Use Orq.ai as the model control layer for CrewAI. Route LLM calls through one OpenAI‑compatible endpoint, capture traces, monitor cost, and manage fallback behavior without rebuilding your CrewAI agent logic, crew definitions, or workflow orchestration.
What is CrewAI?
CrewAI is an open‑source Python framework for building multi‑agent “crews”, with abstractions for roles, tasks, tools, memory, and coordination. It helps teams focus on agent collaboration and workflows while the framework handles messaging and tool execution.
Why use Orq with CrewAI
Trace agent behavior end to end
Trace CrewAI runs end to end, including prompts, task assignments, tool invocations, intermediate steps, model responses, and errors in one place. You get clearer visibility into crew behavior without stitching together custom logs across multiple services.
Model flexibility without rewiring agents
Test new models, add alternative providers, or assign different model tiers to different crews and tasks from Orq.ai, while keeping your CrewAI agent roles, tools, and orchestration code stable. Your CrewAI code still “thinks” it’s talking to a standard LLM client; Orq handles which underlying model is called.
Evaluate real production runs
Use real conversations and traces from CrewAI‑driven agents to build datasets, compare prompt or model changes, and move from subjective tuning to measurable quality checks. Failed or slow runs can be turned into eval inputs before you roll out changes.
Control spend and access centrally
Track token usage and spend per agent, 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 CrewAI‑based application.
How the integration works
Step 1
Point CrewAI traffic at Orq.ai’s router
Configure the LLM client CrewAI uses (for example its OpenAI‑style client) so that its HTTP calls go to Orq.ai’s base URL instead of calling each model provider directly. This gives Orq the context it needs to apply routing rules, capture usage, and enforce fallback behavior.
Step 2
Enable tracing from CrewAI workloads to Orq
Keep your existing logging and monitoring in place while routing LLM calls through Orq’s AI Gateway / Router. Your crews continue to emit logs for tool steps and workflow decisions, and Orq records LLM interactions, latency, and cost metrics for those requests, so you can see both agent‑level and model‑level traces.
Step 3
Define routes, fallbacks, and policies in Orq
Create routes for key CrewAI workflows and assign them model tiers, fallback chains, and region/data policies. Your crews send the request to the configured Orq route, and Orq applies the routing rules you define before invoking OpenAI, Claude, or other models.
Step 4
Monitor, evaluate, and tune
Once connected, use Orq’s dashboards to watch latency, errors, and cost for CrewAI agents, and run evals or experiments on their traces. You can then adjust routes, models, or prompt configurations centrally where supported, without changing your CrewAI agent graph.
Use Cases
Multi‑agent products with real observability
Trace which agent, tool call, and model contributed to a failed workflow or unexpected outcome, combining your own logs from the Claude Agent SDK with Orq’s router‑level traces.
Cost‑aware internal tools
Route routine steps to lower‑cost models while keeping complex reasoning or high‑stakes flows on your strongest Claude routes configured in Orq.
Eval‑driven agent improvements
Reuse failed conversations and agent traces as eval inputs before shipping prompt, tool, or model changes, turning production telemetry into a continuous improvement loop.
Safer experimentation across providers
Test a new provider or model on a small share of SDK traffic, then promote or roll back based on traces and evals, instead of editing each agent definition or HTTP client config by hand.
FAQ
Do I have to change my CrewAI code to use Orq.ai?
In many cases, you can repoint the SDK’s HTTP client (base URL and key) to Orq’s router and keep your existing agent logic as‑is, adding only minimal configuration instead of rewriting agents. The agent definitions, tools, and orchestration stay in the SDK.
Does Orq.ai replace CrewAI’s own runtime or framework?
No. Claude Agent SDK remains your agent framework and runtime; you keep using its abstractions and Anthropic’s safety tooling. Orq.ai sits alongside it as the control plane for models, multi‑provider routing, cost tracking, and evaluation. You still design and run agents using the Claude Agent SDK, but you use Orq to see what they cost, which models they should call, and how LLM behavior compares across providers under the same agent workflow.
Can I keep using my existing LLM providers with CrewAI if I move to Orq.ai?
Yes. You can bring your existing Anthropic API key into Orq.ai and route traffic through Claude, alongside any new models you add later. That way you centralize access, routing, and tracking without losing the Anthropic setup you already rely on. Bring production controls to CrewAI agents
