CrewAI vs LangGraph
Both are open-source Python frameworks for building multi-agent AI systems. They solve the same problem from opposite directions. CrewAI gives you agents with roles and tasks. LangGraph gives you a graph of nodes and edges. This page covers what actually differs between them and when each one makes more sense.
At a glance
Both frameworks are MIT-licensed and actively maintained. The numbers below are from early 2026.
CrewAI
- GitHub stars
- ~45,000
- PyPI monthly downloads
- ~5M
- Contributors
- ~280
- Current version
- 1.9.x
LangGraph
- GitHub stars
- ~25,500
- PyPI monthly downloads
- ~38M
- Contributors
- ~190
- Current version
- 1.0.x
CrewAI has nearly double the GitHub stars, but LangGraph sees roughly 7x more PyPI downloads. Much of that gap comes from LangGraph being pulled in as a transitive dependency through the LangChain ecosystem.
Architecture
CrewAI: agents, tasks, and crews
CrewAI treats multi-agent systems like teams. An Agent has a role, goal, and backstory. A Task has a description, expected output, and an assigned agent. A Crew groups agents and tasks together and runs them in sequence, in parallel, or with a manager agent that delegates work.
Flows (added in 2025) sit on top of crews. They use @start and @listen decorators to chain crews into multi-step, event-driven pipelines with conditional branching and typed state.
LangGraph: nodes, edges, and state
LangGraph is built around directed graphs. You define a state schema (a Python TypedDict), then add nodes (functions that read and update state) and edges (connections between nodes, which can be conditional). The graph compiles into a runnable that executes step by step.
There is no built-in "agent" concept. LangGraph provides create_react_agent as a prebuilt convenience, but most teams assemble agents from nodes, tools, and routing logic.
The core trade-off: CrewAI thinks in "who does what" (roles and tasks). LangGraph thinks in "what happens when" (nodes and edges). CrewAI abstracts the execution flow. LangGraph makes it explicit.
State management
CrewAI: implicit by default
In a sequential crew, each task's output automatically becomes context for the next task. Agents share a memory store (backed by ChromaDB and SQLite) that records facts extracted from task outputs. You don't define state schemas or write reducers. The downside is less visibility into exactly what data flows where.
Flows add explicit state via Pydantic models or dictionaries. This gives you typed, inspectable state across pipeline steps, but crews within a flow still use implicit state passing internally.
LangGraph: explicit always
You define every field upfront as a TypedDict. Nodes receive the full state and return partial updates. Reducer functions control how updates merge (overwrite by default, or append for lists with Annotated[list, operator.add]). Checkpointers save a snapshot after each node, enabling time-travel and resumption after failures.
The upside: you always know exactly what state each node sees. The downside: more boilerplate, especially for simple pipelines where implicit passing would be enough.
Tools
Both frameworks use an @tool decorator to turn functions into tools that agents can call. The syntax is nearly identical. The difference is in how tools connect to agents.
CrewAI
Tools are assigned directly to agents. When you give an agent a list of tools, that agent decides when to call them during task execution. CrewAI also ships a separate crewai-tools package with pre-built tools for web scraping, file I/O, search, and more.
LangGraph
Tools are bound to LLMs via llm.bind_tools(tools), and a dedicated ToolNode executes the tool calls. This separation lets you handle tool execution as a distinct step in the graph with its own error handling, logging, and retry logic. LangGraph inherits LangChain's library of 300+ pre-built tool integrations.
Multi-agent patterns
CrewAI
- Sequential: tasks run in order, output chains forward
- Hierarchical: a manager agent delegates to specialists
- Delegation: agents hand off subtasks to peers at runtime
- Flows: compose multiple crews into multi-step pipelines
These patterns work out of the box with minimal configuration.
LangGraph
- Supervisor: a coordinator routes tasks to sub-agents (subgraphs)
- Swarm: agents hand off to each other via explicit handoff tools
- Map-reduce: fan-out to parallel nodes, fan-in with reducers
- Custom: any topology you can express as a graph
More flexible, but you define every routing decision yourself.
