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.

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

# 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

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).

docker run -p 7438:7438 ghcr.io/yantrikos/yantrikdb:latest
curl -X POST http://localhost:7438/v1/remember -d '{"text":"hello"}'

Single Rust binary. HTTP + binary wire protocol. 2-voter + 1-witness HA cluster via Docker Compose or Kubernetes. Per-tenant quotas, Prometheus metrics, AES-256-GCM at-rest encryption, runtime deadlock detection. See docker-compose.cluster.yml and k8s manifests.

pip install yantrikdb-mcp

Add to your MCP client config — the agent auto-recalls context, auto-remembers decisions, auto-detects contradictions. No prompting needed. See yantrikdb-mcp.

pip install yantrikdb
# or
cargo add yantrikdb

import yantrikdb
db = yantrikdb.YantrikDB("memory.db", embedding_dim=384)
db.set_embedder(SentenceTransformer("all-MiniLM-L6-v2"))
db.record("Alice leads engineering", importance=0.8)
db.recall("who leads the team?", top_k=3)
db.think()  # consolidate, detect conflicts, derive personality

Live numbers from a 2-core LXC cluster with 1689 memories:

For pre-computed embeddings (skip query-time embedding), recall p50 drops to ~5ms.

v0.5.11 — hardened alpha. The embeddable engine has been used in production by the YantrikOS ecosystem since early 2026. The network server is newer — running live on a 3-node Proxmox cluster with multiple tenants for the past few weeks.

A 42-task hardening sprint just completed across 8 epics:

• parking_lot mutexes everywhere with runtime deadlock detection (caught a self-deadlock that would have taken hours to find with std::sync)
• Per-handler Prometheus metrics, structured JSON logging, deep health checks
• Chaos-tested failover (leader kill, network partition, kill-9 mid-write)
• Per-tenant quotas, load shedding, control plane replication
• 1178 core tests + chaos harness + cargo-fuzz + CRDT property tests
• 5 operational runbooks, watchdog with auto-restart

Read the maturity notes: https://yantrikdb.com/server/quickstart/#maturity

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.

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.

• 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

┌──────────────────────────────────────────────────────┐
│                   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

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.

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.

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.

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

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.

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

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

• 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

Pranab Sarkar — ORCID · LinkedIn · [email protected]

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.