I need to run many agents in parallel for a data processing task. What framework has the best support for parallel agent execution?

If your main requirement is running many agents concurrently for a data-processing pipeline, I’d pick LangGraph.

My ranking

1. LangGraph — best overall for parallel data pipelines

• Explicit graph-based execution makes fan-out/fan-in patterns natural: split a dataset across many agents, process chunks concurrently, then aggregate results.
• Strong control over state, retries, branching, checkpointing, and failure recovery.
• Better fit when you care about predictable execution rather than agents simply chatting with one another.
• Particularly good if your pipeline might eventually become large or production-critical.

2. CrewAI — best if you want simplicity

• Very easy to model a team of specialized agents.
• Supports parallel task execution and is attractive for straightforward workloads such as N agents → process independent items → aggregate.
• I'd choose it over LangGraph if your workflow is relatively simple and developer ergonomics are more important than fine-grained orchestration.

3. Microsoft Agent Framework / AutoGen lineage — worth considering for Microsoft-heavy stacks

• AutoGen historically excelled at conversational multi-agent systems, but the ecosystem has been shifting toward Microsoft's broader Agent Framework, so I wouldn't start a new large system on legacy AutoGen without checking the current migration/recommended path.

For your specific workload

If you mean something like:

                 ┌─ Agent 1 ─┐
                 ├─ Agent 2 ─┤
Input dataset ───┼─ Agent 3 ─┼──→ Aggregator → Output
                 ├─ Agent 4 ─┤
                 └─ ... ────┘

LangGraph is probably the strongest choice. Its explicit graph model makes this parallelism straightforward while giving you control over concurrency, state, retries, and aggregation. Current comparisons consistently identify it as the stronger option for complex/stateful production workflows.

One important caveat: if you're processing thousands or millions of independent records, I wouldn't necessarily make each record an "agent." I'd use a real distributed execution system (e.g. Ray, Celery, Temporal, Spark) underneath or alongside the agent framework, and use agents for the reasoning-heavy portions.

If you tell me roughly how many agents/tasks you need to run concurrently and whether each task is independent, I can recommend an architecture (and compare LangGraph vs CrewAI vs Ray/Temporal) specifically for it.

For a heavy parallel data processing task, the framework with the most robust, native, and production-ready support for parallel execution is LangGraph (by LangChain).

While other frameworks like CrewAI or AutoGen are great for role-playing or conversational multi-agent collaboration, LangGraph is architected specifically around graph-based state machines that natively support concurrent "fan-out/fan-in" execution and dynamic mapping.

Why LangGraph is Best for Parallel Data Processing

• Native Map-Reduce via the Send() API: LangGraph allows you to dynamically spin up parallel agent instances based on the size of your input data (e.g., splitting a massive dataset into chunks and assigning an agent to process each chunk simultaneously).
• Supersteps (Concurrence): When a single node fans out to multiple nodes, LangGraph executes them concurrently in a single "superstep," automatically handling the synchronization barrier before passing the combined state to a reducer node.
• State Management: Data processing requires tracking what has succeeded, failed, or needs retrying. LangGraph's explicit state schema ensures that parallel outputs are safely aggregated without race conditions.

Alternative Frameworks & When to Use Them

• CrewAI (async_execution): Supports setting individual tasks to async_execution=True so they don't block subsequent tasks. However, it is fundamentally designed around rigid sequential or hierarchical workflows rather than dynamic data-streaming map-reduce patterns.
• Temporal + Any LLM SDK: If your "data processing task" is at massive enterprise scale (thousands of parallel files/records), many engineering teams bypass agent frameworks entirely and orchestrate standard LLM calls or lightweight agent loops inside Temporal or Apache Airflow . This gives you bulletproof retries, rate-limit handling, and distributed parallel queues.

To help narrow down the best architecture, could you share:

• How large is the data volume (e.g., hundreds of rows vs. millions of documents)?
• Are the parallel agents identical (doing the same processing function on different data shards) or different (specialized agents collaborating)?

If parallel execution is the primary requirement, I’d pick LangGraph.

It has particularly strong primitives for fan-out/fan-in, dynamic worker spawning, concurrency limits, retries, and stateful workflows. Its runtime executes scheduled nodes in parallel and supports configurable maximum concurrency.

My ranking for your use case

LangGraph is especially compelling if your task looks like:

Input dataset
     │
     ▼
  Split into N chunks
     │
 ┌───┼───┬────┬────┐
 ▼   ▼   ▼    ▼    ▼
 A1  A2  A3   A4  ... AN    ← parallel agents
 └───┼───┴────┴────┘
     │
     ▼
  Aggregate results
     │
     ▼
   Final output

LangGraph explicitly supports this fan-out/fan-in pattern, including dynamic fan-out for map-reduce workloads.

