Feature Description
Add support for configuring a different AI model for each subagent. This would allow users to assign the most appropriate model to a specific subagent based on its responsibilities, rather than requiring all subagents to use the same model as the parent agent.
For example, a lightweight and cost-effective model could be used for simple tasks such as file creating a report, analysing ,or documentation generation, while more capable models could be assigned to complex reasoning, architecture design, or debugging tasks.
Use Case
- Optimized credit usage by reserving premium models for tasks that truly require them.
- Improved task performance by matching each subagent with a model that excels at its specific workload.
- Greater flexibility in designing agent workflows.
- Better scalability when running multiple subagents in parallel.
Allowing per-subagent model selection would enable users to balance cost, speed, and quality more effectively, resulting in more efficient and capable multi-agent workflows.
Additional Context
No response
How important is this to you?
None
Feature Description
Add support for configuring a different AI model for each subagent. This would allow users to assign the most appropriate model to a specific subagent based on its responsibilities, rather than requiring all subagents to use the same model as the parent agent.
For example, a lightweight and cost-effective model could be used for simple tasks such as file creating a report, analysing ,or documentation generation, while more capable models could be assigned to complex reasoning, architecture design, or debugging tasks.
Use Case
Allowing per-subagent model selection would enable users to balance cost, speed, and quality more effectively, resulting in more efficient and capable multi-agent workflows.
Additional Context
No response
How important is this to you?
None