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diff --git a/docs/observability/how_to_guides/monitoring/threads.mdx b/docs/observability/how_to_guides/monitoring/threads.mdx
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--- a/docs/observability/how_to_guides/monitoring/threads.mdx
+++ b/docs/observability/how_to_guides/monitoring/threads.mdx
@@ -1,11 +1,16 @@
+import {
+  CodeTabs,
+  PythonBlock,
+  TypeScriptBlock,
+} from "@site/src/components/InstructionsWithCode";
+
 # Set up threads
 
 :::tip Recommended Reading
 Before diving into this content, it might be helpful to read the following:
 
 - [Add metadata and tags to traces](../tracing/add_metadata_tags)
-
-:::
+  :::
 
 Many LLM applications have a chatbot-like interface in which the user and the LLM application engage in a multi-turn conversation. In order to track these conversations, you can use the `Threads` feature in LangSmith.
 
@@ -22,11 +27,183 @@ The key name should be one of:
 - `thread_id`
 - `conversation_id`.
 
-The value should be a UUID, such as `f47ac10b-58cc-4372-a567-0e02b2c3d479`.
+The value can be any string you want, but we recommend using UUIDs, such as `f47ac10b-58cc-4372-a567-0e02b2c3d479`.
+
+### Code example
+
+This example demonstrates how to log and retrieve conversation history from LangSmith to maintain long-running chats.
+
+You can [add metadata to your traces](../tracing/add_metadata_tags) in LangSmith in a variety of ways, this code will show how to do so dynamically, but read the
+previously linked guide to learn about all the ways you can add thread identifier metadata to your traces.
+
+<CodeTabs
+  tabs={[
+    PythonBlock(`import openai
+from langsmith import traceable
+from langsmith import Client
+import langsmith as ls
+from langsmith.wrappers import wrap_openai
+
+client = wrap_openai(openai.Client())
+langsmith_client = Client()
+
+# Config used for this example
+
+langsmith_project = "project-with-threads"
+
+# highlight-next-line
+
+session_id = "thread-id-1"
+
+# highlight-next-line
+
+langsmith_extra={"project_name": langsmith_project, "metadata":{"session_id": session_id}}
+
+# gets a history of all LLM calls in the thread to construct conversation history
+
+def get_thread_history(thread_id: str, project_name: str): # Filter runs by the specific thread and project
+filter_string = f'and(in(metadata_key, ["session_id","conversation_id","thread_id"]), eq(metadata_value, "{thread_id}"))' # Only grab the LLM runs
+runs = [r for r in langsmith_client.list_runs(project_name=project_name, filter=filter_string, run_type="llm")]
+
+    # Sort by start time to get the most recent interaction
+    runs = sorted(runs, key=lambda run: run.start_time, reverse=True)
+    # The current state of the conversation
+    return runs[0].inputs['messages'] + [runs[0].outputs['choices'][0]['message']]
+
+# if an existing conversation is continued, this function looks up the current run’s metadata to get the session_id, calls get_thread_history, and appends the new user question before making a call to the chat model
+
+@traceable(name="Chat Bot")
+def chat_pipeline(question: str, get_chat_history: bool = False): # Whether to continue an existing thread or start a new one
+if get_chat_history:
+run_tree = ls.get_current_run_tree()
+messages = get_thread_history(run_tree.extra["metadata"]["session_id"],run_tree.session_name) + [{"role": "user", "content": question}]
+else:
+messages = [{"role": "user", "content": question}]
+
+    # Invoke the model
+    chat_completion = client.chat.completions.create(
+        model="gpt-4o-mini", messages=messages
+    )
+    return chat_completion.choices[0].message.content
+
+# Start the conversation
+
+chat_pipeline("Hi, my name is Bob", langsmith_extra=langsmith_extra)`),
+    TypeScriptBlock(`import OpenAI from "openai";
+import { traceable, getCurrentRunTree } from "langsmith/traceable";
