Parallel execution with RunnableParallel in LangChain

Introduction

RunnableParallel helps run multiple tasks at the same time. This makes programs faster by doing many things together instead of one after another.

When you want to ask several questions to different chatbots at once.

When you need to process multiple pieces of data at the same time.

When you want to speed up tasks that don't depend on each other.

When you want to combine results from different tools quickly.

Syntax

LangChain

from langchain.schema.runnable import RunnableParallel

parallel_runner = RunnableParallel({"r1": runnable1, "r2": runnable2, ...})
result = parallel_runner.invoke(input_data)

You create RunnableParallel by giving it a dict of keys to tasks (runnables) to run together.

Calling invoke runs all tasks at once and returns their results as a dict.

Examples

This runs two simple tasks in parallel and gets their outputs as a dict.

LangChain

from langchain.schema.runnable import RunnableParallel, RunnableLambda

r1 = RunnableLambda(lambda x: 'Hello')
r2 = RunnableLambda(lambda x: 'World')
parallel = RunnableParallel({"r1": r1, "r2": r2})
output = parallel.invoke(None)

Runs three tasks at the same time using the same input data.

LangChain

parallel = RunnableParallel({"a": runnableA, "b": runnableB, "c": runnableC})
results = parallel.invoke(input_data)

Sample Program

This example shows how to run two simple tasks at the same time and print their results. Each task just returns a message.

LangChain

from langchain.schema.runnable import RunnableParallel, RunnableLambda

# Define two simple runnables that return fixed strings
r1 = RunnableLambda(lambda x: 'Task 1 done')
r2 = RunnableLambda(lambda x: 'Task 2 done')

# Create a RunnableParallel to run both at the same time
parallel_runner = RunnableParallel({"r1": r1, "r2": r2})

# Run the tasks in parallel
results = parallel_runner.invoke(None)

# Print the results
for i, (key, res) in enumerate(results.items(), 1):
    print(f"Result from task {i}: {res}")

Important Notes

RunnableParallel runs tasks at the same time, so tasks should not depend on each other.

Results come back in the same order as the tasks were given (Python 3.7+ dict insertion order).

Use this to speed up independent tasks and improve performance.

Summary

RunnableParallel runs multiple tasks together to save time.

It returns a dict of results matching the keys of tasks.

Great for tasks that can happen at the same time without waiting.

Practice

1. What is the main purpose of using RunnableParallel in langchain?

easy

A. To run multiple tasks at the same time to save time

B. To run tasks one after another in a fixed order

C. To stop tasks from running automatically

D. To run only one task repeatedly

Solution

1. Step 1: Understand RunnableParallel's role

   RunnableParallel is designed to run tasks together, not sequentially.

2. Step 2: Identify the benefit

   Running tasks in parallel saves time by doing them simultaneously.

3. Final Answer:

   To run multiple tasks at the same time to save time -> Option A

4. Quick Check:

   Parallel execution = run tasks together [OK]

Hint: RunnableParallel means tasks run together, not one by one [OK]

Common Mistakes:

• Thinking RunnableParallel runs tasks one after another
• Confusing parallel with repeated single task
• Assuming it stops tasks automatically

2. Which of the following is the correct way to create a RunnableParallel with two tasks named task1 and task2?

easy

A. RunnableParallel{task1, task2}

B. RunnableParallel(task1, task2)

C. RunnableParallel({"task1": task1, "task2": task2})

D. RunnableParallel(task1 + task2)

Solution

1. Step 1: Recall RunnableParallel syntax

   RunnableParallel expects a dictionary {"name": task} as its argument.

2. Step 2: Match options to syntax

   Only RunnableParallel({"task1": task1, "task2": task2}) passes a dict {"task1": task1, "task2": task2}, others use wrong syntax.

