Guides & Tutorials
Practical guides to help you make the most of AgenticAnts.
Getting Started Guides
Monitor Your First Agent
Step-by-step guide to instrument your first AI agent
Multi-Agent Systems
Monitor complex multi-agent collaborations
RAG System Monitoring
Observe retrieval-augmented generation systems
Advanced Guides
Production Best Practices
Deploy monitoring to production safely
Cost Optimization Guide
Reduce AI costs without sacrificing quality
Debugging AI Agents
Debug and troubleshoot agent issues
By Use Case
Customer Support Bots
Monitor conversational AI for customer support:
typescript
// Track customer support agent
const trace = await ants.trace.create({
name: 'customer-support',
input: customerQuery,
metadata: {
customerId: customer.id,
ticketId: ticket.id,
channel: 'chat',
priority: ticket.priority
}
})
// Track satisfaction
await trace.complete({
output: response,
metadata: {
satisfactionScore: feedback.score,
resolved: feedback.resolved
}
})
Content Generation
Monitor content creation agents:
python
# Track blog post generation
trace = ants.trace.create(
name='content-generation',
metadata={
'content_type': 'blog_post',
'target_length': 1500,
'seo_keywords': ['AI', 'agents']
}
)
# Track quality metrics
trace.complete(
output=blog_post,
metadata={
'word_count': len(blog_post.split()),
'readability_score': calculate_readability(blog_post),
'seo_score': analyze_seo(blog_post)
}
)
Code Assistants
Monitor AI code generation:
typescript
// Track code generation
const trace = await ants.trace.create({
name: 'code-assistant',
input: codeRequest,
metadata: {
language: 'python',
complexity: 'medium',
userId: user.id
}
})
// Track code quality
await trace.complete({
output: generatedCode,
metadata: {
linesOfCode: code.split('\n').length,
testCoverage: runTests(code),
lintErrors: lintCode(code)
}
})
Data Analysis
Monitor data analysis agents:
python
# Track data analysis
trace = ants.trace.create(
name='data-analyst',
input=analysis_request,
metadata={
'dataset_size': len(data),
'analysis_type': 'regression'
}
)
# Track analysis results
trace.complete(
output=analysis_results,
metadata={
'confidence': results.confidence,
'r_squared': results.r_squared,
'execution_time': results.time
}
)
Integration Guides
LangChain Guide
typescript
const handler = new AgenticAntsCallbackHandler(ants)
const llm = new ChatOpenAI({ callbacks: [handler] })
// All calls automatically traced
AutoGen Guide
python
from agenticants.integrations import autogen
autogen.auto_instrument(ants)
# All AutoGen agents automatically traced
Framework-Specific Guides
- Next.js + AgenticAnts - Monitor Next.js AI features
- FastAPI + AgenticAnts - Python web services
- Streamlit + AgenticAnts - Data apps
- Vercel AI SDK - Edge functions
Common Patterns
Pattern: Request/Response Logging
typescript
async function loggedAgent(input: string) {
const trace = await ants.trace.create({
name: 'agent-call',
input: input
})
try {
const output = await agent.process(input)
await trace.complete({ output })
return output
} catch (error) {
await trace.error({ error: error.message })
throw error
}
}
Pattern: Multi-Step Workflow
python
def multi_step_workflow(query):
trace = ants.trace.create(name='workflow')
# Step 1
with trace.span('step1') as span:
result1 = step1(query)
span.set_output(result1)
# Step 2
with trace.span('step2') as span:
result2 = step2(result1)
span.set_output(result2)
trace.complete(output=result2)
return result2
Pattern: Retry Logic
typescript
async function withRetry(operation: () => Promise<any>) {
const trace = await ants.trace.create({ name: 'retry-operation' })
for (let attempt = 1; attempt <= 3; attempt++) {
try {
const result = await operation()
await trace.complete({
output: result,
metadata: { attempts: attempt }
})
return result
} catch (error) {
if (attempt === 3) {
await trace.error({ error: error.message })
throw error
}
await new Promise(r => setTimeout(r, 1000 * attempt))
}
}
}
Video Tutorials
- Getting Started (5 min) - Quick intro to AgenticAnts
- LangChain Integration (10 min) - Step-by-step setup
- Cost Optimization (15 min) - Reduce AI spending
- Production Deployment (20 min) - Enterprise setup
Community Guides
Browse community-contributed guides:
- Using AgenticAnts with CrewAI by @developer123
- Monitoring Retrieval Quality by @ml_engineer
- Custom Dashboards Tutorial by @data_scientist
Example Projects
Full working examples on GitHub:
- Customer Support Bot - LangChain + OpenAI
- Code Review Agent - Multi-agent with tools
- Document Q&A - RAG with LlamaIndex
- Data Analyst - AutoGen with pandas
Next Steps
Start with a guide that matches your use case: