14.7 Chapter Summary

In this chapter, we built a complete automated deep research agent system from scratch. Let's review the core points:

(1) TODO-Driven Research Paradigm

We proposed a new research paradigm - TODO-driven research. This paradigm decomposes complex research topics into executable subtasks and completes research through three stages:

• Planning stage: Decompose research topic into 3-5 subtasks, each subtask contains title, intent, and search query
• Execution stage: Execute search and summarization for each subtask, generating structured knowledge
• Reporting stage: Integrate summaries of all subtasks, generate final research report

The advantages of this paradigm are:

1. Strong controllability: Each subtask has clear objectives and scope
2. Reliable quality: Dedicated Agents ensure quality at each stage
3. Easy to debug: Can debug each subtask individually
4. Good scalability: Can easily add new subtasks or modify existing subtasks

(2) Three-Agent Collaboration System

We designed three specialized Agents, each performing their duties:

• TODO Planner (Research Planning Expert): Responsible for decomposing research topics into subtasks
• Task Summarizer (Task Summarization Expert): Responsible for summarizing search results for each subtask
• Report Writer (Report Writing Expert): Responsible for integrating summaries of all subtasks and generating final report

The advantages of this design are:

1. Clear responsibilities: Each Agent focuses on a specific task
2. Prompt optimization: Can customize specialized Prompts for each Agent
3. Easy to maintain: Modifying one Agent does not affect other Agents
4. Quality assurance: Each Agent is an "expert" in their field

(3) ToolAwareSimpleAgent Design

We extended the SimpleAgent of the HelloAgents framework and implemented ToolAwareSimpleAgent. This Agent has tool call listening capability and can:

• Listen to tool calls: Listen to each tool call through callback functions
• Real-time feedback: Push tool call information to the front-end in real-time
• Debugging support: Record all tool calls for easy debugging

This Agent has been integrated into the HelloAgents framework and can be reused in other projects.

(4) Tool System Integration

We fully utilized the tool system of the HelloAgents framework:

• SearchTool: Extended to support more search engines (Tavily, DuckDuckGo, Perplexity, etc.)
• NoteTool: Persist research progress, support recovery and auditing
• ToolRegistry: Unified management of all tools, support custom extensions

Through configuration-based design, users can easily switch search engines without modifying code.

(5) Core Service Implementation

We implemented four core services connecting Agents and tools:

• PlanningService: Call planning Agent, parse JSON, validate format
• SummarizationService: Call summarization Agent, process search results, extract sources
• ReportingService: Call report Agent, integrate summaries, generate report
• SearchService: Schedule search engines, process results, error degradation, result caching

These services each perform their duties and collaborate through clear interfaces, achieving an automated process from research topic to final report.

(6) Front-End Interaction Design

We designed a user-friendly front-end interface:

• Full-screen modal dialog: Immersive experience, clear hierarchy
• SSE real-time progress: Real-time display of research progress, good user experience
• Markdown visualization: Beautiful format, clear structure

Through the Vue 3 + TypeScript + SSE technology stack, we implemented a modern web application.

This knowledge is not only applicable to deep research assistants, but can also be applied to other AI applications. We hope readers can explore more possibilities based on this chapter and build more powerful AI systems.

In the next chapter, we will build a multi-agent system combined with a game engine - Cyber Town, exploring complex interaction and collaboration patterns between Agents. Stay tuned!