Titan-Memory

---

What's New in v2.1

Titan Memory v2.1 adds an optional LLM Turbo Layer for enhanced retrieval quality. Zero new npm dependencies. LLM is OFF by default — the zero-LLM pipeline remains the default fallback. Benchmark scores are under active review and will be updated with verified methodology.

Feature	Description
LLM Turbo Layer	Optional LLM-enhanced classify, extract, rerank, and summarize — provider-agnostic (Anthropic, OpenAI, Groq, Ollama)
LLM Reranking	Highest-impact enhancement: LLM understands "replaced/superseded" semantics and temporal logic for dramatically better recall ordering
Provider-Agnostic Client	Raw fetch(), zero SDK dependencies — same pattern as Voyage AI integration. Supports Anthropic, OpenAI, and any OpenAI-compatible API
Graceful Fallback	Every LLM call wrapped in try/catch. If the LLM is unavailable or errors, the system falls back to the algorithmic pipeline with zero degradation
Per-Capability Toggles	Enable/disable classify, extract, rerank, and summarize independently. Only pay for what you use
Groq Support	Ultra-low latency inference (~350ms/call) via Groq API with Llama 3.3 70B

Verified benchmark scores (April 2, 2026): Overall 74.6/100 with Voyage AI embeddings (16/18 tests passing). Accuracy: 72.8/100, Latency: 90.8/100, Compression: 43.5/100. Two known weak areas: temporal reasoning (41.7% — requires multi-step temporal inference that pure vector search cannot perform) and knowledge updates (48.0% — requires understanding "replaced/superseded" semantics, an inherently LLM-level task). Both improve with LLM Turbo mode enabled. Previously reported figures (84.2%/90.7%) were measured under conditions that could not be independently reproduced. Hash-based offline embeddings produce significantly lower retrieval accuracy — Voyage AI embeddings are recommended for production quality.

v2.1.2 — Security, Testing & Architecture (March 28, 2026)

• Security hardening: WebSocket auth (token validation + localhost bypass), rate limiting (100 req/min per IP), CSP hardened (removed unsafe-inline for scripts), body size pre-check
• Test suite: 1,888 → 2,310 tests (+422), 6 new test files covering compression, LLM client, A2A server, dashboard, MCP tools, and titan.ts branches
• Coverage: Statements 75% → 83%, Branches 60% → 69%, Functions 72% → 79%, Lines 77% → 84%
• Architecture: Extracted 5 delegate classes (Graph, Context, Learning, Validation, Cortex) from titan.ts god class. Replaced 400-line switch in handleToolCall with Map-based tool registry (28 handlers)
• Bug fixes: Fixed cross-project domain-aware dedup, local-embedding partial config, reranker smart fetch (cost reduction)
• Tooling: ESLint installed and configured (flat config, TypeScript support, 0 errors)

v2.1.1 — Performance & Hardening (March 2026)

• Recall pipeline now batches highlight calls per-memory instead of per-sentence — significant latency reduction
• Voyage reranker optimized with topN parameter — halves API cost per recall
• LRU cache eviction on long-term memory cache (capped at 1,000 entries)
• Input validation hardened across dashboard API endpoints
• Request body size limits added to dashboard server
• Dependencies updated to latest compatible versions
• Removed unused production dependency (dotenv)

---

Titan Memory v2.0 closed competitive gaps with industry leaders while maintaining its architectural advantages:

Feature	Description
Voyage AI Reranker	Post-retrieval reranking using Voyage rerank-2 — boosts accuracy beyond raw vector similarity
LoCoMo + LongMemEval Benchmarks	18 benchmarks across 4 categories: accuracy, temporal, knowledge-updates, latency
SimpleMem Compression	Entity extraction, relationship distillation, 30x token compression with fidelity scoring
NOOP/Skip Operation	Explicitly decide NOT to store — prevents memory bloat from routine interactions
Intent-Aware Retrieval	Detect query intent (factual/pattern/timeline/exploration) before retrieval for optimized strategy
Working Memory	Explicit L1 management with focus items, priority-based eviction, and agent scratchpad
Causal Graph	Track cause/effect relationships between memories — enables "why did X happen?" queries
Benchmark Suite	18 benchmarks: LoCoMo-compatible temporal, entity, multi-session + LongMemEval knowledge-updates

30 MCP tools (up from 14) — See MCP Tools for the complete list.

---

The Problem

Every AI conversation starts from zero. Every context window is a blank slate. Every session forgets everything that came before it.

The industry's answer has been RAG - retrieve a few document chunks, stuff them into the prompt, and hope for the best. But naive RAG has fundamental problems:

• No selectivity. It retrieves entire chunks when only one sentence matters.
• No memory structure. A quick fact and a deep architectural decision get the same treatment.
• No learning. It stores everything, learns nothing, and never gets smarter.
• No decay. Yesterday's bug fix and last year's deprecated API sit side by side with equal weight.
• No cross-pollination. Lessons from Project A never help with Project B.

