Context
When handling multiple concurrent requests for different projects in LangSmith, traces may sometimes get mixed up and appear under the wrong project. This can make debugging and monitoring difficult since project-level separation is crucial for tracking user activity accurately.
Answer
To ensure proper project separation during concurrent requests, avoid relying on global project configuration and instead use explicit project assignment methods. Here are the recommended approaches:
1. Configure project name within specific request context
Use the @traceable decorator with explicit project names:
from langsmith import traceable
@traceable(project_name="project-a")
def process_request_a():
# Your logic here
pass
@traceable(project_name="project-b")
def process_request_b():
# Your logic here
pass2. Use tracing_context for proper isolation
Wrap your request handling logic with the tracing_context context manager:
from langsmith.run_helpers import tracing_context
async def handle_request_a():
with tracing_context(project_name="project-a"):
# All traces within this context will use project-a
await process_data()
async def handle_request_b():
with tracing_context(project_name="project-b"):
# All traces within this context will use project-b
await process_data()3. Set project-specific environment variables for containerized deployments
If using containers, configure environment variables per container:
# Container A
LANGSMITH_PROJECT=project-a
# Container B
LANGSMITH_PROJECT=project-b4. Use project-specific client instances
Create separate client instances for different projects:
from langsmith import Client
client_a = Client(project_name="project-a")
client_b = Client(project_name="project-b")
# Use project-specific clients in your traceable functions
@traceable(client=client_a)
def process_a():
pass
@traceable(client=client_b)
def process_b():
passFor more detailed information, refer to the LangSmith Python SDK documentation.