Memory and persistence
CrewAI
CrewAI provides a unified Memory class with four types: short-term (ChromaDB-backed RAG for the current session), long-term (SQLite for cross-session task results), entity (RAG for people, places, and concepts), and contextual (a composite that blends the others). An LLM analyzes content when saving to infer scope, categories, and importance. Memory is extracted automatically after each task.
LangGraph
LangGraph uses checkpointers that save a full state snapshot after each node execution. Implementations exist for Postgres, Redis, SQLite, and MongoDB. This enables time-travel (replay from any checkpoint), fault tolerance (resume after crashes), and branching. For long-term, cross-session memory, a separate Store API lets you share data across threads.
In short: CrewAI uses an LLM to decide what to remember and how important it is. LangGraph saves deterministic snapshots of the full state to a database you control.
Human-in-the-loop and streaming
Human-in-the-loop
CrewAI supports human_input=True on tasks, which prompts the user for feedback before the agent finalizes its output. This works well for CLI tools but requires custom architecture for web-based async HITL.
LangGraph has first-class interrupt() and breakpoint support built on its checkpointing system. A graph can pause for hours or days, persist its state, and resume exactly where it stopped when the human responds. This works natively with web APIs.
Streaming
CrewAI streams task-level output chunks via stream=True on the Crew. LangGraph supports five streaming modes: full state after each node (values), state deltas (updates), individual LLM tokens (messages), custom data from any node (custom), and execution traces (debug). Multiple modes can run simultaneously. For real-time chat interfaces that need token-by-token streaming, LangGraph has more options.
Side-by-side comparison
| CrewAI | LangGraph | |
|---|---|---|
Architecture | Agents / Tasks / Crews (role-based) | StateGraph / Nodes / Edges (graph-based) |
State management | Implicit task output chaining + Flows for explicit state | Explicit TypedDict with reducer functions |
Tool system | Own @tool decorator + BaseTool class | LangChain's @tool + ToolNode in graphs |
Multi-agent patterns | Sequential, hierarchical, delegation, Flows | Supervisor, swarm, custom graph topologies |
Memory | Short-term (ChromaDB), long-term (SQLite), entity memory | Checkpointers (Postgres, Redis, SQLite) + Store API |
Human-in-the-loop | human_input=True on tasks (basic) | interrupt() + breakpoints with checkpoint persistence |
Streaming | Task-level output chunks | 5 modes including token-level streaming |
Ecosystem | Standalone (no LangChain dependency) | Part of LangChain (300+ integrations) |
Learning curve | Lower — working prototype in hours | Higher — days to weeks to master |
License | MIT | MIT |
When to use which
Pick CrewAI when
- You need a working prototype fast (hours, not days)
- Your use case maps to "team of specialists" (researcher, writer, reviewer)
- You want minimal boilerplate and YAML-based configuration
- Your team has limited experience with graph-based programming
- You want a standalone framework without LangChain dependencies
- Your HITL needs are simple (CLI-based approval)
Pick LangGraph when
- You need fine-grained control over execution flow, branching, and loops
- Production-grade persistence and fault tolerance are requirements
- You need async human-in-the-loop with web-based pause/resume
- Token-level streaming matters (real-time chat interfaces)
- You are already in the LangChain ecosystem
- You need deep observability (LangSmith tracing and evaluation)
A common pattern: prototype with CrewAI to validate an idea quickly, then migrate to LangGraph for production when you need checkpointing, streaming, and fine-grained control. Crewship supports both, so you can switch frameworks without changing your deployment infrastructure.
Deployment
Both frameworks are MIT-licensed and can be self-hosted anywhere. Both have official cloud platforms (CrewAI AMP for CrewAI, LangSmith Deployment for LangGraph) and enterprise tiers with SSO, audit logging, and dedicated support.
Crewship is a third-party deployment platform that supports both frameworks. You get a Runs API for one-off executions, a Threads API for multi-turn conversations, automatic versioning, rollbacks, and a Slack chatbot integration. The deployment workflow is the same for both: define a crewship.toml, run crewship deploy.
FAQ
Common questions
Deploy either framework with Crewship
Same Runs API, Threads API, versioning, and Slack integration — regardless of whether you choose CrewAI or LangGraph.