Building an AI-Powered Next.js Application with Custom Agent Framework, and Ollama

1. Introduction

What is Ollama? Ollama allows you to run large language models (LLMs) locally on your machine rather than relying on cloud APIs. This approach provides privacy benefits, reduces costs, and eliminates API latency issues—making it ideal for development and privacy-sensitive applications.

By the end of this tutorial, you'll have created a web application where users can submit goals like "Create a content calendar for social media" or "Analyze quarterly sales data," and watch as an AI agent systematically works through the problem, documenting its reasoning and producing high-quality results.

2. Setting Up the Project

2.1 Prerequisites

Before starting, ensure you have:

• Node.js 18+ installed
• Basic knowledge of React and Next.js
• Ollama installed (we'll cover this in detail)

2.2 Creating a Next.js Application

Let's begin by creating a fresh Next.js project:

bashCopy
1npx create-next-app@latest next-ollama-app
2
3cd next-ollama-app

During the setup, select the following options:

• Would you like to use TypeScript? → Yes (for type safety)
• Would you like to use ESLint? → Yes
• Would you like to use Tailwind CSS? → Yes (for styling)
• Would you like to use the src/ directory? → Yes (for organization)
• Would you like to use App Router? → Yes (for modern routing)
• Would you like to customize the default import alias? → No

2.3 Installing Dependencies

Install the necessary packages:

bashCopy
1npm install dotenv react-markdown

2.4 Setting Up Ollama

1. Visit Ollama's official website and download the installer for your operating system.
2. Install Ollama following the on-screen instructions.
3. Open a terminal and pull the Mistral model (a powerful open-source LLM):

bashCopy
1ollama pull mistral

This will download the model, which may take several minutes depending on your internet connection.

2.5 Creating a Custom Agent Framework

Let's create our own lightweight agent framework:

Create a file at src/lib/agent.ts:

typescriptCopy
1// src/lib/agent.ts
2export interface Step {
3  number: number;
4  description: string;
5  reasoning?: string;
6  output?: string;
7}
8
9export interface AgentResult {
10  goal: string;
11  steps: Step[];
12  output: string;
13}
14
15export type StepCallback = (step: Step) => Promise<void> | void;
16
17export class Agent {
18  private goal: string;
19  private maxSteps: number;
20  private onStepComplete?: StepCallback;
21  private steps: Step[] = [];
22
23  constructor(options: {
24    goal: string;
25    maxSteps?: number;
26    onStepComplete?: StepCallback;
27  }) {
28    this.goal = options.goal;
29    this.maxSteps = options.maxSteps || 5;
30    this.onStepComplete = options.onStepComplete;
31  }
32
33  async execute(): Promise<AgentResult> {
34    // Step 1: Task analysis
35    const taskAnalysis = await this.callOllama(
36      `Analyze this task: "${this.goal}". Break it down into ${this.maxSteps} clear steps that would lead to a high-quality result. Return a JSON array of step descriptions only, no additional text.`
37    );
38    
39    let steps: string[] = [];
40    try {
41      const parsed = JSON.parse(this.extractJSON(taskAnalysis));
42      steps = Array.isArray(parsed) ? parsed : [];
43    } catch (e) {
44      // If parsing fails, try to extract steps using regex
45      const stepRegex = /\d+\.\s*(.*?)(?=\d+\.|$)/gs;
