Langfuse + Strands Agents Observability

Integrate Langfuse's open-source LLM observability platform with AWS Strands Agents using OpenTelemetry.

Core Integration Pattern

python

import os
import base64
from strands import Agent
from strands.telemetry import StrandsTelemetry
from strands.models.bedrock import BedrockModel

# 1. Configure Langfuse credentials
os.environ["LANGFUSE_PUBLIC_KEY"] = "pk-lf-..."
os.environ["LANGFUSE_SECRET_KEY"] = "sk-lf-..."
os.environ["LANGFUSE_BASE_URL"] = "https://cloud.langfuse.com"  # EU default
# os.environ["LANGFUSE_BASE_URL"] = "https://us.cloud.langfuse.com"  # US region

# 2. Configure OTEL exporter for Langfuse
LANGFUSE_AUTH = base64.b64encode(
    f"{os.environ['LANGFUSE_PUBLIC_KEY']}:{os.environ['LANGFUSE_SECRET_KEY']}".encode()
).decode()
os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = f"{os.environ['LANGFUSE_BASE_URL']}/api/public/otel"
os.environ["OTEL_EXPORTER_OTLP_HEADERS"] = f"Authorization=Basic {LANGFUSE_AUTH}"

# 3. Initialize telemetry BEFORE creating agent
strands_telemetry = StrandsTelemetry().setup_otlp_exporter()

# 4. Create agent with trace attributes
agent = Agent(
    model=BedrockModel(model_id="us.anthropic.claude-sonnet-4-20250514-v1:0"),
    system_prompt="Your system prompt here",
    trace_attributes={
        "session.id": "unique-session-id",
        "user.id": "user@example.com",
        "langfuse.tags": ["production", "customer-support"]
    }
)

# 5. Run agent - traces automatically sent to Langfuse
result = agent("User query here")

Installation

bash

pip install strands-agents[otel] langfuse
# Optional: pip install strands-agents-tools for pre-built tools

Key Configuration Reference

Trace Attributes

Pass these in trace_attributes when creating an Agent:

Attribute	Purpose	Example
`session.id`	Group related conversations	`"chat-abc123"`
`user.id`	Track per-user metrics	`"user@domain.com"`
`langfuse.tags`	Filter/organize in UI	`["prod", "v2"]`

Model Providers

Strands supports multiple providers. Configure the model before passing to Agent:

python

# Amazon Bedrock (default)
from strands.models.bedrock import BedrockModel
model = BedrockModel(model_id="us.anthropic.claude-sonnet-4-20250514-v1:0")

# Anthropic direct
from strands.models.anthropic import AnthropicModel
model = AnthropicModel(model_id="claude-sonnet-4-20250514")

# OpenAI
from strands.models.openai import OpenAIModel
model = OpenAIModel(model_id="gpt-4o")

# Ollama (local)
from strands.models.ollama import OllamaModel
model = OllamaModel(model_id="llama3")

Common Patterns

Adding Custom Tools

python

from strands import Agent, tool

@tool
def search_database(query: str) -> str:
    """Search the customer database."""
    # Tool implementation
    return f"Results for: {query}"

agent = Agent(
    model=model,
    tools=[search_database],
    trace_attributes={"session.id": "..."}
)

Combining with Langfuse SDK for Custom Spans

python

from langfuse import observe, get_client

langfuse = get_client()

@observe()
def my_pipeline(user_input: str):
    # Pre-processing traced as custom span
    processed = preprocess(user_input)
    
    # Strands agent call - automatically traced
    result = agent(processed)
    
    # Post-processing traced as custom span
    return postprocess(result)

Multi-Agent Orchestration

python

from strands import Agent
from strands.multiagent import AgentTool

# Create specialized agents
researcher = Agent(model=model, system_prompt="Research specialist...")
writer = Agent(model=model, system_prompt="Writing specialist...")

# Orchestrator can delegate to sub-agents
orchestrator = Agent(
    model=model,
    system_prompt="Coordinate research and writing tasks...",
    tools=[
        AgentTool(researcher, name="researcher"),
        AgentTool(writer, name="writer")
    ],
    trace_attributes={"session.id": "multi-agent-session"}
)

Langfuse Dashboard Features

After traces flow to Langfuse, use these features:

•Trace View: See full agent execution flow, tool calls, LLM generations
•Cost Tracking: Monitor token usage and costs per trace/session/user
•Latency Analysis: Identify slow operations via timeline view
•Session Grouping: View multi-turn conversations together
•Filtering: Use tags, user IDs, session IDs to filter traces
•Evaluation: Add scores to traces for quality monitoring

Troubleshooting

Issue	Solution
No traces appearing	Verify OTEL environment variables are set before `StrandsTelemetry()` call
Auth errors	Check public/secret key pair matches Langfuse project
Missing tool spans	Ensure tools use `@tool` decorator from strands
Bedrock access denied	Enable model access in AWS Bedrock console

Additional Resources

•See references/advanced-patterns.md for async patterns, AgentCore deployment, and evaluation with Ragas
•See references/environment-setup.md for complete environment variable reference