This guide splits open-source AI agent platforms and frameworks into three clear categories, compares the leading options within each, and offers a decision framework based on your team's situation rather than by feature count.
What is an open-source AI workflow builder?
An open-source AI workflow builder is software with an OSI-approved license that lets teams visually assemble, run, inspect, modify, redistribute, and self-host multi-step workflows that use AI models, tools, data, and application integrations.
The workflow-builder framing is narrower than the broader category of AI agent platforms. Buyers evaluating workflow builders should be able to see how steps connect, control branching and execution, connect external systems, choose model providers, inspect runs, and collaborate on deployment and maintenance.
“Source available” is not the same as open source. A product can expose its source code while imposing license restrictions that prevent it from meeting the Open Source Initiative’s definition of open source. That distinction matters when a team needs redistribution rights, plans to offer a hosted service, or wants to modify the software without field-of-use restrictions.
What should you compare in an open-source AI workflow builder?
An open-source AI workflow builder should be evaluated on its license, visual builder, self-hosting options, multi-step orchestration, integrations, model connectivity, operational controls, and team usability.
| Evaluation criterion | What buyers should verify | Why it matters |
|---|---|---|
| License | The exact license for the core repository and any separately licensed enterprise code | Source visibility alone does not guarantee open-source modification or redistribution rights |
| Visual workflow support | Whether the product provides a visual builder for connecting triggers, models, tools, conditions, and outputs | A visual representation makes multi-step logic easier to build and review |
| Self-hosting | Whether the vendor documents a supported self-hosted deployment and which capabilities require a commercial edition | Self-hosting can provide greater control over infrastructure, data handling, and model access |
| Multi-step orchestration | Whether workflows support dependencies, branching, parallel work, retries, and human review | Production workflows usually require more than one prompt followed by one response |
| Integrations | Whether the platform offers maintained application connectors plus API, webhook, or custom-code escape hatches | Integration depth determines whether an AI workflow can act across existing systems |
| Model connectivity | Whether teams can connect multiple hosted providers or local model servers | Model flexibility helps teams balance capability, cost, latency, and data requirements |
| Team usability | Whether teams can collaborate, control access, review changes, and inspect execution history | A builder must remain manageable when workflows move beyond a single developer |
Key Takeaways
- Three categories, not one: Code-first frameworks (LangGraph, CrewAI, AutoGen), visual builders (Dify, n8n), and AI workspaces (Sim) serve fundamentally different buyers and require different evaluations.
- AutoGen is splintering: Microsoft's AutoGen has fractured into maintenance mode, a community-led AG2 fork, and the new Microsoft Agent Framework. Teams need to choose the option that will work best for them.
- CrewAI Flows changed the game: CrewAI's Flows feature adds event-driven orchestration alongside crew-style collaboration, giving teams both flexibility and control in one framework.
- Dify dominates the visual builder space: With over 149,000 GitHub stars and a recent $30M raise, Dify is the most-adopted visual AI agent builder, though it still lacks strong team governance features.
- Workspace platforms bundle what frameworks leave out: Sim combines visual building, team collaboration, knowledge management, and deployment infrastructure in a single package with an Apache 2.0 core; features in
apps/sim/eeuse the separate Sim Enterprise License. - Self-hosting and licensing vary widely: "Open source" means different things across these platforms, from fully permissive MIT licenses to open-core models where enterprise features sit behind paid tiers.
The Three Types of Open-Source AI Agent Platforms
Most "best open-source AI agent platforms" articles rank every tool on the same axis: GitHub stars, LLM support, ease of setup. That ranking ignores the most important question: what kind of tool are you actually looking at?
A code-first framework like LangGraph and a visual builder like Dify solve the same problem the way a custom-built kitchen solves the same problem as a meal kit. Technically, yes, both produce dinner. But the skills, time, and team composition they require are completely different. Lumping them into one comparison is unhelpful when you're trying to make a decision.
Here's how we would divide open-source AI agent platforms for meaningful comparisons.
| Platform Type | Who It's For | Core Trade-off |
|---|---|---|
| Code-first frameworks (LangGraph, CrewAI, AutoGen) | Engineering teams building custom agent logic | Maximum control, but you own the infrastructure and deployment stack |
| Visual/low-code builders (Dify, n8n) | Mixed technical teams shipping quickly | Fast time-to-value, but limited governance and complex-logic ceilings |
Open-source AI workspaces (Apache 2.0 core; separately licensed apps/sim/ee) | Teams needing build + deploy + collaborate in one place | Broad built-in capability, but newer ecosystem compared to established frameworks |
What "open source" means in practice also varies. Code-first frameworks tend to be MIT or Apache 2.0 licensed with full self-hosting, though paid layers (like CrewAI's Enterprise tier or LangSmith for LangGraph observability) sit on top. Visual builders often follow an open-core model with a free community edition and a paid cloud tier for enterprise features. Sim takes the workspace approach with an Apache 2.0-licensed core, self-hosted Docker/Kubernetes deployment, and a managed cloud option. Features in apps/sim/ee, such as SSO, SCIM, access control, audit logs, and white-labeling, use a separate Sim Enterprise License, which is free for development, testing, and internal non-production use, requires an Enterprise subscription for production use, and does not permit modification or redistribution.
Code-First Frameworks: LangGraph, CrewAI, and AutoGen
Code-first frameworks give engineering teams the deepest control over agent logic, state management, and execution flow. They're libraries, not products. You write Python (or TypeScript, in some cases), and you own the deployment, observability, and scaling layers.
That control comes at a cost: these frameworks assume you have engineers who can build and maintain production infrastructure.
LangGraph
LangGraph is the default choice for complex stateful workflows that need explicit control over branching, retries, and human-in-the-loop. It sits underneath the LangChain ecosystem: since LangChain 1.0, LangChain's create_agent runs on LangGraph. It has seen the largest enterprise adoption among code-first agent frameworks.
LangGraph does four things excellently: branching logic that lets you define exactly which path an agent takes based on state, human-led approvals and checkpointing that are now integral features rather than add-ons, durable execution that survives process restarts, and detailed control over every step in the agent's decision chain.
Where it demands investment: setup isn't trivial, especially for teams new to graph-based agent architecture. LangSmith (LangChain's paid observability platform) is the recommended way to monitor and debug LangGraph workflows in production. You don't need it to build or run graphs, but teams that skip it must assemble their own tracing. And LangGraph is fundamentally a developer tool. If your team includes non-engineers who need to build or modify agents, they won't be able to participate without an intermediate layer.
Best for: Teams with strong engineering resources building complex stateful agents where explicit control over every decision branch matters more than speed to first deployment.
CrewAI
CrewAI introduced a mental model that's simple to pick up: instead of defining abstract graph nodes, you define roles. A researcher agent gathers information, a writer agent drafts content, a reviewer agent checks quality. CrewAI is the fastest path from idea to working multi-agent prototype when work decomposes into role-based tasks.
The Flows addition lets you create structured, event-driven workflows that provide a way to connect multiple tasks, manage state, and control the flow of execution in your AI applications. This is a meaningful evolution. Flows give you a structured, event-driven execution engine that sits above individual crews and tasks. A Crew is great at parallel collaboration with multiple agents working on a shared goal, but Crews don't give you sequential control. Think of it this way: a Crew is a team, a Flow is the project plan that coordinates multiple teams.
The open-core dynamic is worth understanding before you commit. CrewAI is open-source and actively encourages community contributions. The MIT-licensed core gives you the framework for free, but CrewAI's AMP Suite provides tracing and observability, a unified control plane for managing and scaling agents, and enterprise integrations as paid enterprise tooling. According to CrewAI's pricing page, the AMP platform adds a visual editor, managed deployment, and governance features; a limited tier is free, while enterprise capabilities require custom pricing. Teams that want a UI, role-based access control, and managed deployments will eventually encounter the paid tier.
Best for: Teams automating multi-step workflows where work naturally breaks into distinct role specializations, and where the Flows layer provides enough orchestration to avoid building a custom control plane.
AutoGen / AG2
This is the platform where you need to understand the landscape before you write a single line of code.
In late 2024, the original creators left Microsoft and forked the project into AG2, retaining control of the original PyPI packages and Discord community. Microsoft, meanwhile, rebuilt AutoGen from scratch as version 0.4 with a completely different architecture. Then, in October 2025, Microsoft announced that AutoGen and Semantic Kernel are merging into a new unified "Microsoft Agent Framework," with AutoGen entering maintenance mode.
As of October 2026, the original AutoGen project has split into three distinct paths: Microsoft Agent Framework (MAF), the official production-grade successor that merges AutoGen's orchestration with Semantic Kernel's enterprise stability; AutoGen v0.7.x, the "stable" maintenance line using the asynchronous actor-model architecture introduced in v0.4; and AG2, a community-led fork that remains backward-compatible with the legacy v0.2 "GroupChat" style.
The conversational multi-agent model that made AutoGen popular is still its core strength. Agents talk to each other to solve problems, which makes it powerful for research and prototyping scenarios where you want agents to reason together dynamically. AutoGen Studio provides a no-code interface for beginners that lets you prototype visually before moving logic to code.
You will, however, need to deal with the fragmentation when you're evaluating AutoGen. This means choosing between the AG2 community fork, Microsoft's transitional 0.4 release, or waiting for Agent Framework 1.0. It's a decision you can't defer, and if you build on the wrong branch, you'll need to consider the migration debt this would incur.
Best for: Research teams, rapid prototypers, and organizations already embedded in the Microsoft ecosystem who can commit to a specific fork and accept the ongoing transition.
Visual Builders: Dify and n8n
Visual builders trade code-level control for speed. They let teams design agent workflows by connecting blocks on a canvas rather than writing Python. The target user is different: product managers, ops teams, and developers who need to prioritize iteration speed over architectural precision.
Dify
Dify's open-source model targets production scalability. With more than 149,000 GitHub stars, it is the most-starred open-source visual builder focused specifically on LLM applications and AI agents, which reflects broad production adoption.
Dify is a production-ready platform for agentic workflow development, handling everything from enterprise QA bots to AI-driven custom assistants. The platform includes a workflow builder for defining tool-using agents, built-in RAG (retrieval-augmented generation) pipeline management, support for multiple AI model providers, and Model Context Protocol (MCP) integration.
The RAG pipeline is Dify's standout feature. It's among the best available in an open-source package. If your primary use case involves document retrieval, knowledge bases, and structured Q&A, Dify's built-in tooling eliminates weeks of integration work.
The self-hosted Community Edition (Docker Compose, single machine or Kubernetes) is free for most uses. Its license is based on Apache 2.0 with added conditions, including a restriction on operating a commercial multi-tenant service without separate permission, so review it before offering Dify as a hosted product. Dify Cloud starts with a free Sandbox tier at 200 message credits and scales to Professional, Team, and Enterprise plans; the Professional tier lists at $590/year and Team at $1,590/year.
Where Dify falls short relative to a purpose-built AI workspace: team governance is limited, agent lifecycle management (versioning, rollback, multi-user editing) lacks depth, and the visual tooling has a ceiling; complex custom logic belongs in code.
Best for: Non-technical users who need to ship quickly and enterprises using Dify as an LLM gateway with strong RAG capabilities.
n8n
n8n is a workflow automation platform that has added AI agent capabilities. Think of it as a workflow automation platform with native AI agent nodes and 400+ app integrations, like Zapier, but self-hostable with unlimited executions.
Its strengths include a mature drag-and-drop interface, the broadest set of service connectors among the tools in this comparison, and proven self-hosting support. n8n crossed 180,000 GitHub stars, reflecting massive community traction in the automation space.
However, it is worth emphasizing that n8n is an AI-augmented workflow tool, not an agent-native platform. Its AI capabilities are additive features on top of an automation engine, not the core architecture. For teams whose primary use case is connecting existing business tools and adding AI reasoning to those connections, it's a great fit. For teams building reasoning-heavy autonomous agents, there are tools that will better suit these needs.
Best for: Teams migrating existing automation workflows toward AI without a full replatform, especially when broad service connectivity matters more than deep agent reasoning.