46      const matches = [...taskAnalysis.matchAll(stepRegex)];
47      steps = matches.map(match => match[1].trim());
48    }
49    
50    // Ensure we have steps
51    if (steps.length === 0) {
52      steps = ["Analyze the problem", "Generate solution", "Refine the output"];
53    }
54    
55    // Execute each step
56    for (let i = 0; i < Math.min(steps.length, this.maxSteps); i++) {
57      const stepNumber = i + 1;
58      const stepDescription = steps[i];
59      
60      // Generate reasoning for this step
61      const reasoning = await this.callOllama(
62        `For the task: "${this.goal}", I am on step ${stepNumber}: "${stepDescription}". Explain your reasoning for how you'll approach this step. Keep it clear and concise.`
63      );
64      
65      // Execute the step
66      const stepPrompt = `
67Task: "${this.goal}"
68Step ${stepNumber}/${Math.min(steps.length, this.maxSteps)}: ${stepDescription}
69Previous steps: ${this.steps.map(s => `Step ${s.number}: ${s.description} -> ${s.output?.substring(0, 100)}...`).join('\n')}
70
71Execute this step and provide the output. Be thorough but focused on just this step.
72`;
73      
74      const stepOutput = await this.callOllama(stepPrompt);
75      
76      // Record the step
77      const step: Step = {
78        number: stepNumber,
79        description: stepDescription,
80        reasoning,
81        output: stepOutput
82      };
83      
84      this.steps.push(step);
85      
86      // Notify via callback if provided
87      if (this.onStepComplete) {
88        await this.onStepComplete(step);
89      }
90    }
91    
92    // Generate final comprehensive output
93    const finalPrompt = `
94You've been working on: "${this.goal}"
95
96You've completed the following steps:
97${this.steps.map(s => `Step ${s.number}: ${s.description}`).join('\n')}
98
99Now, compile all of your work into a comprehensive final output that achieves the original goal. 
100Format your response using Markdown for readability. Include headings, bullet points, and other formatting as appropriate.
101Ensure your response is complete, well-structured, and directly addresses the original goal.
102`;
103    
104    const finalOutput = await this.callOllama(finalPrompt);
105    
106    return {
107      goal: this.goal,
108      steps: this.steps,
109      output: finalOutput
110    };
111  }
112
113  private async callOllama(prompt: string): Promise<string> {
114    try {
115      const response = await fetch('http://localhost:11434/api/generate', {
116        method: 'POST',
117        headers: {
118          'Content-Type': 'application/json',
119        },
120        body: JSON.stringify({
121          model: 'mistral',
122          prompt: prompt,
123          stream: false,
124        }),
125      });
126
127      if (!response.ok) {
128        throw new Error(`Ollama API error: ${response.statusText}`);
129      }
130
131      const data = await response.json();
132      return data.response;
133    } catch (error) {
134      console.error('Error calling Ollama:', error);
135      return `Error: ${error instanceof Error ? error.message : 'Unknown error'}`;
136    }
137  }
138
139  private extractJSON(text: string): string {
140    // Try to extract JSON from the text
141    const jsonRegex = /(\[.*\]|\{.*\})/s;
142    const match = text.match(jsonRegex);
143    return match ? match[0] : '[]';
144  }
145}

