multi-model

name	multi-model
description	Recovery strategy when a subagent is stuck on a hard task. Two escape hatches: spawn a fresh-context instance with the same model, or retry with a different model from the registry.

Multi-Model Retry Pattern

A subagent has clearly stalled — repeating the same wrong fix, hallucinating non-existent APIs, or exceeding a step budget. Two ways out: same model + clean history, or a different model.

Usage

3a. Fresh-context retry (same model)

The current subagent's history has accumulated misleading state. A new instance with the same agent_name and same model but no prior context often makes different choices.

new_sid = call_subagent(
    agent_name="worker",
    request=f"{original_task}\n\nPrevious attempt explored {summary} "
            f"and got stuck. Try a different angle.",
    mode="sync",
    # No model_name → uses the agent's default model.
)

3b. Different-model retry

Some tasks fit certain models better — Claude vs GPT-5 differ on long chains, large diffs, MCP-style tool use, etc.

models = get_available_models()
new_sid = call_subagent(
    agent_name="worker",
    request=original_task,
    mode="sync",
    model_name=alternate_model,    # different from the original
)

Combined recipe

1. Decide the original is stuck (no progress in N tool calls, or final answer is complain-shaped).
2. Try 3a first (cheaper — same model, fresh context). One attempt.
3. Still failing → try 3b with each alternate model in get_available_models(), sync mode, in sequence.
4. If multiple alternates also fail → emit complain with the accumulated evidence. Don't loop forever.

Key points:

• Don't auto-retry on every failure. Distinguish "stuck" from "task genuinely impossible". complain("no info") is signal, not a bug to retry through.
• Cap retries (e.g. ≤2 alternate models). Each retry costs LLM calls.
• For 3b, the new model still has to be in get_available_models(). If the registry only has one model, only 3a is available.

Common Use Cases

• Single-task subagent has burned its step budget without convergence
• Model-specific regression: one model can't handle a certain syntax, another can
• High-value task where you want to "throw bigger model at it" only after the cheap one fails

Requires Sandbox

None — pure orchestration.