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[Feature Request] Implement "Plan Hydration" in Agent Node to Eliminate Redundant Reasoning Tax #22220

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opened 2026-02-21 20:16:12 -05:00 by yindo · 0 comments
yindo commented 2026-02-21 20:16:12 -05:00
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Originally created by @smartass-4ever on GitHub (Feb 13, 2026).

Originally assigned to: @smartass-4ever on GitHub.

Self Checks

  • I have read the Contributing Guide and Language Policy.
  • I have searched for existing issues search for existing issues, including closed ones.
  • I confirm that I am using English to submit this report, otherwise it will be closed.
  • Please do not modify this template :) and fill in all the required fields.

1. Is this request related to a challenge you're experiencing? Tell me about your story.

Currently, the Agent Node (ReAct/Function Calling strategies) operates statelessly regarding its internal execution plan. Even for repetitive enterprise workflows (e.g., "Daily Security Audit" or "Standard Ticket Triage"), the LLM must re-reason and re-construct the execution graph from scratch on every run. This leads to:

High Latency: Users wait 10-20s for "Thinking" steps that were already solved in previous sessions.

Inference Waste: Significant token burn on redundant planning prompts.

Non-Deterministic Behavior: Slight variations in re-planning can lead to inconsistent tool-call sequences for identical intents.

Detailed Description
I propose adding a Plan Hydration Engine to the Agent Node infrastructure. Instead of a "cold start" for every task, the node should implement an Episodic Plan Cache.

Key Components:

Intent Fingerprinting: Generate a hash of the Task Description + Tool Definitions + Agent Instructions.

Hydration Layer: If a matching fingerprint exists in the cache with a high "Success Score," the Agent Node "hydrates" (loads) the previously successful execution plan directly, skipping the initial reasoning loops.

Volatile State Reset: Ensuring that while the plan is hydrated, the state (variables) is fresh to avoid context pollution (similar to my recent executor state fixes in the CrewAI ecosystem).

2. Additional context or comments

No response

3. Can you help us with this feature?

  • I am interested in contributing to this feature.
Originally created by @smartass-4ever on GitHub (Feb 13, 2026). Originally assigned to: @smartass-4ever on GitHub. ### Self Checks - [x] I have read the [Contributing Guide](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md) and [Language Policy](https://github.com/langgenius/dify/issues/1542). - [x] I have searched for existing issues [search for existing issues](https://github.com/langgenius/dify/issues), including closed ones. - [x] I confirm that I am using English to submit this report, otherwise it will be closed. - [x] Please do not modify this template :) and fill in all the required fields. ### 1. Is this request related to a challenge you're experiencing? Tell me about your story. Currently, the Agent Node (ReAct/Function Calling strategies) operates statelessly regarding its internal execution plan. Even for repetitive enterprise workflows (e.g., "Daily Security Audit" or "Standard Ticket Triage"), the LLM must re-reason and re-construct the execution graph from scratch on every run. This leads to: **High Latency**: Users wait 10-20s for "Thinking" steps that were already solved in previous sessions. **Inference Waste**: Significant token burn on redundant planning prompts. **Non-Deterministic Behavior**: Slight variations in re-planning can lead to inconsistent tool-call sequences for identical intents. **Detailed Description** I propose adding a Plan Hydration Engine to the Agent Node infrastructure. Instead of a "cold start" for every task, the node should implement an Episodic Plan Cache. **Key Components**: Intent Fingerprinting: Generate a hash of the Task Description + Tool Definitions + Agent Instructions. Hydration Layer: If a matching fingerprint exists in the cache with a high "Success Score," the Agent Node "hydrates" (loads) the previously successful execution plan directly, skipping the initial reasoning loops. Volatile State Reset: Ensuring that while the plan is hydrated, the state (variables) is fresh to avoid context pollution (similar to my recent executor state fixes in the CrewAI ecosystem). ### 2. Additional context or comments _No response_ ### 3. Can you help us with this feature? - [x] I am interested in contributing to this feature.
yindo added the 💪 enhancement🤖 feat:agent labels 2026-02-21 20:16:12 -05:00
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Reference: langgenius/dify#22220
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