LangGraph vs CrewAI: Let’s Learn About the Differences

LangGraph and CrewAI are modern frameworks for orchestrating complex AI workflows with multiple LLM-driven agents. Both these intelligent systems are capable of sophisticated reasoning, planning, and autonomous action, and are becoming central to modern AI applications. However, they differ in abstraction, interfaces, and enterprise features.

This LangGraph vs CrewAI article compares key attributes of these platforms, like:

• Workflow patterns
• Human-in-loop capabilities
• Parallelism and throttling
• Compliance and security
• Integration options
• Pricing

We do this so you can exactly know when to use which one of these platforms.

LangGraph vs CrewAI: Key Takeaways

🧑‍💻 LangGraph: It’s a framework from LangChain that helps you build stateful, multi-agent applications as graphs. LangGraph provides low-level control over agent workflows with built-in persistence, streaming support, and the ability to create complex branching logic.

🧑‍💻 CrewAI: It’s a high-level framework for orchestrating autonomous AI agents working together as a crew. The platform abstracts away complexity by providing pre-built patterns for agent collaboration, role assignment, and task delegation.

Framework Maturity & Lineage

The table below compared the framework maturity of LangGraph and CrewAI:

CrewAI launched a few months earlier than LangGraph (Nov 2023 vs Jan 2024), and it quickly attracted a large fanbase on GitHub – 33 k stars vs LangGraph’s 15 k.

On the other hand, LangGraph’s 5 800+ commits show a much faster development velocity compared to CrewAI’s 1 520.

When looking at actual usage, LangGraph leads in monthly downloads (~ 6.17 M) compared to CrewAI (~ 1.38 M), indicating broader adoption in production deployments.

LangGraph vs CrewAI: Feature Comparison

Here’s a TL;DR of the features we compare for LangGraph and CrewAI.

If you want to learn more about how each of the above features compares for the two AI agentic framework platforms, read ahead.

In this section, we compare LangGraph and CrewAI across the four most important features:

1. Workflow Deployment Patterns
2. Human-in-the-loop
3. Parallel Agent Execution and Throttling
4. Enterprise Compliance and Security

📚 Related reading: Top LangGraph alternatives

Feature 1. Workflow Deployment Patterns

LangGraph and CrewAI offer solid mechanisms to define and execute agent workflows, each providing varying degrees of abstraction and control.

LangGraph

LangGraph is an orchestration framework designed explicitly to create, deploy, and manage workflows involving stateful, multi-agent systems.

Unlike traditional DAG-based systems, LangGraph leverages a flexible graph-based API where each workflow consists of nodes and directed edges, enabling complex interactions among agents.

Key Workflow Patterns:

• Parallel Execution ("Fan-out/Fan-in"): LangGraph supports parallel execution by branching from a single node into multiple independent tasks (fan-out) and then converging results into a subsequent node (fan-in). This structure enables efficient concurrent execution, significantly speeding up workflows where tasks can run simultaneously.
• Hierarchical Workflows: To manage complexity, LangGraph supports hierarchical agent teams. In this model, top-level agents delegate tasks to specialized sub-agents or entire subgraphs, simplifying oversight and scalability in large workflows. Hierarchical structuring allows each agent to maintain a clear, focused role, improving workflow efficiency and clarity.
• Cyclical (Looping) Workflows: Unlike standard DAGs that prohibit cycles, LangGraph inherently supports cyclical graphs. Cyclical patterns allow workflows to revisit previous nodes, facilitating iterative and adaptive behaviors. For example, workflows can self-correct, request clarifications, or dynamically re-plan tasks based on evolving states or intermediate results.

LangGraph employs a StateGraph to manage shared agent state, maintaining context and memory across workflow nodes. Each workflow execution step is checkpointed, enabling robust recovery and continuity in case of failures.

Additionally, LangGraph supports dynamic conditional routing via methods like addConditionalEdges, allowing the execution path to change according to the workflow’s state at runtime. This flexibility enhances the ability of agents to handle sophisticated, context-driven decisions.

