YantrikDB
⚠ Correction notice (2026-04-19): Phase 3 benchmark writeups committed earlier today (
docs/phase3a/,docs/phase3b/,docs/phase3c/,docs/phase3d/) used a Python simulator for the "structured memory" condition — NOT the actual yantrikdb engine. Rerun with real yantrikdb is indocs/phase3e/. Full audit trail inCORRECTIONS.md. Full corrected findings post coming 2026-04-20.
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.
Shortest path to try it: MCP setup for Claude Code / Cursor / Windsurf → (one pip install, one config block).
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.
📄 New paper (May 2026): Skill as Memory, Not Document — measuring three failure modes when agent skill catalogs scale to 5,000 skills. Code + raw CSVs reproducible in
benchmarks/skill_recall/.
99.9% token savings vs file-based memory
| Memories | File-Based (CLAUDE.md) | YantrikDB | Token Savings | Recall 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 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 —
stalesurfaces important memories that haven't been touched recently.upcomingsurfaces memories with approaching deadlines.
Full cognitive architecture lives in the standalone engine repo. This server repo focuses on deployment, HTTP API, and cluster operations.
Get started in 60 seconds — Claude Code / Cursor / Windsurf
The fastest adoption path. One pip install, one config block, and your agent gets persistent memory that auto-recalls on conversation start, auto-remembers decisions, and flags contradictions — without you prompting it.
pip install yantrikdb-mcp
Add this to your MCP client config — typically ~/.claude.json or .mcp.json in your project for Claude Code, and the equivalent mcp block in settings for Cursor/Windsurf (Claude Code · Cursor · Windsurf):
{
"mcpServers": {
"yantrikdb": {
"command": "yantrikdb-mcp"
}
}
}
That's it. No env vars. Uses a local SQLite memory file at ~/.yantrikdb/memory.db. First call auto-initializes the schema. Restart your client — the yantrikdb MCP server will show up with 15 memory tools (see below).
Want a shared memory across machines or teammates? Point at a YantrikDB cluster instead of local SQLite:
{
"mcpServers": {
"yantrikdb": {
"command": "yantrikdb-mcp",
"env": {
"YANTRIKDB_SERVER_URL": "http://node1:7438,http://node2:7438",
"YANTRIKDB_TOKEN": "ydb_your_database_token"
}
}
}
}
Want it over HTTP/SSE instead of stdio? (For IDE integrations that don't support stdio MCP servers.)
{
"mcpServers": {
"yantrikdb": {
"type": "sse",
"url": "http://your-server:8420/sse",
"headers": { "Authorization": "Bearer YOUR_API_KEY" }
}
}
}
Then start: yantrikdb-mcp --transport sse --port 8420.
The 15 MCP tools your agent gets
| | Tools |
|---|---|
| Core memory | remember · recall · forget · correct |
| Cognition | think (consolidate + conflict-detect) · memory · trigger (proactive insights) |
| Knowledge graph | graph (entities + relations) · category |
| Conflicts & corrections | conflict (list + resolve contradictions) |
| Time | session · temporal (stale/upcoming queries) |
| Behavior | procedure (strategies) · personality (derived traits) |
| Ops | stats (engine health + diagnostics) |
Full tool reference and agent-integration patterns: yantrikdb-mcp → · docs
Other ways to use it
As a network server (binary API + HA cluster)
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.
As an embedded library (Python or Rust)
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
Performance
Live numbers from a 2-core LXC cluster with 1689 memories:
| Operation | Latency | |---|---| | Recall p50 | 112ms (most is query embedding ~100ms) | | Recall p99 | 190ms | | Batch write | 76 writes/sec | | Engine lock acquire | <0.1ms | | Deep health probe | <1ms |
For pre-computed embeddings (skip query-time embedding), recall p50 drops to ~5ms.
Status
v0.5.13 — hardened alpha + RFC 006 Phase 0 observability telemetry shipped. The embeddable engine has been used in production by the YantrikOS ecosystem since early 2026. The network server runs live on a 3-node Proxmox cluster with multiple tenants.
A 42-task hardening sprint just completed across 8 epics:
parking_lotmutexes 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
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 ste