What is .abatch()?
.abatch() is the asynchronous counterpart to .batch() on any LangChain Runnable. Where .batch() processes multiple inputs by dispatching them in a threadpool, .abatch() is a true async coroutine that schedules all inputs as concurrent asyncio tasks and awaits them. The key behavioral difference is that .abatch() must be awaited inside an async def function — calling it without await returns a coroutine object instead of results.
Concurrency is controlled by the max_concurrency parameter. Without it, LangChain attempts to run all inputs simultaneously, which can overwhelm API rate limits for large input lists. A good rule of thumb is to set max_concurrency to the number of concurrent API calls your rate-limit tier allows — for OpenAI's default 3,500 RPM tier, values between 20 and 50 are generally safe. The method preserves input order: results[i] always corresponds to inputs[i] regardless of which task completes first.
.abatch() is available on ChatModel, LLM, Runnable, Chain, RetrievalQA, and all LCEL-constructed pipelines. You can use it in async event handlers, FastAPI route functions, or Celery tasks running under an async executor. For very large lists, consider chunking at the application level with asyncio.Semaphore rather than relying solely on max_concurrency.
Use Cases
- • Async batch processing
- • Concurrent calls
- • Web framework bulk ops
- • Async orchestration
- • Large data processing
- • Scalable operations
Key Features
- ✓ Async batch
- ✓ True parallelism
- ✓ Event loop friendly
- ✓ Concurrency limits
- ✓ Order preservation
- ✓ Async generator
When NOT to Use
Outside async code—use batch(). For single items—use ainvoke().
Notes
Always await the call
The single most common mistake is calling model.abatch(inputs) without await, which returns a coroutine object instead of a list of results. Add await, or use asyncio.run(model.abatch(inputs)) if you need to call it from synchronous code.
Set max_concurrency in production
Without max_concurrency, LangChain submits all inputs as concurrent tasks. On the OpenAI free tier (3 RPM), even 4 simultaneous requests will trigger 429 errors. Always set max_concurrency to match your tier's rate limit.
Error handling — one failure affects all
If one input raises an exception, .abatch() re-raises it and cancels remaining tasks by default. For fault-tolerant pipelines, wrap individual calls in try/except inside a custom async wrapper, or split the list and handle failures per-chunk.
Order is guaranteed
Results are returned in the same order as inputs, not completion order. This is intentional. Do not attempt to sort results by completion time — index correspondence is always preserved.
Method Signature
results = await runnable.abatch(inputs)Parameters
| Parameter | Type | Required | Purpose |
|---|---|---|---|
| inputs | List[Any] | Yes | List of inputs |
Return Value
Type:
Awaitable[List[Any]]
Description:
Awaitable of results
Example Output:
await model.abatch(inputs)Code Examples
Basic async batch
results = await model.abatch(questions)
for q, r in zip(questions, results):
print(f'{q}: {r}')Controlled concurrency with max_concurrency
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-4o-mini")
questions = [
'What is RAG?',
'What is LangGraph?',
'What is a vector store?',
'What is an embedding?',
]
results = await llm.abatch(
questions,
config={"max_concurrency": 2},
)
for q, r in zip(questions, results):
print(f'{q}\n -> {r.content[:80]}')FastAPI endpoint with abatch
from fastapi import FastAPI
from langchain_openai import ChatOpenAI
app = FastAPI()
llm = ChatOpenAI(model="gpt-4o-mini")
@app.post("/batch-classify")
async def batch_classify(texts: list[str]):
prompts = [f"Classify sentiment: {t}" for t in texts]
results = await llm.abatch(prompts, config={"max_concurrency": 10})
return [r.content for r in results]Common Mistakes
❌ results = model.abatch(inputs) # Missing await
✅ results = await model.abatch(inputs)
Related LangChain References
Browse the full LangChain API reference index to explore more classes, methods, and decorators, or start with the LangChain introduction tutorial for end-to-end context on building with .abatch() and the wider framework.
.abatch() FAQ
What does .abatch() do in LangChain?
Process multiple inputs in parallel asynchronously. .abatch() is the asynchronous counterpart to .batch() on any LangChain Runnable. Where .batch() processes multiple inputs by dispatching them in a threadpool, .abatch() is a true async coroutine that schedules all inputs as concurrent asyncio tasks and awaits them. The key behavioral difference is that .abatch() must be awaited inside an async def function — calling it without await returns a coroutine object instead of results. Concurrency is controlled by the max_concurren…
Which LangChain classes support .abatch()?
.abatch() is available on All Runnables. Pin your installed LangChain version and verify the method exists in that release before deploying.
When should I use .abatch()?
Use .abatch() when your LangChain chains, agents, or pipelines need the behavior described in this guide.
What does .abatch() return?
.abatch() returns a Awaitable[List[Any]]. Awaitable of results
Does .abatch() have an async equivalent?
.abatch() does not have a documented async variant. Avoid .abatch() Outside async code—use batch(). For single items—use ainvoke().
Where can I explore more LangChain API reference pages?
Open the LangChain API reference index on DevShelfHub to browse classes, methods, and decorators, each with runnable examples, parameters, common mistakes, and cross-links.