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Sushmita - aka meowy Subscriber for Gaia

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Building a Deep Research Agent with Gaia: Multi-Agent Planning and Execution

Research is no longer just about finding information—it's about intelligent synthesis, strategic planning, and adaptive depth exploration.

Today, I want to share how I built a sophisticated AI research agent using Gaia's decentralized infrastructure that doesn't just search the web, but thinks like a professional researcher.

The Challenge: Beyond Simple Search

Traditional search tools give you results. Research agents give you insights. The difference lies in their ability to:

  • Plan strategically rather than search randomly
  • Evaluate quality rather than just rank by relevance
  • Synthesize findings rather than present raw data
  • Adapt their approach based on what they discover

Sounds complicated? Think of it in this way:

In this case the architecture would be something like this:

This research agent embodies all these capabilities through a multi-agent architecture that mirrors how human researchers actually work.

Architecture: A Symphony of Specialized Agents

The system consists of three core agents working in harmony, let's explore how everything works together.

Planning Agent: The Strategic Mind

Built with Gaia's Llama-3-Groq-8B-Tool model, this agent creates comprehensive research plans:

// Research planning with structured steps
const plan = await planner.createPlan(query);

// Each plan contains:
{
  query: "Original research question",
  steps: [
    {
      id: 1,
      description: "Search for recent developments",
      action: { type: 'search', params: { query: '...' }},
      status: 'pending'
    }
  ]
}
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Execution Agent: The Workhorse

Handles the actual research tasks with multiple specialized tools:

switch (currentStep.action.type) {
  case 'search':
    result = await searchTool.search(query);
    break;
  case 'summarize':
    result = await summarizationTool.synthesize(findings);
    break;
  case 'fetch_webpage':
    result = await webTool.extractContent(url);
    break;
  // Additional tools for papers, GitHub repos, etc.
}
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The Deep Thinker

The most sophisticated component—it decides when and how to dig deeper:

// Dynamic plan expansion based on findings
const additionalSteps = await generateText({
  model: openai('gpt-4'),
  messages: [
    {
      role: "system", 
      content: "Analyze research gaps and create deeper investigation steps..."
    }
  ]
});
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Stateful Research Workflows

Unlike stateless APIs, this research agent maintains context throughout the entire research journey:

interface ResearchSession {
  id: string;
  plan: ResearchPlan;
  currentStepIndex: number;
  artifacts: ResearchArtifact[];
  createdAt: string;
}

// Persistent session storage
const sessions: Record<string, ResearchSession> = {};
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This allows the agent to:

  • Remember previous findings when planning next steps
  • Build upon earlier research in subsequent queries
  • Track progress through complex multi-step investigations
  • Resume research if interrupted

The "Dig Deeper" Innovation

The breakthrough feature is the agent's ability to intelligently expand its own research plan. When you click "Dig Deeper," here's what happens:

1. Gap Analysis

The agent analyzes completed research to identify knowledge gaps:

// The agent examines all artifacts collected so far
const { plan, artifacts } = session;

// And asks: "What haven't we explored yet?"
const analysisPrompt = `
Based on the research conducted so far, identify areas that need deeper investigation.
Current findings: ${JSON.stringify(artifacts)}
What 2-3 additional research steps would provide deeper insights?
`;
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2. Strategic Expansion

Rather than random additional searches, it creates targeted investigation steps:

[
  {
    "id": 4,
    "description": "Investigate the technical implementation challenges",
    "action": {
      "type": "search",
      "params": {
        "query": "technical challenges implementing quantum computing in healthcare"
      }
    },
    "dependencies": [1, 2]
  }
]
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3. Contextual Execution

New steps build upon previous findings using the dependency system:

// Find relevant artifacts from previous steps
if (currentStep.dependencies) {
  for (const depId of currentStep.dependencies) {
    const depArtifact = artifacts.find(a => a.taskId === depId);
    if (depArtifact && depArtifact.taskType === 'search') {
      searchResults = depArtifact.artifact as ProcessedSearchResult[];
    }
  }
}
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Real-Time Research Orchestration

The execution engine manages complex workflows with proper error handling and progress tracking:

export async function POST(request: Request) {
  const { sessionId } = await request.json();
  const session = sessions[sessionId];

  // Execute current step
  const currentStep = plan.steps[currentStepIndex];
  currentStep.status = 'in-progress';

  try {
    // Execute based on step type
    const result = await executeStep(currentStep);

    // Store results as artifacts
    artifacts.push({
      taskId: currentStep.id,
      taskType: currentStep.action.type,
      artifact: result
    });

    currentStep.status = 'completed';
    session.currentStepIndex++;

    return { result, isComplete: currentStepIndex >= plan.steps.length };
  } catch (error) {
    currentStep.status = 'failed';
    throw error;
  }
}
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Advanced Features: Beyond Basic Research

Quality Assessment

The agent doesn't just collect information—it evaluates source credibility, recency, and relevance:

class WebSearchTool {
  processResults(rawResults: any[]): ProcessedSearchResult[] {
    return rawResults.map(result => ({
      title: result.title,
      url: result.url,
      summary: this.extractSummary(result.content),
      source: this.identifySource(result.url),
      date: this.extractDate(result),
      credibilityScore: this.assessCredibility(result),
      keyPoints: this.extractKeyPoints(result.content)
    }));
  }
}
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Intelligent Synthesis

The summarization tool creates coherent narratives from disparate sources:

class SummarizationTool {
  async summarize(query: string, searchResults: ProcessedSearchResult[]): Promise<string> {
    // Combines findings from multiple sources
    // Identifies patterns and contradictions  
    // Creates structured summaries
    // Highlights key insights
  }
}
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Adaptive Interface

