Panoramic Comparison and Analysis of Open Source AI Agent & Workflow Platforms

While organizing the AI Resource Library recently, I noticed a surge of popular open source AI Agent projects in the past two years. This article selects the seven most trending open source projects and analyzes their performance across key dimensions: visualization experience, self-hosting capability, license restrictions, community activity, and sustainability. The seven projects are: Coze Studio/Loop, n8n, Dify, FastGPT, RAGFlow, LangGraph, and Fabric.

Comparison of Popular Open Source AI Agents

Before diving into a detailed comparison, here’s a table summarizing their performance across functional positioning, ecosystem extensibility, visualization experience, self-hosting capability, license restrictions, community activity, and sustainability to help readers quickly build an overall understanding.

Note: Star ratings: ★★★★★ (Highly recommended), ★★★★☆ (Recommended), ★★★★ (Usable)

Comparison insights:

• Coze Studio/Dify/n8n excel in functionality, ecosystem, visualization, and self-hosting, making them ideal for long-term investment.
• FastGPT/RAGFlow/LangGraph have unique strengths for specific scenarios or deep developer customization, with slightly lower but still excellent ratings.
• Fabric is lightweight and flexible, suitable for individuals and small teams to try out, but long-term reliance requires attention to community maintenance.

Core Positioning & Architecture

When comparing the core positioning and architecture of each platform, it’s important to clarify their design intent and main application scenarios. The following list briefly outlines the positioning and architectural features of the seven open source AI Agent and workflow platforms, helping readers quickly grasp their technical direction and applicable domains.

• Coze Studio & Coze Loop (ByteDance open source): Positioned as a one-stop AI Agent visual development and optimization platform, covering the entire process from development and deployment to performance optimization. Coze Studio offers a high-concurrency enterprise-grade platform based on Go microservices and React/TypeScript frontend, with a powerful workflow engine and plugin system, supporting drag-and-drop node construction for Agent workflows. Coze Loop focuses on Prompt development, debugging, and optimization, providing a visual playground, multi-model comparison, automated response quality evaluation, and monitoring. Used together, they enable efficient Agent development and full lifecycle quality assurance. See cozestudio.studio.
• n8n (n8n.io startup): A general open source workflow automation platform known for combining visualization and code flexibility. Originally designed for integrating hundreds of third-party services and APIs for task automation and data orchestration, n8n now includes native AI features and LangChain Agent support. Built with Node.js and a browser-based visual editor, it supports custom code nodes (JavaScript/Python) and branching logic, suitable for both technical and low-code users.
• Dify (LangGenius open source): A production-grade LLM app development and operations platform blending Backend-as-a-Service and LLMOps concepts. Dify provides core components for Prompt orchestration, RAG, Agent framework, model management, and data monitoring. Backend uses Python + Flask + PostgreSQL, frontend is Next.js. Dify supports various app types (chat Q&A, Agent, Workflow) and rich LLM support, emphasizing a “one-stop” solution. Its workflow engine supports visual multi-layered flows and complex logic. See Dify docs and feature specs.
• FastGPT (labring open source): Focused on enterprise knowledge base Q&A and automated workflow construction. FastGPT offers out-of-the-box data preprocessing, vector retrieval, RAG building, and a visual Flow module for complex Q&A workflows. Its main use is enabling users to quickly train dedicated knowledge base AI assistants and automate Q&A and follow-up actions. Supports hybrid retrieval + reranking for accuracy, with multi-format document parsing and vectorization.
• RAGFlow (InfiniFlow open source): A deep document understanding RAG engine for high-quality, explainable retrieval-augmented generation. RAGFlow emphasizes handling complex format documents, extracting insights from unstructured data with traceable references. Its architecture provides end-to-end RAG workflows, including parsing, chunking, indexing, retrieval, and reranking. Supports graph-based workflow orchestration for “Agentic RAG” scenarios.
• LangGraph (LangChain team open source): A framework for building complex AI Agent workflows. LangGraph offers graph-based Agent orchestration, allowing developers to design intelligent agent logic like flowcharts. Built atop LangChain, it emphasizes stateful, long-running agents with persistence and recovery. LangGraph is a code framework (Python library), providing robust execution, human-in-the-loop review, memory storage, and monitoring.
• Fabric (Daniel Miessler open source): A “human augmentation” general AI automation framework. Fabric provides a modular system where crowdsourced AI prompts are packaged as composable instruction units, helping users automate daily tasks. Its philosophy is to build a “universal AI layer,” invoked via CLI or simple config. Fabric is developer-oriented, combining CLI and scriptable workflows.

