| name | ax-python-agent-observability |
| description | Use when writing Python code with `axllm` for agent tracing, centralized and multi-tenant usage accounting, action logs, runtime diagnostics, replay, and production debugging. |
| version | 23.0.14 |
AxAgent Observability For Python
This skill helps an agent write Python code with the generated Ax package axllm. Use the generated package API, examples, and manifests; do not import TypeScript-only APIs unless you are editing the TypeScript package.
When To Use
- Inspect agent traces, runtime envelopes, usage, or action logs.
- Register the process-wide usage observer and attribute model calls by tenant, user, request, run, or feature.
- Attach callbacks for model/tool activity and runtime progress.
- Debug agent loops through generated package state and examples.
Package Facts
- Language: Python.
- Package:
axllm.
- Package API docs:
API.md and axir-api.json.
- Capability manifest:
axir-capabilities.json.
- Runnable examples:
examples/.
- Real network support: yes.
- Scripted no-key transport support: yes.
- Runtime profiles:
javascript-quickjs, python-pyodide.
Core Pattern
from axllm import agent
helper = agent("question:string -> answer:string")
out = helper.forward(llm, {"question": "How should I proceed?"})
Centralized Usage Observer
Use the process-wide usage observer for application accounting across many agents, API routes, tenants, and users. Keep per-agent usage accessors for inspecting one agent instance after a run.
from axllm import set_usage_observer
set_usage_observer(usage_queue.put_nowait)
- The observer receives one normalized event for each completed chat or embedding call that reports provider usage. A fully consumed stream emits once; an unconsumed or cancelled stream may not emit.
- Events include the operation, AI/provider name, model, normalized tokens, streaming flag, optional usage context, and available session or remote request IDs.
- Attach
usageContext in AI service options for stable application or environment defaults. Attach it in call or agent-forward option maps for tenant, user, request, run, and feature attribution.
- Per-call context overrides service defaults. Nested
attributes are shallow-merged.
- The observer is process-wide, best-effort, and fail-open. Registering again replaces the previous observer. Clear it during test teardown or shutdown when appropriate.
- The observer runs on the request path. Production callbacks should synchronously enqueue into a bounded concurrent queue and return immediately, then persist or aggregate out of band. Use a shared durable pipeline across processes or serverless instances.
- Keep identifiers opaque and attributes low-cardinality. Do not attach prompts, responses, secrets, or other sensitive payloads.
- Calculate currency cost downstream against a versioned provider/model pricing table.
- Runnable provider example:
src/examples/python/generation/usage-observer.py.
Relevant API Surface
- AxAI:
ai, OpenAICompatibleClient, OpenAIResponsesClient, DeepSeekResponsesClient, GoogleGeminiClient, AnthropicClient, AxUsageContext, AxUsageEvent, AxUsageObserver, set_usage_observer, AxBalancer, AxBalancerAdaptiveStrategy, AxBalancerStatsStore, AxInMemoryBalancerStatsStore, create_balancer_route_stats, update_balancer_route_stats, sample_balancer_route_health, MultiServiceRouter, ProviderRouter
- Agents And RLM:
agent, AxAgent
- Runtime Profiles:
ProcessCodeRuntime, RuntimeCapabilities, RuntimeEnvelope, javascript-quickjs, python-pyodide
Guardrails
- Start from package examples for exact native syntax before inventing a new call shape.
- Use
provider-api examples only when the user explicitly has provider credentials available.
- Use
no-key examples for deterministic local checks and provider request mapping.
- Treat AxIR as the source of generated package truth: if package docs disagree with source code, update the compiler and regenerate packages.
- Do not copy repo-maintainer skills from
tools/*/skills/ into user packages.
