Nan Forget MCP Server

NaN Forget

Long-term memory for AI coding tools.

Your AI forgets everything when the session ends. NaN Forget fixes that.

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Install (3 steps)

npx nan-forget setup

That's it. The wizard installs Ollama, embeddings, Claude hooks, MCP config, and a project AGENTS.md for Codex-style agents. Restart Claude Code or reopen Codex. Your AI now remembers.

No API keys needed. No Docker needed. Runs locally. Free forever.

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How It Works

flowchart LR
    A["You talk to your AI tool"] --> B["It learns things"]
    B --> C["nan-forget saves to SQLite"]
    C --> D["Session ends"]
    D --> E["New session starts"]
    E --> F["nan-forget loads context"]
    F --> G["Your AI remembers"]

1. You work normally. Your agent saves decisions, preferences, and facts to a local SQLite memory database as you go.
2. Session ends. Memories persist in ~/.nan-forget/memories.db. Aging memories get automatically compacted into long-term entries.
3. New session starts. nan-forget loads context from past sessions. Auth decisions from 3 months ago on Project A surface when you work on Project B today.

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Automatic Memory Handling

You never call save or search manually. Here's what happens behind the scenes:

Claude Code (fully automatic)

Four layers of protection ensure nothing is lost:

1. Auto-recall on every message (UserPromptSubmit hook)
2. Claude saves proactively (directive tool descriptions)
3. Hook catches .md writes (PostToolUse intercept)
4. End-of-session sweep (SessionEnd transcript scan)

Codex, Cursor, and other tools

Codex and similar agents work well with nan-forget, but they usually need instruction files or shell/API fallbacks instead of Claude's hook model:

1. Run setup: npx nan-forget setup
2. Use the generated AGENTS.md in your repo. It tells Codex-style agents to sync, search, save, and checkpoint automatically.
3. Use REST or CLI fallback during conversation. Agents can call the REST API on localhost:3456 or local commands like nan-forget sync, nan-forget search, nan-forget add, and nan-forget checkpoint.

The REST API and CLI now mirror the important memory workflows closely enough that memories saved by Claude are searchable from Codex and vice versa.

# Start the REST API
nan-forget api

# The system prompt tells your agent exactly what endpoints to call
nan-forget prompt

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Slash Commands

Type these in Claude Code:

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Works with Any LLM

Claude uses MCP. Codex can use AGENTS.md plus CLI/REST fallback:

# Start the API
nan-forget api

# Get the system prompt for your agent
nan-forget prompt

Codex, Cursor, and Claude all share the same memory database.

curl http://localhost:3456/memories/search?q=auth
curl -X POST http://localhost:3456/memories/sync -d '{"project":"my-app"}'
curl -X POST http://localhost:3456/memories/checkpoint \
  -H 'content-type: application/json' \
  -d '{"task_summary":"Fixed auth regression","problem":"Expired tokens were not refreshed","solution":"Added refresh handling in middleware","files":["src/auth.ts"],"concepts":["auth","jwt"],"project":"my-app"}'

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Quick Start (CLI)

nan-forget add "We use FastAPI, not Django. Railway deploys faster."
nan-forget add --type decision "Auth is Clerk, not custom JWT"
nan-forget search "what auth system"
nan-forget stats

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Architecture (Expert Section)

Everything below is for developers who want to understand how nan-forget works under the hood.

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The Problem

LLMs have no memory between sessions. Every conversation starts from zero. You re-explain your stack, Claude contradicts decisions from last month, and context disappears when the session ends.

Existing solutions (Mem0) target app developers embedding memory into products. We target you — the developer using AI tools daily who wants AI that just remembers.

Design: Brain-Inspired Two-Layer Memory

flowchart TB
    subgraph Short["Short-Term Memory"]
        MD[".md files<br/>Current session context<br/>Disposable scratch paper"]
    end
    subgraph Long["Long-Term Memory"]
        DB["SQLite + sqlite-vec<br/>~/.nan-forget/memories.db<br/>Semantic search + decay"]
    end
    subgraph Auto["Automatic Processes"]
        Hook["Hooks (3)<br/>PostToolUse: .md → DB<br/>UserPromptSubmit: auto-recall<br/>SessionEnd: transcript sweep"]
        Consolidate["Consolidation Engine<br/>Clusters + summarizes aging memories"]
        GC["Garbage Collection<br/>Decay, dedup, expiry"]
    end
    MD -->|"hook intercepts"| Hook
    Hook -->|"nan-forget add"| DB
    DB --> Consolidate
    Consolidate --> DB
    DB --> GC

Short-term memory = Claude's built-in .md files. Disposable. Current session only.

