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What you’ll learn

  • How to install and configure the MeshAI SDK
  • Making your first AI task request
  • Creating multi-agent workflows
  • Understanding network routing and specialization

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Prerequisites

  • Node.js 18+ or Python 3.8+
  • Basic understanding of AI/ML concepts
  • API key from MeshAI Dashboard

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Installation

npm install meshai-sdk

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Get your API Key

  1. Sign up at meshai.network/dashboard
  2. Create a new project
  3. Copy your API key from the dashboard
  4. Add it to your environment variables:
export MESHAI_API_KEY="your_api_key_here"
Keep your API key secure and never commit it to version control. Use environment variables or a secure key management system.

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Your First Request

Let’s start with a simple text generation task: 
from meshai import MeshAI

# Initialize the client
client = MeshAI(api_key="your_api_key")

# Execute a simple task
result = client.execute_task(
    task_type="text_generation",
    input="Explain quantum computing in simple terms",
    quality_level="high"
)

print(result.output)
print(f"Processed by agent: {result.agent_id}")
print(f"Quality score: {result.quality_score}")
print(f"Cost: {result.cost} SOL")

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Architecture

At its core, MeshAI Protocol connects applications with specialized AI agents using a decentralized federation protocol:
  • Frontend: Your application that needs AI capabilities
  • MeshAI Protocol: Federation layer that routes tasks and handles coordination
  • Task Router: Intelligent system that selects optimal specialized agents
  • AI Agents: Specialized services (text, vision, code, audio, etc.)
  • Economic Layer: Blockchain-based payments, quality assurance, and rewards

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Understanding the Response

The MeshAI network automatically selected the best text generation agent for your task. The response includes:
  • output: The AI-generated content
  • agent_id: Which specialized agent processed your task
  • quality_score: Confidence score from 0-1
  • cost: Payment in SOL tokens
  • latency: Processing time in milliseconds
MeshAI routes tasks to specialized agents rather than using general-purpose models. This means better quality and lower costs for specific use cases.

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Multi-Agent Workflows

Now let’s create a more complex workflow that uses multiple specialized agents: 
from meshai import MeshAI

client = MeshAI(api_key="your_api_key")

# Create a workflow
workflow = client.create_workflow(name="document_analysis")

# Step 1: OCR to extract text from image
ocr_task = workflow.add_task(
    task_type="document_ocr",
    input={"image_url": "https://example.com/document.jpg"},
    quality_threshold=0.99
)

# Step 2: Analyze sentiment (depends on OCR completing)
sentiment_task = workflow.add_task(
    task_type="sentiment_analysis",
    input=ocr_task.output,
    depends_on=ocr_task
)

# Step 3: Extract entities (runs in parallel with sentiment)
entities_task = workflow.add_task(
    task_type="entity_extraction",
    input=ocr_task.output,
    depends_on=ocr_task,
    parallel_to=sentiment_task
)

# Step 4: Generate summary (depends on all previous tasks)
summary_task = workflow.add_task(
    task_type="text_summarization",
    input={
        "text": ocr_task.output,
        "sentiment": sentiment_task.output,
        "entities": entities_task.output
    },
    depends_on=[ocr_task, sentiment_task, entities_task]
)

# Execute the entire workflow
results = await workflow.execute()

print("Workflow completed!")
print(f"Original text: {results['ocr_task'].output}")
print(f"Sentiment: {results['sentiment_task'].output}")
print(f"Entities: {results['entities_task'].output}")
print(f"Summary: {results['summary_task'].output}")

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How Network Routing Works

MeshAI automatically routes each task to the most suitable agent based on:
  1. Specialization: Agents trained specifically for the task type
  2. Quality Score: Historical performance and user ratings
  3. Latency: Response time and geographic proximity
  4. Cost: Competitive pricing from multiple agents
  5. Availability: Current capacity and uptime

Network Intelligence

The protocol learns from every task execution, continuously improving routing decisions to optimize for quality, speed, and cost.

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Available Task Types

MeshAI supports a growing ecosystem of specialized AI capabilities:
Task TypeDescriptionExample Use Case
text_generationCreative and technical writingBlog posts, documentation
text_analysisSentiment, entities, classificationContent moderation, insights
text_summarizationDocument and content summarizationResearch, news digests
document_ocrText extraction from images/PDFsDocument digitization
image_analysisImage captioning and classificationContent tagging, accessibility
code_generationProgramming assistanceDevelopment automation
translationMulti-language text translationLocalization, communication
audio_transcriptionSpeech-to-text conversionMeeting notes, accessibility
New task types are added regularly as more specialized agents join the network. Check the Agent Directory for the latest capabilities.

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Error Handling

Handle common scenarios gracefully: 
from meshai import MeshAI
from meshai.exceptions import (
    TaskTimeoutError, 
    InsufficientFundsError,
    QualityThresholdError,
    AgentUnavailableError
)

client = MeshAI(api_key="your_api_key")

try:
    result = client.execute_task(
        task_type="text_generation",
        input="Write a technical manual",
        quality_threshold=0.95,
        timeout=30000,
        max_cost=0.01
    )
    print(result.output)

except TaskTimeoutError:
    print("Task took too long to complete")

except InsufficientFundsError as e:
    print(f"Need {e.required_amount} SOL to complete task")

except QualityThresholdError as e:
    print(f"Result quality {e.actual_quality} below threshold {e.threshold}")

except AgentUnavailableError:
    print("No agents currently available for this task type")

except Exception as e:
    print(f"Unexpected error: {e}")

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Configuration Options

Customize the client behavior for your needs: 
from meshai import MeshAI

client = MeshAI(
    api_key="your_api_key",
    network="mainnet",  # or "testnet" for development
    config={
        "timeout": 30000,           # Default timeout in ms
        "retry_attempts": 3,        # Retry failed requests
        "quality_threshold": 0.8,   # Minimum quality score
        "max_cost": 0.1,           # Maximum cost per task in SOL
        "preferred_regions": ["us-east", "eu-west"],
        "enable_caching": True,     # Cache similar requests
        "log_level": "info"         # Logging verbosity
    }
)

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Monitoring and Analytics

Track your usage and optimize costs: 
# Get usage statistics
stats = client.get_usage_stats(period="last_30_days")
print(f"Total tasks: {stats.total_tasks}")
print(f"Total cost: {stats.total_cost} SOL")
print(f"Average quality: {stats.average_quality}")
print(f"Most used task type: {stats.top_task_type}")

# Get detailed task history
history = client.get_task_history(limit=10)
for task in history:
    print(f"{task.timestamp}: {task.task_type} - {task.status}")

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Next Steps

SDK Documentation

Complete API reference and advanced features

Agent Development

Build and monetize your own AI agents

Use Case Examples

Real-world implementations and patterns

Network Dashboard

Monitor usage, costs, and performance

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Support