How Sim and n8n compare as workflow builders
Sim, with an Apache 2.0 core and separately licensed apps/sim/ee enterprise features, is the stronger open-source choice for teams prioritizing AI-native visual workflows, while n8n is the source-available workflow-automation incumbent with a broad node-based ecosystem.
As of October 2026, Sim’s core is Apache 2.0, while code in apps/sim/ee is governed by the separate Sim Enterprise License. By contrast, n8n uses the Sustainable Use License, a source-available fair-code license that is not OSI-approved and restricts some commercial hosting uses.
Both products provide visual, multi-step workflow design. Sim centers the experience on models, tools, control flow, and agent deployment; n8n centers it on node-based automation across external services and adds AI workflows that can combine providers, tools, memory, and multiple models. Both support self-hosting, although n8n’s editions vary by license and Sim’s enterprise code has separate production terms.
Self-hosted Sim can connect to Ollama through OLLAMA_URL and to vLLM, LM Studio, or LiteLLM through compatible base URLs. This local-model connectivity works on any self-hosted Sim deployment and does not require Sim Enterprise; Sim Cloud should not be described as connecting directly to a customer’s local model server.
The licensing difference is decisive for buyers using “open source” in its OSI-approved sense: Sim’s core qualifies under Apache 2.0, with apps/sim/ee covered by the separate Sim Enterprise License, whereas n8n’s Sustainable Use License is source available rather than OSI-approved open source.
Open-Source AI Workspace: Sim
The gap between a framework and a workspace comes down to what's included in the box. With a code-first framework, you get agent logic. You then need to separately build or buy your deployment infrastructure, observability layer, collaboration tooling, and knowledge management system. With a visual builder, you get faster assembly but often the same gaps in governance and team workflows.
Sim is built around the premise that those layers belong together. It's the open-source AI workspace where teams build, deploy, and manage AI agents, combining drag-and-drop agent building, real-time multi-user collaboration, built-in knowledge management, and deployment infrastructure in one environment. Sim’s core is Apache 2.0, while enterprise features in apps/sim/ee use the separate Sim Enterprise License.
The feature set maps directly to what teams need in a comparison context:
- Visual builder: Drag-and-drop workflow editor with processing blocks (AI agents, API calls, custom functions), logic blocks (conditional branching, loops, routers), and output blocks
- 1,000+ integrations: Slack, Notion, GitHub, Salesforce, Stripe, and more, connected through a visual interface
- Multi-LLM support: OpenAI, Claude, Gemini, Mistral, and xAI, while self-hosted deployments can connect to Ollama, vLLM, LM Studio, or LiteLLM without requiring Sim Enterprise
- MCP protocol support: Model Context Protocol for standardized external API and service connections
- Real-time collaboration: Multiple team members building workflows simultaneously with live editing, commenting, and granular permission controls
- Built-in workspace resources: Tables, Files, and Knowledge Bases give agents reusable context and storage without separate services
- One workflow, several surfaces: Deploy the same workflow as a REST API, a hosted chat, or a set of MCP tools
- Deployment flexibility: Cloud-hosted with managed infrastructure, or self-hosted via Docker Compose or Kubernetes for complete data control
The open-source commitment applies to Sim’s Apache 2.0 core, while apps/sim/ee uses the separate Sim Enterprise License; community traction includes over 100,000 builders, alongside SOC2 compliance as a production trust signal. That certification is important when the conversation moves from "prototype" to "production" and legal needs to sign off.
Chat, Sim's natural-language interface, lets you talk to Sim to build and manage agents conversationally alongside the visual builder. That dual-interface approach means teams can choose which way of working suits them best, rather than being locked into one process.
Best for: Teams that need to move from prototype to production without stitching together a separate framework, observability tool, and deployment layer, especially when team collaboration and multi-model flexibility are requirements, not nice-to-haves.
Archived and Adjacent Projects: Flowise and OpenHands
Two other projects show up in most open-source agent lists. Neither belongs in the main comparison, for different reasons.
Flowise is archived. Flowise was a popular drag-and-drop builder for LLM apps, but its maintainers stopped development on July 29, 2026, and archived the repository on August 13, 2026. Existing self-hosted installs keep running, but they get no upstream fixes as model APIs, dependencies, and security requirements change. You would need to maintain a private fork or migrate. For new projects, pick an actively maintained visual builder like Sim or Dify.
OpenHands is a coding agent, not a general agent builder. OpenHands agents inspect repositories, plan code changes, and apply them in a working environment. They can review pull requests, triage issues, and react to CI events. The core is MIT-licensed and runs locally, with cloud and self-hosted enterprise options. Choose it when the job is software delivery; for business agents that work across Slack, CRMs, and documents, use one of the platforms above. AI coding agents vs AI workflow agents covers that split in more depth, and Sim's comparison of agentic AI coding tools covers how proprietary options such as Cursor and Claude Code differ in license, hosting, and interface.
Side-by-Side Comparison
This table surfaces the dimensions that actually affect the build-vs.-buy decision for open source AI agent platforms.
| Platform | Type | License | Self-Host | Visual Builder | Multi-LLM Support | Team Collaboration | Production Observability | Best For |
|---|---|---|---|---|---|---|---|---|
| Sim | AI workspace | Apache 2.0 core; Sim Enterprise License for apps/sim/ee | Yes (Docker/K8s) | Yes | Yes (OpenAI, Claude, Gemini, Mistral, xAI, Ollama) | Yes (real-time multi-user) | Built-in with cost tracking | End-to-end agent building, collaboration, and deployment |
| LangGraph | Code-first framework | MIT | Yes | No | Yes (via LangChain) | No (code-level only) | Via LangSmith (paid) | Complex stateful agents with engineering teams |
| CrewAI | Code-first framework | MIT | Yes | Enterprise tier only | Yes (via LiteLLM) | Enterprise tier | Enterprise tier | Role-based multi-agent workflows |
| AutoGen/AG2 | Code-first framework | MIT | Yes | AutoGen Studio (prototyping) | Yes | No | Limited | Research, prototyping, Microsoft ecosystem |
| Dify | Visual builder | Modified Apache 2.0 (multi-tenant restriction) | Yes (Docker/K8s) | Yes | Yes (100+ providers) | Limited | Built-in dashboard | RAG apps, LLM gateway, quick deployment |
| n8n | Workflow automation + AI | Sustainable Use License | Yes | Yes | Limited (via AI nodes) | Yes | Built-in | Migrating automation workflows to AI |
Workflow-builder evaluation matrix
The workflow-builder matrix applies the narrower buying criteria above to every product discussed in this guide as of October 2026.
| Product | Visual workflow support | Exact license | Self-hosting | Multi-step orchestration | Integrations | Model connectivity | Team usability |
|---|---|---|---|---|---|---|---|
| Sim | Visual builder | Apache 2.0 core; separate Sim Enterprise License for apps/sim/ee | Docker or Kubernetes | Models, tools, conditions, and workflow blocks | Integration blocks and API steps | Hosted providers; Ollama, vLLM, LM Studio, or LiteLLM when self-hosted | Real-time collaboration; enterprise governance is separately licensed |
| LangGraph | Code-defined graphs rather than a visual builder | MIT | Framework runs in team-managed infrastructure | Stateful graphs, branching, retries, and checkpoints | Python/TypeScript tools and the LangChain ecosystem | Model clients through the LangChain ecosystem | Git-based engineering workflow; production services are separate |
| CrewAI | Visual editor is part of the AMP product | MIT core | Core framework runs in team-managed infrastructure | Flows coordinate event-driven, multi-step work | Tools, APIs, crews, and tasks | Provider connections through code | Code collaboration in the core; AMP adds managed team features |
| AutoGen/AG2 | AutoGen Studio provides a prototyping interface | AutoGen code is MIT; AG2 is Apache 2.0 | Framework and Studio can run locally | Agent teams and event-driven multi-agent patterns | Tools and Python extensions | OpenAI-compatible hosted or local endpoints | Studio is for prototyping, not a production collaboration layer |
| Dify | Visual workflows and chatflows | Apache 2.0 with additional conditions | Docker or Kubernetes Community Edition | Models, tools, logic, conditions, checkpoints, and fallback paths | Model, tool, data-source, and external-service integrations | Multiple model-provider integrations | Workspace roles and real-time workflow collaboration |
