Loom

Documentation Index

Fetch the complete documentation index at: https://docs.loom.teamecho.ai/llms.txt

Use this file to discover all available pages before exploring further.

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What is Loom?

Loom is the missing “last mile” of agentic memory. Rather than just another storage or retrieval layer, Loom is a decoupled framework that compiles candidate memories into the Minimum Sufficient Context for LLM agents. While naive retrieval approaches treat memory as a search problem, Loom treats it as an associative recommendation problem. Driven by real-world product abstractions from building life-long AI companions, Loom ensures agents truly remember, evolve, and reason without flooding the context window.

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The Cognitive Pipeline

Through its cognitive architecture, Loom achieves this via a robust pipeline:

• Goal-Aware Context — Builds the exact context needed based on the agent’s current task and token budget — no more, no less.
• Associative Recall — Links related memories naturally, but applies strict depth limits to prevent context bloat.
• Read & Write Loop — Pulls information from multiple sources and actively saves high-value insights back to memory.
• Flexible Schemas — Support for custom schemas, AI-generated schemas, and production-tested templates out-of-the-box.
• Decoupled & Inspectable — Plugs into any existing storage (Vector DBs, RAG) while keeping the memory process human-readable and debuggable.

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Schema: A Cognitive Ontology

Unlike standard RAG systems that retrieve noisy, unstructured dialogue chunks, or static markdown file systems that lack active reasoning semantics, a Loom Schema is a dynamic, self-describing cognitive ontology. It acts as an executable “file system for cognition” — structuring memory into an inspectable, actively updatable tree.

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Multi-Resolution Intelligence

Loom’s Schema is Hierarchical & Collapsible, creating a “Variable Focal Length” for the agent’s memory:

• High-Level Abstraction (Low Resolution) — The agent perceives the Core Essence: not “Alice likes hiking on Sundays” but Identity -> High-Energy Adventurer. Lightning-fast, high-dimensional reasoning.
• Granular Specificity (High Resolution) — When precision is needed, the schema unfolds recursively from a general persona down to microscopic nuances of a specific habit.

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Hybrid Schema Strategy

Loom provides three strategies that balance human control with machine intuition:

1. Pre-designed Schemas — Define the “Skeleton of Truth” manually for absolute precision.
2. AI-Generated Schemas — Auto-generate schemas on the fly as new patterns emerge.
3. NS-Inspired Reference Schemas — Battle-tested templates from AI Companionship and Social AI (NatureSelect.ai).

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Benchmark & Results

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Vision: Cognition, Not Just Memory

Memory is a means, not the end. Loom’s “last mile” approach isn’t just about saving context window tokens — it’s about shifting the paradigm from storing raw data to evolving cognition. By distilling noisy interactions into the Minimum Sufficient Context along a structured “skeleton of truth,” Loom bridges the gap between simply knowing what a user said and truly understanding who they are. The ultimate endgame is hidden state modeling — moving beyond explicit dialogue logs to capture underlying intents, beliefs, and emotional baselines. This is the path to AI companions and assistants with genuine intelligence.