This custom agent implementation provides similar functionality to what we'd expect from Mastra:

• Breaking down a task into logical steps
• Reasoning about each step before execution
• Executing steps sequentially
• Providing step-by-step progress updates
• Generating a comprehensive final output

3. Understanding the Frontend (React + Next.js)

Now, let's build a responsive, user-friendly interface for our agent application.

3.1 Creating the Home Page Component

Create or replace the file at src/app/page.tsx with:

tsxCopy
1"use client";
2import { useState, useRef, useEffect } from "react";
3import ReactMarkdown from "react-markdown";
4
5export default function Home() {
6  const [goal, setGoal] = useState<string>("");
7  const [logs, setLogs] = useState<string[]>([]);
8  const [isRunning, setIsRunning] = useState<boolean>(false);
9  const [result, setResult] = useState<string>("");
10  const logsEndRef = useRef<HTMLDivElement>(null);
11
12  // Auto-scroll to the bottom of logs
13  useEffect(() => {
14    if (logsEndRef.current) {
15      logsEndRef.current.scrollIntoView({ behavior: "smooth" });
16    }
17  }, [logs]);
18
19  const handleRunAgent = async () => {
20    if (!goal.trim() || isRunning) return;
21    
22    setIsRunning(true);
23    setLogs(["🤖 Initializing Mastra-inspired agent powered by Ollama..."]);
24    setResult("");
25    
26    try {
27      const response = await fetch("/api/run-agent", {
28        method: "POST",
29        headers: { "Content-Type": "application/json" },
30        body: JSON.stringify({ goal }),
31      });
32      
33      if (!response.ok) {
34        const errorData = await response.json();
35        throw new Error(errorData.error || "Failed to run agent");
36      }
37      
38      // Use streaming for real-time updates
39      const reader = response.body?.getReader();
40      const decoder = new TextDecoder();
41      
42      if (reader) {
43        while (true) {
44          const { done, value } = await reader.read();
45          if (done) break;
46          
47          const text = decoder.decode(value);
48          try {
49            // Handle multiple JSON objects in the same chunk
50            const jsonObjects = text.split('\n').filter(line => line.trim());
51            
52            for (const jsonStr of jsonObjects) {
53              if (!jsonStr.trim()) continue;
54              
55              const data = JSON.parse(jsonStr);
56              
57              if (data.type === "log") {
58                setLogs(logs => [...logs, data.message]);
59              } else if (data.type === "result") {
60                setResult(data.content);
61              }
62            }
63          } catch (error) {
64            console.error("Error parsing stream data:", error);
65          }
66        }
67      }
68    } catch (error: any) {
69      setLogs(logs => [...logs, `❌ Error: ${error.message}`]);
70    } finally {
71      setIsRunning(false);
72      setLogs(logs => [...logs, "✅ Agent execution completed"]);
73    }
74  };
75
76  return (
77    <main className="flex min-h-screen flex-col items-center p-8 max-w-5xl mx-auto">
78      <h1 className="text-4xl font-bold mb-3">AI Agent Workspace</h1>
79      <h2 className="text-xl text-gray-600 mb-8">Powered by Mastra-inspired Architecture + Ollama</h2>
80      
81      <div className="w-full space-y-8">
82        {/* Goal Input Section */}
83        <div className="bg-white p-6 rounded-lg shadow-md">
84          <h3 className="text-lg font-semibold mb-3">What would you like the agent to accomplish?</h3>
85          <div className="flex gap-3">
86            <input
87              type="text"
88              placeholder="e.g., Create a marketing plan for a new product launch"
89              value={goal}
90              onChange={(e) => setGoal(e.target.value)}
91              className="flex-1 p-3 border rounded-md text-gray-800 focus:ring-2 focus:ring-blue-500"
92              disabled={isRunning}
93            />
94            <button
95              onClick={handleRunAgent}
96              disabled={isRunning || !goal.trim()}
97              className={`px-6 py-3 rounded-md font-medium transition ${
98                isRunning ? 
99                "bg-gray-300 text-gray-600" : 
100                "bg-blue-600 text-white hover:bg-blue-700"
101              }`}
102            >
103              {isRunning ? "Working..." : "Run Agent"}
104            </button>
105          </div>
106        </div>
107        
108        {/* Agent Logs Section */}
109        <div className="bg-gray-50 rounded-lg shadow-md">
110          <div className="bg-gray-100 p-4 rounded-t-lg border-b">
111            <h3 className="text-lg font-semibold">Agent Thinking Process</h3>
112          </div>
113          <div className="p-4 max-h-80 overflow-y-auto">
114            {logs.length === 0 ? (
115              <p className="text-gray-500 italic">Agent logs will appear here...</p>
116            ) : (
117              <div className="space-y-2">
118                {logs.map((log, index) => (
119                  <div key={index} className="p-3 bg-white rounded border">
120                    {log}
121                  </div>
122                ))}
123                <div ref={logsEndRef} />
124              </div>
125            )}
126          </div>
127        </div>
128        
129        {/* Result Section */}
130        {result && (
131          <div className="bg-white rounded-lg shadow-md">
132            <div className="bg-green-100 p-4 rounded-t-lg border-b">
133              <h3 className="text-lg font-semibold text-green-800">Agent Result</h3>
134            </div>
135            <div className="p-6 prose max-w-none">
136              <ReactMarkdown>{result}</ReactMarkdown>
137            </div>
138          </div>
139        )}
140      </div>
141    </main>
142  );
143}

3.2 Understanding the Frontend Components

The frontend is built with several key features:

1. State Management:

   • goal: Stores the user's input task
   • logs: Maintains an array of execution logs
   • isRunning: Tracks the agent's execution state
   • result: Stores the final output from the agent

2. Streaming Response Handling:

   • Uses the Fetch API with a reader/decoder to process streamed updates
   • Separates log updates from final results

3. UI Components:

   • A clean input section for submitting tasks
   • A scrollable log window showing the agent's reasoning process
   • A formatted result section using React Markdown for rich text display

4. User Experience Enhancements:

   • Auto-scrolling logs to keep the latest updates visible
   • Disabled inputs during processing
   • Visual feedback for running state

4. Building the Backend (API Route with Custom Agent + Ollama)

Now, let's create the serverless API endpoint that will run our custom agent with Ollama.