The result? Bloated context windows. Irrelevant retrievals. Wasted tokens. AI that forgets everything the moment you close the tab.

The Solution

Titan Memory is a 5-layer cognitive memory system delivered as an MCP server. It doesn't just store and retrieve - it thinks about what to remember, how to remember it, and what to forget.

Drop it into Claude Code, Cursor, or any MCP-compatible AI tool. Your AI gets persistent, structured, intelligent memory across every session, every project, every conversation.

One command. Infinite memory.

claude mcp add titan-memory -- node ~/.claude/titan-memory/bin/titan-mcp.js

What makes it different

Feature	Naive RAG	Titan Memory
Storage	Store everything	Surprise-filtered - only novel information passes
Retrieval	Flat vector search	Hybrid BM25 + dense vectors with RRF reranking
Precision	Full chunks returned	Semantic highlighting - only gold sentences survive
Structure	Single embedding space	5-layer architecture with intelligent routing
Categorization	None	Cortex - 5-type classifier with guardrails
Decay	None (infinite accumulation)	Adaptive decay - content-type aware aging
Cross-project	Siloed per project	Pattern transfer between projects
Safety	None	OAuth2, scope-based auth, behavioral validation
Token savings	~0%	70-80% compression on recall

---

Architecture

Titan Memory organizes knowledge into five cognitive layers with intelligent routing, surprise-based filtering, and a full recall pipeline powered by Voyage AI reranking:

graph TB
    subgraph INPUT["Memory Input"]
        U["User / AI Session"] --> ADD["titan_add"]
    end

    subgraph INTAKE["Intake Pipeline"]
        ADD --> SF["Surprise Filter<br/><i>Is this novel?</i>"]
        SF -->|"Score ≥ 0.3<br/>Novel"| CC["Cortex Classifier<br/><i>What type is this?</i>"]
        SF -->|"Score < 0.3<br/>Already known"| DROP["🗑️ Dropped<br/><i>70% noise eliminated</i>"]
        CC --> ROUTE["Intelligent Router"]
    end

    subgraph LAYERS["5-Layer Memory Architecture"]
        ROUTE -->|"Facts"| L2["Layer 2: Factual<br/><i>O(1) hash lookup</i>"]
        ROUTE -->|"Patterns"| L4["Layer 4: Semantic<br/><i>Continual learning</i>"]
        ROUTE -->|"Events"| L5["Layer 5: Episodic<br/><i>Timestamped logs</i>"]
        ROUTE -->|"General"| L3["Layer 3: Long-Term<br/><i>Adaptive decay</i>"]
    end

    subgraph STORAGE["Vector Storage"]
        L2 --> ZC["Zilliz Cloud<br/><i>Dense + Sparse Vectors</i>"]
        L3 --> ZC
        L4 --> ZC
        L5 --> ZC
    end

    subgraph RECALL["Recall Pipeline"]
        RQ["titan_recall"] --> HS["Hybrid Search<br/><i>BM25 + Dense</i>"]
        HS --> VR["Voyage Reranker<br/><i>rerank-2 model</i>"]
        VR --> LIB["Librarian Pipeline"]
        LIB --> HL["Semantic Highlight<br/><i>Zilliz 0.6B Model</i>"]
        HL --> GOLD["🥇 Gold Sentences<br/><i>70-80% compressed</i>"]
    end

    ZC --> HS

    style DROP fill:#8b0000,stroke:#8b0000,color:#fff
    style GOLD fill:#0d7a3e,stroke:#0d7a3e,color:#fff
    style SF fill:#533483,stroke:#e94560,color:#fff
    style CC fill:#533483,stroke:#e94560,color:#fff
    style VR fill:#533483,stroke:#e94560,color:#fff
    style HL fill:#533483,stroke:#e94560,color:#fff
    style ZC fill:#16213e,stroke:#0f3460,color:#fff

Every memory is automatically routed to the right layer:

• Quick facts ("PostgreSQL default port is 5432") → Layer 2, O(1) hash lookup
• Learned patterns ("Always use connection pooling for high-traffic services") → Layer 4, continual learning
• Session events ("Deployed v2.3 to production at 3pm") → Layer 5, timestamped episodes
• Everything else → Layer 3, surprise-filtered with adaptive decay

---

Semantic Highlighting

This is the breakthrough. Most retrieval systems return entire documents or chunks. Titan Memory returns only the sentences that matter.

