One of the most common questions we get from technical teams planning their first production agent: "Which framework should we use?" It's a fair question — GitHub has dozens of projects, every one ships a demo that looks great, and comparison blog posts contradict each other.
The short answer: the framework is a secondary decision. The primary decision is agent architecture — the pattern by which the agent reasons and acts (ReAct, Plan-and-Execute, reflection). Frameworks *implement* these patterns; they don't invent them. LangGraph, CrewAI, AutoGen, and custom code are four ways to materialise the same patterns in code. Each involves different trade-offs. This article will help you choose the right one — or talk you out of choosing a framework at all where one simply isn't needed.
What frameworks do (and don't do)
Before comparing specific projects, it's worth being clear about what an agent framework actually solves. Typically it provides:
- Step orchestration — how the agent moves from one state to the next (reason-act-observe loop or plan → execute → reflect)
- Context and state management — where history, intermediate results, and cross-call context are stored
- Routing between agents — how a task is handed off from one specialised agent to another
- Tool integration — a standardised way for the agent to call external APIs, vector databases, and code
- Checkpointing and resumability — the ability to restore an agent after a failure without losing progress
What frameworks don't do: they don't share responsibility for prompt correctness, tool quality, guardrails, or observability. Those remain your problem regardless of what you choose.
LangGraph: explicit graph, explicit state
LangGraph builds an agent as a directed graph — nodes are functions (LLM call, tool, condition), edges are state transitions. State is an explicit Python dataclass or dict that is passed between nodes.
What this means in practice:
- Every transition in the graph is visible and testable in isolation
- Checkpointing is built in — the agent can pause and resume from the last node (critical for long-running tasks)
- HITL (human-in-the-loop) is implemented via
interrupt()— the agent stops, waits for human input, then continues. This aligns with the EU AI Act Art. 14 requirement for human oversight in high-risk systems, mandatory from August 2026 - Debuggability is above average: you know exactly which node the agent failed at and with what state
When LangGraph makes sense:
- Production stateful agents with multiple steps (more than 3–4 tool calls per task)
- You need checkpointing — the agent runs for minutes or hours
- HITL on critical actions (financial operations, document submission)
- The team wants a graph visualisation of the flow for review and debugging
- Deployment into a regulated environment requiring an audit trail at node-per-node granularity
When LangGraph is overkill:
- A simple single-step RAG (embed → retrieve → generate) — this is overhead with no payoff
- A quick prototype where you need results in a day, not a week
- The team is unfamiliar with graph-based programming — the learning curve is real
LangGraph is today considered the most production-stable framework for enterprise stateful agents. It is not the easiest entry point, but it delivers the most controllable output.
CrewAI: roles, teams, fast start
CrewAI thinks in terms of roles and teams. You define agents with specific roles (Researcher, Analyst, Writer), each with its own goal and set of tools, and the framework coordinates how they hand off the task between them.
The barrier to entry is low — a basic multi-agent team can be assembled in ~20–35 lines of code. That is CrewAI's main strength: a working multi-agent prototype in hours, not days.
What this means in practice:
- Rapid prototyping is genuinely fast — the syntax is readable, a new team member can get up to speed in hours
- The role-based abstraction is intuitive for teams that think in terms of "who does what"
- In production, CrewAI has fewer tools for explicit state control and checkpointing compared to LangGraph
- Debuggability is weaker — when something fails it is harder to pinpoint exactly which step went wrong and why
When CrewAI makes sense:
- Proof-of-concept or internal prototype where you need to demonstrate value quickly
- Multi-agent scenarios where role-based thinking maps naturally onto the problem (e.g. a pipeline: data gathering → analysis → report)
- A team without a deep background in graph-based programming
- Less critical systems where occasional failure is acceptable
When CrewAI is not enough:
- Production systems requiring reliable checkpointing and resumability
- HITL with a guaranteed stop at critical decision points
- Long agentic tasks (dozens of tool calls) where state must be explicitly managed
- Regulated environments requiring an audit trail
CrewAI is not a bad framework. It is the right tool for a prototype — but moving from a CrewAI prototype to a production system frequently means a partial or full rewrite in LangGraph or custom code.
AutoGen: conversational agents, research character
AutoGen (now AG2) thinks differently from LangGraph or CrewAI. Instead of an explicit graph or roles it has a conversational GroupChat — agents exchange messages and the framework manages who speaks when.
AutoGen reached version 1.0 GA in 2025, signalling maturation. It is popular in research settings and for experimenting with multi-agent dialogue.
What this means in practice:
- Strong for scenarios where a solution emerges from conversation between agents — e.g. one agent writes code, another reviews it and proposes fixes
- Flexible and expressive for experimentation — adding a new agent to the chat is easy
- Less predictable in production — conversational flow is harder to control deterministically than an explicit graph
- Observability and checkpointing are weaker points compared to LangGraph
When AutoGen makes sense:
- Research and experimentation with new agent-to-agent patterns
- Systems where multiple specialists (agents) must iteratively collaborate on an answer
- Coding assistants and code review pipelines
- Prototypes where you can accept lower predictability
When AutoGen is not enough:
- Production systems with requirements for deterministic flow
- Scenarios requiring a guaranteed stop (HITL) at specific actions
- Companies with long-term accountability for output quality — conversational drift is hard to audit
Custom code: sometimes the best choice
This is the answer people don't expect, but in practice we see it more often than one might think: sometimes no framework is needed at all.