4.1 Creating the API Route

Create a file at src/app/api/run-agent/route.ts:

typescriptCopy
1import { NextResponse } from 'next/server';
2import { Agent, Step } from '@/lib/agent';
3
4export const runtime = 'nodejs';
5
6export async function POST(request: Request) {
7  // Initialize the response encoder for streaming
8  const encoder = new TextEncoder();
9  const stream = new TransformStream();
10  const writer = stream.writable.getWriter();
11
12  // Function to send updates to the client
13  const sendUpdate = async (data: any) => {
14    await writer.write(encoder.encode(JSON.stringify(data) + '\n'));
15  };
16
17  // Process the request in the background while streaming updates
18  const processRequest = async () => {
19    try {
20      // Parse the request body
21      const { goal } = await request.json();
22      
23      if (!goal || typeof goal !== 'string') {
24        await sendUpdate({
25          type: 'log',
26          message: '❌ Error: Please provide a valid goal'
27        });
28        writer.close();
29        return;
30      }
31
32      // Initialize the custom agent
33      const agent = new Agent({
34        goal: goal,
35        maxSteps: 5,
36        onStepComplete: async (step: Step) => {
37          await sendUpdate({
38            type: 'log',
39            message: `📝 Step ${step.number}: ${step.description}`
40          });
41          
42          if (step.reasoning) {
43            await sendUpdate({
44              type: 'log',
45              message: `🤔 Reasoning: ${step.reasoning}`
46            });
47          }
48        }
49      });
50      
51      // Log initialization
52      await sendUpdate({
53        type: 'log',
54        message: `🧠 Analyzing task: "${goal}"`
55      });
56      
57      // Execute the agent
58      const result = await agent.execute();
59      
60      // Send the final result
61      await sendUpdate({
62        type: 'result',
63        content: result.output
64      });
65      
66      // Close the stream
67      writer.close();
68    } catch (error: any) {
69      console.error('Agent execution error:', error);
70      await sendUpdate({
71        type: 'log',
72        message: `❌ Error: ${error.message || 'Unknown error occurred'}`
73      });
74      writer.close();
75    }
76  };
77  
78  // Start processing in the background
79  processRequest();
80  
81  // Return the stream response immediately
82  return new NextResponse(stream.readable, {
83    headers: {
84      'Content-Type': 'application/json',
85      'Transfer-Encoding': 'chunked',
86    },
87  });
88}

4.2 Understanding the Backend Architecture

Our API route implements several advanced features:

1. Streaming Response:

   • Uses the Web Streams API to send real-time updates to the frontend
   • Maintains a single connection instead of polling

2. Custom Agent Integration:

   • Initializes our custom Agent class with the user's goal
   • Configures step limits and callback functions
   • Streams progress updates in real-time

3. Progress Tracking:

   • Uses the onStepComplete callback to report each step's progress
   • Separates reasoning logs from final results

4. Error Handling:

   • Robust error catching and reporting
   • Ensures the stream is properly closed even on errors

5. Running and Testing the Application

Now let's run our application and test its capabilities:

5.1 Starting the Development Server

Ensure Ollama is running, then start your Next.js application:

bashCopy
1npm run dev

Open your browser to http://localhost:3000.

5.2 Testing with Different Goals

Try entering various goals to test the agent's capabilities:

Business Planning Examples:

• "Create a marketing strategy for a new fitness app"
• "Develop a 30-day content calendar for a tech startup"
• "Draft a project plan for website redesign"

Creative Tasks:

• "Write a short story about time travel with a twist ending"
• "Create a detailed character profile for a fantasy novel"
• "Develop three unique logo concepts for a sustainable fashion brand"

Analytical Problems:

• "Analyze the pros and cons of remote work for a small business"
• "Compare three different pricing strategies for a SaaS product"
• "Create a SWOT analysis for entering the electric vehicle market"

5.3 Sample Interaction

Here's an example of how the agent might process a goal to "Create a 7-day social media plan for a coffee shop":

Agent Logs:

textCopy
1🤖 Initializing Mastra-inspired agent powered by Ollama...
2🧠 Analyzing task: "Create a 7-day social media plan for a coffee shop"
3📝 Step 1: Define the target audience and social media platforms
4🤔 Reasoning: Before creating content, I need to understand who we're targeting and which platforms would be most effective for a coffee shop. Typically, Instagram and Facebook work well for food/beverage businesses.
5📝 Step 2: Establish content themes and post types
6🤔 Reasoning: Coffee shops can benefit from diverse content including product highlights, behind-the-scenes, customer features, and educational content about coffee.
7📝 Step 3: Create a content calendar for Monday through Wednesday
8🤔 Reasoning: I'll start with the first half of the week, focusing on driving early-week traffic when coffee shops might be slower.
9📝 Step 4: Create a content calendar for Thursday through Sunday
10🤔 Reasoning: For the latter half of the week, I'll focus on weekend promotions and creating content that encourages longer visits and higher purchases.
11📝 Step 5: Add engagement strategies and hashtag recommendations
12🤔 Reasoning: Social media success requires engagement beyond just posting. I'll add strategies for responding to comments and effective hashtags.
13✅ Agent execution completed

Agent Result: The final output would be a comprehensive, day-by-day social media plan formatted in Markdown, including specific post ideas, optimal posting times, hashtag recommendations, and engagement strategies.

