Observability Overview

Capture every LLM call, tool invocation, and agent decision with automatic instrumentation.

What is Observability?

Observability in AI systems means having complete visibility into what your agents are doing, why they're making decisions, and how they're performing. TuringPulse provides:

  • Traces - End-to-end execution paths through your agent
  • Spans - Individual operations within a trace (LLM calls, tools, etc.)
  • Metrics - Quantitative measurements (latency, tokens, cost)
  • Logs - Detailed event data for debugging

Quick Start

Add observability to your agent in three steps:

1. Install the SDK

pip install turingpulse_sdk

2. Initialize

config.py
from turingpulse_sdk import init

init(api_key="sk_live_your_api_key", workflow_name="my-agent")

3. Instrument Your Code

agent.py
from turingpulse_sdk import instrument

@instrument(name="my-agent")
def run_agent(query: str):
    response = llm.chat(query)
    return response
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Zero-Config Instrumentation
If you're using LangGraph, LangChain, or CrewAI, the SDK automatically captures traces without any code changes. Just initialize and go!

What Gets Captured

TuringPulse automatically captures rich telemetry from your AI workflows:

LLM Calls

  • Model name and provider
  • Input messages/prompts
  • Output completions
  • Token counts (input/output)
  • Latency and cost
  • Temperature and other parameters

Tool Invocations

  • Tool name and description
  • Input arguments
  • Output results
  • Execution time
  • Success/failure status

Agent Decisions

  • Reasoning steps (for ReAct agents)
  • Plan steps (for Plan-and-Execute)
  • State transitions
  • Iteration counts

Viewing Traces

Once traces are captured, you can view them in the TuringPulse dashboard:

  • Trace List - Browse all traces with filtering and search
  • Trace Detail - Drill into individual traces with span waterfall
  • Span Inspector - View inputs, outputs, and metadata for each span
  • Timeline View - Visualize execution timing and parallelism

Next Steps