In summary, Coze Studio/Dify/FastGPT focus on complete platforms for low-barrier AI app building with visual interfaces and multi-function integration; LangGraph/Fabric are developer frameworks for maximum flexibility; n8n sits in between as a general workflow tool with added AI modules. Coze Loop and RAGFlow specialize in specific areas: performance tuning and deep RAG engines. Each project’s architecture suits different scenarios.

Plugin & Ecosystem Extensibility

Evaluating plugin and ecosystem extensibility involves understanding how each project supports third-party integration, tool extension, and community contributions. The following list details the plugin mechanisms, extension methods, and ecosystem maturity of the seven platforms.

• Coze Studio & Loop: Unified plugin system for custom plugins integrating third-party APIs, tools, or services into Agent workflows. Official plugin definitions, invocation, and management mechanisms are open sourced, with rich examples and community extensibility. Loop focuses on Prompt testing/model comparison, but can be extended via Studio’s plugin system.
• n8n: Strong extensibility via node mechanism—each integration is a node, with 400+ official nodes and ongoing community contributions. Developers can write custom node plugins in JS/TS, and use Function nodes for inline JS/Python logic. Ecosystem covers a wide range of integrations, with Fair-code license limiting commercial SaaS use but not custom node development.
• Dify: Built-in plugin system for Agent tool extension, supporting OpenAI Plugin spec and OpenAPI spec imports. 50+ official tools (Google Search, DALL·E, Stable Diffusion, Wolfram Alpha, etc.), with developer docs for custom plugins. Also features model and vector DB adapters, forming a complete LLMOps ecosystem.
• FastGPT: Supports plugins for workflow and tool invocation, with “tool call” and “plugin” nodes for reusable logic. Code sandbox for custom code execution. Focused on enterprise integrations (WeChat, DingTalk, CRM, etc.), with OpenAI-compatible API for easy integration.
• RAGFlow: More of a developer library, with extensibility via custom parsers, vector models, retrieval strategies. Extension is typically through custom templates, connectors, or pipeline parameters. Apache2.0 license allows free modification, but third-party plugin market is limited.
• LangGraph: No direct “plugin market,” but integrates seamlessly with LangChain ecosystem (tools, memory, retrievers, etc.). Developers can use LangChain components as LangGraph Agent modules. Ecosystem is the entire LangChain/Python ecosystem.
• Fabric: Extensibility via community-contributed prompt script packs (“skills”). CLI tool installs/updates skill packs, which are collections of prompts and logic for specific tasks. MIT license allows free module development and CLI extension.

Dify and Coze Studio offer robust plugin systems with official/community examples; n8n has a vast ecosystem via nodes; FastGPT supports workflow plugins and open API; LangGraph leverages LangChain’s ecosystem; RAGFlow and Fabric are less plugin-centric. For rich third-party integration and low-code extensibility, n8n, Dify, and Coze Studio are advantageous; for flexible customization, LangGraph and Fabric offer more freedom.

Visualization Experience & Programmability

Assessing visualization and programmability focuses on drag-and-drop workflow building, live debugging, and code extensibility. The following list explains each platform’s visualization friendliness and programming flexibility.

• Coze Studio & Loop: Complete visual workflow editor with drag-and-drop nodes (LLM, condition, API call, etc.), live debugging, and step-by-step input/output inspection. RESTful API and JS SDK for integration and plugin extension. Loop specializes in Prompt development/testing with interactive playground and model comparison.
• n8n: Renowned for Zapier-like visual node editor. Browser-based workflow building, node execution debugging, and parameter configuration. Function nodes allow inline JS/Python for custom logic. Combines “low-code + code” for team collaboration.
• Dify: Graphical app and workflow orchestration tools. Prompt IDE for template editing and live model output. Workflow feature with visual canvas for multi-step flows. Open API and SDK for code integration; open source for backend customization.
• FastGPT: User-friendly visual interface for AI Q&A workflows. Document upload for KB training, graphical configuration for Q&A flows. Simple and advanced modes for zero-config or complex workflows. Code sandbox node and open API for developer integration.
• RAGFlow: No end-user GUI; mainly a backend engine/library. Web demo for feature testing, but actual use is via Python SDK/API. Programmability via YAML/JSON config and library functions.
• LangGraph: No standalone GUI; code-driven workflow definition. LangGraph Studio (cloud tool) for visual prototyping, but not open source. Programmability via Python code, with integration into LangSmith for visual debugging.
• Fabric: CLI and config-driven; users edit YAML/JSON or use command-line parameters to describe tasks. Programmability is via CLI invocation or embedding in scripts/apps.

