> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Rollback Concurrent
This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide](/langsmith/double-texting).
The guide covers the `rollback` option for double texting, which interrupts the prior run of the graph and starts a new one with the double-text. This option is very similar to the `interrupt` option, but in this case the first run is completely deleted from the database and cannot be restarted. Below is a quick example of using the `rollback` option.
## Setup
First, we will define a quick helper function for printing out JS and CURL model outputs (you can skip this if using Python):
```js theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
function prettyPrint(m) {
const padded = " " + m['type'] + " ";
const sepLen = Math.floor((80 - padded.length) / 2);
const sep = "=".repeat(sepLen);
const secondSep = sep + (padded.length % 2 ? "=" : "");
console.log(`${sep}${padded}${secondSep}`);
console.log("\n\n");
console.log(m.content);
}
```
```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# PLACE THIS IN A FILE CALLED pretty_print.sh
pretty_print() {
local type="$1"
local content="$2"
local padded=" $type "
local total_width=80
local sep_len=$(( (total_width - ${#padded}) / 2 ))
local sep=$(printf '=%.0s' $(eval "echo {1.."${sep_len}"}"))
local second_sep=$sep
if (( (total_width - ${#padded}) % 2 )); then
second_sep="${second_sep}="
fi
echo "${sep}${padded}${second_sep}"
echo
echo "$content"
}
```
Now, let's import our required packages and instantiate our client, assistant, and thread.
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import asyncio
import httpx
from langchain_core.messages import convert_to_messages
from langgraph_sdk import get_client
client = get_client(url=)
# Using the graph deployed with the name "agent"
assistant_id = "agent"
thread = await client.threads.create()
```
```js theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { Client } from "@langchain/langgraph-sdk";
const client = new Client({ apiUrl: });
// Using the graph deployed with the name "agent"
const assistantId = "agent";
const thread = await client.threads.create();
```
```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
curl --request POST \
--url /threads \
--header 'Content-Type: application/json' \
--data '{}'
```
## Create runs
Now let's run a thread with the multitask parameter set to "rollback":
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# the first run will be rolled back
rolled_back_run = await client.runs.create(
thread["thread_id"],
assistant_id,
input={"messages": [{"role": "user", "content": "what's the weather in sf?"}]},
)
run = await client.runs.create(
thread["thread_id"],
assistant_id,
input={"messages": [{"role": "user", "content": "what's the weather in nyc?"}]},
multitask_strategy="rollback",
)
# wait until the second run completes
await client.runs.join(thread["thread_id"], run["run_id"])
```
```js theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
// the first run will be interrupted
let rolledBackRun = await client.runs.create(
thread["thread_id"],
assistantId,
{ input: { messages: [{ role: "human", content: "what's the weather in sf?" }] } }
);
let run = await client.runs.create(
thread["thread_id"],
assistant_id,
{
input: { messages: [{ role: "human", content: "what's the weather in nyc?" }] },
multitaskStrategy: "rollback"
}
);
// wait until the second run completes
await client.runs.join(thread["thread_id"], run["run_id"]);
```
```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
curl --request POST \
--url >/threads//runs \
--header 'Content-Type: application/json' \
--data "{
\"assistant_id\": \"agent\",
\"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what\'s the weather in sf?\"}]},
}" && curl --request POST \
--url >/threads//runs \
--header 'Content-Type: application/json' \
--data "{
\"assistant_id\": \"agent\",
\"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what\'s the weather in nyc?\"}]},
\"multitask_strategy\": \"rollback\"
}" && curl --request GET \
--url /threads//runs//join
```
## View run results
We can see that the thread has data only from the second run
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
state = await client.threads.get_state(thread["thread_id"])
for m in convert_to_messages(state["values"]["messages"]):
m.pretty_print()
```
```js theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
const state = await client.threads.getState(thread["thread_id"]);
for (const m of state['values']['messages']) {
prettyPrint(m);
}
```
```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
source pretty_print.sh && curl --request GET \
--url /threads//state | \
jq -c '.values.messages[]' | while read -r element; do
type=$(echo "$element" | jq -r '.type')
content=$(echo "$element" | jq -r '.content | if type == "array" then tostring else . end')
pretty_print "$type" "$content"
done
```
Output:
```
================================ Human Message =================================
what's the weather in nyc?
================================== Ai Message ==================================
[{'id': 'toolu_01JzPqefao1gxwajHQ3Yh3JD', 'input': {'query': 'weather in nyc'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
Tool Calls:
tavily_search_results_json (toolu_01JzPqefao1gxwajHQ3Yh3JD)
Call ID: toolu_01JzPqefao1gxwajHQ3Yh3JD
Args:
query: weather in nyc
================================= Tool Message =================================
Name: tavily_search_results_json
[{"url": "https://www.weatherapi.com/", "content": "{'location': {'name': 'New York', 'region': 'New York', 'country': 'United States of America', 'lat': 40.71, 'lon': -74.01, 'tz_id': 'America/New_York', 'localtime_epoch': 1718734479, 'localtime': '2024-06-18 14:14'}, 'current': {'last_updated_epoch': 1718733600, 'last_updated': '2024-06-18 14:00', 'temp_c': 29.4, 'temp_f': 84.9, 'is_day': 1, 'condition': {'text': 'Sunny', 'icon': '//cdn.weatherapi.com/weather/64x64/day/113.png', 'code': 1000}, 'wind_mph': 2.2, 'wind_kph': 3.6, 'wind_degree': 158, 'wind_dir': 'SSE', 'pressure_mb': 1025.0, 'pressure_in': 30.26, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 63, 'cloud': 0, 'feelslike_c': 31.3, 'feelslike_f': 88.3, 'windchill_c': 28.3, 'windchill_f': 82.9, 'heatindex_c': 29.6, 'heatindex_f': 85.3, 'dewpoint_c': 18.4, 'dewpoint_f': 65.2, 'vis_km': 16.0, 'vis_miles': 9.0, 'uv': 7.0, 'gust_mph': 16.5, 'gust_kph': 26.5}}"}]
================================== Ai Message ==================================
The weather API results show that the current weather in New York City is sunny with a temperature of around 85°F (29°C). The wind is light at around 2-3 mph from the south-southeast. Overall it looks like a nice sunny summer day in NYC.
```
Verify that the original, rolled back run was deleted
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
try:
await client.runs.get(thread["thread_id"], rolled_back_run["run_id"])
except httpx.HTTPStatusError as _:
print("Original run was correctly deleted")
```
```js theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
try {
await client.runs.get(thread["thread_id"], rolledBackRun["run_id"]);
} catch (e) {
console.log("Original run was correctly deleted");
}
```
Output:
```
Original run was correctly deleted
```
***
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