50 lines
1.9 KiB
Markdown
50 lines
1.9 KiB
Markdown
# OpenAI Agents SDK: Orchestrating multiple agents
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## Metadata
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| Field | Value |
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| Institute | Molodetz Institute — Research & Retrieval Division |
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| Subject | cap-protocol |
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| Document type | Source Dossier |
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| Tier | 1 |
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| Source title | OpenAI Agents SDK: Orchestrating multiple agents |
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| Source URL | https://openai.github.io/openai-agents-python/multi_agent/ |
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| Source publisher / author | OpenAI |
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| Source type | vendor docs |
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| Source date (published) | Unknown — not stated |
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| Date researched | 2026-10-25 |
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| Researched by | tai (agent), model: Claude Sonnet 5, on behalf of retoor |
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| Research method | direct page fetch |
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| Confidence | High |
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| Status | Active |
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## Summary
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Short orchestration guide separating LLM-driven from code-driven orchestration and agents-as-tools from handoffs.
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## Key findings
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- LLM-driven orchestration lets the model plan and choose steps; code-driven orchestration puts application code in charge of which agents run in what order, making speed, cost and performance more predictable.
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- Agents as tools: use when a specialist handles a limited subtask 'but should not take over the user-facing conversation'; the manager keeps control of the reply.
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- Handoffs: use when 'routing itself is part of the workflow'; the specialist owns the rest of the turn.
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- Best practices: invest in good prompts, monitor and iterate, let the agent introspect and improve, build specialists that each excel at one task, use evals.
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## Pros / strengths
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Names the decision rule (who owns the reply) in two lines.
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## Cons / limitations / caveats
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Vendor guidance; no data. Overlaps with Anthropic's orchestrator-workers description.
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## Relevance to tai
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tai's `@agent prompt` is a handoff-like human-chosen route; a model-callable `ask_agent` would be the agent-as-tool form. This page backs choosing the tool form when tai must stay in control of the answer.
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## Changelog
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| Date | Change |
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| 2026-10-25 | Initial dossier created. |
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