+import { Client } from "langsmith";
+import { wrapOpenAI } from "langsmith/wrappers";
+
+// Config used for this example
+const langsmithProject = "project-with-threads";
+// highlight-next-line
+const threadId = "thread-id-1";
+
+const client = wrapOpenAI(new OpenAI(), {
+project_name: langsmithProject,
+metadata: { session_id: threadId }  
+});
+const langsmithClient = new Client();
+
+async function getThreadHistory(threadId: string, projectName: string) {
+// Filter runs by the specific thread and project
+const filterString = \`and(in(metadata_key, ["session_id","conversation_id","thread_id"]), eq(metadata_value, "\${threadId}"))\`;
+
+// Only grab the LLM runs
+const runs = langsmithClient.listRuns({
+projectName: projectName,
+filter: filterString,
+runType: "llm"
+});
+
+// Sort by start time to get the most recent interaction
+const runsArray = [];
+for await (const run of runs) {
+runsArray.push(run);
+}
+const sortedRuns = runsArray.sort((a, b) =>
+new Date(b.start_time).getTime() - new Date(a.start_time).getTime()
+);
+
+// The current state of the conversation
+return [
+...sortedRuns[0].inputs.messages,
+sortedRuns[0].outputs.choices[0].message
+];
+}
+
+const chatPipeline = traceable(
+async (
+question: string,
+options: {
+getChatHistory?: boolean;
+} = {}
+) => {
+const {
+getChatHistory = false,
+} = options;
+
+    let messages = [];
+    // Whether to continue an existing thread or start a new one
+    if (getChatHistory) {
+      const runTree = await getCurrentRunTree();
+      const historicalMessages = await getThreadHistory(
+        runTree.extra.metadata.session_id,
+        runTree.project_name
+      );
+      messages = [
+        ...historicalMessages,
+        { role:"user", content: question }
+      ];
+    } else {
+      messages = [{ role:"user", content: question }];
+    }
+
+    // Invoke the model
+    const chatCompletion = await client.chat.completions.create({
+      model: "gpt-4o-mini",
+      messages: messages
+    });
+    return chatCompletion.choices[0].message.content;
+
+},
+{
+name: "Chat Bot",
+project_name: langsmithProject,
+metadata: { session_id: threadId }  
+ }
+);
+
+// Start the conversation
+await chatPipeline("Hi, my name is Bob");`),
+]}
+groupId="client-language"
+/>
+
+After waiting a few seconds, you can make the following calls to contineu the conversation. By passing `getChatHistory: true`,
+you can continue the conversation from where it left off. This means that the LLM will receive the entire message history and respond to it,
+instead of just responding to the latest message.
+
+<CodeTabs
+  tabs={[
+    PythonBlock(`# Continue the conversation (WAIT A FEW SECONDS BEFORE RUNNING THIS SO THE FRIST TRACE CAN BE INGESTED)
+chat_pipeline("What is my name?", get_chat_history=True, langsmith_extra=langsmith_extra)
+
+# Keep the conversation going (WAIT A FEW SECONDS BEFORE RUNNING THIS SO THE PREVIOUS TRACE CAN BE INGESTED)
+
+chat_pipeline("What was the first message I sent you", get_chat_history=True, langsmith_extra=langsmith_extra)`),
+    TypeScriptBlock(`// Continue the conversation (WAIT A FEW SECONDS BEFORE RUNNING THIS SO THE FRIST TRACE CAN BE INGESTED)
+await chatPipeline("What is my name?", { getChatHistory: true });
+
+// Keep the conversation going (WAIT A FEW SECONDS BEFORE RUNNING THIS SO THE PREVIOUS TRACE CAN BE INGESTED)
+await chatPipeline("What was the first message I sent you", { getChatHistory: true });`),
+]}
+groupId="client-language"
+/>
 
 ## View threads
 
-You can view threads by clicking on the `Threads` tad in any project details page. You will then see a list of all threads, sorted by the most recent activity.
+You can view threads by clicking on the `Threads` tab in any project details page. You will then see a list of all threads, sorted by the most recent activity.
 
 ![Thread Tab](./static/convo_tab.png)