Coze Studio, n8n, Dify, and FastGPT provide robust GUIs for drag-and-drop workflow building. LangGraph, RAGFlow, and Fabric are code-driven, suited for developer customization. Programmability is strong across all, with n8n, Coze, and Dify offering APIs and script nodes, and LangGraph/Fabric being pure code frameworks.

Self-hosting Capability

Analyzing self-hosting capability involves deployment methods, resource requirements, operational difficulty, and enterprise suitability. The following list introduces each platform’s self-hosting features.

• Coze Studio & Loop: Both support self-hosting with Docker Compose one-click deployment (min. 2-core CPU, 4GB RAM). Microservice architecture (Go services + frontend), with PostgreSQL backend. Loop is a standalone service for Prompt optimization. No official CI/CD, but open source allows custom deployment.
• n8n: Emphasizes self-hosting friendliness. Multiple deployment options: Docker, NPX, local install. Docker deployment is simple; supports SQLite/PostgreSQL. Queue Mode with Redis for distributed execution. No official pipeline tools, but workflows can be exported/imported.
• Dify: Open source community edition and commercial cloud. Self-hosting requires backend Python service, frontend Next.js, and optional vector DB. Docker Compose deployment; min. 2-core 4GB. AWS templates for enterprise deployment. No explicit CI/CD, but container images and env config allow upgrades.
• FastGPT: Community and commercial editions. Monorepo with frontend/backend code. Helm Chart for K8s and Docker deployment. 4-core 8GB server recommended. Open source for internal use, but no unauthorized SaaS. Commercial edition for SaaS deployment.
• RAGFlow: Default self-hosting via pip install or Docker image. Min. 4-core 16GB recommended. Can be packaged as backend service. Apache2.0 license allows free commercial use.
• LangGraph: Framework library, not a deployable service. Self-hosting means deploying your LangGraph-based app. MIT license, free for commercial integration.
• Fabric: CLI program installable anywhere. MIT license, free for enterprise use. Lightweight, resource usage depends on AI API and task complexity.

Most projects offer Docker/K8s support and moderate deployment difficulty. n8n and Dify are very self-hosting friendly; Coze Studio/Loop are more complex; FastGPT restricts SaaS use; RAGFlow/LangGraph are developer libraries; Fabric is the lightest. All can run in private environments for data ownership and customization.

License Restrictions

Comparing license restrictions involves understanding each open source license’s impact on commercial use, self-hosting, SaaS, and secondary development.

• Coze Studio & Loop: Apache License 2.0, no extra restrictions. Free for commercial use, modification, and distribution.
• n8n: Sustainable Use License (Fair-code). Free for internal use, no unauthorized commercial SaaS. Not OSI-approved open source; commercial licensing required for SaaS.
• Dify: Apache-2.0-based commercial-friendly license. Community edition is open source, but may restrict unauthorized SaaS. Commercial support available.
• FastGPT: FastGPT Open Source License (Apache2.0-based with extra terms): free for backend commercial use, no unauthorized SaaS. Commercial license available.
• RAGFlow: Apache License 2.0. Free for commercial use and modification.
• LangGraph: MIT license. Free for commercial use and modification.
• Fabric: MIT license. Free for commercial use and modification.

Coze Studio/Loop, RAGFlow, LangGraph, Fabric use permissive licenses, fully suitable for commercial use. Dify, FastGPT, n8n add “no unauthorized SaaS” restrictions. Choose based on your business model and compliance needs.

Community Activity & Maintenance Status

Community activity and maintenance status are reflected in GitHub stars, team investment, contributions, update frequency, and long-term support.

