostics— DAG validation, health checks, and AST reference integrity verification.manage_snapshots— Checkpoints and time-travel undo for state rollback.manage_database— Backups, checksum audits, and VCS branch merge operations.manage_data— Bulk import/export, ML trajectories with interleaved fast decision events.query_graph— Subgraph queries, dependency tracing, raw SQL, and sub-1KB compact task slices.use_blackboard— Multi-agent asynchronous topic board for parallel subagents to publish decisions and blockers.
Use cases
- Track multi-turn coding workflows with persistent task queues, blockers, and decision history without re-scanning files.
- Decompose specifications (PRDs, RFCs) into tasks and verify compliance in real time as an agent works.
- Enable parallel multi-agent coordination via a shared blackboard for safe, asynchronous task and blocker updates.
- Analyze agent velocity, cognitive load, token ROI, and critical path to optimize workflow efficiency.
- Time-travel undo and snapshot checkpoints to roll back state or branch workflows without losing history.
State Memory MCP MCP server FAQ
State Memory MCP is a zero-infrastructure MCP server that gives AI coding assistants a deterministic, persistent SQLite graph for tracking workflow state—tasks, decisions, blockers, and their relationships. It eliminates context loss and file re-scanning, reducing latency by 67–74%.
Yes. State Memory MCP is released under the MIT License and is free to use. All state stays local in your workspace; no telemetry or data transmission occurs.
Install globally via npm: `npm install -g @putervision/state-memory-mcp`, then run `state-memory-mcp init` in your project directory. This creates `.state-memory-mcp/`, updates `.gitignore`, and auto-configures your IDE (Cursor, VS Code, Claude, Windsurf, etc.). Restart your IDE to activate.
No. State Memory MCP is 100% local and private. It uses a local SQLite database in `.state-memory-mcp/` and requires no external APIs, authentication, or cloud services.
Yes. The `use_blackboard` tool enables multi-agent coordination via an asynchronous topic board, allowing parallel subagents to safely publish decisions, tasks, and blocker updates to a shared context store.
Node.js >= 18.18.0 is required. The server is compatible with Cursor, Claude Code, Gemini, Copilot, VS Code, Windsurf, and other AI coding assistants that support MCP.
README (reference)
Source of truth, from the repository.
@putervision/state-memory-mcp
@putervision/state-memory-mcp is a zero-infrastructure, deterministic Model Context Protocol (MCP) server that provides AI coding assistants (such as Cursor, Claude Code, Gemini, or Copilot) with a structured, persistent SQLite graph for tracking workflow state—tasks, decisions, artifacts, plans, blockers, and their semantic relationships.
🌐 Official Documentation & Website: statememorymcp.com
⚡ Quick Start & Installation
Prerequisites: Node.js >= 18.18.0
# 1. Install globally
npm install -g @putervision/state-memory-mcp
# 2. Navigate to your project directory
cd your-project
# 3. Initialize state-memory-mcp
# Creates .state-memory-mcp/, updates .gitignore, registers project,
# and scaffolds IDE instructions and MCP configs for Cursor, Claude, VS Code, Windsurf, etc.
state-memory-mcp init
# Done! Restart your IDE or Agent Manager to activate.
Alternative Options
# Run directly via binary (after global install)
state-memory-mcp run
# Re-initialize across all registered workspace projects
state-memory-mcp init-global
🌟 Key Highlights
- 🧠 Deterministic State Memory & Compact TaskSlices: Zero LLM in the loop for memory operations; fast, deterministic SQLite graph traversals, and sub-1KB
TaskSliceextraction for System 1 fast path evaluation. - ⚡ 13 Production-Grade Consolidated MCP Tools: Full CRUD, relationship linking, DAG cycle checks, FTS5 search, TF-IDF RAG, time-travel history rollback, Spec-Driven Development, and auto-healing validation.
- 📉 Efficient Context Management & Decision Thresholding: Offloads context to a local SQLite database, filters sub-0.70 routine decisions to append-only event logs to prevent graph bloat, and preserves high-significance turns.
- 🚀 67%–74% Latency Reduction: Eliminates multi-step file scanning loops; agents retrieve unblocked tasks and blockers in milliseconds.
- 🤝 Multi-Agent Blackboard: Shared Context Store allowing parallel subagents to publish decisions, tasks, and blocker updates safely.
- 🎨 Interactive 3D Visualizer: Browser-based dark-mode 3D WebGL force-directed graph visualizer (
state-memory-mcp view). - 🔗 Dual-MCP Synergy: Pair with
@putervision/vision-memory-mcpfor visual state caching, perceptual hashing, and cryptographic multimodal evidence packs. - 🛡️ 100% Local & Private: Local-first architecture; all state stays inside
.state-memory-mcp/in your workspace.
