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.

sequenceDiagram
    participant Agent
    participant OTEL
    participant Sentry
    participant ObservabilityPlatform (Langfuse/Arize)

    Note over Agent: Agent Startup (bindufy)

    rect rgb(240, 248, 255)
        Note over Agent,OTEL: OpenTelemetry Setup
        Agent->>Agent: Check TELEMETRY_ENABLED
        Agent->>OTEL: Initialize TracerProvider<br/>(service name, version, env)
        Agent->>OTEL: Configure OTLP Exporter<br/>(endpoint, headers)
        Agent->>OTEL: Add BatchSpanProcessor
        OTEL-->>Agent: Tracer ready
    end

    rect rgb(255, 248, 240)
        Note over Agent,Sentry: Sentry Setup
        Agent->>Agent: Check SENTRY_ENABLED
        Agent->>Sentry: Initialize SDK<br/>(DSN, environment, release)
        Agent->>Sentry: Add integrations<br/>(Starlette, SQLAlchemy, Redis)
        Sentry-->>Agent: Error tracking ready
    end

    Note over Agent: Agent Running

    rect rgb(240, 255, 240)
        Note over Agent,ObservabilityPlatform: Runtime Telemetry
        Agent->>OTEL: Create spans (traces)
        OTEL->>ObservabilityPlatform: Export to Langfuse/Arize<br/>(batched, async)
        Agent->>Sentry: Capture errors/performance
        Sentry->>ObservabilityPlatform: Send to Sentry.io
    end

• 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

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

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

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

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

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

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)

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

Use different environments for development/production:

# Development
SENTRY_ENVIRONMENT=development
OLTP_SERVICE_NAME=agent-dev

# Production
SENTRY_ENVIRONMENT=production
OLTP_SERVICE_NAME=agent-prod

import sentry_sdk

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

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

• OpenTelemetry Python
• Langfuse Documentation
• Arize Documentation
• Sentry Python SDK