The Tool Desk
Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →To use different models in a CrewAI workflow, assign a model to each agent when roles need fixed model choices; use a CrewAI Flow router when a workflow must choose a path conditionally. Agent configuration determines which LLM an agent calls. Flow routing determines which step runs next. You can combine both approaches when a workflow needs conditional orchestration and model-specialized agents.
Choose between per-agent models and Flow routing
| Need | Use | What it controls |
|---|---|---|
| A researcher should consistently use one model and a writer another | Assign a model to each agent | The LLM an agent uses when it performs its work |
| A workflow should take different next steps based on a result or state | Use a Flow router | The execution path the workflow follows |
| A structured process needs agents to collaborate on selected tasks | Combine a Flow and a Crew | The Flow coordinates steps; the Crew handles autonomous collaboration |
These controls are related, but they are not interchangeable. A model assignment does not by itself inspect task content and reroute execution. For an explicit branch, use workflow logic to select the route; configure the agents on those paths with the models they should use. CrewAI describes Flows as structured orchestration and Crews as autonomous collaboration, with hybrid designs available when an application needs both (CrewAI core concepts).
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Assign a model to each agent
CrewAI supports configuring a model in an agent’s YAML definition or configuring an LLM in Python. The exact provider identifier, integration package, and setup requirements depend on the provider and CrewAI version. CrewAI’s migration guidance gives an example of assigning different providers to a researcher and a writer; check the guidance for the version you deploy because integration details can change (CrewAI LLM guide; CrewAI LiteLLM migration guide).
YAML configuration
Set the model in the relevant agent configuration, then ensure your CrewAI code loads that configuration as intended. Refer to the LLM guide for the current field names and syntax; do not assume a sample written for another release is compatible.
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Python configuration
Configure the LLM for the agent in Python when you want model setup to live in code rather than agent YAML. Follow the current provider instructions for constructing or identifying the LLM and passing it to the agent. If you mix YAML and Python configuration, verify which values your deployed version uses.
Provider setup and credentials
- Install the provider integration or extra required by the selected model. A model identifier alone does not install its SDK or make credentials available.
- Keep API keys out of committed source code. Use environment variables or a secret-management system, following the provider’s current setup instructions.
- Do not assume providers share package names, key names, supported features, or endpoint conventions.
- For a custom OpenAI-compatible endpoint, check CrewAI’s current requirements for the base URL and credentials, and confirm that the endpoint supports the features your agent uses.
Use a Flow router for conditional model paths
A Flow router method can return a route label that determines which path proceeds. Use this when a prior step’s result, workflow state, or a decision should select the next step. The router controls execution; it does not make a single agent switch models automatically unless your code explicitly implements that behavior. CrewAI’s Flow guide documents conditional routing (CrewAI Flow guide).
- Identify the decision that should change the workflow path, such as a classification or a result from an earlier step.
- Implement a router method that evaluates that decision and returns one of the route labels your Flow handles.
- Define a path for each label and connect the appropriate steps or agents to it.
- Configure each agent on those paths with its intended model, and test the decision outcomes and downstream behavior.
This makes the routing rule explicit and inspectable. Keep the branching condition separate from each agent’s model configuration so it is clear whether a change affects workflow control or the LLM used for a role.
Combine a Flow and a Crew when both are useful
A Flow can manage structured sequencing and conditional branches, while a Crew can coordinate agents that need to collaborate autonomously. A practical design might use a Flow to decide which kind of work is needed, then invoke a Crew whose agents have role-specific model assignments. Not every workflow needs both: use the simpler structure that meets the requirement.
Choose models by testing the work they will do
CrewAI’s configuration and orchestration guides do not establish a universal best model for research, writing, tool use, or another role, and they do not provide a relevant comparative cost or latency benchmark. Evaluate candidate models on representative tasks rather than assuming that a particular provider is best for a job.
- Task quality: Judge outputs against criteria specific to the agent’s role.
- Latency and API cost: Measure them on the workload and usage pattern you expect.
- Context needs: Check that the model can handle the inputs and accumulated context your workflow sends.
- Tools and output formats: Verify the provider and model support the tool calling or structured output your agent requires.
- Privacy and deployment constraints: Confirm the service fits your data-handling and operational requirements.
- Reliability: Check behavior across repeated representative runs, not only one successful output.
Record the CrewAI version, provider, exact model identifier, test workload, date, and relevant region so results can be understood later. Treat those results as specific to the tested configuration, not as a general ranking.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Managed deployment is a separate decision
CrewAI AMP describes managed deployment and operations features, including deployment, monitoring, API access, tool repositories, webhook streaming, and Crew Studio (CrewAI AMP). The cited AMP information does not make AMP a requirement for assigning multiple models or routing a Flow; consider it separately if your team needs hosted operations.
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