diff --git a/versioned_docs/version-2.0/how_to_guides/tracing/trace_with_langchain.mdx b/versioned_docs/version-2.0/how_to_guides/tracing/trace_with_langchain.mdx
index b45d5871..81875c86 100644
--- a/versioned_docs/version-2.0/how_to_guides/tracing/trace_with_langchain.mdx
+++ b/versioned_docs/version-2.0/how_to_guides/tracing/trace_with_langchain.mdx
@@ -223,59 +223,6 @@ await chain.invoke({ input: "What is the meaning of life?" }, { runId: myUuid })
 
 Note that if you do this at the **root** of a trace (i.e., the top-level run, that run ID will be used as the `trace_id`).
 
-## Access run (span) ID for LangChain invocations
-
-When you invoke a LangChain object, you can access the run ID of the invocation. This run ID can be used to query the run in LangSmith.
-
-In Python, you can use the `collect_runs` context manager to access the run ID.
-
-In JS/TS, you can use a `RunCollectorCallbackHandler` instance to access the run ID.
-
-<CodeTabs
-  tabs={[
-    PythonBlock(`from langchain_openai import ChatOpenAI
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_core.output_parsers import StrOutputParser
-from langchain_core.tracers.context import collect_runs\n
-prompt = ChatPromptTemplate.from_messages([
-    ("system", "You are a helpful assistant. Please respond to the user's request only based on the given context."),
-    ("user", "Question: {question}\\n\\nContext: {context}")
-])
-model = ChatOpenAI(model="gpt-3.5-turbo")
-output_parser = StrOutputParser()\n
-chain = prompt | model | output_parser\n
-question = "Can you summarize this morning's meetings?"
-context = "During this morning's meeting, we solved all world conflict."
-with collect_runs() as cb:
-  result = chain.invoke({"question": question, "context": context})
-  # Get the root run id
-  # highlight-next-line
-  run_id = cb.traced_runs[0].id
-print(run_id)`),
-    TypeScriptBlock(`import { ChatOpenAI } from "@langchain/openai";
-import { ChatPromptTemplate } from "@langchain/core/prompts";
-import { StringOutputParser } from "@langchain/core/output_parsers";
-import { RunCollectorCallbackHandler } from "@langchain/core/tracers/run_collector";\n
-const prompt = ChatPromptTemplate.fromMessages([
-  ["system", "You are a helpful assistant. Please respond to the user's request only based on the given context."],
-  ["user", "Question: {question\\n\\nContext: {context}"],
-]);
-const model = new ChatOpenAI({ modelName: "gpt-3.5-turbo" });
-const outputParser = new StringOutputParser();\n
-const chain = prompt.pipe(model).pipe(outputParser);
-const runCollector = new RunCollectorCallbackHandler();\n
-const question = "Can you summarize this morning's meetings?"
-const context = "During this morning's meeting, we solved all world conflict."
-await chain.invoke(
-    { question: question, context: context },
-    { callbacks: [runCollector] }
-);
-// highlight-next-line
-const runId = runCollector.tracedRuns[0].id;
-console.log(runId);`),
-  ]}
-  groupId="client-language"
-/>
 
 ## Ensure all traces are submitted before exiting
 
@@ -329,9 +276,9 @@ This largely builds off of the [previous section](#trace-selectively).
 <CodeTabs
   tabs={[
     PythonBlock(`from langchain.callbacks.tracers import LangChainTracer
-from langsmith import Client\n
+import langsmith as ls\n
 # You can create a client instance with an api key and api url
-client = Client(
+client = ls.Client(
     api_key="YOUR_API_KEY",  # This can be retrieved from a secrets manager
     api_url="https://api.smith.langchain.com",  # Update appropriately for self-hosted installations or the EU region
 )\n
@@ -339,10 +286,10 @@ client = Client(
 # highlight-next-line
 tracer = LangChainTracer(client=client, project_name="test-no-env")
 chain.invoke({"question": "Am I using a callback?", "context": "I'm using a callback"}, config={"callbacks": [tracer]})\n
-# LangChain Python also supports a context manager which allows passing the client and project_name
-from langchain_core.tracers.context import tracing_v2_enabled
+# LangSmith python also supports a context manager which allows passing the client and project_name
+import langsmith as ls
 # highlight-next-line
-with tracing_v2_enabled(client=client, project_name="test-no-env"):
+with ls.tracing_context(client=client, project_name="test-no-env", enabled=True):
     chain.invoke({"question": "Am I using a context manager?", "context": "I'm using a context manager"})`),
     TypeScriptBlock(`import { LangChainTracer } from "@langchain/core/tracers/tracer_langchain";
 import { Client } from "langsmith";\n