See what your agent did and why You keep task input, context, orientation, memories, and skills in the right lifecycle. Runtime hooks then show the turns, tool calls, traces, status, and usage behind the answer. python academy academy/topics/agent-context-observability website/content-src/academy/course.mjs academy See what your agent did and why
ax
Python axllm
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Unit 5 · Build an agent that can use tools

See what your agent did and why

You keep task input, context, orientation, memories, and skills in the right lifecycle. Runtime hooks then show the turns, tool calls, traces, status, and usage behind the answer.

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Unit example (nearest native match)

See the idea in context

from axllm import agent

helper = agent('question:string -> answer:string')
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/short-agents/tools-agent.py
Active practice

Show that you can use it

Answer 2 in a row to learn this · attempt 1
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Source-backed follow-up