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LinearMemory: AI Memory Explorer

Human-readable, auditable memory for AI agents.

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LinearMemory turns AI agent executions into visual, auditable memory. Follow context, searches, recalled knowledge, decisions, tools, errors, reflections, and outcomes through timelines and an interactive 3D graph. It supports semantic search, validated knowledge, evidence-based relationships, and shared memory across agents. Built with MCP, PostgreSQL, Docker, and local embeddings, without storing private chain-of-thought.

PH · launched Sep 6, 2026 · 2 upvotes (top 26%) · 0 views

Niche: ai agent memory infrastructure · 350 launches, 11 in the last 7 days ▼ -58% · Explore this niche →

Category and tags

  • Horizontal
  • Observability & eval
  • Developer
  • AI-native
  • Commercial product
  • SaaS
  • Software Engineering
  • GitHub

Visit website ↗ Source listing ↗

Could you build this?

Partial Visualizing agent logs in a 3D graph is manageable with three.js, but capturing and reconciling complex agent execution states, semantic memory graphs, and evidence tracking requires advanced agentic instrumentation architecture.

What it would actually take: Requires an observability SDK (tracing LangChain/LlamaIndex or raw LLM calls) sending structured telemetry into an event pipeline like Kafka/ClickHouse, combined with a graph database (Neo4j or vector graph index) and a frontend using Three.js/ForceGraph. The difficult part is building an automatic ontological extractor that structures agent decisions, context, and error states reliably in real time.

Competitors

Other products that read as similar to this one : 689 launches clear the similarity bar, closest 8 shown.

Attention rank: #207 of 690 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 311 days after the earliest competitor.

The niche, labels, vibe code rating and competitor comparison on this page are analysis by Nicheloom. If you use them, please credit nicheloom.com. How you may use our data.