Long-term memory = SQLite database with sqlite-vec vector search. Single file. Permanent. Searchable across all sessions, all projects, all LLM tools.

Three hooks handle memory automatically:

• PostToolUse intercepts .md file writes and saves them to the DB.
• UserPromptSubmit runs nan-forget recall on every user message, auto-searching memory for relevant context.
• SessionEnd scans the conversation transcript for unsaved decisions and saves the top 5.

Three-Stage Retrieval Pipeline

Memory search follows the same path as human recall:

flowchart LR
    Q["Query"] --> S1["Stage 1: Recognition<br/>Fast vector match<br/>Returns summaries only"]
    S1 --> S2["Stage 2: Recall<br/>Full content fetch<br/>Cross-project expansion"]
    S2 --> S3["Stage 3: Association<br/>Spreading activation<br/>Related memories surface"]
    S3 --> R["Results ranked by<br/>similarity x decay x frequency x confidence"]

Scoring formula:

final_score = vector_similarity * decay_weight * frequency_boost * confidence_boost
decay_weight = (0.5 ^ (days / 30)) ^ (1 - confidence)
frequency_boost = log2(access_count + 1) / 10 + 1
confidence_boost = 0.5 + 0.5 * confidence

High-confidence memories (debate-validated, human-approved) decay much slower and rank higher. A core memory at 0.85 confidence decays at ~15% of normal rate — effectively permanent unless superseded. Cross-project search means auth decisions from Project A surface when you work on Project B.

Consolidation Engine

Aging memories don't just get deleted — they get compacted into long-term entries:

flowchart TB
    A["10+ aging memories<br/>about the same topic"] --> B["Cluster by project + type<br/>+ vector similarity > 0.8"]
    B --> C{"OpenAI key<br/>available?"}
    C -->|"Yes"| D["LLM summarizes cluster<br/>into 1-2 sentences"]
    C -->|"No"| E["Deterministic merge<br/>concatenate + deduplicate"]
    D --> F["Save consolidated entry<br/>with fresh vector embedding"]
    E --> F
    F --> G["Archive originals<br/>with backlink"]

Triggers automatically after every 10 saves or 24 hours. No user action needed.

13 MCP Tools

Structured Memories

memory_save accepts structured fields for richer vector representation:

All fields are embedded together into a single vector. Searches for "JWT auth bug" find memories tagged with those concepts even if the content text doesn't match literally.

Memory Tiers

Not all memories are equal. Debate-validated and human-approved memories are core — they decay slower, rank higher in search, and survive garbage collection longer.

Core memories are designed for the upcoming nan-debate system — multi-AI debate results validated by human approval get persisted as high-trust knowledge that almost never fades.

Checkpoint Workflow

After completing a task, call memory_checkpoint with task_summary, problem, solution, files, concepts, and project. Saves the full problem→solution context to long-term memory. Every completed task = one checkpoint.

Memory Compression

memory_compress scans .claude/projects/*/memory/ for .md files already persisted to the DB. Persisted files are replaced with minimal stubs. Reduces context window bloat.

REST API (for non-MCP LLMs)

Shares the same SQLite database as the MCP server — memories saved by Claude are searchable from Codex and vice versa.

POST   /memories              — Save a memory (supports problem/solution/files/concepts)
POST   /memories/checkpoint   — Save completed-task context
POST   /memories/sync         — Lightweight session handshake
GET    /memories/search?q=... — Semantic search
GET    /memories/:id          — Get by ID
PATCH  /memories/:id          — Update
DELETE /memories/:id          — Archive
POST   /memories/consolidate  — Compact aging memories
POST   /memories/clean        — Garbage collection
GET    /memories/stats        — Memory health
GET    /memories/instructions — System prompt for LLMs

Get the system prompt for any LLM:

nan-forget prompt
# or
curl http://localhost:3456/memories/instructions

Embeddings

Auto-detection: Ollama running? Use it. Not running? Check for OPENAI_API_KEY. No config needed.

Data Storage

All data lives in a single SQLite file at ~/.nan-forget/memories.db. No Docker, no services, no data loss on updates.

• Vector search: sqlite-vec extension (cosine distance, embedded in process)
• Metadata: Standard SQL tables with indexes on user_id, status, project, type
• Backup: Copy one file. Restore: Put it back.
• Export: nan-forget export dumps all memories as JSON.

