YantrikDB

A memory database that forgets, consolidates, and detects contradictions.

Vector databases store memories. They don't manage them. After 10,000 memories, recall quality degrades because there's no consolidation, no forgetting, no conflict resolution. Your AI agent just gets noisier.

YantrikDB is different. It's a cognitive memory engine — embed it, run it as a server, or connect via MCP. It thinks about what it stores.

The bigger picture: YantrikDB is the memory layer being built on the road to YantrikOS — an AI-native operating system where agents are first-class primitives, not apps on top. Memory was the bottleneck, so we're shipping it first.

99.9% token savings vs file-based memory

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

Reproduce: python benchmarks/bench_token_savings.py

Three things no other database does

1. It forgets

db.record("read the SLA doc by Friday", importance=0.4, half_life=86400)  # 1 day
# 24 hours later, this memory's relevance score has decayed
# 7 days later, recall stops surfacing it unless explicitly queried

2. It consolidates

# 20 similar memories about the same meeting
for note in meeting_notes:
    db.record(note, namespace="standup-2026-04-12")

db.think()
# → {"consolidation_count": 5}  # collapsed 20 fragments into 5 canonical memories

3. It detects contradictions

db.record("CEO is Alice")
db.record("CEO is Bob")  # added later in another conversation

db.think()
# → {"conflicts_found": 1, "conflicts": [{"memory_a": "CEO is Alice",
#                                         "memory_b": "CEO is Bob",
#                                         "type": "factual_contradiction"}]}

Plus: temporal decay with configurable half-life, entity graph with relationship edges, personality derivation from memory patterns, session-aware context surfacing, multi-signal scoring (recency × importance × similarity × graph proximity).

What makes this different

YantrikDB isn't just storage with operations. The engine has a layer that makes agents feel less reactive:

• Proactive triggers — the system surfaces what needs attention: pending conflicts, decaying important memories, approaching deadlines, patterns across domains. Agents don't have to ask what they should care about. The memory tells them.
• Derived personality — stable tendencies extracted from memory patterns over time. "This user prefers X, reacts to Y, values Z." Informs default agent behavior across sessions.
• Procedural memory — strategies that worked before get recorded and reinforced. Agents learn what to do, not just what they know.
• Temporal awareness — stale surfaces important memories that haven't been touched recently. upcoming surfaces memories with approaching deadlines.

Full cognitive architecture lives in t

... View full README on GitHub