• Coze Studio & Loop: Open sourced July 2025, quickly gained 15k+ stars (Studio ~7.3k, Loop ~2k initially). ByteDance-backed, frequent updates, active issues/discussions.
• n8n: Established 2019, 130k+ stars, strong community, official forum/Discord, frequent updates, commercial sustainability.
• Dify: Launched March 2023, 111k+ stars, LangGenius team, weekly releases, 290+ contributors, active Discord/WeChat.
• FastGPT: LabRing team, 25k+ stars, active in China, frequent updates, commercial/community model.
• RAGFlow: 62k+ stars, InfiniFlow startup, rapid versioning, active issues, growing community.
• LangGraph: LangChain team, 17k+ stars, frequent commits, part of LangChain ecosystem.
• Fabric: Daniel Miessler, 33k+ stars, personal project, active but long-term maintenance depends on community.

See the table below for a summary.

n8n is the most stable with company support and a mature community. Dify, LangGraph have professional teams and rapid iteration. Coze is new but promising with ByteDance backing. FastGPT, RAGFlow are popular but depend on startup resources. Fabric is innovative but less certain for long-term reliance.

Roadmap & Sustainability

When analyzing the roadmap and sustainability of each platform, it’s crucial to consider future plans, technical evolution, and long-term maintenance guarantees. The following list summarizes the key roadmap highlights and sustainability prospects of the seven major platforms, helping readers quickly assess their long-term investment value and risks.

• Coze Studio & Loop: As part of ByteDance’s strategic initiative, Coze’s open sourcing is seen as the starting point for building an Agent ecosystem. According to press releases, ByteDance open sourced the Coze suite (Studio, Loop, Eino) to rally developers around Agent ecosystem development. The roadmap is expected to focus on lowering the barrier for Agent app creation, enriching plugins and templates, and integrating more large models. Coze Studio may integrate more algorithmic capabilities (supporting new tool protocols, more LLM interfaces), while Coze Loop may develop smarter Agent evaluation systems (such as automated prompt optimization and online learning). If ByteDance remains committed to the Agent ecosystem, it will likely continue investing resources to improve Coze and apply it in products like Feishu and Volcano Engine for validation. ByteDance’s abundant resources ensure strong sustainability for Coze—provided the Agent direction aligns with company strategy (currently, major tech firms are betting on AGI/Agent, so short-term abandonment is unlikely).
• n8n: n8n’s roadmap is gradually shifting from general automation to “AI-native automation.” Recent versions have introduced AI Nodes and LangChain integration, making AI-driven workflow construction easier. Future plans may include: AI assistants to help users build workflows (lowering usage barriers), more AI service integration nodes, and improved collaboration and version control features. As a commercial product, n8n’s roadmap also includes enterprise features (multi-environment deployment, audit logs, etc.) in new paid versions. With a clear revenue model (cloud subscriptions), n8n can continually improve its open source offering. Despite some concerns about its Fair-code license, n8n has found a practical balance between open source and commercial interests. With over four years of stable development, n8n is expected to grow steadily, especially in the AI automation space. For users, n8n’s sustainability is solid, making it a safe long-term choice—just keep up with new releases for the latest AI features.
• Dify: Dify’s website and documentation clearly outline its development direction, including expanding model support (adapting to the latest GPT-4.5 and domestic models), improving visual orchestration, and strengthening team collaboration and permissions. The roadmap includes plans like supporting file-type variables in Q3 2024 and continuously adding built-in tools and vector DB support. The LangGenius team is building Dify as both an open source and commercial product (cloud service and enterprise edition), so Dify will follow a community edition + paid enhanced edition dual path. As long as the LLM app market remains hot, Dify will keep evolving. Sustainability is ensured by key factors: a dedicated team, a large user base (180,000 developer community), and likely funding (not disclosed but probable). This shows Dify is a long-term project, not a fleeting trend. For long-term consideration, Dify is worth investing in—even if the community edition hits a bottleneck, commercial support is available to avoid isolation.
• FastGPT: Community discussions indicate FastGPT will continue to focus on enterprise knowledge base Q&A scenarios. LabRing has launched a commercial edition, and the roadmap may place advanced features in the paid version to sustain open source iteration—similar to many ChatGPT open source alternatives. FastGPT may enhance large model adaptation (supporting more domestic models), knowledge base management (better data annotation and review), and workflow complexity (more node types). The community edition is already powerful; if the commercial edition is just plugin-based, it won’t affect the core. Sustainability depends on whether commercial revenue can support ongoing development. If LabRing’s cloud/private deployments sell well, the community edition will benefit; otherwise, open source maintenance may slow or seek integration with larger platforms. Given FastGPT’s popularity, community volunteers may also drive development. In summary, FastGPT is rapidly updated in the short term; medium- to long-term prospects depend on commercial success—cautiously optimistic.
• RAGFlow: InfiniFlow is likely to make RAGFlow a core part of its product suite, offering paid services (cloud hosting, enterprise support) around the open source engine. The roadmap already includes text, image, and audio parsing, and “Agentic RAG” support. Future directions may include evolving into a general multimodal RAG platform, enabling unified Q&A across various enterprise data formats, and improving explainability and accuracy (e.g., smarter scoring models). With its open source popularity, RAGFlow could become a de facto standard, attracting more community plugins and improvements. Sustainability depends on InfiniFlow’s ability to monetize enterprise editions (with GUI, visual management, etc.), which would fund ongoing engine development. Its 62k GitHub stars may also attract external partnerships or sponsorships. Notably, RAGFlow appeals to large enterprises and could be integrated or acquired by cloud vendors. Regardless, its Apache 2.0 license ensures that even if official support ends, the community can fork and continue development. Thus, RAGFlow has strong vitality and is suitable as part of a long-term solution.
• LangGraph: LangGraph’s future is closely tied to the LangChain ecosystem. Expect LangGraph to keep pace with LangChain updates, remain compatible with new interfaces, and add features based on user feedback (e.g., more workflow templates, simplified multi-Agent collaboration). LangChain has announced future support for a JS version (already has a JS repo) to broaden its application scope. As LangChain commercializes (LangSmith, private deployment), LangGraph will remain free as the open source component. Since LangChain aims to be the foundation for LLM app development, LangGraph is strategically important, and its roadmap will ensure stability and scalability, possibly including an official local visual tool (currently only online Studio). Sustainability is undeniable—LangChain is booming, well-funded, and has a large team. Choosing LangGraph means betting on the LangChain ecosystem, which is wise for long-term investment, though it requires keeping pace with LangChain’s evolution.
• Fabric: As a personal project, Fabric’s roadmap depends on the author’s vision and availability. Daniel aims to provide an “AI layer for daily life,” so future plans may include more skill modules (covering work and life), improved CLI experience, and possibly a simple GUI or web interface for easier onboarding. Without company backing, these are hobby-driven. Fabric will stay popular in the short term due to its novelty and utility, but long-term sustainability relies on community contributions. Other developers have already proposed improvements and forks (e.g., Home Assistant integration). If a collaborative community forms, Fabric could evolve and persist; if not, it may stagnate if the author loses interest. Thus, Fabric is best approached with caution and not relied on as a sole critical business dependency. It’s ideal for individuals and small teams to experiment with automation, but long-term reliance requires backup plans in case maintenance stops.

