ChatGPT SearchOct 3, 2026
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Data as of Oct 12, 2026A topic in LLM Agent Frameworks and Tooling.
Reviewed by Dimitry Apollonsky
Mentioned in · Jul 8 – Oct 12, 2026
ChatGPT SearchOct 3, 2026
If your main requirement is running many agents concurrently over independent data items, I’d look first at LangGraph, with CrewAI as the simpler alternative.
| Framework | Parallel execution | Control over concurrency | Stateful/retryable workflows | Best fit |
|---|---|---|---|---|
| LangGraph | Strong — graph fan-out/map patterns | High | Excellent | Large data-processing pipelines |
| CrewAI | Good | Medium | Good | Role-based agent teams |
| Microsoft Agent Framework | Strong | High | Strong | Microsoft/Azure-oriented systems |
| AutoGen | Good | Medium | Less compelling for new builds | Existing AutoGen systems |
| Swarms | Strong, especially graph/swarm topologies | High | Varies | High-throughput experimentation |
Your workload sounds more like parallel orchestration than agents having a conversation with one another. LangGraph's graph model lets you explicitly fan work out:
┌─ Agent 1 ─┐
├─ Agent 2 ─┤
Input → split ───┼─ Agent 3 ─┼──→ aggregate → output
├─ Agent 4 ─┤
└─ Agent N ─┘That explicit execution model is useful when you have thousands of records/jobs because you can control fan-out, state, retries, aggregation, and checkpoints rather than relying on emergent agent conversations. Current comparisons also highlight LangGraph's durable state and explicit graph control as advantages for complex production workflows.
CrewAI is attractive if each parallel worker has a well-defined role—e.g. "extract," "validate," "classify," "summarize." Its abstraction is easier to get started with, but you're giving up some low-level execution control.
One important 2026 consideration: I wouldn't start a new project on AutoGen without investigating Microsoft's newer Agent Framework direction first; recent sources report that AutoGen has moved into maintenance mode.
If by "many agents in parallel" you mean hundreds/thousands of simultaneous workers processing independent records, the agent framework itself shouldn't necessarily be your concurrency engine. A good architecture is often:
queue/workflow engine → parallel workers → agent runtime → result store
That lets you independently control worker count, rate limits, retries, backpressure, and LLM API concurrency.
If you tell me roughly how many agents/jobs you want to run concurrently (10, 100, 10,000+) and whether each job is independent, I can narrow this down to a concrete architecture and framework.
Google AI ModeOct 3, 2026
LangGraph and AutoGen currently offer the best native support for high-throughput, parallel agent execution, depending on whether you prefer a graph-based map-reduce architecture or an asynchronous actor model.
For data processing tasks requiring a clean "fan-out/fan-out and fan-in" approach (splitting a large data chunk across many identical worker agents and aggregating the results), LangGraph is exceptionally well-suited due to its explicit structural design.
| Framework | Core Concurrency Model | Best Suited For |
|---|
What is the best framework for orchestrating multiple agents in parallel for data processing workloads?
ChatGPT SearchOct 12, 2026
As of October 2026, the best choice depends on whether you need massive parallel data processing, coordinated AI agents, or reliable production workflows. These are related but distinct problems: a framework that excels at coordinating LLM agents isn't necessarily the best engine for processing millions of records.
My shortlist is Ray, LangGraph, Microsoft Agent Framework, and CrewAI, with each serving a different role.
(Best for data-processing scale)
Ray provides distributed tasks, stateful actors, resource-aware scheduling, and cluster execution. It's especially suitable for parallel ETL, document processing, feature engineering, embedding generation, and batch inference.
| Key Scaling Strength |
|---|
| LangGraph | Directed graphs with Send API and map-reduce | Data processing, batch pipelines, deterministic fan-out | Built-in state management, easy dynamic parallel node creation. |
| AutoGen | Asynchronous Actor Model (asyncio) | Communicating multi-agent swarms, event-driven tasks | Native message-passing across decentralized autonomous agents. |
| CrewAI | Process types (Sequential/Hierarchical/Parallel) | Role-based task delegation and high-level business logic | Simpler syntax, though lower performance on massive dynamic data splits. |
Send mechanism that allows you to dynamically spin up parallel graph nodes based on the size of an input collection (e.g., splitting 1,000 rows into batches). The graph automatically handles the fan-out and aggregates responses back into a single state safely.asyncio ). Every agent acts as an independent actor that can send and receive messages concurrently without blocking the main thread, making it ideal if your data tasks require agents to chat or negotiate dynamically.If you'd like to narrow this down, please share:
I can provide a tailored code snippet or architecture recommendation.
Docs:
(Best for controlled agent workflows)
LangGraph supports graph-based orchestration, conditional execution, and dynamic parallel fan-out/fan-in. Its Send mechanism can dispatch multiple instances of a worker node with different inputs, then combine their results in a map-reduce pattern.
Docs:
(Best for structured multi-agent orchestration)
It offers built-in concurrent, sequential, handoff, group-chat, and manager-led orchestration patterns. Concurrent workflows run agents independently and aggregate their outputs, with support for custom executors and streaming events.
Docs:
CrewAI separates autonomous teams called Crews from structured, event-driven Flows. Flows are particularly useful for predictable execution paths, task dependencies, and combining agent work with application logic.
Docs:
| Requirement | Best fit | Why |
|---|---|---|
| Process millions of records in parallel | Ray | Distributed execution and resource scheduling |
| Fan out AI agents, then merge results | LangGraph | Dynamic map-reduce and explicit workflow control |
| Enterprise multi-agent workflows | Microsoft Agent Framework | Built-in concurrent orchestration and workflow events |
| Quickly build teams of specialized agents | CrewAI | Simple role-based agents and structured Flows |
| Combine heavy data processing with AI agents | Ray + LangGraph | Distributed compute plus sophisticated agent orchestration |
These recommendations reflect each framework's documented capabilities, rather than a standardized benchmark ranking.
If your goal is to process a large collection of records with multiple AI agents, I would start with a distributed processing engine and use agents where reasoning is actually needed.
Input data
Files, database rows, documents, events
Ray task scheduler
Partition records and distribute work across workers
Agent A
Extract
Agent B
Classify
Agent C
Validate
Aggregation and validation
Combine outputs, handle failures, check quality
Output data store
Ray handles the parallel compute layer; LangGraph can coordinate complex agent decisions within individual tasks or processing stages. Ray's distributed task and actor model is documented in its core concepts guide.
A few important engineering considerations:
What kind of data processing are you primarily trying to do?
1. What are the agents processing?
Documents, PDFs, emails, or unstructured text
ETL, database records, or data pipelines
Analytics, classification, or enrichment
Agents that research, reason, and use tools
A mixture of these workloads
2. What is the approximate workload size?
Thousands of tasks
Millions of records or tasks
Continuous, streaming, or event-driven processing
3. Where will it run?
Single machine or local server
Cloud or distributed cluster
Hybrid or on-premises enterprise environment
4. Preferred language
Python
C# / .NET
Other / language-agnostic
[Find my best-fit framework]