Deepen with an LLM

The deepen phase of kontext init is normally driven by the agent you work with. The autopilot runs the same tasks through any LLM you configure as an adapter with an llm op — useful for a first pass over a large repository.

kontext adapters add llm-claude              # or llm-codex, llm-ollama, or your own
kontext init --deepen --llm llm-claude --jobs 3 --max 20

[5/5] deepen   … 1/47 done, running up to 20 task(s) with 'llm-claude' (3 at a time)
  [1/20] mod:libs-schemas ✓ (32.0s)
  [2/20] mod:apps-runner ✓ (41.3s)
  …
      deepen   partial ✓ 20 · ✗ 0 · progress 21/47

Option	Default	Meaning
--llm	init.llm	adapter to use
--jobs	2	tasks in parallel
--max	50	tasks in this run

Set a default in config to drop --llm:

[init]
llm = "llm-claude"

What the model receives

Each task as an agent would see it, plus inlined sources (up to init.task_inline_chars, 24000 characters): the module's docs and key files, or git show --stat of the commits in a decision cluster. Secret files are never attached and attached text is redacted. The model must answer with one JSON object — the same shape as ctx_init_submit:

{
  "summary": "One line: what the module is for",
  "overview": "5–15 lines of Markdown",
  "decisions": [{ "title": "…", "decision": "…", "context": "…", "consequences": "…", "paths": ["…"], "commits": ["abc1234"] }],
  "learnings": [{ "title": "…", "body": "…", "paths": ["…"] }]
}

Prose or code fences around the JSON are tolerated. Failed tasks are reported and stay pending.

Models

Preset	Runs	Variable
llm-claude	claude -p --output-format text --model <model>	model (default sonnet; haiku is cheaper and good for module summaries)
llm-codex	codex exec --sandbox read-only --ephemeral, last message from a file	—
llm-ollama	POST /api/generate on $OLLAMA_HOST	model (default qwen2.5-coder:14b)

kontext adapters add llm-claude --var model=haiku --force

Quality

• Review the diff before committing — the autopilot writes into the working tree only.
• Run a few tasks first (--max 3) and read them; adjust init.task_max_files or init.task_inline_chars if summaries are shallow.
• Decision tasks are the ones to check most carefully: they infer intent from commit messages.
• Mixing is fine: let the autopilot summarize modules and have your agent do the dec: tasks with full tool access.