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Compute the MD5 checksum of a file. Call once before and once after a change to prove whether the file's contents actually changed -- pair both hashes as evidence for task_complete rather than asserting the change happened.

Delete a memory entry by key. Use to remove outdated or incorrect facts from persistent memory.

Run a relational query over agent state. Relations: `memory` (persistent memory entries: key, value, namespace, type, references, createdAt, updatedAt), `tool_calls` (recent tool call history: name, args, result, timestamp), `tasks` (active goal's task list: id, desc, status, rounds, note). Query language is expr-lang: filter(relation, predicate) selects rows (e.g. filter(memory, .type == "error")), map(relation, expr) projects fields, join(left, right, leftField, rightField) equi-joins two relations into rows with left_/right_ prefixed fields, union(a, b) combines two relations with set semantics.

Save a fact to persistent memory. The memory is injected into every LLM call automatically. Use for project conventions, user preferences, key decisions, or any information that should persist across sessions. Keys should be short and descriptive.

Search persistent memory for entries matching a query. Returns up to 20 matching key-value pairs (each value capped), best match first: a key hit outranks a value-only hit, more query words outrank fewer, and a newer update breaks a tie. Use to recall previously saved facts, preferences, or decisions. There is no memory_list tool; the memory graph is already in the system prompt.

Extract text from an image via tesseract OCR. Runs entirely locally -- no API key required. Supports png, jpg/jpeg, tiff, bmp, gif. Requires: file (absolute path to the image). Optional: language (tesseract -l value, e.g. 'eng' or 'eng+fra'; default 'eng'). Does not support PDF -- use load_document with type pdf for text-layer PDFs.