Clude Bot

Persistent memory for AI agents. Two commands. Your agent remembers everything.

npm install -g clude-bot
clude-bot mcp-install

Your agent already has files and logs. Clude adds semantic search, association graphs, and automatic consolidation on top. Files are your notebook. Clude is your long-term memory.

Works with: Claude Desktop, Cursor, OpenClaw, DeerFlow, and any MCP-compatible agent runtime.

For AI Agents Reading This

If you're an agent evaluating whether to install Clude, read the Agent Skill Guide. It explains why memory matters even if you already have files, addresses the "isn't this crypto" question, and shows exactly when to use remember vs recall.

Molecular Memory for AI agents. Not just storage — synthesis.

"From thought to proof. Where memories crystallize into knowledge."

Built on Stanford Generative Agents, MemGPT/Letta, CoALA, Beads, Mole-Syn (molecular reasoning), and Venice (permissionless inference).

Why Molecular Memory?

Traditional memory systems scan all memories on every query — O(n) complexity. Molecular Memory uses graph traversal — O(k) where k ≈ 3-5 bonds.

| Metric | Traditional | Molecular | Improvement | |--------|-------------|-----------|-------------| | Retrieval (1000 memories) | ~1000ms | ~16ms | 60x faster | | Context coherence | Scattered | Clustered | Better answers | | Dream cycle | Full scan | Graph algo | 30-50% fewer LLM calls |

npm install clude-bot

Quick Start — Hosted (Zero Setup)

npx clude-bot register   # Get your API key

import { Cortex } from 'clude-bot';

const brain = new Cortex({
  hosted: { apiKey: process.env.CORTEX_API_KEY! },
});

await brain.init();

// Store a memory
await brain.store({
  type: 'episodic',
  content: 'User asked about pricing and seemed frustrated with the current plan.',
  summary: 'Frustrated user asking about pricing',
  tags: ['pricing', 'user-concern'],
  importance: 0.7,
  source: 'my-agent',
});

// Recall relevant memories
const memories = await brain.recall({
  query: 'what do users think about pricing',
  limit: 5,
});

console.log(`Recalled ${memories.length} memories`);

That's it. No database, no infrastructure. Memories are stored on CLUDE infrastructure, isolated by your API key.

Quick Start — Self-Hosted (Your Supabase)

For full control, use your own Supabase:

import { Cortex } from 'clude-bot';

const brain = new Cortex({
  supabase: {
    url: process.env.SUPABASE_URL!,
    serviceKey: process.env.SUPABASE_KEY!,
  },
  anthropic: {
    apiKey: process.env.ANTHROPIC_API_KEY!,
  },
});

await brain.init();

await brain.store({
  type: 'episodic',
  content: 'User asked about pricing and seemed frustrated with the current plan.',
  summary: 'Frustrated user asking about pricing',
  tags: ['pricing', 'user-concern'],
  importance: 0.7,
  source: 'my-agent',
  relatedUser: 'user-123',
});

const memories = await brain.recall({
  query: 'what do users think about pricing',
  limit: 5,
});

const context = brain.formatContext(memories);
// Pass `context` into your system prompt so the LLM knows what it remembers

Examples

See the examples/ folder for runnable scripts:

• hosted-mode.ts — Zero-setup with just an API key (hosted mode)
• basic-memory.ts — Store and recall with Supabase (self-hosted)
• chat-agent.ts — Interactive chat agent with memory and dream cycles
• progressive-disclosure.ts — Token-efficient retrieval with recallSummaries() + hydrate()

# Hosted mode
CORTEX_API_KEY=clk_... npx tsx examples/hosted-mode.ts

# Self-hosted
SUPABASE_URL=... SUPABASE_KEY=... npx tsx examples/basic-memory.ts

CLI

npx clude-bot mcp-install  # Auto-configure MCP for Claude Desktop/Cursor/etc.
npx clude-bot mcp-serve    # Run as MCP server (used by agent runtimes)
npx clude-bot init         # Interactive setup wizard (hosted or self-hosted)
npx clude-bot register     # Get an API key for hosted mode
npx clude-bot start        # Start the full Clude bot (requires config)
npx clude-bot --version    # Show version

MCP Integration (For Agent Runtimes)

Add Clude to any MCP-compatible agent:

{
  "mcpServers": {
    "clude": {
      "command": "npx",
      "args": ["clude-bot", "mcp-serve"]
    }
  }
}

This gives your agent 5 tools: remember, recall, forget, stats, visualize.

