Telemetry & Performance Tracking

The Telemetry system provides minimal, privacy-focused usage tracking for Praison Labs Agents. It collects only anonymous metrics about feature usage, with no personal data, prompts, or responses tracked. Telemetry is enabled by default but can be easily disabled via environment variables.

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Quick Start

pip install praisonaiagents

from praisonaiagents import Agent, Task, Praison LabsAgents

# Create a simple agent - telemetry is automatically enabled
agent = Agent(
    name="Assistant",
    role="Helper",
    goal="Assist with tasks",
    instructions="You are a helpful assistant."
)

# Create a task
task = Task(
    description="Write a haiku about AI",
    expected_output="A three-line haiku poem",
    agent=agent
)

# Create and run workflow - telemetry tracks automatically
workflow = Praison LabsAgents(agents=[agent], tasks=[task])
result = workflow.start()

print(result)

from praisonaiagents.telemetry import get_telemetry

# Get the telemetry instance
telemetry = get_telemetry()

# Check metrics (stored in memory)
metrics = telemetry.get_metrics()
print(f"Agent executions: {metrics['agent_executions']}")
print(f"Task completions: {metrics['task_completions']}")
print(f"Tool calls: {metrics['tool_calls']}")
print(f"Errors: {metrics['errors']}")

export PRAISONAI_TELEMETRY_DISABLED=true
# or
export PRAISONAI_DISABLE_TELEMETRY=true
# or
export DO_NOT_TRACK=true

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What is Tracked

Praison Labs telemetry collects only minimal, anonymous metrics:

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Disabling Telemetry

Telemetry can be disabled in multiple ways:

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Environment Variables (Recommended)

# Set any of these before running your code
export PRAISONAI_TELEMETRY_DISABLED=true
export PRAISONAI_DISABLE_TELEMETRY=true
export DO_NOT_TRACK=true  # Universal standard

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Programmatically

# Option 1: Disable before importing
from praisonaiagents.telemetry import disable_telemetry
disable_telemetry()

# Then import and use normally
from praisonaiagents import Agent, Task, Praison LabsAgents

# Option 2: Disable at runtime
from praisonaiagents.telemetry import get_telemetry
telemetry = get_telemetry()
telemetry.enabled = False

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How Telemetry Works

Telemetry in Praison Labs is automatic and minimal:

1. Automatic Integration: When you create agents and run workflows, telemetry is automatically integrated
2. Anonymous Tracking: Only counts and types are tracked, never content
3. Memory Storage: Currently metrics are only stored in memory (no network calls)
4. Session-based: Each run gets a unique anonymous session ID
5. Privacy-first: Respects DO_NOT_TRACK standard and multiple opt-out methods

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Advanced Usage

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Accessing Telemetry Data

from praisonaiagents.telemetry import get_telemetry

# Get the global telemetry instance
telemetry = get_telemetry()

# Check if telemetry is enabled
if telemetry.enabled:
    print("Telemetry is active")

# Get current metrics
metrics = telemetry.get_metrics()
print(f"Total agent executions: {metrics['agent_executions']}")
print(f"Total task completions: {metrics['task_completions']}")
print(f"Total tool calls: {metrics['tool_calls']}")
print(f"Total errors: {metrics['errors']}")

# Get session info
print(f"Session ID: {telemetry.session_id}")
print(f"Environment: {telemetry._environment}")

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Manual Tracking (Advanced)

from praisonaiagents.telemetry import get_telemetry

# Get telemetry instance
telemetry = get_telemetry()

# Manually track events (usually automatic)
telemetry.track_agent_execution(agent_name="MyAgent", success=True)
telemetry.track_task_completion(task_name="MyTask", success=True)
telemetry.track_tool_usage(tool_name="web_search", success=True)
telemetry.track_error(error_type="ValueError")

# Track feature usage
telemetry.track_feature_usage("custom_feature")

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Integration with External Services

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PostHog Integration (Coming Soon)

Praison Labs telemetry includes built-in PostHog support for anonymous analytics:

# PostHog integration is automatic when available
# Metrics are sent anonymously with session IDs only
# No configuration needed - it just works

# To verify PostHog is available:
try:
    import posthog
    print("PostHog integration available")
except ImportError:
    print("PostHog not installed - metrics stored locally only")

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AgentOps Integration

For more advanced monitoring, you can use AgentOps alongside the built-in telemetry:

import agentops
from praisonaiagents import Agent, Task, Praison LabsAgents

# Initialize AgentOps
agentops.init(api_key="your-api-key")

# Use Praison Labs normally - both telemetry systems work together
agent = Agent(
    name="DataAnalyst",
    role="Analyst",
    goal="Analyze data"
)

task = Task(
    description="Analyze sales trends",
    agent=agent
)

workflow = Praison LabsAgents(agents=[agent], tasks=[task])
result = workflow.start()

# End AgentOps session
agentops.end_session("Success")

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Backward Compatibility

The telemetry module maintains compatibility with the previous interface:

from praisonaiagents.telemetry import TelemetryCollector

# Legacy interface still works
collector = TelemetryCollector()
collector.start()

with collector.trace_agent_execution("MyAgent"):
    # Agent execution code
    pass

collector.stop()

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Best Practices

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Troubleshooting

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Next Steps