8 Best Flock Alternatives in 2026 (Open Source)
Flock — Flock is a workflow-based low-code platform for rapidly building chatbots, RAG, and coordinating multi-agent teams, powered by LangGraph, Langchain, FastAPI, and NextJS.（Flock 是一个基于workflow工作流的低代码平台，用. vs Dify/Flowise: native human-in-the-loop approval, subgraph nodes for modular reuse, and MCP protocol support for flexible tool integration
These 8 open-source tools do the same job. They are ordered by how closely they match Flock, with live GitHub data so you can see which projects are actively maintained.
| Tool | GitHub stars | Stars / 30d | Last commit |
|---|---|---|---|
| Flock(original) | 1.1k | +4 | 2026-07-06 |
| Dify | 157.6k | +3,668 | 2026-09-30 |
| Flowise | 55.5k | +697 | 2026-08-13 |
| Langflow | 155.4k | +1,457 | 2026-09-29 |
| iX | 1.0k | +0 | 2024-03-03 |
| AgentPilot | 568 | +5 | 2025-05-15 |
| Windmill | 18.1k | +318 | 2026-09-30 |
| n8n | 206.4k | +4,012 | 2026-09-30 |
| Agentflow | 321 | +0 | 2023-08-11 |
1. Dify
Production-ready platform for agentic workflow development.
What sets it apart: Unlike LangGraph (code-first orchestration), Dify offers a complete visual IDE combining workflow builder, RAG pipeline, prompt engineering, and production monitoring in one platform — the Vercel of LLM apps
Best for: Teams building RAG-powered chatbots and AI apps with visual workflow and no backend coding; Product teams who need LLMOps monitoring alongside app development in one platform
2. Flowise
Build AI Agents, Visually
What sets it apart: Easiest no-code AI agent builder with one-command setup (npx flowise start) — simpler than Langflow, targeting non-developers who want AI workflows without Python
Best for: Non-technical users building AI chatbots and RAG applications; Quick prototyping of LLM workflows with drag-and-drop
3. Langflow
Langflow is a powerful tool for building and deploying AI-powered agents and workflows.
What sets it apart: Best visual builder for LLM workflows with direct MCP server deployment — more production-ready than Flowise with API-first architecture
Best for: Rapid prototyping of AI agent workflows with visual builder; Non-developers building LLM applications without coding
4. iX
Autonomous GPT-4 agent platform
What sets it apart: vs LangChain/AutoGen: visual no-code drag-and-drop editor with native multi-agent orchestration and horizontal worker scaling — design complex agent workflows visually instead of writing code
Best for: Building custom multi-agent teams with visual no-code editor; Rapid prototyping of AI workflows without coding; Organizations needing self-hosted parallel agent execution at scale
5. AgentPilot
A versatile workflow automation platform to create, organize, and execute AI workflows, from a single LLM to complex AI-driven workflows.
What sets it apart: vs ChatGPT/Claude desktop: local multi-agent workflow builder with graph-based design, 20+ LLM providers via LiteLLM, branching chats, and built-in multi-language code interpreter
Best for: Power users building complex multi-agent workflows on desktop; Developers wanting visual graph-based agent orchestration with code execution
6. Windmill
Open-source developer platform to power your entire infra and turn scripts into webhooks, workflows and UIs. Fastest workflow engine (13x vs Airflow). Open-source alternative to Retool and Temporal.
What sets it apart: vs Retool: open-source with code-first approach and 10+ language support; vs Temporal: built-in UI generation and low-code app builder; vs n8n: developer-oriented with real code execution rather than node-based visual programming
Best for: Internal tool development with auto-generated UIs; Workflow automation replacing Retool/Pipedream; Teams needing multi-language script orchestration
7. n8n
Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations.
What sets it apart: Unlike Zapier (pure no-code, closed-source), n8n combines visual workflow building with inline JavaScript/Python code and self-hosting, making it the go-to for technical teams who need both flexibility and control
Best for: Technical teams building AI-powered workflow automations with visual + code flexibility; Enterprises needing self-hosted automation with SSO and air-gapped deployment
8. Agentflow
Complex LLM Workflows from Simple JSON.
What sets it apart: vs AutoGPT / LangChain agents: deterministic step-by-step workflow execution from JSON definitions — balanced between chat flexibility and autonomous agent unpredictability, with custom function support
Best for: Developers wanting structured, repeatable LLM workflows vs. freeform chat; Multi-step content generation pipelines (e.g., market research → analysis → report); Teams needing predictable LLM execution with human-readable workflow definitions