CrewAI

from crewai import Crew, Process

# Example: Creating a crew with a sequential process
crew = Crew(
    agents=my_agents,
    tasks=my_tasks,
    process=Process.sequential
)

# Example: Creating a crew with a hierarchical process
# Ensure to provide a manager_llm or manager_agent
crew = Crew(
    agents=my_agents,
    tasks=my_tasks,
    process=Process.hierarchical,
    manager_llm="gpt-4o"
    # or
    # manager_agent=my_manager_agent
)

CrewAI orchestrates task execution by agents through predefined Process types. These processes are akin to project management strategies in human teams, ensuring that tasks are distributed and executed efficiently according to a specified strategy.

The default process type in CrewAI is the Sequential Process, where tasks are executed one after another in a linear progression, ensuring an orderly and systematic workflow.

The output of a preceding task serves as the context for the subsequent task, facilitating a clear flow of information. Each task within a sequential process must have an agent explicitly assigned to it.

In contrast, the Hierarchical Process organizes tasks within a managerial hierarchy, where tasks are delegated and executed based on a structured chain of command. This pattern of CrewAI is exactly the same as LangGraph.

To enable this, a manager language model (manager_llm) or a custom manager agent (manager_agent) must be specified within the crew. This manager agent is responsible for overseeing task execution, including planning, delegating tasks to specific agents based on their capabilities, reviewing their outputs, and assessing task completion.

CrewAI also has a Consensual Process type planned for future development. This aims to introduce a more democratic approach to task management, where agents engage in collaborative decision-making regarding task execution, fostering a collective intelligence model. However, this process is not yet implemented in the current codebase.

Bottom line: LangGraph delivers parallel, hierarchical, and cyclical graph patterns with dynamic routing for fine-grained, stateful agent control. CrewAI focuses on sequential and hierarchical processes today; simple to set up, manager-led, and expanding toward a future consensual model for collaborative decision-making.

Feature 2. Human-in-the-loop

Integrating human oversight into AI workflows, often termed Human-in-the-Loop (HIL or HITL), is vital to ensure reliability, validation, and ethical alignment in agentic systems.

LangGraph

Long-running or sensitive agent workflows often need human oversight. LangGraph has first-class features for this. Its persistence layer (checkpointer) allows the graph to pause and resume.

You can design a node that explicitly waits for human approval before proceeding: the system will halt execution until a human provides input through the UI.

What’s more, LangGraph also supports breakpoints and ‘time travel’ debugging. What does this mean? You can inspect the agent’s thought process, modify state or pending actions, and resume execution from any point.

This functionality allows developers to intervene mid-graph, validate or correct outputs, and ensure agents follow business rules.

CrewAI

CrewAI likewise supports human input during execution. In CrewAI’s task model, a task definition can include a human_input=True parameter. When enabled, after an agent generates its result, the framework will prompt you for additional input or confirmation before finalizing the answer.

For example, an analyst agent might draft a report and then ask you to approve or refine the findings before the crew moves on. This pattern is useful when an agent might be unsure or a final human check is required.

In addition, the hierarchical crew model inherently involves a manager agent reviewing and validating sub-tasks, which provides a layer of oversight (the manager agent can itself be a human or a strong LLM).

Bottom line: Both platforms allow you to interleave human feedback steps, but LangGraph emphasizes checkpointing and replay, while CrewAI provides explicit prompts and manager roles for human-in-the-loop scenarios.

Feature 3. Parallel Agent Execution and Throttling

The ability to execute tasks concurrently and manage resource consumption is vital for the performance and cost-efficiency of agentic applications.

