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Copy file name to clipboardexpand all lines: docs/multi_agent.md
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@@ -34,4 +34,4 @@ While orchestrating via LLM is powerful, orchestrating via LLM makes tasks more
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- Running the agent that performs the task in a `while` loop with an agent that evaluates and provides feedback, until the evaluator says the output passes certain criteria.
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- Running multiple agents in parallel, e.g. via Python primitives like `asyncio.gather`. This is useful for speed when you have multiple tasks that don't depend on each other.
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We have a number of examples in [`examples/agent_patterns`](https://github.com/openai/openai-agents-python/examples/agent_patterns).
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We have a number of examples in [`examples/agent_patterns`](https://github.com/openai/openai-agents-python/tree/main/examples/agent_patterns).
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