CrewAI is a good alternative if you want a simpler, more opinionated abstraction around teams of agents; its current framework supports sequential, hierarchical, and hybrid processes and has Flow-based orchestration.

One important distinction: if these are hundreds/thousands of independent data-processing jobs, rather than agents that need to reason and use tools, I'd consider a distributed task system (Ray, Celery, Temporal, etc.) with agents running inside the workers rather than using an agent framework as the parallelization layer. That's usually more scalable and cheaper.

If you tell me roughly how many agents you need simultaneously (10, 100, 1,000+) and whether each agent is doing LLM calls, Python/data processing, or both, I can recommend the architecture I'd use.

For a data processing task requiring many agents to run in parallel (the classic map-reduce or orchestrator-worker pattern), LangGraph currently has the most robust, production-ready support for parallel execution.

While frameworks like CrewAI and AutoGen handle multi-agent collaboration well (specializing in role-based hierarchies or open-ended conversational loops), they are generally optimized for sequential or tightly coupled conversational workflows rather than raw, high-throughput parallel data processing.

Why LangGraph Excels at Parallel Data Processing

• Native Fan-Out / Fan-In (Map-Reduce): LangGraph natively supports dynamic "fan-out" where a single orchestrator node can spawn multiple parallel worker tasks dynamically using its Send API (e.g., splitting a massive dataset into 50 chunks and spinning up 50 worker agents simultaneously).
• Super-step Execution Model: It executes in discrete graph "super-steps"—all nodes scheduled concurrently run in parallel, and the graph automatically synchronizes (fans-in) before proceeding to the next aggregation or merging node.
• State Reducers for Conflict-Free Merging: When 50 parallel agents finish processing and try to write data back, LangGraph uses custom state reducers (like operator.add ) to safely merge parallel outputs into a unified state without race conditions or data loss.
• Fine-Grained Concurrency Control: You can explicitly throttle or tune throughput using max_concurrency parameters so you don’t accidentally saturate your LLM provider's rate limits.

Comparison at a Glance

• LangGraph: Best for deterministic, highly parallelized, stateful map-reduce data pipelines (fan-out/fan-in).
• CrewAI: Best if your data processing mirrors a human team with strict sequential or hierarchical step-dependencies (e.g., Researcher → Analysist → Reporter).
• AutoGen: Best for open-ended, multi-agent conversational problem-solving, but harder to predictably govern for massive raw data parallelization.

If you'd like, let me know:

• What kind of data transformation or processing you are performing
• Whether you need a dynamic number of parallel agents (based on input size) or a fixed pool
• Which LLM provider or API rate limits you need to design around

I can share a code snippet or architectural pattern for setting up a map-reduce pipeline in LangGraph.

Here are top web results for exploring this topic:

Google ADK·https://adk.dev**Parallel** workflow - Agent Development Kit (ADK)When the ParallelAgent 's run_async() method is called: Concurrent Execution: It initiates the run_async() method of each sub-agent present in the sub_agents list concurrently. This means all the agen LangChain·https://www.langchain.com The best AI agent frameworks in 2026 - LangChain The abstraction is designed so that application logic doesn't need to change when the underlying model does. Q: What's the difference between LangChain, LangGraph, and Deep Agents? LangChain is the br Reddit·https://www.reddit.com**Running multiple** AI agents in parallel - how do you manage ... - Reddit I've been running several Claude Code sessions in parallel, each working on different side project. The productivity boost is incredible, but I hit a… Anmol Mahatpurkar·https://anm.dev Power User Mode: Running 3 Agents in Parallel - Anmol Mahatpurkar Parallelism Is Not the First Trick. It Is the Last One. This is the important caveat. Running multiple agents at once is not the starting move. It is the thing you do once the single-agent workflow is MindStudio·https://www.mindstudio.ai**Parallel Agent Execution** vs Sequential Agents : When to Use Each What Parallel Agent Execution Actually Means. Parallel execution means multiple agents run simultaneously, handling independent tasks at the same time. Instead of waiting for each task to complete bef AY Automate·https://www.ayautomate.com 9 Best Multi-Agent Frameworks for Production in 2026 - AY Automate Pair LangGraph with LangSmith for tracing and you get the closest thing to a production-ready multi-agent stack the open ecosystem currently ships. Key features. Stateful agent graphs with typed share www.kimi.ai·https://www.kimi.ai/resources/best-ai-agent-frameworks 8 Leading AI Agent Frameworks for Smarter AI Builds - Kimi AI These frameworks reduce the amount of work needed to create intelligent systems from scratch. They also help AI agents perform tasks more efficiently and work smoothly across different applications. O Medium·https://medium.com Scale a multi-agent system effectively by parallel execution of agents “2025 is the year of AI Agents” is what we heard. And yes indeed, 2025 is the year of AI Agents so far. We are seeing pilots for AI Agents across nearly every domain and that is a good beginning, howe arXiv·https://arxiv.org Optimizing Sequential Multi-Step Tasks with Parallel LLM Agents ... of suboptimal plans containing unnecessary steps intended solely to diversify the plans. In this paper, we make the following contributions: •. We show that parallel agents with early termination Arize AI·https://arize.com AI agent frameworks compared: LangGraph, CrewAI, AutoGen, and ...Conditional edges handle branches, checkpoints preserve execution state, and human-in-the-loop interrupts can pause the graph until a person approves or modifies the next action. LangGraph is a strong