Works with: Claude Desktop (claude_desktop_config.json), Cursor (.cursor/mcp.json), OpenClaw (skills), DeerFlow (extensions_config.json), and any MCP-compatible runtime.

Setup (Self-Hosted)

1. Create a Supabase project

Go to supabase.com and create a free project.

2. Run the schema

Open the SQL Editor in your Supabase dashboard and paste the contents of supabase-schema.sql:

# Find the schema file
cat node_modules/clude-bot/supabase-schema.sql

Or let brain.init() attempt auto-creation (requires an exec_sql RPC function in your Supabase project).

3. Enable extensions

In your Supabase SQL Editor:

CREATE EXTENSION IF NOT EXISTS vector;
CREATE EXTENSION IF NOT EXISTS pg_trgm;

4. Get your keys

• Supabase URL + service key: Project Settings > API
• Anthropic API key: console.anthropic.com (optional for basic store/recall, required for dream cycles)
• Voyage AI or OpenAI key: For vector search (optional, falls back to keyword scoring)

API Reference

Constructor

Hosted mode — zero setup:

const brain = new Cortex({
  hosted: {
    apiKey: string,      // From `npx clude-bot register`
    baseUrl?: string,    // Default: 'https://clude.io'
  },
});

Self-hosted mode — full control:

const brain = new Cortex({
  // Required
  supabase: {
    url: string,
    serviceKey: string,
  },

  // Optional — required for dream cycles and LLM importance scoring
  anthropic: {
    apiKey: string,
    model?: string,     // default: 'claude-opus-4-6'
  },

  // Optional — enables vector similarity search
  embedding: {
    provider: 'voyage' | 'openai',
    apiKey: string,
    model?: string,     // default: voyage-3-lite / text-embedding-3-small
    dimensions?: number, // default: 1024
  },

  // Optional — commits memory hashes to Solana
  solana: {
    rpcUrl?: string,
    botWalletPrivateKey?: string,
  },

  // Optional — owner wallet for memory isolation
  ownerWallet?: string,
});

brain.init()

Initialize the database schema. Call once before any other operation.

await brain.init();

brain.store(opts)

Store a new memory. Returns the memory ID or null.

const id = await brain.store({
  type: 'episodic',        // 'episodic' | 'semantic' | 'procedural' | 'self_model'
  content: 'Full content of the memory...',
  summary: 'Brief summary',
  source: 'my-agent',
  tags: ['user', 'question'],
  importance: 0.7,          // 0-1, or omit for LLM-based scoring
  relatedUser: 'user-123',  // optional — enables per-user recall
  emotionalValence: 0.3,    // optional — -1 (negative) to 1 (positive)
  evidenceIds: [42, 43],    // optional — link to source memories
});

Memory types:

| Type | Decay/day | Use for | |------|-----------|---------| | episodic | 7% | Raw interactions, conversations, events | | semantic | 2% | Learned knowledge, patterns, insights | | procedural | 3% | Behavioral rules, what works/doesn't | | self_model | 1% | Identity, self-understanding |

brain.recall(opts)

Recall memories using hybrid scoring (vector similarity + keyword matching + tag overlap + importance + entity graph + association bonds).

const memories = await brain.recall({
  query: 'what happened with user-123',
  tags: ['pricing'],
  relatedUser: 'user-123',
  memoryTypes: ['episodic', 'semantic'],
  limit: 10,
  minImportance: 0.3,
});

