Open source alternative to Relevance AI and Voiceflow
Last verified
Manage a team of AI agents operating 7x24 across Discord, Slack, and Telegram, with scheduling, memory, and 10,000+ tools and plugins.
LobeHub's IM gateway puts agents directly in your existing channels, so team members can delegate tasks without leaving the messaging tool they already use. Agents receive requests, hand work to external tools through MCP skills, and report back in-thread.
Your team does not use these messaging platforms, or the goal is a customer-facing chat interface rather than internal team automation.
The 10,000+ MCP-compatible skill library connects agents to the APIs and tools developers already use. The Agent Builder starts from a text description, auto-configures the agent, and makes it available immediately across channels and projects.
You need a visual drag-and-drop no-code flow designer; LobeHub's configuration approach is more developer-oriented than a point-and-click interface.
Commercial agent orchestration services charge per execution, per seat, or both. LobeHub shifts costs to model API usage and hosting, which are easier to predict and control at scale. The White-Box Memory keeps all agent history on your own infrastructure.
Your organization lacks the technical capacity to manage a self-hosted service. A managed service may be worth the recurring cost if DevOps resources are limited.
LobeHub's structured White-Box Memory lets agents learn from your work over time and adapt their behavior. Unlike opaque memory systems, you can inspect and edit what each agent retains, which is useful for regulated industries or organizations with strict data governance requirements.
You are running short-lived, stateless tasks where memory between sessions adds no value. For simple one-off queries, the memory overhead is unnecessary.
Today's AI agents are task-driven, one-off tools. They lack shared context, live in isolation, and require manual hand-offs between conversations, windows, and models. Users end up toggling between fragmented chat sessions, losing context each time, with no way to form structured, recurring workflows.
The deeper challenge is coordination. When you work with multiple AI agents, there is no way to assign them to channels, schedule work while you are offline, share context between agents, or see a unified view of what your AI team actually did. Without persistent memory, each conversation starts from zero, and nothing the agent learned yesterday carries into today's work.
Agents join your existing messaging channels as first-class members. The product website shows an agent reading 2,340 messages and 128 docs in a #general channel in one session, then operating inside that same channel for follow-up requests. No separate app is required; your agents work where your team already communicates.
The Agent Builder turns a plain-text description of what you need into a configured agent. Connect it to any capability in the MCP-compatible skill library, which covers over 10,000 tools and plugins, so agents have access to the integrations they need without manual API wiring.
Agent Groups assemble the right agents for a task and let them work in parallel with shared context. The Pages feature lets multiple agents write and refine content together. Projects organize work by context, and the Workspace gives teams a shared space with clear ownership and visibility across the organization.
Personal Memory builds a structured, editable record of how you work. Unlike global or opaque memory in other systems, LobeHub's memory is transparent: you can inspect what each agent remembers and correct it. Agents update their memory from your interactions and use it to act at the right moment.
The Schedule feature lets you configure timed agent runs. Agents execute at the right time without requiring you to stay online to trigger them. This covers recurring tasks like daily reports, monitoring jobs, or batch operations assigned to specific agents in a project.
Both tools let you build and run AI agents that connect to external tools and automate workflows. The key difference is deployment model: Relevance AI is a managed cloud service with no self-hosting path, while LobeHub runs on your own infrastructure.
| Feature | LobeHub | Relevance AI |
|---|---|---|
| Self-hosting | Yes (Docker, Vercel, and others) | No |
| License | Apache-2.0 + LobeHub Community License | Proprietary |
| Agent memory | Structured, editable personal memory | Cloud-managed |
| IM gateway | Discord, Slack, Telegram native | Via integrations |
| Pricing | Free to self-host (API key costs only) | Per-usage cloud plans |
LobeHub is the better choice when your team needs self-hosted infrastructure, wants to avoid per-execution cloud pricing, or needs agents that live natively inside Discord, Slack, or Telegram. Relevance AI is worth considering for teams that prefer a fully managed, no-code cloud experience where owning the infrastructure is not a goal.