LangGraph

import operator
from typing import Annotated, Any

from typing_extensions import TypedDict

from langgraph.graph import StateGraph, START, END


class State(TypedDict):
    # The operator.add reducer fn makes this append-only
    aggregate: Annotated[list, operator.add]


def a(state: State):
    print(f'Adding "A" to {state["aggregate"]}')
    return {"aggregate": ["A"]}


def b(state: State):
    print(f'Adding "B" to {state["aggregate"]}')
    return {"aggregate": ["B"]}


def c(state: State):
    print(f'Adding "C" to {state["aggregate"]}')
    return {"aggregate": ["C"]}


def d(state: State):
    print(f'Adding "D" to {state["aggregate"]}')
    return {"aggregate": ["D"]}


builder = StateGraph(State)
builder.add_node(a)
builder.add_node(b)
builder.add_node(c)
builder.add_node(d)
builder.add_edge(START, "a")
builder.add_edge("a", "b")
builder.add_edge("a", "c")
builder.add_edge("b", "d")
builder.add_edge("c", "d")
builder.add_edge("d", END)
graph = builder.compile()

👀 Note: In this example code above, we fan out from Node A to B and C and then fan in to D.

Efficient orchestration often requires parallelizing independent tasks and controlling execution rates. LangGraph supports true parallel execution of independent branches.

LangGraph runs such branches concurrently in ‘supersteps’: it accumulates each branch’s updates (using state reducers if needed) and then proceeds only once all branches succeed. Importantly, the superstep is transactional: if any branch fails, none of that superstep’s state updates are applied. This prevents partial failures.

What’s more, LangGraph also allows deferred execution (wait until all branches have caught up) and retry policies on failing branches.

For throttling, LangGraph relies on the deployment environment. In LangGraph Cloud, task queues are horizontally scalable. They can apply concurrency limits on workers, but the core SDK doesn’t impose hard rate limits on calls; it assumes your code or environment controls API usage.

CrewAI

CrewAI can run multiple agents in parallel because a Crew inherently has multiple agents working on tasks.

For example, in a flow, you could start() multiple agents at once and use @listen to gather their outputs. The platform lets you define parallel workflows where tasks or even crews run concurrently (with the system managing dependencies).

For throttling, CrewAI provides built-in limits in its hierarchical process: you can set a ‘Max Requests Per Minute’ parameter so that the manager agent will not exceed a given rate of LLM API calls.

Bottom line: Both frameworks support concurrent agent execution; LangGraph’s model is via parallel graph branches with transactional consistency, while CrewAI provides broad concurrency out-of-the-box and explicit rate-limiting knobs for team agents.

Feature 4. Enterprise Compliance and Security

For enterprise adoption, robust security and compliance features are non-negotiable.

LangGraph

You can self-host the open-source LangGraph anywhere; this gives you full control over security. But the managed LangGraph Platform adds enterprise-grade security controls.

By default, it uses LangSmith API keys for authentication and allows custom auth handlers (OAuth, SAML, etc.) for single sign-on. It implements role-based authorization that lets you restrict access to specific graphs or assistants (LangGraph calls these ‘resources’).

For compliance, LangChain’s deployment options include a hybrid SaaS control plane with self-hosted data plane (data stays in your VPC) or fully self-hosted deployments.

All in all, LangGraph supports private VPC deployments and configurable network isolation. Features like the Persistence Layer (for sensitive data) can run on your own Postgres, which ensures no data leak.

The open-source core also lets you integrate any secrets management or RBAC system, since you control the server environment.

CrewAI

CrewAI offers a full Enterprise Edition with built-in compliance. The platform is ‘HIPAA & SOC2 compliant’ and supports on-premise deployment.

Access to crews and APIs is secured via bearer tokens (you start a trial and get API keys), and you can manage user roles through their management UI.

CrewAI Enterprise plan gives you access to a web-based management dashboard to create and deploy crews, and includes user/permission management - teams, roles, and permissions. Because

CrewAI can run fully on-premises or in your cloud, you retain control of data.

Bottom line: Both platforms aim for enterprise needs: LangGraph relies on the robustness of its managed Platform, which includes API keys, private networks, audit logs via LangSmith, while CrewAI’s Enterprise plan is explicitly built for compliance, supporting HIPAA/SOC2, on-prem install, secure tokens, and fine-grained RBAC.

LangGraph vs CrewAI: Integration

The ability of an agentic framework to integrate with existing tools, services, and infrastructure is paramount for its practical utility in an enterprise environment.

LangGraph

LangGraph is part of the LangChain ecosystem, so it ‘fits in nicely’ with all LangChain integrations. You can use any LangChain ChatModel or LLM - OpenAI, Azure, Amazon, Anthropic, etc., inside LangGraph nodes.

