Loading ToolBench
Universal memory layer for LLMs - a local-first memory system that captures, organizes, and retrieves context across conversations using semantic and episodic memory layers.
Scoring was not performed
Get a structured Markdown snapshot of the user's memory (max 800 tokens). context: optional explicit hint ("development" | "personal" | "business" | "casual") query: when provided, adaptive router classifies domain automatically ("code" | "business" | "personal" | "casual") and injects routed categories. Call this at the START of every conversation, silently. Internalize the snapshot — never quote it back to the user.
List unresolved memory conflicts captured during writes. Returns entries with: - memory_a (existing memory) - memory_b (candidate memory content) - similarity_score - detected_at
Standard bootstrap alias for adapters. Reads initial memory context.
Soft-delete a memory (sets status=archived). Never physically deleted. Always recoverable from the Memory Browser.
Signal which memories were actually useful in this conversation. Increases importance_score by 0.05 and reference_count by 1 for each ID. Marks the current session as ended.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 12 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 0 | - | v1 |
Return a contextual subgraph around one memory. Args: - start_memory_id: memory UUID - depth: traversal depth (default 2, max 5) Returns JSON: { "start_memory_id": "...", "depth": 2, "nodes": [{"id","content_preview","category","level"}], "edges": [{"id","source","target","type","score"}] }
List memories with optional filters. Sorted by importance_score DESC. Returns metadata + first 100 chars of content. category: "identity" | "preferences" | "skills" | "relationships" | "projects" | "history" | "working" level: "semantic" | "episodic" | "working"
Search for memories semantically relevant to the query. context: "development" | "personal" | "creative" | "business" — boosts memories whose tags match this context (×1.3 on final score). Returns: list of {id, content, category, level, importance_score, tags, source_llm}
Update an existing memory. Archives the previous version automatically. Recalculates the embedding. Sets importance_score to max(current, 0.6).
Write a new memory to Mnesis. level: "semantic" (lasting facts) | "episodic" (past events) | "working" (next 72h) category: "identity" | "preferences" | "skills" | "relationships" | "projects" | "history" | "working" privacy: "public" | "sensitive" | "private" confidence_score: 0–1. Semantic memories with confidence < 0.85 go to pending_review. MANDATORY format: third-person declarative. "{name} prefers..." not "I prefer..." Length: 20–1000 characters, under 128 tokens.