3. Final Answer:

   RunnableParallel({"task1": task1, "task2": task2}) -> Option C

4. Quick Check:

   Dict of tasks = {"task1": task1, "task2": task2} [OK]

Hint: Use curly braces {} to pass {"name": task} dictionary [OK]

Common Mistakes:

• Passing tasks as separate positional arguments
• Using invalid set syntax {}
• Trying to add tasks with + operator

3. Given the code:

parallel = RunnableParallel({"taskA": taskA, "taskB": taskB})
results = parallel.invoke()
print(results)

If taskA returns 'Hello' and taskB returns 'World', what will be printed?

medium

A. {'taskB': 'World', 'taskA': 'Hello'}

B. ['HelloWorld']

C. 'Hello World'

D. {'taskA': 'Hello', 'taskB': 'World'}

Solution

1. Step 1: Understand RunnableParallel output order

   RunnableParallel returns a dict with results in the order keys are defined.

2. Step 2: Match task results to output dict

   taskA under 'taskA' returns 'Hello' first, taskB under 'taskB' returns 'World' second, so {'taskA': 'Hello', 'taskB': 'World'}.

3. Final Answer:

   {'taskA': 'Hello', 'taskB': 'World'} -> Option D

4. Quick Check:

   Order of results matches dict definition order [OK]

Hint: Results dict order matches task definition order [OK]

Common Mistakes:

• Reversing the order of task results
• Thinking results are combined into one string
• Expecting a list instead of dict output

4. What is wrong with this code snippet?

parallel = RunnableParallel(task1, task2)
results = parallel.invoke()

medium

A. RunnableParallel requires tasks inside a dictionary, not separate arguments

B. invoke() method does not exist on RunnableParallel

C. You must call run() instead of invoke()

D. RunnableParallel cannot run more than one task

Solution

1. Step 1: Check RunnableParallel constructor usage

   RunnableParallel expects a dictionary of tasks, not separate positional arguments.

2. Step 2: Identify the error in code

   Passing task1, task2 as separate positional arguments causes a TypeError.

3. Final Answer:

   RunnableParallel requires tasks inside a dictionary, not separate arguments -> Option A

4. Quick Check:

   Tasks must be in a dictionary [OK]

Hint: Always use a dictionary or named kwargs for RunnableParallel tasks [OK]

Common Mistakes:

• Passing tasks as separate positional arguments
• Using wrong method name instead of invoke()
• Thinking RunnableParallel runs only one task

5. You want to run three independent tasks taskX, taskY, and taskZ in parallel and combine their results into a single string separated by commas. Which code correctly does this?

hard

A. parallel = RunnableParallel(taskX, taskY, taskZ)
results = parallel.invoke()
combined = ','.join(results)
print(combined)

B. parallel = RunnableParallel({"taskX": taskX, "taskY": taskY, "taskZ": taskZ})
results = parallel.invoke()
combined = ','.join(results.values())
print(combined)

C. results = [taskX(), taskY(), taskZ()]
combined = ','.join(results)
print(combined)

D. parallel = RunnableParallel([taskX, taskY, taskZ])
combined = parallel.invoke().join(',')
print(combined)

Solution

1. Step 1: Create RunnableParallel with dictionary of tasks

   parallel = RunnableParallel({"taskX": taskX, "taskY": taskY, "taskZ": taskZ}) results = parallel.invoke() combined = ','.join(results.values()) print(combined) correctly passes tasks as a dictionary to RunnableParallel.

2. Step 2: Invoke and join results properly

   This calls invoke() to get dict results, then joins the values with commas correctly.

3. Step 3: Check other options for errors

   parallel = RunnableParallel(taskX, taskY, taskZ) results = parallel.invoke() combined = ','.join(results) print(combined) passes tasks incorrectly as positional; C runs tasks sequentially; D uses invalid list and misuses join.

4. Final Answer:

   Using RunnableParallel({"taskX": taskX, "taskY": taskY, "taskZ": taskZ}) and ','.join(results.values()) -> Option B

5. Quick Check:

   Dict tasks + invoke + join values = correct [OK]

Hint: Pass tasks as dict, invoke, then ','.join(results.values()) [OK]

Common Mistakes:

• Passing tasks without dictionary syntax
• Calling join() on the wrong object
• Running tasks sequentially instead of parallel