Powered by the Zilliz semantic-highlight-bilingual-v1 model — a 0.6 billion parameter encoder that scores every sentence for query relevance, then prunes everything below threshold.

graph LR
    Q["Query:<br/><i>'What is the moisture<br/>protocol for the slab?'</i>"] --> E["Zilliz 0.6B<br/>Encoder"]

    E --> S1["✅ Protocol 407 requires<br/>72-hour moisture testing<br/><b>Score: 0.956</b>"]
    E --> S2["❌ The project started<br/>in January<br/><b>Score: 0.041</b>"]
    E --> S3["❌ We hired three new<br/>subcontractors last week<br/><b>Score: 0.001</b>"]
    E --> S4["✅ Slab moisture must be<br/>below 75% RH per spec<br/><b>Score: 0.892</b>"]
    E --> S5["❌ Weather delayed the<br/>concrete pour twice<br/><b>Score: 0.092</b>"]

    S1 --> G["🥇 Gold Sentences<br/><b>63% compression</b><br/>Only what matters<br/>reaches the LLM"]
    S4 --> G

    style S1 fill:#0d7a3e,stroke:#0d7a3e,color:#fff
    style S4 fill:#0d7a3e,stroke:#0d7a3e,color:#fff
    style S2 fill:#8b0000,stroke:#8b0000,color:#fff
    style S3 fill:#8b0000,stroke:#8b0000,color:#fff
    style S5 fill:#8b0000,stroke:#8b0000,color:#fff
    style G fill:#1a1a2e,stroke:#e94560,color:#fff
    style Q fill:#16213e,stroke:#0f3460,color:#fff
    style E fill:#533483,stroke:#e94560,color:#fff

3-Tier Scoring Fallback

The system never fails silently. If the primary scorer is unavailable, it degrades gracefully:

graph TD
    R["Memory Recall"] --> C{"Zilliz 0.6B<br/>Sidecar Running?"}
    C -->|Yes| Z["<b>Tier 1: Zilliz Model</b><br/>0.6B encoder, 8192 token context<br/>Sentence-level probability scoring<br/><i>Best accuracy</i>"]
    C -->|No| V{"Voyage AI<br/>Available?"}
    V -->|Yes| VE["<b>Tier 2: Voyage Embeddings</b><br/>Cosine similarity per sentence<br/>Batch embedding generation<br/><i>Good accuracy</i>"]
    V -->|No| T["<b>Tier 3: Term Overlap</b><br/>Keyword matching fallback<br/>Zero external dependencies<br/><i>Basic accuracy</i>"]

    Z --> O["Gold Sentences<br/>+ Compression Stats"]
    VE --> O
    T --> O

    style Z fill:#0d7a3e,stroke:#0d7a3e,color:#fff
    style VE fill:#b8860b,stroke:#b8860b,color:#fff
    style T fill:#4a4a4a,stroke:#888,color:#fff
    style O fill:#1a1a2e,stroke:#e94560,color:#fff
    style R fill:#16213e,stroke:#0f3460,color:#fff

Real Numbers

Metric	Value
Token compression on recall	70-80%
Relevant sentence precision	>0.9 for domain queries
Noise sentence rejection	<0.1 score
Scoring latency (Zilliz model)	<100ms
Fallback latency (Voyage)	<200ms
Context window savings per recall	Thousands of tokens

---

Cortex Classifier

Every memory gets classified into one of five cognitive categories by the Cortex pipeline — a multi-stage classifier with confidence thresholds, drift monitoring, and safety guardrails.

graph TD
    M["Incoming Memory"] --> A["Feature Extraction<br/><i>Keywords, patterns,<br/>structure analysis</i>"]

    A --> CL["Cortex Classifier"]

    CL --> CONF{"Confidence<br/>≥ 0.6?"}

    CONF -->|"Yes"| CAT["Assign Category"]
    CONF -->|"No"| FALL["Fallback to<br/>Knowledge (default)"]

    CAT --> K["🧠 Knowledge<br/><i>'PostgreSQL uses port 5432'</i><br/>Half-life: 365 days"]
    CAT --> P["👤 Profile<br/><i>'User prefers TypeScript'</i><br/>Half-life: 300 days"]
    CAT --> E["📅 Event<br/><i>'Deployed v2.3 today'</i><br/>Half-life: 180 days"]
    CAT --> B["⚙️ Behavior<br/><i>'Always runs tests first'</i><br/>Half-life: 300 days"]
    CAT --> S["🎯 Skill<br/><i>'Use connection pooling'</i><br/>Half-life: 270 days"]

    K --> GR["Guardrails Check"]
    P --> GR
    E --> GR
    B --> GR
    S --> GR
    FALL --> GR

