The following optimization scheme can be used in combination with the DeepAgents feature:
- Toolchain Configuration: Add the
tool="code_analyzer"and mount specialized tools such as the AST parser - contextual enhancement: Documentation of the code specification will
style_guide.pdfPre-posted virtual file system for reference - hierarchical analysis: Set up the analysis process as
文件结构→模块关系→函数逻辑Three progressive stages - Validation of results: Configuration
unit_test_agentAutomated tests are recommended for the generated code - Cache Utilization: Enabling of large codebases
agent.enable_cache=TrueAvoid duplication of analysis - Professional Tips: Setting up targeted system prompts such as
"严格遵循PEP8规范，发现潜在安全漏洞时高亮警告"
These methods can significantly improve the professionalism of code analysis.
This answer comes from the articleDeep Agents: a Python toolkit for rapidly building AI agents for complex tasksThe
May not be reproduced without permission:AI productivity tools " Improving the accuracy and efficiency of AI agents in code analysis tasks
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