iterative-retrieval

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Pattern for progressively refining context retrieval to solve the subagent context problem

AI & Automation 496 stars 41 forks Updated 1 months ago MIT

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Skill Content

# Iterative Retrieval Pattern Solves the "context problem" in multi-agent workflows where subagents don't know what context they need until they start working. ## The Problem Subagents are spawned with limited context. They don't know: - Which files contain relevant code - What patterns exist in the codebase - What terminology the project uses Standard approaches fail: - **Send everything**: Exceeds context limits - **Send nothing**: Agent lacks critical information - **Guess what's needed**: Often wrong ## The Solution: Iterative Retrieval A 4-phase loop that progressively refines context: ``` ┌─────────────────────────────────────────────┐ │ │ │ ┌──────────┐ ┌──────────┐ │ │ │ DISPATCH │─────▶│ EVALUATE │ │ │ └──────────┘ └──────────┘ │ │ ▲ │ │ │ │ ▼ │ │ ┌──────────┐ ┌──────────┐ │ │ │ LOOP │◀─────│ REFINE │ │ │ └──────────┘ └──────────┘ │ │ │ │ Max 3 cycles, then proceed │ └─────────────────────────────────────────────┘ ``` ### Phase 1: DISPATCH Initial broad query to gather candidate files: ```javascript // Start with high-level intent const initialQuery = { patterns: ['src/**/*.ts', 'lib/**/*.ts'], keywords: ['authentication', 'us...

Details

Author
vibeeval
Repository
vibeeval/vibecosystem
Created
2 months ago
Last Updated
1 months ago
Language
C#
License
MIT

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