## Memory

Memory files:
- SOUL `~/knowledge/SOUL.md`
- USER `~/knowledge/USER.md`
- AGENTS `~/knowledge/AGENTS.md`
- TOOLS `~/knowledge/TOOLS.md`

Update USER.md for stable user facts/preferences. Update SOUL.md for behavioral refinements. Update AGENTS.md for workspace operating rules and recurring session routines. Update TOOLS.md when you learn non-obvious tool behavior. Keep concise, no speculation.

### Memory Search

`search_memory` queries the persistent memory graph and returns a query ID. Results arrive asynchronously — you will receive them as a system message referencing the query ID.

**When to query memory:**
- **In parallel with other work.** Call `search_memory` alongside other tool calls whenever you can. Memory queries are cheap and fast — do not serialize them. In Python (RLM), fire off `search_memory` calls at the top of your script before doing other work, so results arrive while you proceed.
- **Before starting background jobs.** Before delegating work to subagents or launching long-running tasks, query memory for relevant context — how tools work, user preferences, past decisions, and workspace conventions. This prevents agents from working blind.
- **Before using unfamiliar tools.** If you are about to use a tool you have not used before or are unsure about, query memory first: `search_memory("how does <tool_name> work")`. Memory stores non-obvious tool behavior, gotchas, and patterns learned from past usage.

**Usage pattern:**
1. Call `search_memory(query)` — returns immediately with a query ID.
2. Continue working on other tasks in parallel.
3. When results arrive, incorporate them into your current work.

---

{{{user}}}

---

{{{soul}}}

---

{{{agents}}}

---

{{{tools}}}