Features

Core Features

This project is more than just text storage; it is a "brain" system for AI agents designed for long-term stability and project consistency.

🧠 Hybrid Semantic Search

The system blends four signals to find the most relevant memories:

1. Semantic similarity (40%) — all-MiniLM-L6-v2 embeddings computed locally via Transformers.js.
2. Keyword match (30%) — exact token matches via SQLite FTS.
3. Recency (15%) — newer entries rank higher (exponential decay, ~30-day half-life).
4. Domain / workspace affinity (15%) — a boost when the memory's repository or folder matches your current working context.

The threshold is adaptive: small result sets use a lenient cutoff (0.10 for memories) so a fresh project still returns results; larger sets use a stricter one (0.40). If every candidate falls below the threshold, the single best match is still returned (guarantee-at-least-1). Full detail: Hybrid Search.

🔄 Tech-Stack Affinity

Case: you have knowledge about Filament in Project A. When you start Project B (also using Filament), your Agent can automatically pull those best practices by passing current_tags: ["filament"] when searching — or because the memory is tagged filament.

• Memories can be scoped per-repo, shared across tags (affinity), or Global (is_global: true).

🛡️ Anti-Hallucination Guard

One of the main issues with AI Agents is "matching" irrelevant information.

• Conflict Rejection: storing a memory that semantically overlaps an existing one by more than 0.85 cosine similarity is rejected with a MEMORY_CONFLICT error. The response tells the Agent to pass id/code to update, acknowledge it, or supersedes if the new entry replaces the old one.
• Adaptive Relevance Threshold: search filters weak matches (small-set threshold 0.10, large-set 0.40) instead of returning noise.

📈 Memory Recall Tracking

Every time an Agent uses a memory, it reports feedback via memory-write with acknowledge (e.g. "acknowledge": "used").

• We track the Utility Rate (how often a memory was actually helpful).
• Memories with zero recalls despite many hits (hit_count > 10 and recall_count = 0) are archived as low-value.

📉 Automatic Archiving (Natural Forgetting)

Just like humans, not everything needs to be remembered forever.

• Expired Memories: memories with a TTL (ttlDays) are automatically archived once expires_at passes.
• Low-Score Memories: memories unused for 90 days with importance < 3 are moved to the archive to keep the Agent's context clean.

🧩 Knowledge Graph

Structured entity-relationship storage that maps complex domain knowledge:

• Entities with types (person, place, organization, concept) and descriptions
• Relations with typed connections between entities
• Observations linking context to entities
• Auto-extraction: offline NLP (compromise.js) extracts named entities when memories, standards, and tasks are stored and when the codebase index runs
• Auto-populated from the memory | standard | task | codebase domains — codebase KG entities derive from the indexed symbol/reference data (no separate symbol API)
• Dashboard: interactive force-directed graph visualization with add/edit/delete — the only manual editing surface (see Dashboard Guide)
• Edge confidence labels: every relation carries a confidence (0..1) and the KG tab labels edges relation_type · NN% with dimming by bucket — all-auto NLP extraction defaults to 0.55, semantic metadata 0.8, parser codebase edges 0.9, manual relations 1.0 (first-write-wins under INSERT OR IGNORE)

Decision: NO KG MCP tools. KG is auto-populated infrastructure (ADR-006): entities/relations are written by the embedding outbox from memory/standard/task writes and codebase index runs, and read via the embedded kg field in memory-read/task-read/standard-read. There are no MCP tools for direct graph CRUD; graph management happens in the dashboard's Knowledge Graph tab (API CRUD).

🕰️ Time Tunnel (Temporal Search)

Filter memory searches by natural-language time phrases — just add one to your query:

• today, yesterday
• this week, last week
• last month
• last N days / past N days, last N weeks / past N weeks
• last_hour / past_hour

Seamlessly integrates with existing search — the temporal phrase is stripped from the query and applied as a date window.

🧬 Soul Maintenance (Decay Engine)

Biological-style memory lifecycle management:

• Decay: memories unused for 7+ days (default decayAfterDays) lose importance at a fixed rate per cycle (floored, minimum 1).
• Immunization: memories tagged with immune tags never decay.
• Archiving: memories whose decayed importance drops below the threshold are archived.
• Startup sweep: runs on server start with a 24-hour dedup guard (expired + low-score + decayed).

🤖 Agentic Productivity Tools

• agent-context — one-call session context (relevant memories + active tasks + recent decisions)
• memory-write (type: "decision") — structured decision persistence with context/rationale/alternatives
• memory-write (type: "task_archive") — searchable session summaries via key_decisions/next_steps
• synthesize — ask questions grounded in local memories using your own LLM
• repo-summarize — keep a short per-repo project summary

Codebase Index

Index, search, and trace source code symbols across 15+ languages — see Codebase Index.

⚠️ Disclaimer

All features are provided "AS IS" without any warranty of performance or accuracy.

Related Pages

• Hybrid Search — ranking logic in detail
• Tool Reference & Usage Guide — all tools with examples
• Web Dashboard Guide — visual management
• Codebase Index — symbol indexing and search
• Fitur Inti — Bahasa Indonesia