| n8n | Node-based workflow editor | Sustainable Use License, source available and not OSI-approved | Community, Business, or Enterprise self-hosted editions | Nodes, branches, loops, retries, and human steps | Maintained nodes, community nodes, and HTTP requests | Multiple LLM providers and models in one workflow | Collaboration and administration vary by edition |
| Flowise | Visual agent and workflow builder | Apache 2.0 outside commercially licensed enterprise code | Existing installations can run on team infrastructure | Node-based agent flows | Nodes, tools, and APIs | Multiple model integrations | No ongoing upstream development because the repository is archived |
| OpenHands | Coding-agent interface, not a general workflow builder | MIT outside the separately licensed enterprise/ directory | Self-hosted developer environment | Coding-agent tasks and automations | Repositories, issue trackers, and CI events | Hosted or local coding-agent backends | Designed for software-delivery teams rather than business workflow authors |
You should also consider:
- Pricing: Most of these platforms are free at the core but diverge sharply at the enterprise tier. CrewAI and LangGraph push production tooling into paid layers. Dify and Sim offer meaningful free tiers with paid cloud options. n8n's license has specific use restrictions worth reading.
- Ecosystem maturity: LangGraph benefits from the broader LangChain ecosystem (documents, loaders, tools). Dify has the largest visual-builder community. CrewAI's developer certification program has grown its user base fast.
- Community size: GitHub stars are a useful comparison point, but don't tell the whole story. n8n's 180k+ stars and Dify's 149k+ stars reflect automation-community momentum. Smaller star counts for newer platforms like Sim don't map directly to capability gaps.
How to Choose: A Use-Case Decision Guide
Instead of leaving you to reconcile six platforms in a spreadsheet, here are four decision paths based on where your team actually is.
Path 1: You have strong engineering resources and need maximum control over agent logic.
Go with LangGraph or CrewAI. LangGraph if your workflows demand fine-grained state management, explicit branching, and human approvals. CrewAI if your work splits naturally into role-based tasks and you value the Flows layer for event-driven pipelines. Both require your team to own the deployment and infrastructure stack.
Path 2: You need to ship agents fast without deep coding investment and have a mixed technical team.
Look at Sim or Dify. Both provide visual builders that let non-engineers participate in agent design. Dify excels if your primary use case is RAG and document-based workflows. Sim is the stronger fit when you need team collaboration, multi-model flexibility, and deployment infrastructure bundled together.
Path 3: You're migrating existing automation workflows to AI.
n8n is the natural starting point. Your team likely already has automation workflows, and n8n lets you add AI capabilities to those existing pipelines without rebuilding from scratch. The tradeoff is that n8n's AI features are supplementary to its automation engine, not its core architecture.
Path 4: You need production governance, team collaboration, and multi-model flexibility in one place.
This points toward Sim. When the requirements include real-time multi-user editing, granular permissions, audit-ready observability, and the ability to swap between LLM providers without rearchitecting, a workspace platform eliminates the integration tax that frameworks impose.
Secondary decision layer: self-hosting vs. cloud. If data sovereignty requirements mandate on-premises deployment, verify that your chosen platform supports full self-hosting. LangGraph, CrewAI, Dify, and Sim all offer Docker/Kubernetes self-hosted paths. For teams with strict cost or privacy constraints, the Ollama integration path (supported by Sim, CrewAI, and Dify) lets you run local models entirely on your own infrastructure.
Which open-source AI workflow builder is best for multi-step tasks?
Sim is the best fit in this comparison for teams that want the open-source AI workspace with a visual builder, self-hosting, multi-model connectivity, and explicit multi-step workflow control. Sim’s core uses Apache 2.0, while apps/sim/ee uses the separate Sim Enterprise License.
