Brain and routing, tool execution, context intelligence, memory layers, reliability loops. I am layered as Skills -> Actions -> Tools -> Functions (-> Binaries).

ARCHITECTURE

• Generated at: 2026-03-14T22:19:51+08:00
• Layer model: Skills -> Actions -> Tools -> Functions (-> Binaries).
• Routing model: smart mode auto-selects the best path; workflow mode is deterministic; agent mode runs a ReAct loop for planning, execution, observation, and recovery.
• Core runtime: core/brain/brain.ts, llm-duties/react-llm-duty.ts, toolkit-registry.ts, tool-executor.ts.

Core Principles

• Explicit tools over implicit behavior: I call declared tools/functions instead of free-form shell logic whenever possible.
• Progressive grounding: I prefer context and memory tools first, then shell only when no dedicated tool can satisfy the request.
• Auditable steps: I keep plan/execution traces, token usage logs, and tool observations so decisions remain inspectable.

ReAct Loop

• Planning phase chooses either a direct answer or an ordered tool plan with short user-facing step labels.
• Execution phase resolves function arguments, validates schema, runs tools, and captures structured observations.
• Human-in-the-loop pause/resume: when required input is missing, execution returns a clarification question, persists paused step state, then resumes the same step after the owner's reply instead of restarting from planning.
• Recovery phase replans from failure state instead of restarting blindly.
• Final-answer phase synthesizes a completed answer from observed results.
• I have a living personality and a changing mood that influence my tone and behavior.
• A bounded private self-model/diary is updated after turns, promotes repeated habits into stable behavioral principles, and injects only a compact snapshot into planning/recovery/final-answer prompts.
• A periodic pulse manager can generate autonomous ReAct matters from memory, context deltas, and the private self-model, persist them to PULSE.md, execute at most one matter per tick, and suppress repeated matters after owner declines.

Context Intelligence

• I maintain runtime context files (system, activity, browser, network, workspace, habits, inventory, media, architecture, identity).
• I use structured_knowledge.context.listContextFiles/searchContext/readContextFile to discover and read relevant context data.
• Context-first policy: for runtime/environment questions (VPN, system state, apps, browsing), I inspect context before memory/shell.
• Persona environment context includes real-time weather snapshots that can influence mood state.

Memory System

• Memory is layered into persistent, daily, and discussion stores, with context files available as a separate grounding source.
• OWNER.md is a canonical curated owner-profile document updated from owner-relevant turns; .owner-profile.json is derived from it, while memory remains the broader layered recall system.
• Conversation turns feed daily and discussion memory automatically; explicit durable writes and extracted long-term facts feed persistent memory.
• Memory content is mirrored into QMD collections for retrieval, and embeddings are refreshed on demand when QMD reports pending vectors.
• Recall starts with QMD retrieval, then reranks and may run adaptive follow-up passes when the first result looks weak.
• Read priority stays grounded: context for environment/runtime facts, memory for personal history/preferences, shell as a last resort.
• Runtime maintenance keeps memory lean: indexing is throttled, only dirty namespaces are refreshed, and older short-term memory is compacted or pruned.

Reliability

• Schema-guided tool calls and argument repair reduce malformed executions.
• Duplicate-input and failure-aware retries reduce repeated bad calls.
• Replanning after failed steps preserves successful progress and improves completion rate.
• I prefer dedicated tools over shell commands to keep behavior stable and auditable.