LangSmith - PraisonAI

Monitor agent execution, LLM calls, tool usage, and token costs in LangSmith.

Quick Start

Install Dependencies

pip install praisonaiagents praisonai-tools opentelemetry-sdk opentelemetry-exporter-otlp

Set Environment Variables

export LANGSMITH_API_KEY=lsv2_xxx
export LANGSMITH_PROJECT=my-project

Run Your Agent

from praisonai_tools.observability import obs
from praisonaiagents import Agent

obs.init(provider="langsmith")

agent = Agent(
    name="Assistant",
    instructions="You are a helpful assistant.",
    model="gpt-4o-mini",
)

response = agent.chat("What is AI?")
print(response)

That’s it — three lines of setup. Every LLM call, tool call, and agent step is automatically traced to your LangSmith dashboard.

How It Works

What Gets Traced	Details
Agent lifecycle	Start/end timing, agent name, role
LLM calls	Input messages, output, model, token usage
Tool calls	Tool name, arguments, results
Token usage	Prompt tokens, completion tokens, total
Errors	Stack traces, error messages

Configuration Options

Option	Environment Variable	Description
`api_key`	`LANGSMITH_API_KEY`	Your LangSmith API key
`project`	`LANGSMITH_PROJECT`	Project name (default: `"default"`)
`endpoint`	`LANGSMITH_ENDPOINT`	API endpoint (default: `https://api.smith.langchain.com`)
`tracing`	`LANGSMITH_TRACING`	Set to `true` to enable (auto-detected)

Common Patterns

Single Agent
Multi-Agent Team
With Tools
Explicit Tracing

from praisonai_tools.observability import obs
from praisonaiagents import Agent

obs.init(provider="langsmith")

agent = Agent(
    name="Assistant",
    instructions="You are a helpful assistant.",
    model="gpt-4o-mini",
)

response = agent.chat("What is AI?")
print(response)

from praisonai_tools.observability import obs
from praisonaiagents import Agent, Task, PraisonAIAgents

obs.init(provider="langsmith", project_name="my-team")

researcher = Agent(
    name="Researcher",
    instructions="Search for information.",
    model="gpt-4o-mini",
)

writer = Agent(
    name="Writer",
    instructions="Write clear summaries.",
    model="gpt-4o-mini",
)

task1 = Task(description="Research AI trends", agent=researcher)
task2 = Task(description="Summarize findings", agent=writer)

agents = PraisonAIAgents(agents=[researcher, writer], tasks=[task1, task2])
agents.start()

from praisonai_tools.observability import obs
from praisonaiagents import Agent, tool

obs.init(provider="langsmith")

@tool
def search_web(query: str) -> str:
    """Search the web for information."""
    return f"Results for: {query}"

agent = Agent(
    name="Researcher",
    instructions="Search and answer questions.",
    tools=[search_web],
    model="gpt-4o-mini",
)

response = agent.chat("Latest AI news")
print(response)

from praisonai_tools.observability import obs
from praisonaiagents import Agent

obs.init(provider="langsmith", auto_instrument=False)

agent = Agent(
    name="Assistant",
    instructions="You are helpful.",
    model="gpt-4o-mini",
)

with obs.trace("chat-session"):
    response = agent.chat("What is AI?")
    print(response)

What You See in LangSmith

Diagnostics & Verification

Check your setup with the built-in doctor:

Python
CLI

from praisonai_tools.observability import obs

obs.init(provider="langsmith")
results = obs.doctor()
print(results)

{
    "enabled": true,
    "provider": "langsmith",
    "connection_status": true,
    "connection_message": "LangSmith API key configured"
}

# Health check
python -m praisonai_tools.observability.cli doctor

# Health check (JSON)
python -m praisonai_tools.observability.cli doctor --json

# Verify traces in LangSmith (requires LANGSMITH_API_KEY)
python -m praisonai_tools.observability.cli verify --project "My First App"

# Verify with JSON output
python -m praisonai_tools.observability.cli verify --project "My First App" --json

PraisonAI Branding

Every agent and workflow span automatically includes PraisonAI branding in LangSmith metadata:

Metadata Key	Value	Description
`praisonai.version`	`0.2.20`	SDK version used
`praisonai.framework`	`praisonai`	Framework identifier

Workflow spans also capture structured input (agent names, task descriptions) and output.

Best Practices

Use project names to organize traces

Set LANGSMITH_PROJECT or pass project_name to group traces by environment or feature.

obs.init(provider="langsmith", project_name="production-chatbot")

Use auto-instrumentation for most cases

Auto-instrumentation traces everything automatically. Only use explicit obs.trace() when you need custom trace boundaries or additional metadata.

Set environment variables in .env files

Keep API keys out of code. Use .env files or your deployment platform’s secret management.

# .env
LANGSMITH_API_KEY=lsv2_xxx
LANGSMITH_PROJECT=my-project

Monitor token usage for cost control

LangSmith traces include token counts for every LLM call. Use this to identify expensive operations and optimize prompts.

Observability Overview

All supported observability providers

Langfuse

Alternative open-source observability

Langfuse

Traceloop

​Quick Start

​How It Works

​Configuration Options

​Common Patterns

​What You See in LangSmith

​Diagnostics & Verification

​PraisonAI Branding

​Best Practices

​Related

Observability Overview

Langfuse

Quick Start

How It Works

Configuration Options

Common Patterns

What You See in LangSmith

Diagnostics & Verification

PraisonAI Branding

Best Practices

Related