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Bindu integrates with OpenTelemetry (OTEL) and Sentry to provide comprehensive observability and error tracking for your agents. Monitor performance, trace execution flows, and debug issues using industry-standard platforms.

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Architecture

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OpenTelemetry Setup

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Supported Platforms

  • Arize - AI observability platform for monitoring and debugging ML models
  • Langfuse - Open-source LLM engineering platform with tracing and analytics
  • Any OTEL-compatible platform - Supports standard OTLP protocol

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Configuration

Enable OpenTelemetry tracing via environment variables (see .env.example): 
# Enable telemetry
TELEMETRY_ENABLED=true

# OTEL endpoint (platform-specific)
OLTP_ENDPOINT=https://cloud.langfuse.com/api/public/otel/v1/traces

# Service name for your agent
OLTP_SERVICE_NAME=research-agent

# Authentication headers (platform-specific)
OLTP_HEADERS={"Authorization":"Basic <base64-encoded-credentials>"}

# Optional: Enable verbose logging
OLTP_VERBOSE_LOGGING=true

# Optional: Additional configuration
OLTP_SERVICE_VERSION=1.0.0
OLTP_DEPLOYMENT_ENVIRONMENT=production
OLTP_BATCH_MAX_QUEUE_SIZE=2048
OLTP_BATCH_SCHEDULE_DELAY_MILLIS=5000

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Platform-Specific Setup

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Langfuse

  1. Create Account: Sign up at cloud.langfuse.com
  2. Generate API Keys:
    • Navigate to Settings → API Keys
    • Create new key pair (public and secret)
  3. Encode Credentials: 
    # Base64 encode "public-key:secret-key"
echo -n "pk-xxx:sk-xxx" | base64
  4. Configure Environment: 
    TELEMETRY_ENABLED=true
OLTP_ENDPOINT=https://cloud.langfuse.com/api/public/otel/v1/traces
OLTP_SERVICE_NAME=your-agent-name
OLTP_HEADERS={"Authorization":"Basic <base64-encoded-credentials>"}
OLTP_VERBOSE_LOGGING=true

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Arize

  1. Create Account: Sign up at arize.com
  2. Get Credentials:
    • Navigate to Settings → API Keys
    • Copy Space ID and API Key
  3. Configure Environment: 
    TELEMETRY_ENABLED=true
OLTP_ENDPOINT=https://otlp.arize.com/v1
OLTP_SERVICE_NAME=your-agent-name
OLTP_HEADERS={"space_id":"<your-space-id>","api_key":"<your-api-key>"}
OLTP_VERBOSE_LOGGING=true

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Sentry Error Tracking

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Configuration

Enable Sentry via environment variables (see .env.example): 
# Enable Sentry
SENTRY_ENABLED=true

# Sentry DSN (from your Sentry project)
SENTRY_DSN=https://<key>@<org-id>.ingest.sentry.io/<project-id>

# Optional: Environment name
SENTRY_ENVIRONMENT=production

# Optional: Release version
SENTRY_RELEASE=1.0.0

# Optional: Performance monitoring
SENTRY_TRACES_SAMPLE_RATE=1.0
SENTRY_PROFILES_SAMPLE_RATE=1.0
SENTRY_ENABLE_TRACING=true

# Optional: Privacy settings
SENTRY_SEND_DEFAULT_PII=false
SENTRY_DEBUG=false

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Setting Up Sentry

  1. Create Account: Sign up at sentry.io
  2. Create Project:
    • Select Python as platform
    • Copy the DSN from project settings
  3. Configure Environment: 
    SENTRY_ENABLED=true
SENTRY_DSN=https://xxx@xxx.ingest.sentry.io/xxx
SENTRY_ENVIRONMENT=production
SENTRY_RELEASE=1.0.0
  4. Restart Agent: Sentry initializes on startup

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Agent Configuration

No code changes needed - observability is configured via environment variables: 
config = {
    "author": "your.email@example.com",
    "name": "research_agent",
    "description": "A research assistant agent",
    "deployment": {"url": "http://localhost:3773", "expose": True},
    "skills": ["skills/question-answering"],
}

bindufy(config, handler)

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Best Practices

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Sampling

For high-traffic agents, use sampling to reduce costs: 
# Sample 10% of traces
SENTRY_TRACES_SAMPLE_RATE=0.1

# Sample 10% of profiles
SENTRY_PROFILES_SAMPLE_RATE=0.1

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Environment Separation

Use different environments for development/production: 
# Development
SENTRY_ENVIRONMENT=development
OLTP_SERVICE_NAME=agent-dev

# Production
SENTRY_ENVIRONMENT=production
OLTP_SERVICE_NAME=agent-prod

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Custom Sentry Context

import sentry_sdk

sentry_sdk.set_context("business", {
    "plan": "premium",
    "credits": 100
})

sentry_sdk.set_tag("feature", "pdf-processing")