If your agent executes a fixed sequence of steps — e.g. receives a document, calls 2–3 tools, returns a structured output — Python with direct SDK calls (Anthropic, OpenAI) and your own retry logic can be cleaner, faster, and easier to maintain than any framework.
When custom code makes sense:
- Agent logic is simple and stable — fewer than 3–4 steps, no branching
- The team has a strong Python background and frameworks add more complexity than value
- You want a minimal dependency footprint — every framework pulls in dozens of transitive packages
- You are building a specific pipeline, not a generic agent runtime
Watch out for this trap:
Custom code quickly turns into an unmanageable "hobby framework" — you add state, retry, checkpointing one by one. Once the agent grows to 5+ steps with branching, LangGraph or another framework becomes the rational choice. Custom code is a good starting point, not always a good destination.
Decision framework
Rather than a table (which would be unreadable) here is the decision logic from practice:
Start with: How many steps and how much complexity?
- Fewer than 3–4 steps, fixed sequence → custom code or CrewAI
- 5+ steps, branching, need for resumability → LangGraph
- Multi-agent dialogue, research, coding review → AutoGen
Then: Is this a prototype or production?
- Prototype, PoC, demo → CrewAI or AutoGen (speed to first result)
- Production, SLA, regulated environment → LangGraph or custom code (control)
Then: Is HITL required?
- Yes, with a guaranteed stop → LangGraph (
interrupt()) or custom code with an explicit await - No, or "human-on-the-loop" is sufficient → any framework
Finally: What are the observability requirements?
This is the point companies most often underestimate. Without observability — capturing traces at the level of every node and tool call — you cannot diagnose failures in production. For LangGraph the natural integration is LangSmith; for other frameworks Langfuse (self-hostable, framework-agnostic) and Arize Phoenix both work well. More on this topic in the article on AI agent observability.
What all frameworks have in common: tool calling
One pattern holds regardless of which framework you choose: tool calling reliability is not free. Most production agent incidents do not happen because of bad reasoning — they happen due to malformed tool arguments, unhandled tool errors, or the agent calling a tool with nonsensical parameters.
The minimum viable production setup:
- Strict JSON schema validation on the input and output of every tool
- Retry logic with exponential backoff for transactional errors
- A maximum retry count (e.g. 3) with a fallback — the agent must be able to "give up" gracefully
- Logging of every tool call with parameters and result
A deeper look at this topic is in the article on reliable tool calling.
Frameworks vs. patterns: the right order
Let's return to where we started. Before choosing a framework, answer these questions:
- 1.What pattern do I need? ReAct (loop), Plan-and-Execute (plan first), reflection (self-critique)? More on patterns in AI agent architectures.
- 2.What are the requirements for reliability and auditability? Regulated environment, financial operations, healthcare — LangGraph or custom code applies here.
- 3.What is the time horizon? A two-week prototype vs. a system that will run for two years — these are fundamentally different decisions.
The framework is just a wrapper. A bad pattern in an elegant framework will still fail. The right pattern in simple custom code can run without issues for years.
Frequently asked questions
Can I combine frameworks?
Yes, but with caution. Combining LangGraph for orchestration with a CrewAI crew inside a single node is technically possible, but debugging becomes more complex. In practice we recommend choosing one framework for the entire system. If that is not feasible (e.g. you are integrating an existing CrewAI prototype into a new LangGraph system), isolate the integration into a dedicated node with a clearly defined API contract.
Do I have to migrate from CrewAI to LangGraph when going to production?
Not necessarily. Some production systems run reliably on CrewAI — it depends on agent complexity and requirements. The rule of thumb from practice: if your agent runs for longer than a minute, has more than 5 steps, or needs guaranteed HITL, migration is worth it. Shorter, simpler agents can stay on CrewAI.
Is AutoGen suitable for production?
AutoGen 1.0 GA (2025) is more mature than its 0.x versions. For well-defined use cases (coding assistants, review pipelines) a production deployment is realistic. For scenarios requiring full auditability of every step and guaranteed HITL, LangGraph remains the better choice.
What model should I use with a framework?
The framework is model-agnostic — it works with any model that supports tool calling. In practice we recommend testing with a frontier model (Claude Sonnet, GPT class) during development, and if cost is critical, progressively testing cheaper models (Haiku tier, open-weight Qwen 3.x or Llama 4) in the production environment. The costs of different models in agentic scenarios are covered in AI agent costs in production.
How do frameworks handle security — guardrails?
Short answer: they don't — that is your responsibility. A framework provides flow structure, not a security layer. Guardrails (input validation, prompt injection detection, tool permission scope, output filtering) must be added explicitly — either through dedicated tools (NeMo Guardrails, Guardrails AI) or your own validation logic. More in the article on guardrails for AI agents.
*Choosing a framework is an important decision, but not the most important one when building an agent. At MP Industrial Solutions we help clients navigate the entire journey — from selecting a pattern and framework through production deployment to monitoring and guardrails. If you are planning your first production agent or evaluating whether your prototype is ready for production, we are happy to walk through your specific use case with you.*