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Documentation Index

Fetch the complete documentation index at: https://docs.ntrp.io/llms.txt

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

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Overview

ntrp’s memory system stores knowledge as a graph of facts, observations, and dreams. Unlike simple chat history, this is structured long-term memory that grows, consolidates, and surfaces relevant context automatically.

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Data model

Facts ────────► Observations
  │                  │
  └──── Entities ────┘

Dreams (cross-domain insights)

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Facts

Atomic pieces of information extracted from conversations and sources.
“Alice joined the backend team in March 2025”
Each fact has:
  • Vector embedding for semantic search
  • Timestamps (created_at, happened_at)
  • Access tracking with decay (less-accessed facts fade)
  • Entity references (people, projects, places)

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Observations

Higher-level patterns synthesized from multiple facts.
“Alice transitioned from mobile to backend development, taking on infrastructure responsibilities”
Observations track their source facts and maintain a change history as new evidence arrives.

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Dreams

Cross-domain insights that connect seemingly unrelated facts.
“Pattern: work stress correlates with reduced exercise frequency”
Generated during weekly consolidation by clustering facts across domains. Dreams are disabled by default — enable them in Settings > Memory.

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Write path

When ntrp learns something new:
  1. Extract — fact is created from conversation or source data
  2. Embed — vector encoding for similarity search
  3. Link — entities extracted and connected in the knowledge graph
  4. Queue — fact queued for consolidation

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Consolidation

Consolidation now runs as a builtin automation with dual triggers: every 30 minutes and after 5 minutes of idle time (configurable in Settings > Memory).
  1. Per-fact — search for similar observations, decide whether to update existing or create new
  2. Temporal pass — find trends and transitions across an entity’s facts
  3. Observation merge — deduplicate similar observations (cosine > 0.90)
  4. Fact merge — deduplicate near-identical facts
  5. Dream pass — cluster facts, discover cross-domain patterns
  6. Archival pass — archive old, low-value facts that have been consolidated

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Extraction

Fact extraction from conversations is also a builtin automation. It fires every 10 user messages or after 5 minutes of idle time, extracting durable facts from the conversation without running a full agent loop. Both builtin automations are visible in the automations viewer under the Internal tab.

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Archival

Facts and observations that have been fully consolidated, have low access decay, and exceed an age threshold are automatically archived. Archived items are excluded from retrieval but not deleted — they can be restored if needed.

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Retrieval

When you ask ntrp something, it retrieves context in two phases:
  1. Observations — vector + full-text hybrid search, top 5 results
  2. Facts — vector + FTS, reranked (consolidated and archived facts excluded)
This gives the agent both high-level understanding (observations) and specific details (facts).

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Tools

The agent uses these tools to interact with memory:
  • remember — store a fact (requires approval)
  • recall — semantic search across facts and observations
  • forget — delete facts matching a query