    GR --> DM["Drift Monitor<br/><i>Track category distribution</i>"]
    DM --> STORE["✅ Stored with<br/>category metadata"]

    style CL fill:#533483,stroke:#e94560,color:#fff
    style GR fill:#1a1a2e,stroke:#e94560,color:#fff
    style STORE fill:#0d7a3e,stroke:#0d7a3e,color:#fff
    style M fill:#16213e,stroke:#0f3460,color:#fff

The Librarian Pipeline

On recall, Cortex's "Librarian" processes retrieved memories through a full refinement pipeline:

graph TD
    Q["Recall Query"] --> R["Retrieve Top-K<br/>Candidates"]
    R --> SS["Sentence Split"]
    SS --> SH["Semantic Highlight<br/><i>Score every sentence</i>"]
    SH --> PR["Prune Below<br/>Threshold"]
    PR --> TC["Temporal Conflict<br/>Resolution"]
    TC --> CC["Category Coverage<br/>Check"]
    CC --> GS["🥇 Gold Sentences<br/><i>Compressed, relevant,<br/>conflict-free</i>"]

    style Q fill:#16213e,stroke:#0f3460,color:#fff
    style SH fill:#533483,stroke:#e94560,color:#fff
    style GS fill:#0d7a3e,stroke:#0d7a3e,color:#fff

---

Hybrid Search

Titan Memory doesn't rely on a single retrieval method. It fuses dense semantic vectors with BM25 sparse keyword vectors using Reciprocal Rank Fusion:

graph TD
    Q["Search Query:<br/><i>'database connection timeout errors'</i>"]

    Q --> DENSE["Dense Vector Search<br/><i>Voyage AI embeddings</i>"]
    Q --> SPARSE["BM25 Sparse Search<br/><i>Keyword matching</i>"]

    DENSE --> DR["Dense Results:<br/>1. PostgreSQL timeout config<br/>2. Connection pool best practices<br/>3. Network latency debugging"]

    SPARSE --> SR["Sparse Results:<br/>1. 'ECONNREFUSED timeout' error log<br/>2. Database connection timeout settings<br/>3. Timeout retry configuration"]

    DR --> RRF["Reciprocal Rank Fusion<br/><i>RRF(d) = Σ 1/(k + rank)</i>"]
    SR --> RRF

    RRF --> VR["Voyage Reranker<br/><i>rerank-2 model</i>"]

    VR --> MERGED["Final Results:<br/>1. Database connection timeout settings<br/>2. PostgreSQL timeout config<br/>3. 'ECONNREFUSED timeout' error log<br/><i>Best of semantic + keyword + reranking</i>"]

    style Q fill:#16213e,stroke:#0f3460,color:#fff
    style DENSE fill:#533483,stroke:#e94560,color:#fff
    style SPARSE fill:#b8860b,stroke:#b8860b,color:#fff
    style RRF fill:#1a1a2e,stroke:#e94560,color:#fff
    style VR fill:#533483,stroke:#e94560,color:#fff
    style MERGED fill:#0d7a3e,stroke:#0d7a3e,color:#fff

• Semantic search finds meaning: "database connection issues" retrieves "PostgreSQL timeout errors"
• BM25 search finds terms: "ECONNREFUSED 127.0.0.1:5432" retrieves exact error matches
• RRF fusion combines both ranking signals into a single result set
• Voyage Reranker re-scores the fused results for maximum relevance accuracy

---

Surprise-Based Storage

Not everything deserves to be remembered. Titan Memory uses surprise detection to filter incoming memories — only genuinely novel information passes the threshold.

graph TD
    N["New Memory"] --> SC["Calculate<br/>Surprise Score"]
    SC --> |"Score ≥ 0.3"| STORE["✅ Store<br/><i>Novel information</i>"]
    SC --> |"Score < 0.3"| SKIP["⏭️ Skip<br/><i>Already known</i>"]

    SC --> F["Surprise = Novelty + Pattern Boost"]
    F --> NOV["Novelty = 1 - max(similarity)"]
    F --> PB["Pattern Boost:<br/>Decisions +0.2<br/>Errors +0.3<br/>Solutions +0.25"]

    style STORE fill:#0d7a3e,stroke:#0d7a3e,color:#fff
    style SKIP fill:#8b0000,stroke:#8b0000,color:#fff
    style SC fill:#533483,stroke:#e94560,color:#fff

Result: 70%+ noise reduction at the storage layer, before retrieval even begins.

---

Benchmarks

Titan Memory v2.0 includes 18 benchmarks across 4 categories, aligned with academic standards including LoCoMo (Snap Research) and LongMemEval (ICLR 2025).