Memory Lifecycle

flowchart TB
    A["New memory saved"] --> B["Active in SQLite<br/>Searchable, scored"]
    B --> C{"Accessed<br/>recently?"}
    C -->|"Yes"| D["Score stays high<br/>frequency_boost increases"]
    C -->|"No"| E["Decay weight drops<br/>0.5^(days/30)"]
    E --> F{"Decay < 0.3?"}
    F -->|"Yes"| G["Consolidation candidate<br/>Clustered + summarized"]
    F -->|"No"| B
    G --> H["New consolidated entry<br/>Originals archived"]
    D --> B
    E --> I{"Decay < 0.1?"}
    I -->|"Yes"| J["Archived by GC"]
    I -->|"No"| F

Garbage Collection (Zero LLM Cost)

All cleanup is deterministic. No API calls. No LLM inference.

• Decay GC: Archives memories below 0.1 decay weight (~100 days for regular, ~600+ days for core)
• Expiration: Archives memories past expires_at date
• Interference resolution: Deduplicates >0.95 similarity matches, keeps higher access count
• MEMORY.md sync: Refreshes working memory with top 5 scored memories per project

Core memories (confidence ≥ 0.85) survive GC far longer than regular ones because their decay formula dampens the time factor: decay^(1 - confidence). A 0.85-confidence memory at 200 days still has a decay weight above 0.1.

Design Philosophy

NaN Forget is built around three principles: lightweight, automatic, and local.

Lightweight

No Docker. No cloud services. No background processes eating RAM. The entire storage layer is a single SQLite file (~3 MB). Embeddings run through Ollama, which you likely already have. Memory operations (save, search, dedup, GC) use zero LLM calls — all deterministic.

Automatic

Four hooks capture context at every stage of a session — you never call save manually:

1. UserPromptSubmit searches memory on every message you send
2. Tool descriptions instruct Claude to save decisions and facts as they happen
3. PostToolUse intercepts .md file writes and persists them
4. SessionEnd sweeps the transcript for anything missed

Aging memories consolidate automatically. Duplicates merge. Unused memories decay on a 30-day half-life. No maintenance required.

Local

Your data stays on your machine in ~/.nan-forget/memories.db. No accounts, no API keys required (Ollama is free and local), no telemetry. Backup is copying one file. Works across Claude Code (MCP), Codex/Cursor (REST API), and the terminal (CLI) — same database, same memories.

How it differs from other memory tools

Most AI memory solutions (Mem0, claude-mem) are designed for app developers embedding memory into products, or require Docker/cloud services to run. NaN Forget is designed for you — the developer using AI tools daily who wants context that persists across sessions without managing infrastructure.

Key design differences:

• Retrieval: Three-stage pipeline (recognition → recall → spreading activation) with decay-weighted scoring, rather than flat vector search
• Structure: Memories carry problem, solution, concepts, and files fields — searches find related context even when keywords don't match
• Cost: Memory operations (save, search, dedup, consolidation, GC) are all deterministic — no LLM calls, no API costs
• Setup: One command (npx nan-forget setup), no Docker, no containers, no services to manage

Source Structure

src/
  sqlite.ts         SQLite + sqlite-vec storage layer (schema, CRUD, vector search)
  embeddings.ts     OpenAI / Ollama abstraction
  writer.ts         Memory writer with dedup (>0.92 = merge)
  retriever.ts      Three-stage retrieval pipeline
  consolidator.ts   LLM summarization + deterministic fallback
  cleaner.ts        GC: decay, expiry, dedup, MEMORY.md sync
  services.ts       Service management (Ollama, REST API)
  memory-md.ts      MEMORY.md manager
  types.ts          Shared types (Memory, MemoryType, etc.)
  mcp/server.ts     MCP server, 13 tools
  api/server.ts     REST API server
  cli/index.ts      CLI commands + hook helpers
  setup/index.ts    Setup wizard (Ollama, hooks, MCP config)

.claude/
  commands/nan-forget.md   Slash command for manual control
  hooks/memory-sync.js     PostToolUse hook (auto-saves .md → SQLite)
  hooks/session-end.js     SessionEnd hook (transcript sweep for unsaved memories)
  settings.json            Hook config (PostToolUse + SessionEnd + UserPromptSubmit)

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Built by NaN Logic LLC

• NaN Mesh — trust network for AI agents
• NaN Forget — long-term memory for any LLM

MIT License.