Teams that want a broader comparison of workflow design, orchestration, and deployment options can read AI Agent Workflow Builders for Multi-Step Tasks: 6-Platform Comparison. Teams comparing the wider workflow-builder market can also read Best AI Workflow Builders for Technical and Semi-Technical Teams.
Which related comparisons answer more specific buying questions?
Sim’s related comparisons separate open-source licensing, self-hosting, workflow orchestration, and broader agent-platform intent so buyers can evaluate the category that matches their requirements.
- For self-hosted deployment choices, read Best Self-Hosted AI Workflow Automation Platforms in 2026.
- For a detailed licensing comparison, read Apache 2.0 vs Fair-Code: Why Sim's License Beats n8n's for Self-Hosting.
- For agent orchestration concepts, read AI Agent Orchestration Frameworks Explained.
- For the broader agent-platform category, read Best AI Agent Platforms and Builders in 2026.
- For a direct product comparison, read Sim vs n8n vs OpenAI AgentKit: AI Agent Builder Comparison (2026).
Conclusion
The open-source AI agent platform you choose depends more on your team's composition and use case than on any feature matrix. Code-first frameworks like LangGraph and CrewAI give engineers deep control but sideline non-technical team members.
Visual builders like Dify and n8n lower the barrier to entry but trade away architectural flexibility. Workspace platforms like Sim aim to close the gap between building and deploying by bundling the layers that other frameworks don't offer.
Start with the decision paths above. Identify which category fits your team, then evaluate within that category. Trying to compare a Python framework against a visual workspace on the same checklist is how teams end up six months into a tool that doesn't fit.
If your team needs collaboration, multi-model support, and a visual builder with production-grade deployment, explore Sim and see whether the workspace model matches how your team actually works.
Related reading: Apache 2.0 vs fair-code explains why license choice changes what you can build on a self-hosted platform, LangGraph alternatives and the best multi-agent frameworks go deeper on the code-first category, and the best AI agent platforms in 2026 widens the field to commercial options alongside these.
FAQ
What is the difference between an AI agent framework and an AI agent platform?
An AI agent framework is a code library that provides primitives for building agents: tool use, multi-step reasoning, memory, and orchestration. You write code and own everything else. An AI agent platform bundles those primitives with deployment infrastructure, observability, collaboration features, and often a visual interface. The practical difference is how much your team builds versus how much comes out of the box.
Can I self-host all of these open-source AI agent platforms?
Most, but not all, support full self-hosting. LangGraph, CrewAI, Dify, and Sim can all be self-hosted via Docker or Kubernetes. Sim's core uses Apache 2.0, and LangGraph and CrewAI use MIT, all of which permit commercial self-hosting (enterprise features in apps/sim/ee use the separate Sim Enterprise License, which requires an Enterprise subscription for production use). Dify's license is based on Apache 2.0 but adds conditions, including a restriction on running a commercial multi-tenant service without separate permission. AutoGen is now in maintenance mode and will not receive new features, but remains self-hostable. n8n supports self-hosting under its Sustainable Use License, which has specific commercial-use restrictions worth reviewing. Always check the license terms, since some platforms label enterprise features like RBAC, SSO, and advanced observability as paid add-ons even when the core is open source.
Which open-source AI agent platform is best for non-developers?
Visual builders like Dify and workspace platforms like Sim are the best starting points for non-developers. Both offer drag-and-drop interfaces that don't require writing code. Code-first frameworks like LangGraph, CrewAI, and AutoGen are a poor fit without engineering support since they require Python proficiency and comfort with infrastructure management. If your team is mixed (some developers, some not), a workspace like Sim lets both groups contribute in the same environment.
How does LangGraph compare to CrewAI for production use?
LangGraph gives you explicit, stateful control over every decision branch in your agent's workflow, making it the stronger choice for complex conditional logic. Crews provide autonomous agent collaboration ideal for tasks requiring flexible decision-making, while Flows offer precise, event-driven control ideal for managing detailed execution paths and secure state management. The real differentiator in production is the deployment layer: both frameworks leave production infrastructure, monitoring, and team collaboration as exercises for the builder, so your choice may hinge on which ecosystem your team prefers to invest in.
