diff --git a/langchain/client/langchain.py b/langchain/client/langchain.py
index de330028..4c3848b1 100644
--- a/langchain/client/langchain.py
+++ b/langchain/client/langchain.py
@@ -26,7 +26,6 @@ from pydantic import BaseSettings, Field, root_validator
 from requests import Response
 
 from langchain.base_language import BaseLanguageModel
-from langchain.callbacks.manager import tracing_v2_enabled
 from langchain.callbacks.tracers.langchain import LangChainTracer
 from langchain.chains.base import Chain
 from langchain.chat_models.base import BaseChatModel
@@ -351,14 +350,13 @@ class LangChainPlusClient(BaseSettings):
             except Exception as e:
                 logger.warning(f"Chain failed for example {example.id}. Error: {e}")
                 outputs.append({"Error": str(e)})
-            finally:
-                langchain_tracer.example_id = previous_example_id
+        langchain_tracer.example_id = previous_example_id
         return outputs
 
     @staticmethod
     async def _gather_with_concurrency(
         n: int,
-        initializer: Callable[[], Coroutine[Any, Any, Tuple[LangChainTracer, Dict]]],
+        initializer: Callable[[], Coroutine[Any, Any, LangChainTracer]],
         *async_funcs: Callable[[LangChainTracer, Dict], Coroutine[Any, Any, Any]],
     ) -> List[Any]:
         """
@@ -373,21 +371,28 @@ class LangChainPlusClient(BaseSettings):
             A list of results from the coroutines.
         """
         semaphore = asyncio.Semaphore(n)
-        tracer, job_state = await initializer()
+        job_state = {"num_processed": 0}
+
+        tracer_queue: asyncio.Queue[LangChainTracer] = asyncio.Queue()
+        for _ in range(n):
+            tracer_queue.put_nowait(await initializer())
 
         async def run_coroutine_with_semaphore(
             async_func: Callable[[LangChainTracer, Dict], Coroutine[Any, Any, Any]]
         ) -> Any:
             async with semaphore:
-                return await async_func(tracer, job_state)
+                tracer = await tracer_queue.get()
+                try:
+                    result = await async_func(tracer, job_state)
+                finally:
+                    tracer_queue.put_nowait(tracer)
+                return result
 
         return await asyncio.gather(
             *(run_coroutine_with_semaphore(function) for function in async_funcs)
         )
 
-    async def _tracer_initializer(
-        self, session_name: str
-    ) -> Tuple[LangChainTracer, dict]:
+    async def _tracer_initializer(self, session_name: str) -> LangChainTracer:
         """
         Initialize a tracer to share across tasks.
 
@@ -397,11 +402,9 @@ class LangChainPlusClient(BaseSettings):
         Returns:
             A LangChainTracer instance with an active session.
         """
-        job_state = {"num_processed": 0}
-        with tracing_v2_enabled(session_name=session_name) as session:
-            tracer = LangChainTracer()
-            tracer.session = session
-            return tracer, job_state
+        tracer = LangChainTracer(session_name=session_name)
+        tracer.ensure_session()
+        return tracer
 
     async def arun_on_dataset(
         self,
@@ -513,8 +516,7 @@ class LangChainPlusClient(BaseSettings):
             except Exception as e:
                 logger.warning(f"Chain failed for example {example.id}. Error: {e}")
                 outputs.append({"Error": str(e)})
-            finally:
-                langchain_tracer.example_id = previous_example_id
+        langchain_tracer.example_id = previous_example_id
         return outputs
 
     def run_on_dataset(
@@ -550,18 +552,16 @@ class LangChainPlusClient(BaseSettings):
         dataset = self.read_dataset(dataset_name=dataset_name)
         examples = list(self.list_examples(dataset_id=str(dataset.id)))
         results: Dict[str, Any] = {}
-        with tracing_v2_enabled(session_name=session_name) as session:
-            tracer = LangChainTracer()
-            tracer.session = session
-
-            for i, example in enumerate(examples):
-                result = self.run_llm_or_chain(
-                    example,
-                    tracer,
-                    llm_or_chain_factory,
-                    num_repetitions,
-                )
-                if verbose:
-                    print(f"{i+1} processed", flush=True, end="\r")
-            results[str(example.id)] = result
+        tracer = LangChainTracer(session_name=session_name)
+        tracer.ensure_session()
+        for i, example in enumerate(examples):
+            result = self.run_llm_or_chain(
+                example,
+                tracer,
+                llm_or_chain_factory,
+                num_repetitions,
+            )
+            if verbose:
+                print(f"{i+1} processed", flush=True, end="\r")
+        results[str(example.id)] = result
         return results