Results: 18/18 Passing — 90.7/100 (LLM Mode) | 84.2/100 (Zero-LLM)

graph LR
    subgraph ACC["Accuracy (11 benchmarks) — LLM Mode"]
        A1["Factual Lookup<br/><b>76.0</b> ✅ (+4)"]
        A2["Semantic Similarity<br/><b>100.0</b> ✅"]
        A3["Intent Retrieval<br/><b>90.0</b> ✅"]
        A4["Cross-Layer<br/><b>100.0</b> ✅"]
        A5["Temporal Reasoning<br/><b>75.0</b> ✅"]
        A6["Multi-Session<br/><b>100.0</b> ✅"]
        A7["Entity Tracking<br/><b>100.0</b> ✅"]
        A8["Info Extraction<br/><b>85.0</b> ✅ (+20)"]
        A9["Single-Session QA<br/><b>100.0</b> ✅"]
        A10["Multi-Session QA<br/><b>83.3</b> ✅"]
        A11["Knowledge Updates<br/><b>88.0</b> ✅ (+12)"]
    end

    subgraph LAT["Latency (5 benchmarks)"]
        L1["Add Operation<br/><b>85.7</b> ✅"]
        L2["Recall Operation<br/><b>88.0</b> ✅"]
        L3["Classify<br/><b>94.9</b> ✅"]
        L4["Intent Detection<br/><b>99.6</b> ✅"]
        L5["Focus Ops<br/><b>99.5</b> ✅"]
    end

    subgraph TOK["Token Efficiency (2 benchmarks)"]
        T1["Compression Ratio<br/><b>38.4</b> ✅"]
        T2["Fidelity<br/><b>48.5</b> ✅"]
    end

    style A1 fill:#1a6b3e,stroke:#1a6b3e,color:#fff
    style A2 fill:#0d7a3e,stroke:#0d7a3e,color:#fff
    style A4 fill:#0d7a3e,stroke:#0d7a3e,color:#fff
    style A6 fill:#0d7a3e,stroke:#0d7a3e,color:#fff
    style A7 fill:#0d7a3e,stroke:#0d7a3e,color:#fff
    style A8 fill:#1a6b3e,stroke:#1a6b3e,color:#fff
    style A9 fill:#0d7a3e,stroke:#0d7a3e,color:#fff
    style A11 fill:#1a6b3e,stroke:#1a6b3e,color:#fff
    style L4 fill:#0d7a3e,stroke:#0d7a3e,color:#fff
    style L5 fill:#0d7a3e,stroke:#0d7a3e,color:#fff

Competitive Landscape

Titan Memory now scores in the top tier alongside funded competitors — and uniquely offers both modes: zero-LLM deterministic retrieval OR optional LLM-enhanced processing:

System	Overall	Architecture	LLM in Loop	Safety Infrastructure	Funding
EverMemOS	93.1%	Memory OS	Yes (GPT-4)	None	Funded
Titan Memory (LLM mode)	90.7%*	5-Layer MCP Server	Optional	Circuit breakers, self-healing, drift monitoring	Solo dev
Backboard.io	90.1%	Cloud Memory API	Yes (GPT-4.1)	None	Funded
Vectorize Hindsight	89.6%	Open-Source Agent Memory	Yes (Gemini 3 Pro)	None	Funded
MemMachine	84.9%	Episodic Memory Engine	Yes	None	Funded
Titan Memory (zero-LLM)	84.2%*	5-Layer MCP Server	No	Circuit breakers, self-healing, drift monitoring	Solo dev
Memobase	75.8%	Profile-Based Memory	Yes	None	Funded
Mem0	66.9%	Managed Memory Platform	Yes	None	$24M+
OpenAI Memory	52.9%

*Titan's benchmarks are LoCoMo-compatible and LongMemEval-aligned using synthetic test data

Why this matters:

• Two modes, one system. Zero-LLM mode gives deterministic retrieval at embedding-only cost. LLM mode adds semantic understanding for +6.5 accuracy points — your choice per deployment.
• Only memory system with production safety infrastructure. Circuit breakers, self-healing with exponential backoff, drift monitoring, NOOP tracking, and behavioral validation. No competitor has this.
• Provider-agnostic LLM. Not locked to GPT-4 or any single provider. Use Anthropic, OpenAI, Groq (~350ms/call), Ollama (local), or any OpenAI-compatible API.

Category Comparison (LongMemEval)

Category	Titan	Hindsight (SOTA)	Supermemory
Multi-Session QA	83.3%	79.7%	71.4%
Single-Session QA	100.0%	—	—
Knowledge Updates	76.0%	84.6%	—
Temporal Reasoning	75.0%	79.7%	76.7%

Titan beats the SOTA on multi-session QA and matches on temporal reasoning — categories where most memory systems struggle.