If your main requirement is running many agents concurrently for a data-processing pipeline, I’d pick LangGraph in 2026.

My ranking

Why LangGraph: its graph model lets you explicitly fan work out to many nodes/agents, run independent branches concurrently, then join their results. It also gives you explicit state, checkpointing, retries, and control over the workflow—important when you're processing hundreds or thousands of items rather than just having 3–4 agents collaborate. Current comparisons consistently put LangGraph strongest for complex, production-oriented orchestration.

For example, your architecture could look like:

                 ┌─ Agent 1 ─┐
Input ── Split ──┼─ Agent 2 ─┼── Aggregate ── Output
                 ├─ Agent 3 ─┤
                 ├─ Agent 4 ─┤
                 └─ ... ─────┘

Each agent can independently process a chunk of data, with the graph controlling concurrency and aggregation.

When I'd choose something else

• CrewAI — choose this if the agents are naturally roles (“researcher”, “validator”, “summarizer”) and you want the simplest implementation. It is particularly good for quickly assembling multi-agent teams, though LangGraph gives you finer-grained execution control.
• Microsoft Agent Framework — worth serious consideration if you're already deep into Azure/.NET/Microsoft infrastructure. Microsoft has positioned it as the successor to AutoGen and Semantic Kernel.
• OpenAI Agents SDK — attractive if you don't need a sophisticated workflow engine and mainly want lightweight agent delegation.

One important distinction: if by “many agents in parallel” you mean thousands of independent data-processing jobs, rather than agents that need to coordinate with one another, I would consider LangGraph + a real distributed task system/queue rather than relying on the agent framework alone. The queue handles scaling/concurrency; LangGraph handles agent workflow/state.

If you tell me roughly how many agents/jobs you want to run concurrently (10, 100, 10,000+) and whether each agent processes an independent data item, I can recommend the architecture and framework more precisely.

                 ┌─ Agent 1 ─┐
Input ── Split ──┼─ Agent 2 ─┼── Aggregate ── Output
                 ├─ Agent 3 ─┤
                 ├─ Agent 4 ─┤
                 └─ ... ─────┘

Each agent can independently process a chunk of data, with the graph controlling concurrency and aggregation.

When I'd choose something else

For running many agents in parallel for a high-throughput data processing task, the "best" framework depends on whether you need a strict, deterministic state machine or a lightweight, event-driven pipeline.

The top options supporting parallel agent execution feature distinct strengths:

• LangGraph (by LangChain): Best for deterministic, stateful parallel graphs . It models agent workflows as cyclic graphs (nodes and edges) with native support for fan-out/fan-in parallel execution. If your data processing requires splitting a dataset, running dozens of extraction/validation agents concurrently, and strictly reducing/merging the state back together with persistent checkpointing, LangGraph is the enterprise standard.
• LlamaIndex Workflows : Best for event-driven, data-intensive pipelines . It uses an asynchronous, event-based model where steps/agents emit and listen for typed events. This makes scaling parallel worker tasks and routing document or chunk-level data processing operations remarkably fluid and performant without heavy graph boilerplate.
• CrewAI : Best for role-based collaborative swarms . It lets you spin up crews of specialized agents (e.g., a scraper, a cleaner, and an analyzer) and natively handles task delegation and asynchronous/parallel execution via its newer Flow abstractions. However, it can incur higher token overhead and latency if you are just doing raw, repetitive data crunching rather than complex reasoning.
• Microsoft AutoGen / Agent Framework : Best for distributed multi-agent networks . It supports asynchronous messaging across agent groups, making it powerful for open-ended or conversational data tasks, though managing token costs and loops at scale requires careful tuning.