It also supports LangChain’s memory systems and retrievers – for example, you can invoke a vector-store search or knowledge graph call as part of a graph node.

What’s more, LangGraph works with LangSmith for tracing and observability; this means you can export LangGraph agent runs to LangSmith to visualize execution paths and debug code.

You can insert prebuilt agents, like ReAct or retrieval agents from LangChain, as nodes. On the deployment side, LangGraph offers a Visual Studio for prototyping agents and an API for managing Deployments and Assistants.

LangGraph’s documentation summarizes its integrations perfectly.

CrewAI

CrewAI comes with its own integrations and also uses external Python libraries. It natively includes a library of 40+ built-in tools that comprise:

• LLMs: Antropic, Facebook Llama, Google Cloud
• Services: Elint Data, Fathom, Hubspot
• Education: Amazon AWS, DeepLearning, IBM
• Applications: Box, Chroma, Cloudera
• Integrations: Arize, AgentOps, LangTrace
• Infrastructure: Microsoft Azure, MongoDB, Nexla

For other integrations, CrewAI is fully open-source Python: you can call any SDK or API by importing it in an agent’s code.

Many teams use CrewAI with Slack or other SaaS tools; CrewAI provides Zapier connectors and webhook support.

Bottom line: LangGraph gives you the full LangChain integration stack, whereas CrewAI offers a broader and more diverse integration landscape with a vast network of partners - LLMs, core infrastructure, and a wide array of business applications.

There’s some confusion around CrewAI’s relationship with LangChain:

• Today, CrewAI explicitly states it is “built entirely from scratch; completely independent of LangChain or other agent frameworks.”
• Yet, in out research from the past blog posts and posts, especially earlier in 2024, we found out - CrewAI was ‘built on top of LangChain,’ and its architecture previously integrated LangChain’s agent components.

So, CrewAI started by leveraging LangChain primitives, but has since been refactored to operate independently.

LangGraph vs CrewAI: Pricing

In this section, we will explain the cost implications of adopting an agentic framework. Both LangGraph and CrewAI offer open-source options and managed services, but their pricing models for cloud deployments differ significantly.

LangGraph

LangGraph comes with an open-source plan that’s free to use. If you install the LangGraph Python or JS package, you get the MIT-licensed code to design agents with no licensing cost or usage fees. This open-source plan has a limit of executing 10,000 nodes per month.

Apart from the free plan, LangGraph offers three paid plans to choose from:

• Developer: Includes up to 100K nodes executed per month
• Plus: $0.001 per node executed + standby charges
• Enterprise: Custom-built plan tailored to your business needs

📚 Related reading: LangGraph Pricing Guide

CrewAI

CrewAI’s core framework is also MIT-licensed and open-source. But the platform offers several paid plans to choose from:

• Basic: $99 per month
• Standard: $6,000 per year
• Pro: $12,000 per year
• Enterprise: $60,000 per year
• Ultra: $120,000 per year

👀 Note: To see CrewAI’s pricing plans, you must sign up for its free plan.

Which Agentic AI Framework Is Best For You?

Choose LangGraph if your focus is building highly customized, explicitly orchestrated agent workflows. LangGraph excels when you require fine-grained control over individual agent actions, conditional branching, parallelism, and detailed state management.

Its low-level graph API is ideal for developers who need transparency, debugging capabilities, and integration flexibility within the LangChain ecosystem.

Opt for CrewAI if your goal is rapid deployment of role-based, collaborative agent teams, i.e., ‘crews,’ with minimal setup. CrewAI is best when you want intuitive abstractions for agent interactions, built-in support for hierarchical task delegation, and a comprehensive toolbox of pre-integrated functionalities (web search, file I/O, database interaction). It's particularly effective for scenarios that benefit from structured agent roles, simplified human oversight, and straightforward parallel task management.

📚 Related comparison articles to read:

• Kubeflow vs MLflow
• MLflow vs W&B
• Metaflow vs MLflow
• Flyte vs Airflow
• Prefect vs Airflow