Runtimes

You already picked an agent runtime — AgentCore, watsonx.ai, or something else. This section is the shortest path from "we standardized on X" to "we have Waxell governance + observability on every run."

Each page is a focused 5-minute deploy guide for one runtime: prereqs, the smallest possible code change, the exact --env / config wiring, and verification you can copy-paste.

If your runtime hosts your Python code (most do), Waxell drops in unchanged — pip install waxell-observe, waxell.init(), you're done. The pages below cover the runtime-specific quirks: env-var wiring, network egress, image versions, the things that actually trip people up.

Pick your runtime

Amazon Bedrock AgentCore (BYO code) →

Bring your own Python agent, deploy it to AgentCore Runtime's managed microVM via the agentcore CLI. Waxell installs inside the microVM as a normal pip dep and produces full client-side spans with cost + token counts — same shape as running the agent on any other Python host.

Best for: teams who write their agent loop in Python and want AWS to handle the hosting, scaling, and isolation.

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IBM watsonx.ai →

Wrap your ibm-watsonx-ai SDK calls with Waxell so every Granite / Llama / Mistral call on watsonx Foundation Models shows up in your governance dashboard with real cost (sourced from IBM's published per-million-token rates).

Best for: teams running Python agents against watsonx Foundation Models, whether locally, on IBM Cloud Code Engine, or on any other host.

Don't see your runtime?

Anywhere Python runs, Waxell runs. If your runtime hosts an arbitrary Python entrypoint (Lambda, ECS, Cloud Run, Azure Container Apps, Modal, Replicate, your laptop), the generic install guide is the path — the runtime is invisible to Waxell.

We're prioritizing the runtimes our customers ask for most. If yours isn't here yet, tell us and it goes to the top of the list.