In summary, most projects show positive roadmaps and growth potential. n8n, Dify, LangGraph have clear company-driven product evolution, ensuring ongoing improvements and long-term investment. Coze benefits from major corporate backing and will likely expand rapidly as long as the Agent direction remains relevant. RAGFlow is growing fast and may become a lasting open source pillar in the RAG field. FastGPT’s future depends on commercial viability—short-term usability is high, but long-term maintenance needs monitoring. Fabric is innovative but less certain due to lack of formal support. Teams looking to adopt these projects should choose based on their risk tolerance: try new projects for cutting-edge features, but select platforms with guaranteed ongoing support for mission-critical systems, and monitor community activity and version upgrades to adjust strategies promptly.

Summary

Open source AI Agent and workflow platforms each have unique strengths; selection should be based on actual needs. Enterprises and teams are advised to prioritize platforms like Coze Studio, Dify, and n8n, which have mature ecosystems, user-friendly visualization, and ongoing investment—ensuring long-term technical evolution and community support. Developers or those with special requirements can opt for flexible solutions like FastGPT, RAGFlow, or LangGraph, but should have sufficient technical expertise. Fabric is suitable for individuals to quickly experiment with automation. Always pay attention to license restrictions for SaaS/commercial use to ensure compliance. When choosing, focus on ecosystem activity, maintenance status, and future roadmap, and prioritize platforms with clear development plans and community support to reduce technical risk and benefit from ongoing innovation.

References:

1. Coze Studio Official Site – ByteDance open source AI Agent visual development platform
2. Dify Official Docs – Production-grade LLM app development and operations platform
3. Dify Feature Specifications – Detailed features and technical specs
4. FreeCodeCamp Open Source LLM Agent Handbook – Open source LLM Agent development guide
5. n8n GitHub Repository – Official codebase for open source workflow automation platform