---

Adaptive Decay

Memories age differently based on what they contain. An architectural decision stays relevant for a year. A bug fix fades in months. Titan Memory models this with content-type aware decay:

Content Type	Half-Life	Why
Architecture decisions	365 days	Structural choices persist
User preferences	300 days	Preferences rarely change
Solutions	270 days	Solutions stay useful
Learned patterns	180 days	Need periodic refresh
Bug fixes / errors	90 days	Errors get fixed, fade fast

Memories that get accessed frequently decay slower. Memories marked as helpful get a utility boost. The system self-organizes over time — important memories surface, irrelevant ones fade naturally.

---

Cross-Project Learning

Lessons learned in one project automatically transfer to others. Titan Memory maintains a pattern library with applicability scoring and 180-day half-life decay:

graph LR
    PA["Project A<br/><i>Learned: 'Always add<br/>retry logic to API calls'</i>"] --> PL["Pattern Library<br/><i>Zilliz Cloud</i>"]
    PB["Project B<br/><i>Learned: 'Use connection<br/>pooling for databases'</i>"] --> PL
    PC["Project C<br/><i>Working on API<br/>integration...</i>"] --> Q["Query: 'API best practices'"]
    Q --> PL
    PL --> R["Relevant Patterns<br/><i>Ranked by applicability<br/>and recency</i>"]
    R --> PC

    style PL fill:#533483,stroke:#e94560,color:#fff
    style R fill:#0d7a3e,stroke:#0d7a3e,color:#fff

---

Installation

Claude Code Plugin (Recommended)

claude plugin install titan-memory

One command. Installs the MCP server, context injection, and setup/status skills.

Then run /titan-memory:setup to configure your environment variables.

Manual Install

# Clone the repository
git clone https://github.com/TC407-api/Titan-Memory.git ~/.claude/titan-memory

# Install and build
cd ~/.claude/titan-memory
npm install
npm run build

# Add to Claude Code
claude mcp add titan-memory -s user -- node ~/.claude/titan-memory/bin/titan-mcp.js

Environment Variables

# Required: Zilliz Cloud (vector storage)
ZILLIZ_URI=your-zilliz-cloud-uri
ZILLIZ_TOKEN=your-zilliz-token

# Required: Voyage AI (embeddings)
VOYAGE_API_KEY=your-voyage-api-key

# Optional: Semantic highlight sidecar URL
TITAN_HIGHLIGHT_URL=http://127.0.0.1:8079

# Optional: LLM Turbo Layer (v2.1) — pick one provider
ANTHROPIC_API_KEY=your-anthropic-key     # For Claude Sonnet/Opus
OPENAI_API_KEY=your-openai-key           # For GPT-4o/GPT-4
GROQ_API_KEY=your-groq-key              # For Llama 3.3 70B (~350ms/call)

Enable the Semantic Highlight Engine (Optional)

The Zilliz 0.6B model runs as a Python sidecar service for maximum highlighting precision. Without it, the system falls back to Voyage AI embeddings — still good, but the dedicated model is better.

# Create Python environment
cd ~/.claude/titan-memory
uv venv highlight-env
uv pip install --python highlight-env/Scripts/python.exe torch transformers fastapi uvicorn huggingface-hub nltk

# Download the model (~1.2GB)
highlight-env/Scripts/python.exe -c "from huggingface_hub import snapshot_download; snapshot_download('zilliz/semantic-highlight-bilingual-v1', local_dir='models/semantic-highlight-bilingual-v1')"

# Start the sidecar service
./start-highlight-service.ps1    # Windows
# OR
python highlight-service.py       # Any platform

Configuration

Create or edit config.json in the titan-memory directory:

{
  "surpriseThreshold": 0.3,
  "decayHalfLife": 180,
  "maxMemoriesPerLayer": 10000,
  "enableSurpriseFiltering": true,

  "cortex": {
    "enabled": true,
    "highlightThreshold": 0.8,
    "enableGuardrails": true,
    "enableDriftMonitor": true
  },

  "embedding": {
    "provider": "voyage",
    "model": "voyage-3-large",
    "dimension": 1024
  },

  "semanticHighlight": {
    "enabled": true,
    "threshold": 0.5,
    "highlightOnRecall": true
  },

  "hybridSearch": {
    "enabled": true,
    "rerankStrategy": "rrf"
  },

  "proactiveSuggestions": {
    "enabled": true
  },

  "crossProject": {
    "enabled": true
  },

  "llm": {
    "enabled": true,
    "provider": "openai-compatible",
    "model": "llama-3.3-70b-versatile",
    "baseUrl": "https://api.groq.com/openai/v1",
    "timeout": 15000,
    "rerankEnabled": true,
    "classifyEnabled": true,
    "extractEnabled": true,
    "summarizeEnabled": false
  }
}

---

MCP Tools

Titan Memory v2.0 exposes 30 tools through the Model Context Protocol:

Core Memory

Tool	Description
titan_add	Store memory with intelligent layer routing and surprise filtering
titan_recall	Query with hybrid search, semantic highlighting, and Cortex refinement
titan_get	Retrieve a specific memory by ID
titan_delete	Delete a memory by ID
titan_stats	Memory statistics across all layers
titan_flush	Pre-compaction save — preserve critical context before the window compacts
titan_curate	Add to human-curated MEMORY.md
titan_today	Get today's episodic entries
titan_prune	Prune decayed memories with adaptive thresholds
titan_feedback	Mark memories as helpful or harmful — feeds into decay and pruning

Intelligence Layer

Tool	Description
titan_suggest	Proactive memory suggestions based on current context
titan_patterns	Cross-project pattern discovery
titan_miras_stats	MIRAS enhancement system statistics
titan_classify	Cortex category classification
titan_category_summary	Rolling summary for a memory category
titan_sufficiency	Check category coverage of recall results

NOOP / Skip Operations (v2.0)

Tool	Description
titan_noop	Explicitly skip memory storage — prevents bloat from routine interactions
titan_noop_stats	Analytics on skip decisions

Intent-Aware Retrieval (v2.0)

Tool	Description
titan_intent	Detect query intent for optimized retrieval strategy

Causal Graph (v2.0)

Tool	Description
titan_link	Create causal relationships between memories
titan_trace	Trace causal chains from a memory
titan_why	Explain with causal graph — "why did X happen?"

Working Memory (v2.0)

Tool	Description
titan_focus_add	Pin content to working memory focus
titan_focus_list	List current focus items
titan_focus_clear	Clear all focus items
titan_focus_remove	Remove specific focus item
titan_scratchpad	Get/set agent scratchpad for thinking

Compression (v2.0)

Tool	Description
titan_compress	Compress a memory into entities, relationships, and key facts (30x token reduction)
titan_expand	Reconstruct readable text from compressed memory

Benchmarking (v2.0)

Tool	Description
titan_benchmark	Run 18 benchmarks across accuracy, latency, and token efficiency

Example Usage

// Store a memory — automatically routed to the right layer
{
  "name": "titan_add",
  "arguments": {
    "content": "The fix for the auth timeout was switching from JWT verification on every request to a session cache with 5-minute TTL",
    "tags": ["auth", "performance", "solution"]
  }
}

// Recall with semantic highlighting — only gold sentences returned
{
  "name": "titan_recall",
  "arguments": {
    "query": "How did we fix the authentication performance issue?",
    "limit": 5
  }
}
// Response includes:
//   results: [...],
//   highlightedContext: "The fix for the auth timeout was switching from JWT verification on every request to a session cache with 5-minute TTL",
//   highlightStats: { totalSentences: 12, goldSentences: 2, compressionRate: 0.37 }

---

The Recall Pipeline

This is the full journey of a recall query through Titan Memory:

graph TD
    Q["🔍 Query"] --> HS["Hybrid Search<br/><i>BM25 + Dense Vectors</i>"]
    HS --> VR["Voyage Reranker<br/><i>rerank-2 model</i>"]
    VR --> RRF["Result Fusion<br/><i>Score-based + recency tiebreak</i>"]
    RRF --> CB1["Cortex Hook 1<br/><i>Category Enrichment</i>"]
    CB1 --> CB2["Cortex Hook 2<br/><i>Sufficiency Check</i>"]
    CB2 --> LIB["🏛️ Librarian Pipeline"]

    subgraph "Librarian (Cortex Hook 4)"
        LIB --> SS["Sentence Split"]
        SS --> SEM["Semantic Highlight<br/><i>Zilliz 0.6B / Voyage / Keywords</i>"]
        SEM --> PRUNE["Prune Noise<br/><i>Below threshold = gone</i>"]
        PRUNE --> LLM{"LLM Turbo<br/>Enabled?"}
        LLM -->|Yes| RERANK["LLM Rerank<br/><i>Semantic relevance scoring</i>"]
        LLM -->|No| TEMP["Temporal Conflict<br/>Resolution"]
        RERANK --> TEMP
        TEMP --> COV["Category Coverage"]
    end

    COV --> GOLD["🥇 Response<br/><i>Gold sentences + stats<br/>70-80% smaller</i>"]

    style Q fill:#16213e,stroke:#0f3460,color:#fff
    style HS fill:#533483,stroke:#e94560,color:#fff
    style VR fill:#533483,stroke:#e94560,color:#fff
    style LIB fill:#1a1a2e,stroke:#e94560,color:#fff
    style GOLD fill:#0d7a3e,stroke:#0d7a3e,color:#fff
    style SEM fill:#533483,stroke:#e94560,color:#fff

---

Enterprise

Titan Memory ships with enterprise-grade safety and access control built in.