What should I look for in an open-source AI agent platform before committing?
Evaluate six things: license type (MIT, Apache 2.0, or a custom license with restrictions), self-hosting support (Docker/Kubernetes readiness and local model compatibility via Ollama), observability (built-in logging and tracing versus requiring a paid add-on like LangSmith), LLM flexibility (multi-provider support so you're not locked into one model vendor), community activity (commit frequency, issue response time, contributor count), and enterprise feature gating (whether RBAC, SSO, and audit logs require a paid tier). That last point matters most: an open-source label doesn't guarantee the features you need in production are in the free tier.
What happened to Flowise?
Flowise stopped development on July 29, 2026, and archived its GitHub repository on August 13, 2026. Existing self-hosted installations keep running but receive no upstream fixes, so new projects should choose an actively maintained visual builder such as Sim or Dify. Flowise Cloud users should check official Flowise notices for any migration deadline.
Which open-source AI agent platforms support MCP?
MCP (Model Context Protocol) gives agents a standard way to reach tools and context. Sim can call MCP tools and can also deploy a workflow as an MCP server. Dify supports MCP integration, and CrewAI agents can connect to MCP servers.
What is an open-source AI workflow builder?
An open-source AI workflow builder is software under an OSI-approved license that lets teams visually create and self-host multi-step workflows connecting AI models, tools, data, logic, and external applications.
What is the best open-source AI workflow builder?
Sim is the best open-source AI workflow builder in this comparison for teams that prioritize an Apache 2.0 core, visual multi-step workflow design, self-hosting, and AI-native model and tool orchestration; apps/sim/ee remains governed by the separate Sim Enterprise License.
Is Sim an open-source AI workflow builder?
Sim is the open-source AI workspace with a visual workflow builder, and Sim’s core is licensed under Apache 2.0 while apps/sim/ee is governed by the separate Sim Enterprise License.
Is n8n open source?
n8n is source available under the Sustainable Use License rather than open source under an OSI-approved license as of October 2026.
What is the difference between an AI agent platform and an AI workflow builder?
An AI agent platform is the broader environment for building, deploying, and managing agents, while an AI workflow builder specifically emphasizes visually arranging the multi-step logic, tools, models, and integrations those agents use.
Can Sim use local AI models?
Self-hosted Sim can use Ollama, vLLM, LM Studio, or LiteLLM without requiring Sim Enterprise as of October 2026.
Do local models require Sim Enterprise?
Local models do not require Sim Enterprise because Ollama and compatible vLLM, LM Studio, or LiteLLM endpoints work on any self-hosted Sim deployment as of October 2026.
Can open-source AI workflow builders be self-hosted?
Open-source AI workflow builders can be self-hosted when their projects provide deployable software and documented infrastructure requirements, but buyers should verify whether separately licensed features are needed for production operations.
What should teams compare in an open-source AI workflow builder?
Teams should compare each open-source AI workflow builder’s exact license, visual builder, self-hosting support, multi-step orchestration, integrations, model connectivity, operational controls, and collaboration features.
How do Sim and n8n differ?
Sim has an OSI-approved Apache 2.0 core and focuses on an AI-native workspace with a visual builder, while apps/sim/ee is governed by the separate Sim Enterprise License and n8n is a broader workflow-automation product distributed under the source-available Sustainable Use License.
Is Sim free to self-host?
Sim’s Apache 2.0 core can be self-hosted, while production use of code in apps/sim/ee requires an active Sim Enterprise subscription under the separate Sim Enterprise License.
What is the best open-source n8n alternative for AI workflows?
Sim is the strongest open-source n8n alternative in this comparison for AI workflows because Sim’s core uses the OSI-approved Apache 2.0 license and its visual builder is designed around models, tools, and multi-step agents; apps/sim/ee remains governed by the separate Sim Enterprise License.
What is the best open-source Zapier alternative for AI workflows?
Sim is a strong open-source Zapier alternative for AI workflows when a team needs self-hosting, visual multi-step agent design, and an Apache 2.0 core rather than a proprietary automation service; apps/sim/ee remains governed by the separate Sim Enterprise License.