Setting different LangSmith project names for individual chains at runtime

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1

I’m working with LangSmith and need to run multiple chains in the same application but track them under separate project names.

The docs mention using the LANGCHAIN_PROJECT environment variable to set the project name. But this approach doesn’t work when you need different project names for different chains running in the same service.

I found a workaround by digging through the source code, but I’m not sure if this is the right way to do it:

  1. Create the chain first
  2. Make a custom tracer with the specific project name
  3. Override the chain’s callback system
my_chain = LLMChain(llm=self._model, prompt=my_prompt, verbose=True)
custom_tracer = LangChainTracer(project_name="my_project")
my_chain.callbacks = CallbackManager(handlers=[custom_tracer])

Is there a better way to handle this? I’m worried this method might break when the library gets updated.

2

Your approach works, but there’s a cleaner way without touching callback managers directly.

I hit this same issue months ago tracking different product feature chains. Use the invoke method with config parameters instead of setting callbacks on the chain.

my_chain = LLMChain(llm=self._model, prompt=my_prompt, verbose=True)

# Pass project config for each call
result = my_chain.invoke(
    input_data,
    config={"callbacks": [LangChainTracer(project_name="my_project")]}
)

Keep one chain instance but route traces to different projects per call. Way more flexible than hardcoding callbacks.

You can also set the project name in RunnableConfig if you’re using newer LCEL syntax. Same concept, feels more natural.

We’ve run this pattern in production for months across multiple microservices. Zero issues with library updates since you’re using the intended API.

3

Dynamic project switching gets tricky with concurrent requests. I hit this building a chat service where different conversation threads needed separate LangSmith tracking. Cleanest solution? Use LangSmith’s run configuration through the client directly. Skip chain-level modifications: ```python
from langsmith import Client

client = Client()
my_chain = LLMChain(llm=self._model, prompt=my_prompt)

Set project per execution

with client.tracing_v2_enabled(project_name=“specific_project”):
result = my_chain.invoke(input_data)

This isolates project switching at the client level instead of messing with chain behavior. Works great with async operations and won't interfere with other concurrent chains using different projects. I've been running this for four months - handles thousands of requests daily with zero hiccups or compatibility issues.
4

Been there. Dealt with this exact mess when juggling multiple LangSmith chains across projects. Your workaround does the job, but yeah - you’re right to worry about updates breaking everything.

Real problem? You’re doing all this tracking manually. New chain or service means setting up custom tracers again, managing callbacks again, and crossing your fingers nothing explodes.

I just automated the whole thing with Latenode. Built scenarios that auto-route different chains to their LangSmith projects based on whatever triggers I set. Done with manual tracer setup and callback headaches.

Best part - Latenode handles orchestration. Scale up, add chains, whatever. It works. Plus you get error handling and monitoring without writing more code.

New chain configs take me minutes now instead of hours debugging callback managers and wondering if the next update will nuke my setup.

Check it out: https://latenode.com

5

Had this exact problem building a multi-tenant app where each customer needed separate tracking. That manual tracer approach you’re thinking about? It’ll bite you later with maintenance hell.

Use context managers instead. Don’t mess with chain instances or pass configs around - just wrap your chain execution:

from langsmith import tracing_context

my_chain = LLMChain(llm=self._model, prompt=my_prompt)

with tracing_context(project_name="project_a"):
    result_a = my_chain.invoke(input_data)

with tracing_context(project_name="project_b"):
    result_b = my_chain.invoke(other_input)

Keeps your chains clean and makes project switching obvious at runtime. Way safer than callback manipulation and handles nested calls properly. I’ve used this for six months - zero breaking changes so far.