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LangGraph, introduced by the LangChain team in 2023, promised a powerful way to orchestrate multi-agent workflows for LLM applications. The platform gives fine-grained control over agent behaviors by making each thought and action an explicit graph node, as opposed to letting LLMs chat freely.
Sounds great in theory, right? But here's what we've discovered after months of real-world usage: many ML engineers and LLMOps teams are hitting walls in production. Between constantly shifting APIs, layers of abstraction that obscure what's actually happening, and deployment constraints that feel unnecessarily restrictive, teams are starting to look elsewhere.
So, we set out to test 8 of the best LangGraph alternatives for orchestrating AI agents at scale. Our list spans everything from full-fledged MLOps platforms to specialized multi-agent frameworks. In this post, we break down their features, talk about why you might choose each one, and highlight the pros and cons we discovered along the way.
Whether you’re building autonomous research agents or adding LLM-powered steps to your ML pipeline, if you read ahead, you will find an option that fits your needs.
TL;DR
- Why Look for LangGraph Alternatives: LangGraph’s fast-evolving codebase and complex abstractions introduced a lot of instability and debugging headaches for us. We often ran into broken tutorials after updates and had to dig through five layers of abstraction just to customize an agent’s behavior. Moreover, LangGraph sometimes slowed down or hit concurrency limits in our more complex workflows.
- Who Should Care: If you're an ML engineer, LLMOps practitioner, or part of an AI platform team deploying multi-agent systems in production, this is for you. You likely need reliable agent orchestration with observability, cost controls, and enterprise features (security, RBAC, etc.), so exploring alternatives to LangGraph is worth your time.
- What to Expect: In our roundup below, you'll find a range of options. For example, ZenML offers a pipeline-centric approach (complete with versioning and experiment tracking), while Microsoft’s AutoGen framework caters to conversational agent teams. We cover everything from lightweight libraries that prioritize developer experience to enterprise-grade orchestration platforms with slick UIs — and a lot in between.
The Need for a LangGraph Alternative?
As popular and sophisticated as LangGraph is for agent orchestration, it has some inherent issues that we (and many others) encountered in production. Here are the major reasons we felt the need to look beyond LangGraph:
Reason 1. Constantly Shifting APIs Create Version Instability
LangGraph sits on top of LangChain, a library that still changes week to week. We saw new releases rename classes, move modules, or deprecate methods with little warning and no clear migration path.
We even hit situations where code from a month-old tutorial wouldn’t work anymore because the underlying library had changed. That forced us to pin our setup to older versions or rewrite parts of our pipelines just to keep things running.
This kind of churn makes it really tough to adopt LangGraph in environments where you need predictable release cycles and long-term maintenance. Frankly, it’s discouraging if you value stability.
Reason 2. Over-Abstracted Design Makes Debugging Hard
LangGraph hides agent logic behind several layers of abstractions – graphs, sub-graphs, state objects, and decorators. When an agent produces a bad answer, engineers must dig through multiple indirections to see which prompt, tool, or state mutation caused it.
And honestly, the official documentation didn’t make things much easier for us. Many docs pages referenced experimental features, and core concepts were scattered across tutorials, code comments, and blog posts. The result? A steep learning curve and some very opaque failure modes that slowed us down when things went wrong.
Reason 3. Opinionated Deployment Limits Scalability
By default, LangGraph runs agents in a single-threaded loop; true concurrency requires a separate LangGraph Server.
This server adds another queue, database, and API layer to your stack, yet still keeps advanced features, background jobs, horizontal scaling, and double-message handling behind a managed cloud plan.
Teams that need to self-host for compliance end up maintaining niche infrastructure with no built-in autoscaling, while those who stay on the open-source path must engineer their own distributed runner.
Evaluation Criteria
When we started looking at alternatives to LangGraph, we knew we had to consider how each tool would perform in a production-grade AI workflow. Here are the three big criteria we focused on:
1. Built-in Observability and Cost Governance
In our view, any viable framework has to expose every message hop, tool call, and token spent. We look for things like trace viewers, time-travel debuggers, per-run cost meters, and alerting hooks that integrate with our existing monitoring tools (APM, SIEM, etc.). Without first-class observability, an agent system can quickly turn into an expensive black box.
2. Integration & Extensibility
We realized that agent workflows never operate in a vacuum. They need to connect to all the other pieces of your stack — retrievers, vector databases, schedulers, CI/CD pipelines, you name it.
They might even kick off model fine-tuning jobs or other sub-pipelines that LangGraph itself doesn’t track. So when we evaluated alternatives, we favored platforms that offered:
- Ready connectors for LLMs, embeddings, vector stores, queues, and schedulers
- A simple SDK or plugin layer to wrap in-house tools without forking core code
- Multi-language support (Python is table stakes; TS, Go, or gRPC widen adoption)
- Templates or a marketplace that speeds up common agent patterns
The goal is to compose agents with the rest of your stack and monitor every upstream or downstream component they rely on.
3. Enterprise Readiness & Deployment Flexibility
For any solution to work in production at an enterprise level, it needs to check the right boxes for security, compliance, and scalability. Specifically, we looked for:
- Security: RBAC, secrets management, VPC or on-prem deployment, network policy controls, and policy hooks for SOC 2 or GDPR.
- Auditability: Immutable logs of who ran what, when, and with which data.
- Scalability Options: Single-node dev runs, autoscaling Kubernetes jobs, or a managed SaaS; your choice, not the vendor’s.
- Isolation & Multi-Tenancy: Clean separation of projects and budgets.
What are the Best Alternatives to LangGraph
Here's a comparison of the top LangGraph alternatives:
1. ZenML
ZenML is an open-source MLOps framework that provides a pipeline-centric approach to machine learning workflows.
Unlike LangGraph (designed specifically for multi-agent conversations), ZenML is a general pipeline orchestrator for ML, but it has recently extended its capabilities to LLMops. ZenML allows you to define steps in a pipeline (data prep, model train, etc.) and run them on various ‘stacks’ with automatic artifact tracking, model versioning, and more.
While ZenML isn’t a drop-in replacement for LangGraph’s agent graphs, it provides the foundations to productionize AI workflows.
Essentially, ZenML can orchestrate and manage the surrounding pipeline in which an LLM agent might live: data pipelines, prompt versioning, model deployment triggers, and experiment tracking.
Feature 1. Prompt Versioning
In ZenML, every prompt, whether a one-off system message or a chain of prompts, is stored as an immutable artifact the moment it is executed inside a pipeline step.
The framework automatically hashes the prompt content, tags it with the code and data revision that produced it, and writes that record to the artifact store alongside run metadata.
Because prompts are treated exactly like datasets or models, you gain full lineage: you can trace which prompt version generated which output, compare token-level differences between versions, and correlate those changes with evaluation metrics logged in the same run.
The ZenML CLI (and ZenML Pro dashboard) lets you visualise these versions, roll back to an earlier prompt if performance regresses, or enforce RBAC-gated reviews before a new prompt is promoted to production.
In short, prompt versioning is baked into the pipeline lifecycle, turning what is usually ad-hoc string tweaking into a reproducible, auditable process that scales with the rest of your MLOps stack.
Feature 2. Artifact Visualization
ZenML's Model Control Plane provides a unified approach to model management, bringing together pipeline lineage, artifacts, and business context.
It treats a ZenML Model as a first-class entity, grouping relevant pipelines, artifacts, metadata, and business metrics. This enables built-in versioning and stage management, where each training run can produce a new Model Version, automatically tracked with lineage to the data and code that created it.
All in all, ZenML provides a comprehensive view of a model's lifecycle, addressing a critical need for governance in production by linking models directly to their creation and usage context.
Feature 3. Flexible Orchestration Options Across Different Stacks
ZenML's architecture is designed for flexibility, which allows you to define and swap out various stack components (orchestrator, experiment tracker, model deployer) to suit different workflows and infrastructure needs.
It supports multi-cloud scalability, enabling deployment across AWS, GCP, Azure, or on-prem environments with unified resource management.
This pluggable architecture and vendor-agnostic approach prevent lock-in and allow integration with preferred tools like Kubeflow, MLflow, Skypilot, and more.
How Does ZenML Compare to LangGraph?
While ZenML wasn’t built as an ‘agent framework,’ it addresses many production pain points that LangGraph has.
ZenML provides stronger experiment management (prompt and artifact versioning) and metadata tracking. It is more stable – ZenML has a controlled release cycle and focuses on backward compatibility, as opposed to LangGraph’s rapid changes.
👀 Note: ZenML does not (currently) offer the kind of interactive, branching conversation management that LangGraph does. You wouldn’t use ZenML to implement a complex multi-turn agent loop without writing a fair bit of custom code. Instead, you might combine ZenML with one of the other frameworks in this list: for example, orchestrate a CrewAI or LlamaIndex AgentWorkflow within a ZenML pipeline to benefit from both worlds (ZenML for pipeline reliability + another library for agent logic).
ZenML is a great choice if you care about robust MLOps foundations around your LLM agents – versioning, reproducibility, and deployment portability. It trades off some of the specialized agent control LangGraph has for a far wider scope and stability.
Pros and Cons
ZenML brings mature MLOps practices (lineage tracking, model registry, pipelines) to LLM workflows. It’s highly extensible and tool-agnostic – you’re not locked to a single LLM provider or vector DB. The focus on observability and reproducibility (prompt versioning, artifact caching) is excellent for debugging and governance.
But remember, ZenML is not a specialized multi-agent orchestration tool, so you don’t get plug-and-play agent dialog management as in LangGraph.
2. CrewAI
CrewAI is a dedicated multi-agent framework that takes inspiration from human “crews” or teams. It enables you to define multiple AI agents, each with a specific role like Researcher, Writer, Critic, and orchestrates their collaboration in a structured way.
Features
- CrewAI makes it easy to create role-based agents with distinct personalities and responsibilities. All you need to do is define each agent’s backstory, role description, and tools it can use in a YAML or Python config.
- By default, CrewAI runs agents in a sequential turn-taking process – one agent completes its task, then hands off to the next, and so on. This deterministic pipeline is simpler to reason about and ensures each agent gets the necessary context from the previous ones.
- Comes with an expanding set of built-in tools (web search, code execution, calculators, etc.), and importantly, a simple way to add custom tools. Tools are just Python functions that an agent can call when you give it proper permissions.
- It provides a replay mechanism to debug agent runs. After a crew run, you can inspect each step’s input/output and even replay certain steps with modifications, similar to how you might debug a failed pipeline.
Pros and Cons
CrewAI’s structured approach - role specialization and step-wise execution - makes multi-agent systems more deterministic and debuggable. The learning curve for the platform is not that steep – you can define agents and tasks in plain Python with clear decorators.
That said, we think CrewAI lacks flexibility in comparison to more free-form systems. The sequential process can be limiting if you want agents to truly converse back-and-forth arbitrarily.
3. Microsoft AutoGen
AutoGen is an open-source multi-agent framework from Microsoft Research that focuses on enabling conversational interactions among AI agents and humans to solve tasks. AutoGen’s philosophy is to orchestrate multiple LLM-based agents by letting them talk to each other in natural language, possibly involving tool use and human feedback, to collaborate on complex problems.
Features
- AutoGen’s primary feature is agents that can send and receive messages in a conversation loop. Any agent can talk to any other agent, making it a flexible ‘many-to-many’ communication model
- AutoGen agents are capable of using tools and executing code as part of the conversation. Notably, if an AssistantAgent sends a Python code block in its message, the UserProxyAgent can be configured to automatically run that code in a sandbox (like Docker) and return the output.
- Doesn’t require everything to be fully autonomous. You can easily include a human user in the conversation, for example, by not auto-responding on the UserProxyAgent and instead awaiting actual user input.
- The platform is built as a thin layer, originally part of the FLAML library. It maximizes the use of natural language for orchestration: to change an agent’s behavior, you often just tweak its system prompt or conversation rules rather than writing new code.
Pros and Cons
AutoGen offers perhaps the most flexible agent orchestration because it imposes little structure beyond the conversation. This makes it ideal for open-ended, complex tasks where it’s not obvious how to break down the problem – the agents can negotiate the approach among themselves. It’s relatively easy to set up and requires minimal boilerplate to get multi-agent workflows going.
The flip side of AutoGen’s freedom is unpredictability and potential inefficiency. Since agents converse freely, it can be hard to guarantee when or how the process will converge.
4. LlamaIndex AgentWorkflow
LlamaIndex has introduced AgentWorkflow, a system designed to simplify the building and orchestration of AI agent systems by building upon LlamaIndex's existing Workflow abstractions.
Features
- AgentWorkflow supports various agent architectures, including
FunctionAgentfor LLMs with function calling capabilities andReActAgentfor any LLM, with easy extensibility for custom agent types. - LlamaIndex provides a
Contextobject that all agents can access to read and write a shared state. In an AgentWorkflow, this is how agents communicate indirectly. - Each agent in the workflow can have a set of tools (functions) it’s allowed to use, just like LangChain agents. LlamaIndex makes it easy to incorporate its existing toolkit or external functions.
- One of the touted advantages of LlamaIndex’s approach is focusing on significant events rather than every node/edge, which helps you handle conditional flow more easily.
Pros and Cons
LlamaIndex’s AgentWorkflow is developer-friendly and highly structured without being low-level. You get the benefits of planning out a workflow (less chance of agents looping infinitely or forgetting steps) with the flexibility of letting agents still operate in natural language within their steps.
A significant issue is that after an agent hands off control, the receiving agent may fail to immediately respond to your requests, causing the workflow to halt unless custom modifications are made.
5. Semantic Kernel Agent Orchestration
Semantic Kernel (SK) is Microsoft’s open-source SDK for building AI integrations, primarily known in the .NET world (with Python support as well). In May 2025, the Semantic Kernel team introduced a multi-agent orchestration framework within SK.
Features
- Semantic Kernel supports multiple patterns for agent collaboration, including Sequential (pipeline-like processing), Concurrent (simultaneous independent work), Group Chat (collaborative conversation with optional human input), Handoff (dynamic control transfer between agents), and Magentic (flexible, general-purpose dynamic collaboration for complex tasks).
- Allows developers to define different types of agents for specific tasks - data scraping, API interaction, NLP - enhancing modularity and adaptability.
- All orchestration patterns share a consistent interface for construction and invocation, simplifying development. You can define agents, create orchestrations, and invoke them asynchronously with consistent results.
Pros and Cons
Semantic Kernel’s multi-agent orchestration is built by a large tech company for enterprise developers, so expect high quality and attention to real-world needs. The predefined patterns - sequential, concurrent, group chat, and handoff - cover most common multi-agent designs.
One consideration is that SK’s multi-agent features are relatively new (as of 2025), so they may not be as battle-tested as other parts of Semantic Kernel.
6. SuperAGI
SuperAGI is an autonomous AI agent framework that has gained significant traction on GitHub (15k+ stars). It brands itself as a ‘dev-first’ platform for building, managing, and deploying useful autonomous agents quickly.
Features
- Supports defining agent workflows that can involve multiple steps and even multiple agents working in sequence. It comes with the ability to create ReAct-style workflows where the LLM agent plans actions and executes them iteratively.
- Comes with a variety of built-in toolkits for Twitter, Google search, file management, email, JIRA, etc., and you can add more via their plugin-like architecture.
- Provides Memory management for agents – they can store and retrieve memories of past interactions or data. It supports connecting to multiple popular vector databases (like Pinecone, Weaviate, etc.) to enhance the agent’s context.
- Comes with a polished web UI. There’s a console where you can watch agents’ decisions in real-time, give them user inputs, or validate their actions.
Pros and Cons
SuperAGI’s biggest pro is its comprehensiveness and ease of use. It wraps a lot of powerful capabilities like concurrency, memory, tools, and UI into a single coherent platform. For teams that want to hit the ground running with autonomous agents, SuperAGI can save weeks of development time.
However, because SuperAGI does a lot, it’s also relatively complex. The platform itself has many moving parts - database, API server, frontend, and possibly background workers. Getting it set up (if not using their cloud) might require some DevOps work.
7. PydanticAI
PydanticAI is a Python agent framework for production-grade Generative AI applications, integrating Pydantic with Large Language Models (LLMs). It aims to replicate FastAPI's developer experience for GenAI.
Features
- Leverages Pydantic models to validate and structure LLM outputs, enhancing data quality and reliability.
- Compatible with various LLMs - OpenAI, Anthropic, Gemini, Deepseek, Ollama, Groq, Cohere, Mistral - with an extensible interface for others.
- Seamlessly integrates for real-time debugging, performance monitoring, and behavior tracking of LLM applications.
- Uses Python's control flow and agent composition, promoting standard Python best practices.
Pros and Cons
The strong typing and validation is PydanticAI’s killer feature. It directly tackles one of the hardest parts of using LLMs: making sure outputs are correct and usable. By catching errors early, you can handle them or retry instead of getting garbage downstream.
PydanticAI’s approach, while robust, might be a bit intimidating for those not used to type-driven development. There’s some upfront work to define all your schemas and think in terms of data models.
8. Marvin by Prefect
Marvin is an open-source library (developed by Prefect) described as an ‘ambient intelligence’ and multi-agent orchestration tool. It is actually closely related to PydanticAI; in fact, Marvin 3.0 is the PydanticAI approach discussed above.
Features
- Marvin provides capabilities to parse natural language into type-safe schemas (AI Models), transform enums into classifiers, apply complex business logic with AI (AI Functions), and create interactive AI assistants (AI Applications).
- Sensible defaults work out-of-the-box, accelerating development while allowing customization.
- Built-in conversation history and memory management for agents, ensuring coherent dialogues and decisions.
- Obtains structured results from LLMs, often using Pydantic models, ensuring data consistency and reliability.
Pros and Cons
Marvin’s marriage of LLMs with proven workflow orchestration concepts is a big win for production scenarios. You get the best of both worlds: LLM flexibility and traditional software reliability.
But Marvin is still evolving, and some might find it a bit too code-oriented. If you prefer designing agent workflows in a GUI or a low-code environment, Marvin’s approach might feel too manual.
Which One is the Best LangGraph Alternative You Should Use?
Choosing the right LangGraph alternative depends on your specific requirements:
- For MLOps teams: ZenML provides the best integration with existing ML workflows while treating agents as specialized pipelines.
- For multi-agent systems: CrewAI offers intuitive role-based patterns for complex agent interactions.
- For enterprise deployments: Semantic Kernel brings Microsoft's enterprise support and Azure integration.
- For rapid prototyping: SuperAGI's visual tools accelerate agent development.
- For type-safe development: PydanticAI ensures robust agent implementations through validation.
The key is matching tool capabilities to your team's expertise and project requirements. Consider starting with lighter-weight options like Marvin for simple use cases before adopting platforms like ZenML for production deployments.
But if you’re ready to build production-grade agent systems with enterprise support, book a demo call with ZenML and discover how pipeline-based orchestration can transform your agent workflows while providing the observability and governance features your team needs.
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