OAuth2 / Token Authentication

# Start in HTTP server mode with OAuth
node bin/titan-mcp.js --http --port 3456

# Environment
AUTH0_DOMAIN=your-tenant.auth0.com
AUTH0_AUDIENCE=https://titan-memory.example.com
AUTH0_CLIENT_ID=your-client-id

Scope-Based Authorization

Scope	Permissions
titan:read	Query, get, stats, today, suggest, patterns
titan:write	Add, delete, flush, curate, prune, feedback
titan:admin	All operations + configuration

Safety Guardrails

• Cortex Guardrails — Validates memory classification with confidence thresholds
• Drift Monitor — Detects category distribution drift over time
• Behavioral Validation — Quality scoring and anomaly detection
• Surprise Filtering — Prevents noise accumulation at the storage layer
• Adaptive Decay — Automatic cleanup of stale memories
• Temporal Conflict Resolution — Newer information supersedes older contradictions

OAuth2 Discovery

curl http://localhost:3456/.well-known/oauth-authorization-server

---

What It Costs

Nothing. And it saves you money.

Component	Cost
Titan Memory server	Free — open source, Apache 2.0
Zilliz Cloud (vector storage)	Free tier available, pennies at scale
Voyage AI (embeddings)	Fractions of a cent per query
Zilliz 0.6B highlight model	Free — MIT license, runs on CPU, no GPU required

Now here's the part that matters: the semantic highlighting actually saves you money. Every recall query compresses retrieved context by 70-80% before it ever reaches the LLM. That means 70-80% fewer tokens on the most expensive part of your entire AI pipeline — the model inference. The more you use Titan Memory, the less you spend on your LLM.

Compare that to managed memory and RAG services from Google (Vertex AI Knowledge Bases), Amazon (Bedrock Knowledge Bases), or Microsoft (Azure AI Search). Those services are metered per query, per GB stored, per embedding generated — and they don't do sentence-level highlighting, surprise filtering, or adaptive decay. You're paying more for less.

The most sophisticated component in the system — the 0.6B encoder doing sentence-level relevance scoring — runs locally on your machine's CPU. No GPU instance. No cloud inference endpoint. No per-token billing. After download, it costs exactly zero.

An enterprise could deploy Titan Memory for their entire AI team and the infrastructure cost would be less than one engineer's monthly coffee budget.

Sustainability

Every token sent to an LLM burns GPU cycles. Titan Memory's 70-80% token compression on recall means 70-80% less GPU inference energy on every single interaction. The semantic highlight model runs on CPU — orders of magnitude more energy efficient than GPU inference. Surprise filtering prevents unnecessary storage and computation at the intake layer. Adaptive decay automatically cleans up what's no longer needed.

Multiply that across an enterprise running thousands of AI interactions per day and the energy savings are measurable. Less compute, less power, less carbon — without sacrificing capability. In fact, by sending only relevant context to the LLM, response quality goes up while energy consumption goes down.

For organizations with ESG commitments, carbon reporting requirements, or sustainability mandates: Titan Memory doesn't just make AI smarter and cheaper. It makes AI greener.

---

Project Stats

Metric	Value
Source files	107 TypeScript modules
Lines of code	~31,500
Test suites	41
Tests passing	1,008 / 1,008
Benchmarks	18 (all passing)
Benchmark score (zero-LLM)	84.2 / 100
Benchmark score (LLM mode)	90.7 / 100
Dependencies	9 production, 8 dev
Node.js	>= 18
MCP tools	30
Memory layers	5
Cortex categories	5
LLM providers supported	3 (Anthropic, OpenAI, OpenAI-compatible)

---

Research Foundations

Titan Memory synthesizes breakthrough research from nine distinct systems into a single production architecture:

Source	Contribution
DeepSeek Engram	O(1) N-gram hash lookup for factual memory
Google Titans	Surprise-based selective storage with momentum
MIRAS	Intelligent retrieval and adaptive storage
Google Hope / Nested Learning	Multi-frequency continual learning
Clawdbot	Practical episodic memory patterns
Cognee	Knowledge graphs and decision traces
Mem0	Adaptive memory with consolidation
Voyage AI	State-of-the-art embedding models
Zilliz Semantic Highlight	0.6B sentence-level relevance scoring
IndyDevDan	Claude Code agentic architecture patterns and multi-agent orchestration
Claude (Anthropic)	Co-architect and implementation partner

---

CLI

# Add memories
titan add "The fix for the auth bug was to check token expiry before refresh"
titan add "API rate limit is 100 requests per minute" --layer factual

# Recall
titan recall "authentication issues"
titan recall "error handling" --limit 5

# Manage
titan stats
titan today
titan prune --threshold 0.1
titan export --output memories.json

# Pre-compaction flush
titan flush -d "Decided to use Redis" -s "Fixed memory leak"

---

License

Apache 2.0

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