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Chain of Agents(CoA)
A training-free framework for long-context tasks that splits an oversized input into chunks, assigns each chunk to a worker agent that reads its chunk plus a running communication unit passed from the previous worker, then hands the final accumulated communication to a manager agent that produces the answer. The sequential read-and-summarize chain mitigates long-context focus loss and beats RAG and full-context baselines.
In 30 seconds
- What
- Splits input into chunks, routes each to worker agents that read their chunk plus accumulated prior findings, then a manager synthesizes the final answer.
- When to use
- Documents or contexts exceed a single model's window and need systematic extraction across many pages without losing earlier findings.
- Watch out
- Worker agents may miss or misinterpret context that spans chunk boundaries, degrading answer quality if splits fall at critical junctures.
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Chain of Agents: Overview
A training-free framework for long-context tasks that splits an oversized input into chunks, assigns each chunk to a worker agent that reads its chunk plus a running communication unit passed from the previous worker, then hands the final accumulated communication to a manager agent that produces the answer. The sequential read-and-summarize chain mitigates long-context focus loss and beats RAG and full-context baselines.
- Input split into manageable chunks
- Worker agents read their chunk plus the prior communication unit
- A running evidence summary accumulates left to right
- Manager agent synthesizes the final answer
- Training-free, interpretable, and model-agnostic
- Handles inputs beyond a single model context window
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References
The papers, specifications, and repositories this pattern is based on.
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