The same content can be analyzed in the workflow's LLM, but in the ReAct mode (using the same model), it often results in a "data too long" error.

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Dify version

v1.11.4

Cloud or Self Hosted

Self Hosted (Docker)

Steps to reproduce

NONE

✔️ Expected Behavior

I hope the output from the workflow and the output from the ReAct mode are consistent.

❌ Actual Behavior

These images all pertain to the same issue, and the tool has returned identical data in each case.

Workflow LLM output:

ReAct output(Unstable)：
success_output:

data too long:

[dosubot]

This happens because ReAct mode builds much longer prompts than workflow LLM mode, even with the same input and model. In ReAct, the prompt includes not just your query, but also system instructions, tool metadata, the agent’s scratchpad (thought/action/observation history), and all previous messages. This can easily overflow the model’s context window and trigger a “data too long” error, especially if your agent uses many tools or has a long history source.

Workflow LLM nodes use a more controlled, template-driven approach to prompt construction, with explicit token-budget logic to avoid exceeding model limits source.

To reduce “data too long” errors in ReAct mode, try limiting the number of tools, reducing the agent’s history length, trimming the scratchpad, or splitting large inputs. Make sure your model’s max_length and context window are set appropriately. If possible, upgrade to a newer Dify version—recent changes have removed some hardcoded input limits, but prompt assembly in ReAct mode is still more prone to overflow source.

There’s no universal fix, but keeping the agent’s context and tool use lean is the most effective mitigation.

To reply, just mention @dosu.

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