YantrikDB FAQ — Cognitive Memory, MCP, Cluster Mode, Pricing

Common questions about YantrikDB, cognitive memory for AI agents, MCP integration, cluster mode, and licensing.

What is YantrikDB?

YantrikDB is a cognitive memory database for AI agents. It gives LLMs and agents persistent long-term memory with semantic recall, a knowledge graph, contradiction detection, autonomous consolidation, and temporal decay.

It ships in four shapes — pick the one that fits:

• Embeddable Rust library — cargo add yantrikdb. Link it into your app.
• Python package — pip install yantrikdb. For Python apps and notebooks.
• MCP server — pip install yantrikdb-mcp. Drop-in memory for Claude Code, Cursor, Windsurf.
• Network database — yantrikdb serve. Multi-tenant HTTP gateway with Raft cluster mode and at-rest encryption.

How is YantrikDB different from a vector database?

A vector database stores embeddings and returns top-k by cosine similarity. YantrikDB models how memory actually works. Every memory has importance, valence, half-life (decay), source attribution, and validity windows. The engine has an integrated knowledge graph with typed entity edges, contradiction detection between stored claims, autonomous consolidation that merges duplicates and surfaces patterns, and proactive triggers.

A vector DB is one of five indexes inside YantrikDB, not the whole picture.

Does YantrikDB work with Claude Code, Cursor, and Windsurf?

Yes. YantrikDB ships an MCP server (yantrikdb-mcp) that drops into any MCP-compatible AI client — Claude Code, Cursor, Windsurf, Copilot, and Claude Desktop. 24 tools: remember, recall, think, graph, conflict detection, personality.

pip install yantrikdb-mcp

What is cognitive memory?

Cognitive memory models the human memory system:

• Encoding — what to store (with importance, valence, source)
• Consolidation — merging duplicates and surfacing patterns
• Retrieval — importance- and recency-weighted recall, not just similarity
• Forgetting — temporal decay so old low-importance memories fade
• Contradiction handling — detecting and resolving conflicting beliefs

Vector search is one component; cognitive memory is the architecture.

Can I run YantrikDB without an internet connection?

Yes. YantrikDB runs fully offline. Since v0.7.0, the default install bundles a static embedder (potion-base-2M, ~7.9 MB, pure Rust) so no ONNX Runtime or sentence-transformers download is needed. The engine is a single SQLite file plus an HNSW index — zero network calls.

Optional higher-quality embedders (potion-base-8M at ~92% MiniLM, potion-base-32M at ~95% MiniLM) are downloadable and SHA-256 pinned; once downloaded they also work offline.

Is YantrikDB free?

Yes — open source under AGPL-3.0. Free to use, modify, and self-host.

The AGPL obligation: if you offer YantrikDB as a network service (SaaS), you must publish your source code modifications. For closed-source SaaS or proprietary embedded use, contact the maintainers about commercial licensing.

What’s the difference between yantrikdb, yantrikdb-server, and yantrikdb-mcp?

Repo	What it is	Install
yantrikos/yantrikdb	Embeddable Rust engine + Python bindings	cargo add yantrikdb / pip install yantrikdb
yantrikos/yantrikdb-server	Multi-tenant network database — HTTP, wire protocol, cluster	brew install yantrikos/tap/yantrikdb
yantrikos/yantrikdb-mcp	MCP server wrapper for Claude Code / Cursor / Windsurf	pip install yantrikdb-mcp

How do I install YantrikDB?

See the full install guide and quickstart. One-liners:

# Embeddable engine (Python)
pip install yantrikdb

# Embeddable engine (Rust)
cargo add yantrikdb

# MCP server for AI clients
pip install yantrikdb-mcp

# Network server (macOS/Linux)
brew install yantrikos/tap/yantrikdb

# Docker
docker pull ghcr.io/yantrikos/yantrikdb:latest

What hardware does YantrikDB need?

Single-node: any 64-bit Linux, macOS, or Windows machine with 1 GB RAM and 1 GB disk handles tens of thousands of memories. A Raspberry Pi 4 runs a working single-agent instance.

Production cluster: 3-node deployment with 4 GB RAM and SSD recommended. See cluster setup.

Does YantrikDB support clustering and replication?

Yes. YantrikDB Server cluster mode:

• openraft consensus — leader election, log replication, automatic failover
• mTLS cluster transport — encrypted peer-to-peer
• Witness daemon — safe HA with only 2 data nodes
• Multi-tenant — each database replicates independently
• At-rest encryption (AES-256-GCM)

As of v0.8.13, every committed mutation flows through the durable commit log so followers materialize engine state via deterministic apply on every replica.

What does the AGPL license mean for my use?

AGPL-3.0:

• ✅ Free to use, modify, distribute
• ✅ Self-host, internal use, embed in your AGPL-compatible app
• ⚠️ Offer YantrikDB as a network service to external users → publish source code modifications under AGPL
• ⚠️ Closed-source SaaS or proprietary embedded use → commercial license required (contact maintainers)

How does YantrikDB detect contradictions?

YantrikDB stores facts as structured claims with polarity (positive / negative), validity windows, source attribution, and confidence. The conflict detector compares claims about the same entity-relation-target tuple. When two claims with opposite polarity and overlapping validity coexist, a conflict record fires with both source memories cited.

See Wirecard showcase for a real-world worked example: eight polarity contradictions on €1.9 billion across six years of public record.

What is temporal decay in YantrikDB?

Temporal decay models how memories fade over time. Each memory has a configurable half-life (default 168 hours / 7 days). After one half-life, retrieval relevance is halved. Recent memories outrank older ones at equal semantic similarity, while old high-importance memories stay accessible.

Importance and access frequency counter-balance decay — frequently-recalled memories stay sharp. See relevance scoring for the mathematical model.

Is YantrikDB production-ready?

Embedded and single-node: yes — powers real workloads on a 3-node Proxmox homelab with multiple tenants.

Cluster mode: structurally production-ready in v0.8.13. Empirical RYW validation on a real 2-node cluster is ongoing. For multi-node deployments, follow the interim cluster-routing runbook.

How does YantrikDB compare to mem0?

mem0 is an LLM-driven memory layer — it uses an LLM to extract and store facts from conversations, backed by a configurable vector store.

YantrikDB is a cognitive memory database — it stores structured memories with importance, decay, polarity, and a knowledge graph at the substrate level, regardless of how the agent generates them.

mem0 is great for adding memory to LLM apps quickly. YantrikDB is for systems where memory mechanics — contradiction handling, validity windows, autonomous consolidation, audit-ready substrate — need to live below the LLM rather than alongside it.

Does YantrikDB support skills as a first-class primitive?

Yes. As of v0.8.11, the skill substrate exposes:

• POST /v1/skills/define — register a skill (skill_id, body, applies_to, skill_type)
• GET /v1/skills/{skill_id} — exact lookup
• POST /v1/skills/search — semantic search with optional filters
• POST /v1/skills/{id}/outcome — append-only outcome event log
• POST /v1/skills/{id}/forget — tombstone

Schema-not-semantics design line: the engine validates shape (skill_id pattern, body length, applies_to format) but does NOT enforce semantic ontology — that’s the agent-layer’s job. Outcome events are append-only; success-rate aggregation, ECU routing, and other pedagogy compose client-side.

Where can I get help?

• 📖 Documentation — start here
• 💬 GitHub Discussions — questions and ideas
• 🐛 GitHub Issues — bug reports
• 📺 Live agi.yantrikdb.com — see YantrikDB powering an AGI experiment in real time