LangBot vs building your own bot
Should you write your own Slack/Telegram/WeCom bot around an LLM API, or use LangBot? Compare the work involved: adapters, sessions, streaming, group chats, knowledge bases, monitoring and upgrades.
Last reviewed:
In short
A single-platform bot that forwards messages to an LLM is a weekend project. The work grows with every platform and requirement: webhook verification and encryption, message formats, streaming limits, group-chat triggers, sessions, knowledge retrieval, tool calls, monitoring and keeping up with platform API changes. LangBot packages that layer so you only write what is specific to your business.
| LangBot | Building it yourself | |
|---|---|---|
| Natural-language control | Tell the Workspace Assistant or an authorized agent what to change: routing, pipelines, replies, group actions; every write is confirmed or permission-gated | Build it yourself |
| Time to first bot | Minutes at cloud.langbot.app, no deployment | Write, host and operate it yourself |
| Models | LangBot Models built in, plus any OpenAI/Anthropic-compatible API with fallback models | Integrate each provider and its billing yourself |
| Set-up help | Workspace Assistant and a web panel | Edit code and config |
| Extending it | Stable plugin interfaces and a Workspace API with explicit permissions | Your own codebase |
| Platform adapters | Ready for 18+ platforms | Write and maintain one per platform |
| Sessions, triggers, rate limits | Configured per pipeline | Implement yourself |
| Knowledge base, MCP, Skills, sandbox | Built in | Implement or integrate yourself |
| Custom business logic | Plugins (Python SDK) or an external agent platform | Anywhere in your code |
| Monitoring | Conversation and run logs in the web panel | Build your own |
What LangBot gives you out of the box
No deployment: cloud.langbot.app
Sign up and build a bot in your browser. Free plan, public URL included.
cloud.langbot.app- Sign up
- Pick a platform
- Bot online
Details ›
Sign up at cloud.langbot.app and build a bot in the browser with a setup wizard. There is a free plan, the public HTTPS URL webhook platforms need is included, and a Dedicated environment is available for production. Self-hosting stays free and unlimited.
Official model service: LangBot Models
Top models ready to use. No API key to apply for.
- Claude
- GPT
- Gemini
- DeepSeek
- Qwen
- GLM
- Kimi
- Grok
No API key neededDetails ›
Claude, GPT, Gemini, DeepSeek, Qwen, GLM, Kimi, Grok and more, plus embedding, rerank and speech models, with no model API key to obtain. Cloud plans include monthly credits; self-hosted instances signed in with a LangBot Space account get the models added automatically with a free starter quota. Your own API keys keep working alongside.
Control your bots in natural language
Say what should change. Agents and the assistant carry it out after you approve.
In the support group, only answer when someone @mentions the bot, and welcome new members.Routing: reply on @mentiongroup.member_joined → welcomeApplied after you confirmDetails ›
Agents, a unified API and standard platform events and APIs work together. Every Workspace operation (bots, routing, pipelines, models, knowledge bases, monitoring) is one catalog shared by the HTTP API, the MCP server, the Workspace Assistant and plugins, and adapters expose standard platform APIs such as send, reply, edit and recall messages, query groups and members, and, where the platform allows, mute or remove members. So you can change how a bot behaves by telling the Workspace Assistant, or let an authorized agent act on the platform from a chat instruction. Writes need your confirmation or an explicit permission grant.
Workspace Assistant
Describe the goal; review the proposed changes; confirm.
Proposed changes
- Create pipeline “Support”
- Attach knowledge base “FAQ”
- Bind to the WeCom bot
ConfirmDetails ›
Describe what you want in plain language. The assistant inspects your existing models, knowledge bases and pipelines, proposes a configuration and applies it only after you confirm each change.
Rich APIs and flexible tools
One set of operations behind HTTP, MCP, CLI and webhooks; agents get MCP, Skills, a sandbox and knowledge bases.
One Workspace API
HTTP APIMCP ServerlbctlWebhooksAgent tools
MCPSkillsSandboxRAGEventsDetails ›
An HTTP Service API with API keys, LangBot's own MCP server, the
lbctlCLI and outgoing event webhooks for your systems. Agents can use MCP tools (stdio and HTTP), Skills, a code sandbox (Docker, nsjail or E2B), knowledge bases with rerank, and platform events such as member joins, reactions and card clicks.Rich, stable plugin interfaces
Seven component types in an isolated runtime, with permissions you grant.
Plugin Runtime · isolated process
RunnerToolCommandEventListenerKnowledgeEngineParserPageCertified PluginsDetails ›
Plugins run in a separate Plugin Runtime process and are hot-pluggable. The Python SDK offers Runner, Tool, Command, EventListener, KnowledgeEngine, Parser and Page components, and a Workspace API whose operations a plugin must declare and a member must grant. The LangBot Space marketplace lists plugins, MCP servers and Skills, with reviewed Certified Plugins.
Open source, with official backing
Apache 2.0, official hosting and enterprise support.
Apache 2.0Free commercial useOfficial CloudEnterprise SupportDetails ›
Apache 2.0 with free commercial use, an official managed service, and optional Enterprise Support with contracts and invoices.
Build it yourself when
You target one platform, the logic is unusual, and you have time to maintain the platform integration.
Use LangBot when
You need several platforms, non-developers manage the bot, or you want knowledge bases, tools and monitoring without building them. Custom logic still fits: write a plugin, or put it in a Dify, n8n, Coze or Langflow app that LangBot calls.