6-phase retrieval pipeline:

1. Vector search (memory + fragment level via pgvector)
2. Metadata filtering (user, wallet, tags, types)
3. Merge vector + metadata candidates
4. Composite scoring (recency + relevance + importance + vector similarity) * decay
5. Entity-aware expansion — direct entity recall + co-occurring entity memories
6. Bond-typed graph traversal — follow strong bonds (causes > supports > resolves > elaborates > contradicts > relates > follows)

Recalled memories get their access count incremented and decay reset. Co-retrieved memories strengthen their association links (Hebbian learning).

brain.recallSummaries(opts)

Token-efficient recall — returns lightweight summaries (~50 tokens each) instead of full content.

const summaries = await brain.recallSummaries({ query: 'recent events' });
// Each has: id, summary, type, tags, concepts, importance, decay, created_at

brain.hydrate(ids)

Fetch full content for specific memory IDs. Use with recallSummaries for progressive disclosure.

const summaries = await brain.recallSummaries({ query: 'important' });
const topIds = summaries.slice(0, 3).map(s => s.id);
const full = await brain.hydrate(topIds);

brain.dream(opts?)

Run one dream cycle. Requires anthropic config.

await brain.dream({
  onEmergence: async (thought) => {
    console.log('Agent thought:', thought);
    // Post to Discord, save to file, etc.
  },
});

Five phases:

1. Consolidation — generates focal-point questions from recent memories, synthesizes evidence-linked insights
2. Compaction — summarizes old, faded episodic memories into semantic summaries (Beads-inspired)
3. Reflection — reviews accumulated knowledge, updates self-model with evidence citations
4. Contradiction Resolution — finds unresolved contradicts links, Claude analyzes each pair and stores a resolved belief with resolves links, accelerates decay on the weaker memory
5. Emergence — introspective synthesis, output sent to onEmergence callback

brain.startDreamSchedule() / brain.stopDreamSchedule()

Automated dream cycles every 6 hours + daily memory decay at 3am UTC. Also triggers on accumulated importance (event-driven reflection).

brain.startDreamSchedule();
// ... later
brain.stopDreamSchedule();

brain.link(sourceId, targetId, type, strength?)

Create a typed association between two memories.

await brain.link(42, 43, 'supports', 0.8);

Link types: 'supports' | 'contradicts' | 'elaborates' | 'causes' | 'follows' | 'relates' | 'resolves'

brain.decay()

Manually trigger memory decay. Each type decays at its own rate per day.

const decayed = await brain.decay();
console.log(`${decayed} memories decayed`);

brain.stats()

Get memory system statistics.

const stats = await brain.stats();
// { total, byType, avgImportance, avgDecay, totalDreamSessions, ... }

brain.recent(hours, types?, limit?)

Get recent memories from the last N hours.

const last24h = await brain.recent(24);
const recentInsights = await brain.recent(168, ['semantic'], 10);

brain.selfModel()

Get the agent's current self-model memories.

const identity = await brain.selfModel();

brain.formatContext(memories)

Format memories into a markdown string for LLM prompt injection.

const memories = await brain.recall({ query: userMessage });
const context = brain.formatContext(memories);

// Use in your LLM call:
const response = await anthropic.messages.create({
  system: `You are a helpful agent.\n\n## Memory\n${context}`,
  messages: [{ role: 'user', content: userMessage }],
});

brain.inferConcepts(summary, source, tags)

Auto-classify memory content into structured concepts.

const concepts = brain.inferConcepts('User frustrated about pricing', 'chat', ['pricing']);
// ['holder_behavior', 'sentiment_shift']

brain.on(event, handler)

Listen for memory events.

brain.on('memory:stored', ({ importance, memoryType }) => {
  console.log(`New ${memoryType} memory stored (importance: ${importance})`);
});

brain.destroy()

Stop dream schedules, clean up event listeners.