Voiceflow focuses on building conversational AI products and customer-facing chatbot flows, while LobeHub targets internal team workflows with multi-agent coordination and IM-native deployment.
| Feature | LobeHub | Voiceflow |
|---|---|---|
| Self-hosting | Yes | No |
| License | Apache-2.0 + LobeHub Community License | Proprietary |
| Primary use case | Internal multi-agent team operations | Customer-facing conversation design |
| IM integration | Discord, Slack, Telegram native | Via API integrations |
| Agent scheduling | Built-in 7x24 scheduler | Not a core feature |
LobeHub is a stronger fit for teams that need agents coordinating internally across messaging tools, with scheduling and persistent memory, rather than building polished customer-facing conversation products. Voiceflow is worth considering when the goal is a managed interface for end-user conversation design and the team does not need self-hosted infrastructure.
Deploy LobeHub using Docker with an initialization script.
```bash
bash <(curl -fsSL https://lobe.li/setup.sh)
docker compose up -d
```LobeHub is released under Apache-2.0 with LobeHub Community License additional terms, a fair-code arrangement. You may self-host it and use it commercially, including as a frontend or backend service, without modifying the source code. Distributing a derivative work of LobeHub requires a separate commercial license from LobeHub LLC.
Yes. The LobeHub Community License explicitly permits self-hosting and commercial use as a frontend or backend service, as long as you are not distributing a derivative work. This covers running LobeHub for your team, your company, or your clients on infrastructure you control, paying only for the hosting and model API keys you supply.
LobeHub supports multiple model providers. The GitHub repository documents integrations with OpenAI GPT models, Anthropic Claude, Google Gemini, DeepSeek, and GLM models among others. You configure models through environment variables, and you can restrict or extend the model list using the OPENAI_MODEL_LIST setting. LobeHub also supports a proxy URL for teams using third-party model providers.
The simplest path is Docker: run the initialization script from the README, then start the service with docker compose. One-click deployment is also available on Vercel, Zeabur, Sealos, RepoCloud, and Alibaba Cloud for teams using existing cloud accounts. All paths require configuring at least one model provider API key before agents can respond.
Agent Groups assemble multiple specialized agents to work on a task in parallel, with shared context. A single agent handles one conversation or task at a time, while an Agent Group routes work to the right agents, allows them to collaborate on the same project or document through the Pages feature, and returns a coordinated result. This is useful for tasks that benefit from parallel review, such as a draft reviewed by a research agent and an editing agent at the same time.
Alternative to
Relevance AI
Visual AI workflow builder that deploys as an API or MCP server.
Alternative toRelevance AI
Visual AI workflow builder with RAG, agents, and self-hosting
Alternative to
Microsoft Copilot Studio
Self-hosted AI agent canvas for every business function
Alternative toClaude Managed Agents
Bash microharness for agents that think continuously
Alternative to
Vapi
Open source voice agent builder with telephony and BYOK AI support
Self-host LobeHub on a Hostinger VPS
Keep it running 24/7, even when your laptop is off. A Hostinger VPS gives it a server of its own.
Choose a plan
Starts from $6.49/mo
Apply
Extra 10% off yearly plans
Install LobeHub
On a server that stays on
Start building
Focus on the app, not the server
Sponsors
Alternative to
Superhuman
AI email assistant that drafts replies and clears your inbox
Alternative to
Grok Bot
AI teammates you own: your keys, your model, your machine.
Live monitoring on LinkedIn, X, Reddit & more. Agentic social listening for startups.
The projects we add and every new guide. Free, unsubscribe any time.
Claude Code to 
OpenCode
Jira to 
Windshift
Notion to 
AppFlowy
Slack to 
Mattermost
Linear to 
GitLab
Wispr Flow to 
Handy
Arc to 
Zen Browser
CapCut to 
Concat
Grok Bot to 
rakazo
Superhuman to 
Inbox Zero
Airtable to 
AITable
Muse to 
OpenClaw
Railway to 
Coolify
GitHub Copilot to 
Cline
Datadog to 
NetdataConfluence to 
Wiki.js
Microsoft Teams to 
Rocket.Chat
Pinecone to 
Qdrant
Zapier to 
n8n
1Password to 
KeePassXC
Devin AI to 
OpenHands
Firebase to 
Supabase
Contentful to 
Strapi
Adobe Premiere Pro to 
Shotcut