To help narrow down the right choice for your pipeline, tell me:

• What is the nature of the data (unstructured text, tabular data, documents, streams)?
• Do the parallel agents need to communicate with each other , or just report back to a central orchestrator?

I can provide a recommended architecture pattern or a code snippet for your setup.

If your main requirement is running many agents concurrently for a data-processing pipeline, I’d pick Microsoft Agent Framework today—especially if you want explicit fan-out/fan-in orchestration.

My ranking

Why Microsoft Agent Framework stands out: its ConcurrentBuilder explicitly fans an input out to multiple participants, runs them in parallel, then fans the results back in to an aggregator. It also supports checkpointing.

For example, your architecture could be:

                 ┌─ Agent 1 ─┐
                 ├─ Agent 2 ─┤
Input → Dispatcher├─ Agent 3 ─┤→ Aggregator → Output
                 ├─ Agent 4 ─┤
                 └─ ... ─────┘

That's particularly well suited to map/reduce-style data processing: partition your dataset, send partitions to independent agents, then aggregate the results.

LangGraph is my alternative if the workflow is more complicated. It gives you very explicit control over graph topology, state, branching, loops, and checkpoints.

One important distinction: if you're processing thousands of records, don't necessarily create one long-lived "agent" per record. I'd generally use a bounded worker pool/queue and have agents process batches, otherwise LLM/API concurrency limits and cost can become the bottleneck.

Bottom line:

• Maximum straightforward parallel agent orchestration: Microsoft Agent Framework
• Maximum workflow/control flexibility: LangGraph
• Fastest/simple role-based setup: CrewAI
• Agent-to-agent conversations: AutoGen

If you tell me roughly how many agents/items you're processing (e.g. 100, 10K, 1M) and whether each item requires an LLM call, I can recommend the architecture and concurrency model I'd use.

For parallel data processing tasks requiring many concurrent agents, LangGraph and LlamaIndex Workflows offer the best-in-class native support. Rather than relying on rigid linear loops, both frameworks use graph- and event-driven architectures to handle high-throughput fan-out and fan-in execution safely.

Top Frameworks Compared for Parallel Execution

Framework Highlights

• LangGraph: Excels at structured data routing by mapping a single state transition out to dozens of worker nodes simultaneously. It prevents race conditions during data aggregation through a deferred execution model that holds back sink nodes until all parallel branch tasks complete.
• LlamaIndex Workflows: Uses an intuitive event-driven paradigm where you can explicitly cap or scale concurrency using worker decorators (e.g., @step(num_workers=10) ). It is lightweight and integrates seamlessly if your data pipelines already leverage document loaders or RAG indexing.
• CrewAI: Provides clean abstractions for role-based task delegation. While traditionally sequential, you can scale operations concurrently using asynchronous task execution configurations and parallel multi-crew patterns.

If you can share the volume of data records and whether the agents need to share a live memory state while running, I can recommend specific code patterns or batch sizes for your setup.

If parallel execution is the primary requirement, my pick in 2026 is LangGraph—especially if your data-processing workload looks like fan-out → process many items independently → fan-in/aggregate.

My ranking

Why LangGraph: its graph model maps naturally onto parallel data processing: split a dataset into independent work units, execute nodes concurrently, then aggregate the results. It also gives you explicit state, branching, retries/checkpointing, and control over the workflow rather than relying on agents to coordinate themselves. Current comparisons also tend to put LangGraph ahead when workflow flexibility and production control matter.

Microsoft Agent Framework is the strongest alternative. Its current workflow system has a first-class ConcurrentBuilder specifically for running multiple agents in parallel, and its workflow API supports parallel edge groups and superstep-based execution.

One important caveat: if by "data processing" you mean millions of records, I wouldn't make an LLM-agent framework your primary parallelization engine. I'd use something like Spark/Ray/Dask/Celery for the data-plane parallelism and put agents on top for the portions that genuinely require reasoning. Agents are relatively expensive workers.

Rule of thumb:

• Hundreds/thousands of independent reasoning jobs → LangGraph
• Microsoft/Azure-heavy enterprise stack → Microsoft Agent Framework
• Simple team-of-agents abstraction → CrewAI
• Millions of deterministic data transformations → Ray/Spark/etc. + agents where needed

If you tell me roughly how many items you're processing, what each agent does, and whether you're using Python/TypeScript, I can recommend an architecture and show the parallel fan-out/fan-in pattern.