6. Expanding the Project

Once you have the basic application working, consider these enhancements to create a more powerful agent system:

6.1 Adding Specialized Agent Tools

Extend your custom agent with specialized tools for different tasks:

typescriptCopy
1// src/lib/tools.ts
2export interface Tool {
3  name: string;
4  description: string;
5  execute: (input: string) => Promise<string>;
6}
7
8// Sample search tool
9export const searchTool: Tool = {
10  name: 'search',
11  description: 'Search the web for information',
12  async execute(query: string): Promise<string> {
13    // This is a mock implementation - in a real app, you'd integrate with a search API
14    return `Simulated search results for: ${query}\n\n1. First relevant result\n2. Second relevant result\n3. Third relevant result`;
15  }
16};
17
18// Then enhance your Agent class to use tools
19// In src/lib/agent.ts, modify the execute method:
20
21async execute(): Promise<AgentResult> {
22  // ... existing code
23  
24  // Add tool usage where appropriate
25  if (this.tools && this.tools.length > 0) {
26    const toolDescriptions = this.tools.map(t => `${t.name}: ${t.description}`).join('\n');
27    
28    // Let the agent decide whether to use a tool
29    const toolUsage = await this.callOllama(`
30      For the task: "${this.goal}", I'm on step ${currentStep.number}: "${currentStep.description}".
31      I have these tools available:
32      ${toolDescriptions}
33      
34      Should I use a tool for this step? If yes, specify which tool and the exact input to provide to the tool.
35      Return your answer in JSON format: { "useTool": boolean, "toolName": string, "toolInput": string }
36    `);
37    
38    try {
39      const toolDecision = JSON.parse(this.extractJSON(toolUsage));
40      if (toolDecision.useTool) {
41        const tool = this.tools.find(t => t.name === toolDecision.toolName);
42        if (tool) {
43          const toolResult = await tool.execute(toolDecision.toolInput);
44          // Use the tool result in further processing
45          currentStep.output = `Used ${tool.name} with input: ${toolDecision.toolInput}\n\nResult: ${toolResult}`;
46        }
47      }
48    } catch (e) {
49      // If JSON parsing fails, continue without tool usage
50    }
51  }
52  
53  // ... rest of execute method
54}

6.2 Implementing a Database for Conversation History

Add persistence to your application with a database:

typescriptCopy
1// Using Prisma with SQLite for simplicity
2// 1. Install Prisma: npm install prisma @prisma/client
3// 2. Initialize Prisma: npx prisma init --datasource-provider sqlite
4
5// 3. Create schema.prisma:
6// prisma/schema.prisma
7/*
8generator client {
9  provider = "prisma-client-js"
10}
11
12datasource db {
13  provider = "sqlite"
14  url      = "file:./dev.db"
15}
16
17model Session {
18  id        String   @id @default(uuid())
19  goal      String
20  output    String   @default("")
21  logs      String   @default("[]") // JSON string of logs
22  createdAt DateTime @default(now())
23}
24*/
25
26// 4. Run migration: npx prisma migrate dev --name init
27// 5. Generate client: npx prisma generate
28
29// 6. Create a database service
30// src/lib/db.ts
31import { PrismaClient } from '@prisma/client';
32
33const prisma = new PrismaClient();
34
35export async function saveSession(goal: string, output: string, logs: string[]) {
36  return await prisma.session.create({
37    data: {
38      goal,
39      output,
40      logs: JSON.stringify(logs),
41    },
42  });
43}
44
45export async function getSessions() {
46  return await prisma.session.findMany({
47    orderBy: {
48      createdAt: 'desc',
49    },
50  });
51}
52
53export async function getSession(id: string) {
54  return await prisma.session.findUnique({
55    where: { id },
56  });
57}

