Relevance-Conditioned Scoring
Relevance gates every other signal multiplicatively. A perfectly relevant old memory surfaces. An irrelevant high-importance memory doesn’t. This is the key insight — patented and proven.
The memory engine for AI that actually knows you.
Not another vector database. YantrikDB models how memory actually works — temporal decay, semantic consolidation, contradiction detection, and proactive triggers. One embedded engine. Five unified indexes. Zero servers.
pip install yantrikdbcargo add yantrikdbbrew install yantrikos/tap/yantrikdbdocker pull ghcr.io/yantrikos/yantrikdbv0.5.5 · Early adopters welcome
The embeddable engine is mature and used in production by the YantrikOS ecosystem. The network database server is new — running live on a homelab cluster but not yet battle-tested at scale. Read the maturity notes →
The Problem
Section titled “The Problem”Every AI memory solution does the same thing:
Store everything. Embed. Retrieve top-k. Inject into context. Hope it helps.
That doesn’t model how memory works. It treats all memories as equal. Old memories never fade. Contradictions are never detected. Nothing is ever consolidated. The AI never proactively remembers anything.
YantrikDB fixes all of this.
Cognitive State Graph
Typed nodes (beliefs, goals, intents, preferences) with typed edges (supports, contradicts, causes, predicts). Your AI doesn’t just remember — it reasons about what it knows.
Autonomous Cognition
Consolidation merges related memories. Conflict detection flags contradictions. Pattern mining discovers recurring themes. All automatic via db.think().
Proactive Triggers
Decaying memories, unresolved conflicts, emerging patterns — YantrikDB tells your AI when to act, grounded in real data. Not engagement farming.
Five Unified Indexes
Vector (HNSW), graph, temporal, decay heap, and key-value — all in one embedded SQLite database. No server. No infrastructure. Just a file.
MCP Server
pip install yantrikdb-mcp — instant persistent memory for Claude Code, Cursor, Windsurf, and any MCP-compatible AI agent.
Try It Live
Section titled “Try It Live”The real YantrikDB engine — Rust compiled to WebAssembly — running in your browser. No server. No API calls. Click through to see record(), recall(), relate(), and think() in action.
Three ways to use YantrikDB
Section titled “Three ways to use YantrikDB”YantrikDB ships in three forms. Pick the one that fits your stack:
📦 Embeddable engine
Drop the Rust crate or Python package into your app. Zero servers, single-process, fastest possible. Best for desktop apps, agents that own their memory, and CLI tools.
cargo add yantrikdbpip install yantrikdb🌐 Network database
Run yantrikdb serve and get a multi-tenant database with HTTP + wire protocol, replication, automatic failover, encryption at rest, and a psql-style REPL. Best for self-hosted agents, homelab clusters, and shared memory across services.
brew install yantrikos/tap/yantrikdbdocker pull ghcr.io/yantrikos/yantrikdb🔌 MCP server
Plug-and-play memory for Claude Code, Cursor, Windsurf and any MCP-compatible agent. 15 tools for remember/recall/relate/think. The fastest way to give an existing AI assistant persistent memory.
pip install yantrikdb-mcpAll three share the same underlying engine and convergent semantics. You can start embedded and migrate to clustered later — your data works the same way.
Architecture
Section titled “Architecture”| Index | What It Does | Example Query |
|---|---|---|
| Vector (HNSW) | Semantic similarity search | ”What did the user say about work?” |
| Graph | Entity relationships & reasoning | ”Who works at what company?” |
| Temporal | Time-aware retrieval | ”What happened last Tuesday?” |
| Decay Heap | Importance with biological time decay | Memories fade like human memory |
| Key-Value | Instant fact lookup | ”User’s timezone is CST” |
All five indexes query the same data. A single recall() call blends signals from all of them into one relevance-conditioned score.
How It’s Different
Section titled “How It’s Different”| Vector DB | RAG Pipeline | YantrikDB | |
|---|---|---|---|
| Storage | Flat embeddings | Chunked documents | Typed memories with metadata |
| Retrieval | Cosine top-k | Hybrid search | Relevance-conditioned scoring |
| Time | Ignored | Ignored | Temporal decay + recency |
| Contradictions | Undetected | Undetected | Automatic conflict detection |
| Consolidation | None | None | Autonomous merging |
| Proactive | Never | Never | Trigger-based notifications |
| Graph | Separate system | None | Built-in cognitive state graph |
Benchmark: Token Savings vs File-Based Memory
Section titled “Benchmark: Token Savings vs File-Based Memory”Benchmarked with 15 diverse queries across 4 scales. File-based memory (CLAUDE.md, memory files) loads everything into context every conversation. YantrikDB’s selective recall retrieves only the 3–5 memories relevant to the current task.
| Memories | File-Based | YantrikDB | Savings | Precision |
|---|---|---|---|---|
| 100 | 1,770 tokens | 69 tokens | 96% | 66% |
| 500 | 9,807 tokens | 72 tokens | 99.3% | 77% |
| 1,000 | 19,988 tokens | 72 tokens | 99.6% | 84% |
| 5,000 | 101,739 tokens | 53 tokens | 99.9% | 88% |
Selective recall cost is O(1). File-based memory cost is O(n).
At 500 memories, file-based memory already exceeds 32K context windows. At 5,000 memories, it doesn’t fit in any context window — not even 200K. YantrikDB stays at ~70 tokens per query with recall latency under 60ms. Precision improves with more data: the opposite of file-based memory, which degrades as context fills up.
Works with Claude Code, Cursor, Windsurf, Copilot, Kilo Code — any MCP-compatible agent. Run the benchmark yourself: python benchmarks/bench_token_savings.py
Patent
Section titled “Patent”U.S. Patent Application No. 19/573,392 (filed March 2026) — covers relevance-conditioned scoring, the cognitive state graph, and the unified system architecture.
Open source under AGPL-3.0. The patent protects the methods, not the code. Use it freely. Read more →
Ecosystem
Section titled “Ecosystem”| Component | Description | License |
|---|---|---|
| yantrikdb | Cognitive memory engine (Rust + Python bindings) | AGPL-3.0 |
| yantrikdb-server | Multi-tenant network database with replication, auto-failover, encryption | AGPL-3.0 |
| yantrikdb-witness | Vote-only daemon for 2-node Raft cluster failover | AGPL-3.0 |
| yantrikdb-protocol | Wire protocol codec (frames, opcodes, MessagePack) | AGPL-3.0 |
| yql | Interactive REPL client (like psql for cognitive memory) | MIT |
| yantrikdb-mcp | MCP server for Claude Code, Cursor, Windsurf & more | MIT |
| Cortex | OpenClaw/ClawDBot plugin — personality traits, bond evolution, context assembly | MIT |
Distribution: crates.io · Docker Hub (GHCR) · Homebrew tap · PyPI
Open source. Get started →