Unit 6 · Solve long and complex tasks

Ask small questions mid-investigation

You use llmQuery() for a focused semantic question over narrowed context and child agents for tool-using subtasks. The runtime can also upgrade exploration, answer directly, or repair failed code.

llmQuery()9 focused minutesNot started

Unit example (nearest native match)

See the idea in context

investigator = agent(signature, {
    "contextFields": ["logs"],
    "contextPolicy": {"preset": "lean", "budget": "balanced"},
    "runtime": {"language": "JavaScript"},
})

Run itIn your own project

pip install axllm

from axllm import ai, ax

llm = ai('openai', api_key=os.environ['OPENAI_API_KEY'])
classify = ax('review:string -> sentiment:class "positive, negative, neutral"')

Set OPENAI_APIKEY in your environment before running provider-backed code.

In the ax repo

From a clone of the ax repo:

npm run example -- python src/examples/python/long-agents/incident-log-forensics.py

Active practice

Show that you can use it

Answer 2 in a row to learn this · attempt 1

Keep exploring

See the idea in context

Show that you can use it

Source-backed follow-up