WeAreBorg

Memory infrastructure for AI

Models execute. WeAreBorg remembers.

Model-agnostic memory infrastructure for AI applications and agents—so knowledge survives every model swap.

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WeAreBorg

WeAreBorg

What it is

A persistent memory layer shared by humans, models, agents, and applications. Entities own memory. Models are replaceable.

WeAreBorg decides what an AI should remember, what it should forget, and what it needs to recall right now—scoped to who is acting and what they are trying to do.

What WeAreBorg is not

  • Not another AI model
  • Not a vector-database wrapper for chat logs
  • Not a virtual-employee framework
  • Not a place for hardcoded domain objects in the core

The central loop

Context in. Execution out. Reflection extracts what deserves to persist. Future context gets sharper.

  1. 01

    context()

    Resolve actor, scopes, and intent into a structured Memory Pack—not a raw dump of similar text.

  2. 02

    Execute

    Any model or agent runs the work. Continuity does not depend on which provider you chose today.

  3. 03

    reflect()

    Turn completed work into candidate memories, decisions, and relationships—without treating chatter as truth.

  4. 04

    remember()

    Persist with lifecycle, provenance, confidence, and scope—so knowledge can supersede and evolve.

Built around memory operations

The product interface is the API. Applications and agents call the same primitives.

remember()

Store facts, decisions, procedures, and events.

recall()

Search relevant memories with explainable ranking.

context()

Primary retrieval: what should be remembered for this execution.

reflect()

Extract candidates after an interaction ends.

supersede()

Replace outdated truth without erasing history.

promote()

Move validated knowledge to broader scopes.

Principles that keep knowledge free

Models do not own memory. Entities do.

Workers, projects, users, and domains persist across GPT, Claude, Gemini, or local models.

Context is resolved, not dumped.

Retrieval combines scopes, relationships, importance, confidence, freshness—and then budgets what fits.

Similarity is not memory.

Vector search finds neighbors. WeAreBorg decides relevance for the job at hand.

Give your agents a memory that lasts.

Start with a tenant, connect your tools, and let agents share durable memory instead of restarting from zero.

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