GAAMA: Graph Augmented Associative Memory for Agents

Dimensions overall score 7.0

• GAAMA provides AI agents with a hierarchical, graph-augmented memory system that captures associative relationships for more coherent and personalized multi-session interactions. Current approaches either rely on flat retrieval-augmented generation (RAG), which loses structural relationships between memories, or use memory compression and vector retrieval that cannot capture the associative structure of multi-session conversations.problem 35%
  Evidencepartial

  GAAMA provides AI agents with a hierarchical, graph-augmented memory system that captures associative relationships for more coherent and personalized multi-session interactions. Current approaches either rely on flat retrieval-augmented generation (RAG), which loses structural relationships between memories, or use memory compression and vector retrieval that cannot capture the associative structure of multi-session conversations.

  Implicationpartial

  Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.

  Verificationpartial

  partial

• AI agents that interact with users across multiple sessions require persistent long-term memory to maintain coherent, personalized behavior. Current approaches either rely on flat retrieval-augmented generation (RAG), which loses structural relationships between memories, or use memory compression and vector retrieval that cannot capture the associative structure of multi-session conversations.method 35%
  Evidencepartial

  AI agents that interact with users across multiple sessions require persistent long-term memory to maintain coherent, personalized behavior. Current approaches either rely on flat retrieval-augmented generation (RAG), which loses structural relationships between memories, or use memory compression and vector retrieval that cannot capture the associative structure of multi-session conversations.

  Implicationpartial

  Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.

  Verificationpartial

  partial

• ScienceToStartup currently rates this 7.0/10 on the public viability pass. On the LoCoMo-10 benchmark (1,540 questions across 10 multi-session conversations), GAAMA achieves 78.9\% mean reward, outperforming a tuned RAG baseline (75.0\%), HippoRAG (69.9\%), A-Mem (47.2\%), and Nemori (52.1\%). A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.result 35%
  Evidencepartial

  ScienceToStartup currently rates this 7.0/10 on the public viability pass. On the LoCoMo-10 benchmark (1,540 questions across 10 multi-session conversations), GAAMA achieves 78.9\% mean reward, outperforming a tuned RAG baseline (75.0\%), HippoRAG (69.9\%), A-Mem (47.2\%), and Nemori (52.1\%). A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.

  Implicationpartial

  Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.

  Verificationpartial

  partial

• AI Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.why now 35%
  Evidencepartial

  AI Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.

  Implicationpartial

  Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.

  Verificationpartial

  partial

Competitive landscape

GAAMA provides AI agents with a hierarchical, graph-augmented memory system that captures associative relationships for more coherent and personalized multi-session interactions.