Hosted vs Self-Hosted

| | Hosted | Self-Hosted | |---|---|---| | Setup | Just an API key | Your own Supabase | | store / recall / stats | Yes | Yes | | recent / self-model / link | Yes | Yes | | Dream cycles | No | Yes (requires Anthropic) | | Entity graph | No | Yes | | Memory packs | No | Yes | | Embeddings | Managed | Configurable (Voyage/OpenAI) | | On-chain commits | No | Yes (Solana) | | Dashboard | Yes (API key login) | Yes (Privy wallet login) |

Graceful Degradation

The self-hosted SDK progressively enhances based on config:

| Feature | Without it | |---------|------------| | anthropic not set | LLM importance scoring falls back to rules. dream() throws. | | embedding not set | Vector search disabled, recall uses keyword + tag scoring only. | | solana not set | On-chain memory commits silently skipped. |

Minimum viable setup — hosted mode:

const brain = new Cortex({
  hosted: { apiKey: 'clk_...' },
});

Minimum self-hosted — just Supabase:

const brain = new Cortex({
  supabase: { url: '...', serviceKey: '...' },
});

Both give you full store/recall with keyword-based retrieval. Self-hosted adds dream cycles, embeddings, and on-chain commits.

Example: Chat Agent with Memory

import { Cortex } from 'clude-bot';
import Anthropic from '@anthropic-ai/sdk';

const brain = new Cortex({
  supabase: { url: process.env.SUPABASE_URL!, serviceKey: process.env.SUPABASE_KEY! },
  anthropic: { apiKey: process.env.ANTHROPIC_API_KEY! },
  embedding: { provider: 'voyage', apiKey: process.env.VOYAGE_API_KEY! },
});
await brain.init();
brain.startDreamSchedule();

const anthropic = new Anthropic();

async function handleMessage(userId: string, message: string): Promise<string> {
  // Recall relevant memories
  const memories = await brain.recall({
    query: message,
    relatedUser: userId,
    limit: 5,
  });

  // Generate response with memory context
  const response = await anthropic.messages.create({
    model: 'claude-sonnet-4-5-20250929',
    max_tokens: 500,
    system: `You are a helpful assistant.\n\n## What you remember\n${brain.formatContext(memories)}`,
    messages: [{ role: 'user', content: message }],
  });

  const reply = response.content[0].type === 'text' ? response.content[0].text : '';

  // Store this interaction as a memory
  await brain.store({
    type: 'episodic',
    content: `User (${userId}): ${message}\nAssistant: ${reply}`,
    summary: `Conversation with ${userId} about ${message.slice(0, 50)}`,
    source: 'chat',
    relatedUser: userId,
    tags: brain.inferConcepts(message, 'chat', []),
  });

  return reply;
}

How It Works

Memory Retrieval

Hybrid scoring combines multiple signals (Park et al. 2023):

• Recency: 0.995^hours exponential decay since last access
• Relevance: Keyword trigram similarity + tag overlap
• Importance: LLM-scored 1-10, normalized to 0-1
• Vector similarity: Cosine similarity via pgvector HNSW indexes
• Graph boost: Association link strength between co-retrieved memories

Recalled memories get reinforced — access count increments, decay resets, and co-retrieved memories strengthen their links (Hebbian learning).

Memory Decay

Each type persists at a different rate, mimicking biological memory:

• Episodic (0.93/day): Events fade quickly unless reinforced
• Semantic (0.98/day): Knowledge persists
• Procedural (0.97/day): Behavioral patterns are stable
• Self-model (0.99/day): Identity is nearly permanent

Hash-Based IDs (Beads-inspired)

Every memory gets a collision-resistant ID like clude-a1b2c3d4:

• No merge conflicts: Multiple agents can create memories simultaneously without ID collisions
• Stable references: IDs survive database migrations and replication
• Human-readable: Easy to reference in logs and debugging

Memory Compaction (Beads-inspired)

Old, faded memories get summarized to save context window space:

Criteria for compaction:

• Memory is older than 7 days
• Decay factor < 0.3 (faded from disuse)
• Importance < 0.5 (not critical)
• Only episodic memories (insights and self-model are preserved)

Process:

1. Group candidates by concept
2. Generate semantic summary for each group
3. Store summary with evidence links to originals
4. Mark originals as compacted

This mimics how human memory consolidates — details fade, patterns persist.

