yantrikdb-server
Reduces hallucination and inconsistency in AI agent systems by maintaining coherent, deduplicated memory state with automatic stale-data purging—critical for…
Cognitive memory database for AI agents — consolidates duplicates, detects contradictions, fades stale memories via temporal decay. Rust, AGPL, ships as library / MCP server / HTTP cluster.
- Ask Claude to identify conflicting information across multiple data sources within a knowledge base.
- Generate a consolidated memory view that removes duplicate facts stored across different timestamps.
- Automate cleanup of outdated information by applying temporal decay to aging database records.
Reduces hallucination and inconsistency in AI agent systems by maintaining coherent, deduplicated memory state with automatic stale-data purging—critical for production reliability in multi-turn workflows.
AI platform teams building stateful agents requiring consistent fact retrieval and contradiction detection across extended conversation histories.
https://github.com/yantrikos/yantrikdb-server
By yantrikos
How to Get It
See repository README
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Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
After installing, paste this into Claude:
Identify conflicting information across multiple data sources within a knowledge base
Trust Signals Auto-scanned
Community Pulse Growing
Discussed on Hacker News, Reddit
- Show HN: A memory database that forgets, consolidates, and detects contradiction — Hacker News · 48 pts
1 mentions across 1 sources
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 154 GitHub stars; contributors unknown; last commit 5d ago; license AGPL-3.0.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
- License (AGPL-3.0) may restrict commercial use. Check with your legal team.
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Evaluation
Scored from trust signals (evidence-eval-v1): 154 GitHub stars; contributors unknown; last commit 5d ago; license AGPL-3.0.