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To use different models in a CrewAI workflow, assign a model to each agent that should use it consistently. Use a CrewAI Flow router when the workflow must choose which execution path to take based on a result or state. These controls solve different problems: agent configuration selects an LLM; Flow routing selects what runs next.
Choose between fixed model assignments and conditional routing
If each role should always use a particular model, configure that model on the corresponding agent. For example, a research agent and a writing agent can use different provider/model identifiers. This is role-based selection, not automatic switching in response to each prompt.
If a workflow needs to branch—for instance, taking one path when an earlier step produces a particular result—use a Flow router. The router returns a route label, and that label determines which path proceeds. A router does not, by itself, change the model configured on an agent.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchCrewAI describes Crews as a way to coordinate autonomous collaboration and Flows as a way to structure orchestration. A workflow can combine them: use Flow logic to control the sequence and a Crew for collaborative work within an appropriate step. CrewAI core concepts explain the distinction, and the Flow guide documents conditional routing.
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Set a model for each agent
CrewAI documents model configuration in agent YAML and in Python. Choose the configuration style that fits your project, then set a distinct model identifier for each agent whose role needs a different model. The identifiers, provider integrations, and package requirements depend on the providers you use; follow the current setup instructions for your CrewAI release. CrewAI’s LLM guide covers configuration and provider setup.
Keep credentials out of source code
Supply API credentials through environment variables or a secret-management system rather than committing keys in YAML or Python files. Use the provider’s current credential names and setup instructions; do not assume that providers share the same key format or configuration.
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Install the required provider integrations
Different agents can use models from different providers, but the appropriate provider integration must be available in the environment. CrewAI’s LiteLLM migration guide includes a multi-provider example and discusses provider integrations and custom OpenAI-compatible endpoints. Because this is versioned integration guidance, check it against the CrewAI version and packages deployed in your project. A custom endpoint may require its own base URL and credentials, and its capabilities may differ from native integrations.
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Use a Flow router when the next step depends on a result
Model assignments are static for the agent configuration; a Flow router adds explicit conditional execution. Implement a router method that evaluates the relevant workflow state or prior result and returns a route label. Define the paths associated with those labels so the Flow continues along the intended branch. The CrewAI Flow guide describes router methods and route-based execution.
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If different branches should call different models, have each branch invoke an agent configured with the intended model. The routing decision then selects the agent or step to run; the selected agent’s configuration determines its LLM. Make the decision rule explicit so the workflow’s behavior can be understood and tested.
Evaluate models for your own roles
CrewAI’s configuration documentation does not establish a universally best model for research, writing, tool use, or another role, and the cited guides do not provide comparative cost or latency benchmarks. Select models by evaluating representative tasks from your own workflow rather than assuming that provider choice alone predicts performance.
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- Task quality: Check whether outputs meet the role’s factual, stylistic, or decision-making requirements.
- Latency and API cost: Measure these using your own workload and current provider terms.
- Context needs: Confirm that the model can handle the prompts, history, and source material the agent receives.
- Tool calling and structured output: Verify support for the features your agent actually uses.
- Privacy and deployment constraints: Make sure the provider and endpoint meet your requirements for data handling and deployment.
- Reliability: Assess behavior over repeated representative runs, including failures and recovery needs.
For results that remain interpretable, record the CrewAI version, provider, exact model identifier, relevant region, test date, and workload. Treat those results as measurements of your setup, not as a general ranking.
When managed deployment is relevant
CrewAI AMP is presented as a managed option for deploying and operating workflows, with features including deployment, monitoring, API access, a tool repository, webhook streaming, and Crew Studio. The AMP material does not make AMP a requirement for assigning different models to agents or routing a Flow. See CrewAI AMP for its described capabilities.
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For component definitions, CrewAI also documents annotations for agents, tasks, crews, LLMs, and related elements in its annotations guide.
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