OpenClaw + Orq: Production observability for OpenClaw agents

Trace OpenClaw runs end to end, including prompts, tool invocations, workflow steps, LLM calls, and errors in one place. You get clearer visibility into how each agent decided, called tools, and produced answers, without stitching together logs across multiple services.

Test new models, add alternative providers, or assign different model tiers to different OpenClaw agents and workflows from Orq.ai, while keeping your OpenClaw definitions and orchestration code stable. Your agents still “think” they’re talking to a single LLM client; Orq handles which underlying model is called.

Use real OpenClaw traces like requests, intermediate steps, tool chains, and LLM interactions to build datasets, compare prompt or model changes, and move from subjective tuning to measurable quality checks. Failed or slow agent runs can be turned into eval inputs before you roll out changes.

Track token usage and spend per agent, tool, team, and workflow so you can see which routes drive cost. Add budgets, rate limits, and approved‑model lists at the Orq platform layer instead of enforcing governance separately inside each OpenClaw‑based application.

Configure the LLM client OpenClaw uses so that its HTTP calls go to Orq.ai’s base URL and API key instead of calling each provider directly. This gives Orq the context it needs to apply routing rules, capture usage, and enforce fallback behavior behind your agents and workflows.

Keep your existing logging and monitoring in place while routing LLM calls through Orq’s AI Gateway / Router. Your OpenClaw agents continue to emit logs for tool steps and workflow decisions, and Orq records LLM interactions, latency, and cost metrics, so you can see both agent‑level and model‑level traces.

Create routes for key OpenClaw workflows (for example customer‑support agents, internal copilots, or multi‑tool orchestrators) and assign them model tiers, fallback chains, and region/data policies. OpenClaw sends LLM requests to the configured Orq routes, and Orq applies routing before invoking OpenAI, Gemini, Claude, or other models.

Once connected, use Orq’s dashboards to watch latency, errors, and cost for OpenClaw agents, and run evals or experiments on their traces. You can adjust routes, models, or prompt configurations centrally where supported, while OpenClaw continues to coordinate tools, memory, and workflows.

Trace which OpenClaw agent, tool, workflow step, and model contributed to a failed workflow or unexpected outcome, combining your own application logs with Orq’s router‑level LLM traces.

Route routine steps like simple QA, summarization, or routing decisions to lower‑cost models while keeping complex reasoning, multi‑tool orchestration, or safety‑critical flows on your strongest routes configured in Orq.

Reuse failed conversations, mis‑routed tool calls, and slow agent runs as eval inputs before shipping prompt, workflow, or model changes, turning production telemetry into a continuous improvement loop.

Test a new model or provider on a small share of OpenClaw traffic, then promote or roll back based on traces and evals, instead of editing each agent definition or LLM client config by hand.