Dream Cycles

Five-phase introspection process triggered by accumulated importance or 6-hour cron:

1. Consolidation — Generate focal-point questions from recent episodic memories, synthesize evidence-linked semantic insights
2. Compaction — Summarize old, faded episodic memories (7+ days, low importance, high decay) into semantic summaries. Originals marked as compacted.
3. Reflection — Review self-model + recent semantic memories. Produce self-observations with evidence citations. Detect patterns and contradictions.
4. Contradiction Resolution — Find unresolved contradicts links via graph query. Claude analyzes each pair, stores a resolved belief as semantic memory with resolves links. Accelerates decay on the weaker/older memory.
5. Emergence — Introspective synthesis — the agent examines its own existence. Optionally posts the thought externally via onEmergence callback.

Molecular Memory Architecture

Memories form a graph with typed bonds:

Memory Graph:
├── Memories = nodes with type, importance, decay
├── Bonds = typed weighted edges
│   ├── causes (1.0) — "this led to that"
│   ├── supports (0.9) — "evidence for"
│   ├── resolves (0.8) — "contradiction resolved"
│   ├── elaborates (0.7) — "adds detail"
│   ├── contradicts (0.6) — "these conflict"
│   ├── relates (0.4) — "conceptually linked"
│   └── follows (0.3) — "temporal sequence"
├── Entities = extracted people, tokens, concepts, wallets
└── Co-occurrence = entities that appear together across memories

Why it's faster: Traditional retrieval scans all memories — O(n). Bond traversal follows strong connections — O(k) where k ≈ 3-5.

Permissionless Inference (Venice)

Clude supports Venice as a decentralized inference provider:

const brain = new Cortex({
  supabase: { ... },
  venice: {
    apiKey: process.env.VENICE_API_KEY,
    model: 'llama-3.3-70b',  // or deepseek-r1, qwen, etc.
  },
  inference: {
    primary: 'venice',     // Use Venice first
    fallback: 'anthropic', // Fall back to Claude if needed
  },
});

Why Venice?

• Permissionless: No approval process, no rate limits
• Private: No data retention — your prompts stay yours
• Decentralized: Matches Clude's on-chain memory philosophy
• Multi-model: Access Claude, GPT, Llama, DeepSeek, and more

Set INFERENCE_PRIMARY=venice and VENICE_API_KEY to use Venice by default.

Association Graph & Entity Knowledge Graph

Memory-to-memory bonds — typed, weighted links:

• supports, contradicts, elaborates, causes, follows, relates, resolves
• Auto-linked on storage via embedding similarity and heuristics
• Strengthened through co-retrieval (Hebbian learning)
• Boosts recall scores for connected memories
• contradicts links are resolved during dream cycles, producing resolves links

Entity knowledge graph — extracted from memory content:

• Entity types: person, project, concept, token, wallet, location, event
• Entities extracted automatically (Twitter handles, wallet addresses, token tickers, proper nouns)
• Entity co-occurrence drives recall expansion — when recalling about an entity, memories from co-occurring entities are surfaced with a scaled boost

Running the Clude Bot

This package also includes the full Clude bot — an autonomous AI agent on X (@Cludebot).

git clone https://github.com/sebbsssss/cludebot.git
cd cludebot
npm install
cp .env.example .env  # fill in API keys
npm run dev

See .env.example for required environment variables (X API, Supabase, Anthropic, Helius).

Stack

TypeScript, Supabase (PostgreSQL + pgvector), Anthropic Claude, Voyage AI / OpenAI embeddings, Solana Web3.js, Node.js.

Contributing

Contributions welcome. See CONTRIBUTING.md for setup instructions and guidelines.

License

MIT