🛠️ MCP Tool Suite
@putervision/state-memory-mcp provides 13 production-grade consolidated MCP tools organized across 5 core workflow domains:
- Graph & Relationships:
manage_nodes(node CRUD, FTS5/TF-IDF vector search, atomic batch mutations, observation notes, thresholded fast decision logging),manage_edges(typed DAG links, multimodal visual state linking). - Task Execution & Work Queue:
manage_tasks(topological dependency queue, blocker detection, task completion with artifacts, auto-prune),manage_sessions(agent attribution, turn tracking, context bootstrap). - Spec-Driven Development (SDD):
manage_specs(PRD/RFC parsing, requirement-to-task decomposition, live acceptance criteria verification, compliance scoring). - Analytics, Audit & Diagnostics:
get_analytics(velocity, burndown, token ROI, cognitive load, critical path),get_events(SHA-256 tamper-evident event ledger),run_diagnostics(DAG validation, health checks, AST reference integrity). - Data, Snapshots & Multi-Agent:
manage_snapshots(checkpoints, time-travel undo),manage_database(backups, checksum audits, VCS branch merge),manage_data(bulk import/export, ML trajectories with interleaved fast decision events),query_graph(subgraphs, dependency tracing, raw SQL, sub-1KBcompact_slice),use_blackboard(multi-agent asynchronous topic board).
👉 For complete parameter specifications, return schemas, and example payloads, see the Tools Reference Guide and Formal API Reference.
🚀 Architecture & State Graph Lifecycle
AI Agent Prompt / Task
│
▼
┌─────────────────────────────────┐
│ Agent Session Attribution │ ──▶ manage_sessions(action: "start")
└────────────────┬────────────────┘
│
▼
┌─────────────────────────────────┐
│ Context & Task Prioritization │ ──▶ get_analytics(action: "summary")
│ │ ──▶ manage_tasks(action: "next")
└────────────────┬────────────────┘
│
▼
┌─────────────────────────────────┐
│ Deterministic Graph Mutation │ ──▶ manage_nodes(action: "create"|"update")
│ (Tasks, Decisions, Blockers) │ ──▶ manage_edges(action: "add"|"link_visual")
└────────────────┬────────────────┘
│
▼
┌─────────────────────────────────┐
│ Spec & Integrity Verification │ ──▶ manage_specs(action: "compliance"|"verify")
│ │ ──▶ run_diagnostics(action: "validate")
└────────────────┬────────────────┘
│
▼
┌─────────────────────────────────┐
│ Persistent SQLite Storage │ ──▶ .state-memory-mcp/graph.db (WAL mode)
│ Append-Only Event Ledger │ ──▶ SHA-256 Cryptographic Audit Chain
└─────────────────────────────────┘
📚 Documentation Directory
Explore dedicated guides and deep dives in the docs/ directory:
| Guide | Description |
|---|---|
| 🏗️ Architecture & Codebase Distillation | High-signal architectural overview, module inventory, data flows, and design decisions. |
| 🚀 v0.10 → v1.0 Migration Guide | Step-by-step migration guide, legacy tool mapping table, and STATE_MEMORY_COMPAT mode. |
| 💡 Value Proposition & Theory | Cognitive Externalization, FSM Formalism, First-Hop Determinism & Benchmark metrics. |
| 📋 State Memory Concepts | Node Types (task, decision, blocker...), Status Values, Typed Edges & Seeding Guidelines. |
| ⚙️ Configuration & IDE Setup | Auto-Initialization details, Environment Variables table, and Editor Configs (Cursor, VS Code, Claude, Antigravity, Windsurf). |
| 🛠️ CLI Command Reference | CLI flags (init, run, view, inspect, metrics, audit, doctor, backup, restore, merge) & Git Scanner. |
| ⏱️ Sessions, Snapshots & SDD | Session Lifecycle, Event Audit Trail, Snapshots, Trajectories, Sub-directory support & Spec-Driven Development. |
| 🧰 Tools, Resources & Prompts | Complete reference for all 13 Consolidated MCP Tools, read-only state-memory:/// Resources, and Prompt templates. |
| 📘 Formal API Reference | Formal parameters, return schemas, and code signatures for all MCP endpoints. |
| 🎨 3D Visualizer Guide | Viewing and exporting the interactive WebGL 3D Force-Directed Graph visualizer. |
| 🗄️ Database Schema | SQLite tables, columns, indexes, and schema migration history. |
📖 Agent Playbook: 5-Step Canonical Workflow
When an autonomous AI agent enters a repository with state-memory-mcp:
1. Orient & Bootstrap ──▶ manage_sessions(action: "start") + get_analytics(action: "summary")
2. Task Selection ──▶ manage_tasks(action: "next") + manage_tasks(action: "find_blockers")
3. Trace Context ──▶ query_graph(action: "trace") + manage_specs(action: "compliance")
4. Execute & Record ──▶ manage_nodes(action: "create", type: "decision") + manage_edges(action: "link_visual")
5. Validate & Close ──▶ run_diagnostics(action: "validate") + manage_tasks(action: "complete") + manage_sessions(action: "end")
🧪 Testing
# Run full unit, integration, and performance benchmark test suite across all 113 test files (418 tests)
npm run test
⚖️ License & Disclaimers
Developed and maintained by PuterVision. Released under the MIT License.
- Local Storage Guarantee: All graph data, decision records, and event logs remain 100% local in your workspace. No telemetry or project data is ever transmitted.
- Trademarks & Non-Affiliation: Product names (Cursor, Claude Code, Gemini, Windsurf, VS Code, GitHub, SQLite) are property of their respective owners and used solely for compatibility identification.
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