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Memori

Persistent memory from agent trace, not just conversation

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Memori launched its new agent-native memory infrastructure, enabling agents to create structured, long-term memory directly from agent trace — including execution paths, tool results, workflow steps, outcomes, and decision-making logic. This allows memory to also be generated from what an agent actually does. Benchmark results: 81.95% accuracy on LoCoMo using only 1,294 tokens per query, roughly 5% of full-context cost, saving users 95%+ on inference spend. 15K GitHub stars, 200000+ downloads

PH · launched May 28, 2026 · 168 upvotes (top 85%) · 0 views

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

Category and tags
  • Horizontal
  • Data infrastructure
  • Developer
  • AI-native
  • Commercial product
  • Open Source
  • Developer Tools
  • Artificial Intelligence

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More like this: all 350 launches in ai agent memory infrastructure →

Could you build this?

Partial While storing JSON execution logs in a vector or graph database is easy, automatically synthesizing structured long-term memory, causal logic, and deduplicated knowledge graphs from arbitrary execution traces requires non-trivial algorithmic pipelines.

What it would actually take: Requires an ingestion pipeline for OpenTelemetry-style agent traces, an automated parsing/summarization worker that identifies decision forks and outcomes, and an entity-relationship graph database (like Neo4j or Memgraph) paired with vector search. Designing deterministic distillation pipelines that avoid memory corruption or hallucination loops across thousands of agent runs requires deep LLM systems expertise.

Competitors

Other products that read as similar to this one : 641 launches clear the similarity bar, closest 8 shown.

Attention rank: #543 of 642 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 203 days after the earliest competitor.

Related data

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