6.3 Implementing Multi-Agent Workflows

Create complex workflows with multiple specialized agents:

typescriptCopy
1// src/lib/workflow.ts
2import { Agent, AgentResult } from './agent';
3
4export async function runResearchAndSynthesisWorkflow(topic: string, updateCallback: (message: string) => Promise<void>) {
5  await updateCallback(`Starting research workflow on: ${topic}`);
6  
7  // Research agent gathers information
8  const researchAgent = new Agent({
9    goal: `Research key facts about: ${topic}`,
10    maxSteps: 3,
11    onStepComplete: async (step) => {
12      await updateCallback(`Research step ${step.number}: ${step.description}`);
13    }
14  });
15  
16  await updateCallback("Starting research phase...");
17  const researchResult = await researchAgent.execute();
18  
19  // Analysis agent evaluates the research
20  const analysisAgent = new Agent({
21    goal: `Analyze these research findings and identify key insights: ${researchResult.output}`,
22    maxSteps: 2,
23    onStepComplete: async (step) => {
24      await updateCallback(`Analysis step ${step.number}: ${step.description}`);
25    }
26  });
27  
28  await updateCallback("Starting analysis phase...");
29  const analysisResult = await analysisAgent.execute();
30  
31  // Synthesis agent creates final output
32  const synthesisAgent = new Agent({
33    goal: `Create a comprehensive report on ${topic} using this research and analysis:
34    Research: ${researchResult.output}
35    Analysis: ${analysisResult.output}`,
36    maxSteps: 3,
37    onStepComplete: async (step) => {
38      await updateCallback(`Synthesis step ${step.number}: ${step.description}`);
39    }
40  });
41  
42  await updateCallback("Starting synthesis phase...");
43  const finalResult = await synthesisAgent.execute();
44  
45  await updateCallback("Workflow complete!");
46  
47  return {
48    topic,
49    research: researchResult.output,
50    analysis: analysisResult.output,
51    synthesis: finalResult.output
52  };
53}

6.4 Optimizing Model Selection and Configuration

Allow users to customize the LLM parameters:

typescriptCopy
1// src/lib/modelConfig.ts
2export interface ModelConfig {
3  modelName: string;
4  temperature: number;
5  maxTokens: number;
6}
7
8export const availableModels = [
9  { name: 'mistral', label: 'Mistral (Balanced)' },
10  { name: 'llama3', label: 'Llama 3 (Creative)' },
11  { name: 'codellama', label: 'CodeLlama (Technical)' },
12];
13
14// Then update your Agent class to use these configurations:
15private async callOllama(prompt: string): Promise<string> {
16  try {
17    const response = await fetch('http://localhost:11434/api/generate', {
18      method: 'POST',
19      headers: {
20        'Content-Type': 'application/json',
21      },
22      body: JSON.stringify({
23        model: this.modelConfig.modelName || 'mistral',
24        prompt: prompt,
25        temperature: this.modelConfig.temperature || 0.7,
26        max_tokens: this.modelConfig.maxTokens || 2048,
27        stream: false,
28      }),
29    });
30
31    if (!response.ok) {
32      throw new Error(`Ollama API error: ${response.statusText}`);
33    }
34
35    const data = await response.json();
36    return data.response;
37  } catch (error) {
38    console.error('Error calling Ollama:', error);
39    return `Error: ${error instanceof Error ? error.message : 'Unknown error'}`;
40  }
41}

7. Conclusion

In this comprehensive tutorial, we've built a sophisticated Next.js application that leverages a custom agent framework and Ollama to create an AI-powered task automation system. This combination offers several key advantages:

1. Local Privacy: By running models through Ollama, you maintain control of your data without sending it to external API services.

2. Cost Efficiency: Eliminate per-token or per-request charges by running inference locally.

3. Architectural Flexibility: Our custom agent implementation provides a structured framework that can be extended as needed.

4. Realtime Feedback: The streaming architecture keeps users informed of progress throughout the execution.

While we couldn't use the actual Mastra package due to availability issues, our custom implementation follows similar architectural principles, delivering a comparable experience. In a production environment, you might choose to use a commercially available agent framework like Mastra when it becomes publicly available, or continue to evolve your custom solution to meet your specific needs.

The agent-based approach we've implemented demonstrates how complex goals can be broken down into manageable steps with explicit reasoning at each stage. This not only produces better results but also creates transparency that builds user trust in the AI system.

Additional Resources

• Ollama GitHub Repository
• Next.js Documentation
• LangChain.js (complementary framework)
• Prompt Engineering Guide

By mastering these technologies, you're well-positioned to build the next generation of intelligent applications that blend the best of human creativity with AI capabilities.