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Palo Bloom

Palo Bloom is a persistent, personalized AI memory infrastructure system. It does not generate conversational responses. Its role is to enrich input with memory-aware context before that input is passed to an external LLM, or to produce embeddings and memory representations for downstream similarity and personalization workflows.

Palo Bloom operates on a bring-your-own-vector-store model, with optional Mpalo-managed storage where applicable. You bring your storage provider or use Mpalo storage, while Mpalo handles encoding, retrieval, memory traversal, and personalization through a single API call. Storage costs are either paid directly to the provider or included in the subscription, depending on the deployment.

Core Concepts

Every input is encoded into a memory representation by Palo Bloom. The system is designed to preserve the semantic content of an interaction, its place in time, and the contextual signals needed for later retrieval.

Palo Bloom is built to support:

  • -- Memory-aware retrieval
  • -- Personalization across sessions
  • -- Temporal continuity
  • -- Memory traversal for deeper context when needed
  • -- Memory mapping for more advanced retrieval paths

Each user can accumulate memory in a way that reflects their own patterns over time, so the system becomes more aligned with that user's history, style, and usage context.

Retrieval Modes

Palo Bloom exposes three tiers of memory operation:

Memory Recall

The default retrieval path -- semantic relevance and context retrieval. Best for most production use cases.

Memory Traversal

Multi-hop retrieval across a user's personal timeline. Activated when broader historical context is needed.

Memory Mapping

Advanced multi-path retrieval for high-fidelity synthesis over extended histories. Highest depth, highest precision.

We recommend using deeper retrieval modes on new memory storage so the model converges to your individuality faster, then switching to simple recall once the memory layer is established. Your memory storage grows with you.

Memory Behavior

Palo Bloom supports controlled persistence and controlled forgetting. Users can remove memory explicitly, and the system can surface older material for cleanup when it is no longer useful. This keeps the memory layer aligned with user intent while avoiding unnecessary accumulation.

What Palo Bloom Is For

Palo Bloom is meant for products that need memory to be a native part of the experience rather than an add-on. It is useful when continuity, personalization, and long-term context matter more than a single isolated response.

Palo Bloom can also power any system that requires personalization beyond what standard similarity search provides -- music recommendation, content curation, behavioral modeling. Standard similarity doesn't capture the personal connections between users and their history. Palo Bloom does.

Quickstart → Memory Engines