Setting different LangSmith project names for individual chains at runtime

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1

I’ve been working with LangSmith and need some guidance. The docs mention using the LANGCHAIN_PROJECT environment variable to set your project name, which works fine for basic setups.

My issue is that I’m running several different chains in the same application, and I want each one to log to its own LangSmith project. Setting a single environment variable doesn’t help here since I need to switch project names dynamically based on which chain is executing.

I found a potential workaround by digging through the LangChain source code, but I’m worried this approach might break when they update their API. Here’s what I came up with:

  1. Create the chain normally
  2. Make a custom LangChain tracer with the desired project name
  3. Override the chain’s callback system with this tracer
my_chain = LLMChain(llm=chat_model, prompt=my_prompt, verbose=True)
custom_tracer = LangChainTracer(project_name="my_specific_project")
my_chain.callbacks = CallbackManager(handlers=[custom_tracer])

Is this the right way to handle per-chain project naming? I’d appreciate any suggestions for a more stable approach.

2

Custom tracers aren’t as fragile as you think. I’ve been using similar patterns for 8 months across multiple production apps without any breaking changes. The LangChainTracer interface stays pretty stable. There’s actually a cleaner way that doesn’t mess with the entire callback system. Just pass the project name directly when you run the chain using the config parameter:

from langchain_core.runnables import RunnableConfig

config = RunnableConfig(
    callbacks=[LangChainTracer(project_name="specific_project_name")]
)
result = my_chain.invoke(input_data, config=config)

This keeps your chain definitions clean and lets you switch projects dynamically at runtime. Way more maintainable than messing with the chain’s callback manager directly, and it works consistently across different chain types.

3

Yeah, the environment variable limitation is super annoying with multiple chains. Hit this same issue about six months back building a system with different processing pipelines that each needed their own tracking. The custom tracer approach works, but there’s actually an easier way using context managers that I’ve found way more reliable. Just temporarily override the project setting with langsmith.utils.tracing_context. You can wrap chain execution without touching the chain itself: ```python
from langsmith.utils import tracing_context

with tracing_context(project_name=“chain_specific_project”):
result = my_chain.invoke(input_data)

4

the metadata approach sounds interesting, but i’m not convinced it actually works. i tried something similar and langsmith completely ignored the metadata field. maybe there’s a specific version requirement i’m missing? i’ve been stuck using env variables and it’s a nightmare with multiple chains running at once.

5

I’ve been fighting this same issue at work for years. Environment variables suck when you’re juggling multiple projects per chain.

What actually works is automating the project switching. Skip the manual tracer creation and callback mess - build automation that assigns project names based on your chain config.

I created a system that reads chain definitions and auto-routes logging to the right LangSmith project. No more hardcoded env vars or custom tracer maintenance.

The trick is a centralized automation layer handling project name logic. Chain starts, automation checks its type/config, sets the right project context. Your chains stay clean and API changes won’t break your custom setup.

Latenode makes this dead simple. Create workflows that auto-manage LangSmith project assignments with whatever rules you want. Way more reliable than doing it manually.

6

Managing project names across multiple chains is way more complex than it should be. I’ve dealt with this on dozens of production systems - all these manual approaches just create maintenance headaches.

The real problem isn’t setting project names. It’s managing chain execution, logging, and project organization at scale. You end up with scattered logic everywhere.

What works is automating your chain management. Don’t patch project names at runtime - build workflows that handle the entire pipeline. Project assignment, execution monitoring, result processing, all of it.

I built something similar using workflow automation that reads chain configs and routes everything to the right LangSmith projects automatically. Handles project creation, execution, even cleanup. No more custom tracers or context manager juggling.

Latenode nails this. You can create workflows that manage your entire LangChain setup. Chain detection, automatic project assignment, execution orchestration - all automated. Way more reliable than manual switching and actually scales when you’ve got dozens of chains.

7

totally agree! using invoke() with the tracer makes it so much smoother. i also avoid the callback mess and it keeps things tidy. never ran into issues either, so should work out for you too. gd luck!

8

Been dealing with this exact problem for the past year managing about 15 different chains in production. Your custom tracer approach works but there’s a much simpler solution nobody mentioned.

Just set the project name directly in your chain’s metadata when you create it. LangSmith picks this up automatically:

my_chain = LLMChain(
    llm=chat_model, 
    prompt=my_prompt, 
    metadata={"langsmith_project": "my_specific_project"}
)

No context managers, no custom tracers, no config overrides. The metadata approach has been rock solid for me across LangChain updates.

I actually have a factory function that builds chains with project names based on the chain type:

def create_chain(chain_type, llm, prompt):
    project_name = f"production_{chain_type}_chains"
    return LLMChain(
        llm=llm, 
        prompt=prompt,
        metadata={"langsmith_project": project_name}
    )

Each chain automatically logs to its own project without any runtime switching logic. Way cleaner than juggling project contexts every time you invoke a chain.