yantrikdb 0.4.1

A Cognitive Memory Engine for Persistent AI Systems

YantrikDB — A Cognitive Memory Engine for Persistent AI Systems

The memory engine for AI that actually knows you.

Get Started in 60 Seconds

For AI agents (MCP — works with Claude, Cursor, Windsurf, Copilot)

pip install yantrikdb-mcp

Add to your MCP client config:

{
  "mcpServers": {
    "yantrikdb": {
      "command": "yantrikdb-mcp"
    }
  }
}

That's it. The agent auto-recalls context, auto-remembers decisions, and auto-detects contradictions — no prompting needed. See yantrikdb-mcp for full docs.

As a Python library

pip install yantrikdb

import yantrikdb
from sentence_transformers import SentenceTransformer

# Single file, no server, no config
db = yantrikdb.YantrikDB("memory.db", embedding_dim=384)
db.set_embedder(SentenceTransformer("all-MiniLM-L6-v2"))

# Record memories with importance, domain, and emotional valence
db.record("Alice is the engineering lead", importance=0.8, domain="people")
db.record("Project deadline is March 30", importance=0.9, domain="work")
db.record("User prefers dark mode", importance=0.6, domain="preference")

# Semantic recall — ranked by relevance, recency, importance, and graph proximity
results = db.recall("who leads the team?", top_k=3)
# → [{"text": "Alice is the engineering lead", "score": 1.0}, ...]

# Knowledge graph — entity relationships
db.relate("Alice", "Engineering", "leads")
db.get_edges("Alice")
# → [{"src": "Alice", "dst": "Engineering", "rel_type": "leads", "weight": 1.0}]

# Cognitive maintenance — consolidate, detect conflicts, mine patterns
db.think()
# → {"consolidation_count": 2, "conflicts_found": 0, "patterns_new": 1}

db.close()

As a Rust crate

[dependencies]
yantrikdb = "0.4"

The Problem

Current AI memory is:

Store everything → Embed → Retrieve top-k → Inject into context → Hope it helps.

That's not memory. That's a search engine with extra steps.

Real memory is hierarchical, compressed, contextual, self-updating, emotionally weighted, time-aware, and predictive. YantrikDB is built for that.

Why Not Existing Solutions?

Solution	What it does	What it lacks
Vector DBs (Pinecone, Weaviate)	Nearest-neighbor lookup	No decay, no causality, no self-organization
Knowledge Graphs (Neo4j)	Structured relations	Poor for fuzzy memory, not adaptive
Memory Frameworks (LangChain, Mem0)	Retrieval wrappers	Not a memory architecture — just middleware
File-based (CLAUDE.md, memory files)	Dump everything into context	O(n) token cost, no relevance filtering

Benchmark: Selective Recall vs. File-Based Memory

Memories	File-Based	YantrikDB	Token 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%

At 500 memories, file-based exceeds 32K context windows. At 5,000, it doesn't fit in any context window — not even 200K. YantrikDB stays at ~70 tokens per query. Precision improves with more data — the opposite of context stuffing.

Architecture

Design Principles

• Embedded, not client-server — single file, no server process (like SQLite)
• Local-first, sync-native — works offline, syncs when connected
• Cognitive operations, not SQL — record(), recall(), relate(), not SELECT
• Living system, not passive store — does work between conversations
• Thread-safe — Send + Sync with internal Mutex/RwLock, safe for concurrent access

Five Indexes, One Engine

┌──────────────────────────────────────────────────────┐
│                   YantrikDB Engine                    │
│                                                      │
│  ┌──────────┬──────────┬──────────┬──────────┐       │
│  │  Vector  │  Graph   │ Temporal │  Decay   │       │
│  │  (HNSW)  │(Entities)│ (Events) │  (Heap)  │       │
│  └──────────┴──────────┴──────────┴──────────┘       │
│  ┌──────────┐                                        │
│  │ Key-Value│  WAL + Replication Log (CRDT)          │
│  └──────────┘                                        │
└──────────────────────────────────────────────────────┘

1. Vector Index (HNSW) — semantic similarity search across memories
2. Graph Index — entity relationships, profile aggregation, bridge detection
3. Temporal Index — time-aware queries ("what happened Tuesday", "upcoming deadlines")
4. Decay Heap — importance scores that degrade over time, like human memory
5. Key-Value Store — fast facts, session state, scoring weights

Memory Types (Tulving's Taxonomy)

Type	What it stores	Example
Semantic	Facts, knowledge	"User is a software engineer at Meta"
Episodic	Events with context	"Had a rough day at work on Feb 20"
Procedural	Strategies, what worked	"Deploy with blue-green, not rolling update"

All memories carry importance, valence (emotional tone), domain, source, certainty, and timestamps — used in a multi-signal scoring function that goes far beyond cosine similarity.

