[Feature]: Support running multiple agents (same or different providers) in parallel within the same thread #6911
Replies: 2 comments
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Building on this request, I would also love a structured adversarial-dialogue mode and cross-provider subagent delegation. The key requirement is that an agent should be able to spawn subagents using different models or providers from its own, while keeping all work in the same thread. For example:
This could support two complementary workflows:
A possible adversarial workflow:
Useful controls would include:
This would go beyond merely running several independent tasks. It would let models with different strengths and failure modes actively review one another and produce a more thoroughly challenged solution. Agreement would not be treated as proof that an answer is optimal, and unresolved dissent would remain visible to the user. This also overlaps with the cross-provider orchestration idea in #3138. |
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Adding a concrete use case for the cross-provider part of this. I came to T3 Code from oh-my-pi, where model roles let a Claude orchestrator dispatch subagents to other providers per role — cheap mechanical work to a small OpenAI model, review passes to a different one, while the orchestrator itself stayed on Claude. Being able to mix providers within one agent's workflow, rather than switching the whole thread, was the feature I used most, and it's the one thing I lost migrating over. Right now I'm hand-rolling it: my Claude orchestrator shells out to One design note from building the workaround, since I don't think it's obvious: permission rules don't cross the provider boundary. My harness-level deny rules on destructive project commands have zero effect inside a delegated Codex process, because that process only reads its own config. I had to re-implement the same policy as a Codex |
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Before submitting
Area
Not sure
Problem or use case
Today a thread in t3code is tied to a single agent running one task at a time. There is no way to run multiple agents in parallel within the same thread, whether multiple instances of the same provider (e.g. several Claude agents working on different parts of a task) or a mix of different providers (e.g. Claude and Codex together). This limits how quickly independent sub-tasks in the same conversation/thread can be worked on, since everything is serialized through a single agent.
Proposed solution
Allow spawning and running several agents concurrently inside the same thread, similar to Cursor's "Multitask"/parallel agents mode: either multiple instances of the same provider or a mix of different providers (e.g. Claude + Codex + OpenCode) working side by side on separate sub-tasks, with their outputs surfaced back into the shared thread/conversation.
Why this matters
Parallel agents let users get through multi-part tasks much faster by working on independent sub-tasks simultaneously instead of waiting for one agent to finish each step in sequence. It also lets users combine the strengths of different providers on the same problem (e.g. one agent handling backend changes while another handles frontend or tests) without leaving the thread.
Smallest useful scope
A minimal version could support spawning 2 additional agents inside a thread (same provider, running on clearly separated sub-tasks), with their output appended to the thread once done.
Alternatives considered
Currently the only workaround is opening separate threads for each sub-task and manually merging the results, which loses shared context and adds manual coordination overhead. Cursor's Multitask mode is the closest existing example of this pattern.
Risks or tradeoffs
Running multiple agents concurrently increases cost (multiple provider sessions billed at once) and adds complexity around merge conflicts when agents edit overlapping files or state. The UI/thread model also needs a clear way to show which agent produced which output, and how partial/conflicting results are reconciled.
Examples or references
Cursor's "Multitask" mode, which lets multiple agents work in parallel within the same session, is a comparable existing implementation of this pattern.
Contribution
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