Setting different LangSmith project names for individual chains at runtime

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1

I know from the docs that you can use the LANGCHAIN_PROJECT environment variable to set your project name in LangSmith.

But what if I need to run several chains in one application and want each chain to use a different project name? How can I set the project name for each chain individually when the code runs?

I found what might be a working solution but I had to dig into the LangChain source code to figure it out. I’m worried this approach might not be the right way and could break when they update their API.

Here’s my current approach:

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

Is this the correct way to handle multiple project names? Any better suggestions would be really helpful.

2

Look, those solutions work but you’re fixing the wrong thing. Stop manually managing tracers and configs - automate the whole chain orchestration instead.

I hit this same problem running multiple LangChain workflows for different teams. Instead of patching project names everywhere, I built simple automation that handles routing based on input context.

Set up triggers that auto-route requests to the right chain with the right project name. No more hardcoded project names in your app code. The automation reads request context and sets everything up.

Your chains become stateless services called with proper context. Way cleaner than remembering to add config parameters every time you invoke something.

I handle dozens of chains this way now. Each team gets their own project tracking without touching any LangChain code. Automation handles all routing and project assignment.

Latenode makes this super easy. Just create workflows that route based on your business logic and let it handle all LangSmith project management automatically.

3

Both approaches work, but there’s an even simpler solution. Just use the with_config method when invoking your chain - no need to mess with callback managers. my_chain.with_config({"project_name": "my_project"}).invoke(input_data) does the trick. I’ve been using this for months and it’s way cleaner than manually handling tracers.

4

Creating chains with different project names gets way easier with a factory pattern. I got sick of juggling config parameters across 20+ chains in multiple services, so I built this:

class ChainFactory:
    def create_with_project(self, chain_type, project_name):
        chain = LLMChain(llm=self.llm, prompt=self.get_prompt(chain_type))
        tracer = LangChainTracer(project_name=project_name)
        chain.callbacks = CallbackManager(handlers=[tracer])
        return chain

Set the project once during creation and you’re done. Each chain knows its project from the start - no runtime config mess or wrapper functions.

I keep chain types mapped to project names in a config file. Adding new projects or changing mappings doesn’t require code changes. Been using this for eight months across multiple LangChain versions with zero problems.

5

Had the same issue tracking different product features in LangSmith. Your approach works, but there’s an easier way I found after trying everything.

Skip setting project names at the chain level. Handle it when you invoke instead:

def run_with_project(chain, inputs, project_name):
    return chain.invoke(
        inputs, 
        config={"callbacks": [LangChainTracer(project_name=project_name)]}
    )

# Usage
result = run_with_project(my_chain, {"input": data}, "feature_x_project")

Keeps your chains clean and you can route to different projects without hardcoding. I use this across 15+ chains in our app.

Treat project routing as a runtime decision, not chain config. Makes testing easier too - just point everything at a test project by changing one parameter.

Survived three major LangChain updates without breaking.

6

Your callback manager approach works fine and isn’t fragile at all. I’ve used something similar for over a year with zero issues. The trick is adding your custom tracer to existing callbacks instead of replacing everything.

my_chain = LLMChain(llm=self._language_model, prompt=my_prompt)
custom_tracer = LangChainTracer(project_name="my_specific_project")
my_chain.callbacks.add_handler(custom_tracer)

This keeps all default handlers while adding your project tracer. I run multiple chains like this in production and it’s handled several LangChain updates just fine. Tracer classes are part of their stable callback interface - not some internal hack. If you’re still worried about API changes, just wrap it in try-catch and fall back to environment variables.

7

Skip the manual tracer creation - there’s a cleaner way. Just pass the project name directly through the invoke method with config parameters. Way more stable since you’re using the official API instead of messing with internal stuff.

my_chain = LLMChain(llm=language_model, prompt=my_prompt)
result = my_chain.invoke(
    {"input": "your input here"},
    config={"project_name": "my_specific_project"}
)

This works across all chain types and you don’t need to import any internal tracer classes. I’ve run this in production for months without problems, even through multiple LangChain updates. The config approach is documented and won’t break like directly messing with callback managers will.