Key Capabilities

Relevance-Conditioned Scoring

Not just vector similarity. Every recall combines:

• Semantic similarity (HNSW) — what's topically related
• Temporal decay — recent memories score higher
• Importance weighting — critical decisions beat trivia
• Graph proximity — entity relationships boost connected memories
• Retrieval feedback — learns from past recall quality

Weights are tuned automatically from usage patterns.

Conflict Detection & Resolution

When memories contradict, YantrikDB doesn't guess — it creates a conflict segment:

"works at Google" (recorded Jan 15) vs. "works at Meta" (recorded Mar 1)
→ Conflict: identity_fact, priority: high, strategy: ask_user

Resolution is conversational: the AI asks naturally, not programmatically.

Semantic Consolidation

After many conversations, memories pile up. think() runs:

1. Consolidation — merge similar memories, extract patterns
2. Conflict scan — find contradictions across the knowledge base
3. Pattern mining — cross-domain discovery ("work stress correlates with health entries")
4. Trigger evaluation — proactive insights worth surfacing

Proactive Triggers

The engine generates triggers when it detects something worth reaching out about:

• Memory conflicts needing resolution
• Approaching deadlines (temporal awareness)
• Patterns detected across domains
• High-importance memories about to decay
• Goal tracking ("how's the marathon training?")

Every trigger is grounded in real memory data — not engagement farming.

Multi-Device Sync (CRDT)

Local-first with append-only replication log:

• CRDT merging — graph edges, memories, and metadata merge without conflicts
• Vector indexes rebuild locally — raw memories sync, each device rebuilds HNSW
• Forget propagation — tombstones ensure forgotten memories stay forgotten
• Conflict detection — contradictions across devices are flagged for resolution

Sessions & Temporal Awareness

sid = db.session_start("default", "claude-code")
db.record("decided to use PostgreSQL")  # auto-linked to session
db.record("Alice suggested Redis for caching")
db.session_end(sid)
# → computes: memory_count, avg_valence, topics, duration

db.stale(days=14)    # high-importance memories not accessed recently
db.upcoming(days=7)  # memories with approaching deadlines

Full API

Operation	Methods
Core	record, record_batch, recall, recall_with_response, recall_refine, forget, correct
Knowledge Graph	relate, get_edges, search_entities, entity_profile, relationship_depth, link_memory_entity
Cognition	think, get_patterns, scan_conflicts, resolve_conflict, derive_personality
Triggers	get_pending_triggers, acknowledge_trigger, deliver_trigger, act_on_trigger, dismiss_trigger
Sessions	session_start, session_end, session_history, active_session, session_abandon_stale
Temporal	stale, upcoming
Procedural	record_procedural, surface_procedural, reinforce_procedural
Lifecycle	archive, hydrate, decay, evict, list_memories, stats
Sync	extract_ops_since, apply_ops, get_peer_watermark, set_peer_watermark
Maintenance	rebuild_vec_index, rebuild_graph_index, learned_weights

Technical Decisions

Decision	Choice	Rationale
Core language	Rust	Memory safety, no GC, ideal for embedded engines
Architecture	Embedded (like SQLite)	No server overhead, sub-ms reads, single-tenant
Bindings	Python (PyO3), TypeScript	Agent/AI layer integration
Storage	Single file per user	Portable, backupable, no infrastructure
Sync	CRDTs + append-only log	Conflict-free for most operations, deterministic
Thread safety	Mutex/RwLock, Send+Sync	Safe concurrent access from multiple threads
Query interface	Cognitive operations API	Not SQL — designed for how agents think

Ecosystem

Package	What	Install
yantrikdb	Rust engine	cargo add yantrikdb
yantrikdb	Python bindings (PyO3)	pip install yantrikdb
yantrikdb-mcp	MCP server for AI agents	pip install yantrikdb-mcp

Roadmap

• V0 — Embedded engine, core memory model (record, recall, relate, consolidate, decay)
• V1 — Replication log, CRDT-based sync between devices
• V2 — Conflict resolution with human-in-the-loop
• V3 — Proactive cognition loop, pattern detection, trigger system
• V4 — Sessions, temporal awareness, cross-domain pattern mining, entity profiles
• V5 — Multi-agent shared memory, federated learning across users

Research & Publications

• U.S. Patent Application 19/573,392 (March 2026): "Cognitive Memory Database System with Relevance-Conditioned Scoring and Autonomous Knowledge Management"
• Zenodo: YantrikDB: A Cognitive Memory Engine for Persistent AI Systems

Author

Pranab Sarkar — ORCID · LinkedIn · [e-mail]

License

AGPL-3.0. See LICENSE for the full text.

The MCP server is MIT-licensed — using the engine via the MCP server does not trigger AGPL obligations on your code.