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Memory Engines

Palo Bloom engines are the foundation of your memory-augmented applications. Every engine shares a core processing pipeline designed to handle episodic memory encoding, retrieval, and contextual enrichment. Selection should be based on your throughput and complexity requirements.

Engine Selection

Engines are categorized by their depth of memory processing and capacity for long-horizon recall.

Engine API Name Best For
Palo Nano mpalo-palo-nano High-throughput, cost-sensitive, real-time personalization; efficient for high-frequency batch operations.
Palo Bloom mpalo-palo Flagship production engine for agents and copilots.
Palo Research mpalo-palo-research Complex research agents, deep recalling, multi-session synthesis; ideal for high-latency-tolerant precision applications.

Usage rates and pricing tiers are defined in your account dashboard. Optional features like Memory Traversal and Memory Mapping are invoked via the API and billed as additional operations.

Beyond Retrieval: Memory-Aware Embeddings

Palo Bloom does more than handle conversational context. Our engines produce dense, memory-aware embeddings that embed the user's personal history and behavioral context directly into the vector representation.

These embeddings are optimized for advanced workflows:

Advanced RAG

Go beyond keyword or simple semantic similarity. Retrieval can prioritize content that aligns with the user's evolving context and past history.

Personalized Recommendations

Recommendations that evolve with user intent, whether for media, content, or product discovery -- memory-biased at the embedding level.

Predictive Behavior Modeling

Use the episodic structure within the embedding to build applications that anticipate user needs based on past temporal patterns.

Memory Operations

Memory Recall

The default path for semantic relevance and context retrieval.

Memory Traversal

Enables multi-hop retrieval across a user's personal timeline.

Memory Mapping

Advanced multi-path retrieval for applications requiring high-fidelity synthesis over extended histories.

Capabilities

  • -- Input Handling: Each engine supports text, image, and (in beta) audio processing.
  • -- Storage: Operate using your own vector store or Mpalo-managed storage.
  • -- Engine Migration: As your needs grow, you may migrate memory between engine tiers via the API. This process ensures your memory is re-optimized for the target engine's capabilities.

Known Limitations

Known limitations regarding non-Latin scripts, complex visual styling, and spatial reasoning are tracked in the Project Known Issues registry. These are subjects of active iterative improvement.