The frontend provides real-time feedback on which agents are active:

// Visual indicators for active processes
const [activeFeatures, setActiveFeatures] = useState({
  search: false,    // Web search in progress
  planning: false,  // Creating research plan
  quality: false,   // Evaluating source quality
  synthesis: false, // Synthesizing findings
  depth: false      // Digging deeper
});
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Why Gaia Makes This Possible

Decentralized Intelligence

Unlike centralized AI services, Gaia's distributed architecture ensures:

  • No single point of failure for critical research workflows
  • Data privacy - research stays within your infrastructure
  • Cost efficiency - run your own nodes instead of paying per API call
  • Customization - fine-tune models for specific research domains

Tool-Calling Excellence

Gaia's Llama-3-Groq-8B-Tool model excels at structured tool usage:

// The model understands complex tool interactions
const tools = {
  search: { /* Web search capabilities */ },
  summarize: { /* Content synthesis */ },
  fetch_webpage: { /* Content extraction */ },
  dig_deeper: { /* Plan expansion */ }
};

// And orchestrates them intelligently
const result = await streamText({
  model: openai(GAIA_MODEL),
  tools,
  maxSteps: 5, // Multi-step reasoning
});
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Real-World Research Scenarios

Academic Research 

Query: "Latest developments in quantum computing for healthcare applications"

Plan Generated:
1. Search recent academic papers on quantum healthcare
2. Fetch specific papers from top conferences  
3. Analyze implementation challenges
4. Summarize potential applications
5. [Dig Deeper] Investigate regulatory considerations
6. [Dig Deeper] Explore current pilot programs
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Market Analysis 

Query: "Competitive landscape for AI-powered customer service tools"

Plan Generated:
1. Search major players in AI customer service
2. Fetch company websites and product pages
3. Analyze pricing models and features
4. Summarize competitive positioning
5. [Dig Deeper] Investigate recent funding rounds
6. [Dig Deeper] Analyze customer reviews and case studies
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Technical Investigation 

Query: "Performance comparison of Next.js vs React for large-scale applications"

Plan Generated:
1. Search performance benchmarks
2. Fetch GitHub repositories with comparisons
3. Analyze real-world case studies
4. Summarize trade-offs and recommendations  
5. [Dig Deeper] Investigate deployment patterns
6. [Dig Deeper] Explore migration strategies
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Performance and Scalability

The agent delivers impressive performance across key metrics:

  • Research Plan Generation: ~3-5 seconds for complex topics
  • Step Execution: ~2-4 seconds per research step
  • Deep Research Expansion: ~5-8 seconds for additional planning
  • Session Management: Handles multiple concurrent research workflows
  • Memory Efficiency: Stores only essential artifacts and findings

Error Handling and Resilience

The system includes comprehensive error handling for real-world reliability:

try {
  const result = await executeStep(currentStep);
  currentStep.status = 'completed';
} catch (error) {
  currentStep.status = 'failed';

  // Detailed error reporting
  return NextResponse.json({ 
    error: 'Step execution failed',
    details: error.message,
    stepId: currentStep.id,
    recoveryOptions: ['retry', 'skip', 'modify']
  });
}
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Setting Up Your Research Agent

Quick Start with Gaia API 

// Simple configuration
const GAIA_API_ENDPOINT = 'API_ENDPOINT';
const GAIA_MODEL = 'Llama-3-Groq-8B-Tool';

// Add your API key
process.env.GAIA_API_KEY = 'your_key_here';
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Self-Hosted for Maximum Control 

# Install and run your own Gaia node
curl -sSfL 'https://github.com/GaiaNet-AI/gaianet-node/releases/latest/download/install.sh' | bash
gaianet init --config https://raw.githubusercontent.com/GaiaNet-AI/node-configs/main/llama-3-groq-8b-tool/config.json
gaianet start

# Point your application to local node
const GAIA_API_ENDPOINT = 'http://localhost:8080/v1';
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Future Enhancements: The Research Agent Evolution

Knowledge Base Integration

Gaia's knowledge base system could enhance the agent with:

  • Domain-specific expertise - Specialized knowledge for different fields
  • Historical context - Learning from previous successful research patterns
  • Quality heuristics - Improved source evaluation based on domain knowledge
  • Citation networks - Understanding relationships between sources

Multi-Modal Research

Expanding beyond text to include:

  • Image analysis for visual research
  • Video content processing for multimedia sources
  • Audio transcription for podcast and interview analysis
  • Document parsing for PDFs and technical papers

Collaborative Research

Multiple agents working together:

  • Specialist agents for different domains
  • Verification agents for fact-checking
  • Synthesis agents for cross-domain insights
  • Presentation agents for formatted output

Open Source Impact

The complete research agent is available as an open-source project, enabling:

  • Educational use - Understanding multi-agent AI systems
  • Research applications - Academic and professional research workflows
  • Commercial adaptation - Building specialized research tools
  • Community contribution - Collaborative improvements and extensions

Conclusion: The Future of AI-Powered Research

This research agent represents a fundamental shift from reactive search to proactive investigation. By combining Gaia's decentralized AI infrastructure with sophisticated multi-agent orchestration, we've created a system that doesn't just find information—it conducts research.

The implications extend far beyond individual productivity:

  • Academic researchers can explore complex topics more comprehensively
  • Business analysts can conduct thorough market investigations
  • Students can learn research methodologies through AI guidance
  • Organizations can build institutional knowledge through systematic investigation

As AI agents become more sophisticated, the boundary between human and artificial research capabilities continues to blur. The key insight from this project is that the future lies not in replacing human researchers, but in amplifying their capabilities through intelligent, strategic, and adaptive AI partners.

Resources:

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