Choose Your Execution Layer
Carryall handles who can access what (authorization + audit). You still need something to handle what agents actually do — planning tasks, receiving messages, coordinating tool calls. That's the execution layer.
At a Glance
| OpenClaw | LangChain | AutoGen | |
|---|---|---|---|
| Install | npm install -g openclaw | pip install langchain | pip install autogen |
| Language | Node.js / TypeScript | Python | Python |
| MCP Support | Native (3,200+ skills) | Plugin-based | Limited |
| Channels | 20+ built-in | Manual | Manual |
| Carryall Integration | Plugin (auto-discovers tools) | Custom BaseTool wrapper | Custom agent wrapper |
| UI | Built-in Control UI | None (code-based) | None (code-based) |
| Setup Time | ~20 min | ~30 min | ~30 min |
Detailed Comparison
OpenClaw
RecommendedSelf-hosted, multi-channel personal AI assistant framework. Hub-and-spoke architecture with a WebSocket gateway routing messages across 20+ platforms. MCP is first-class — every Carryall tool becomes a native skill automatically.
Best for: Personal assistants, Telegram/Discord/WhatsApp bots, teams who want a UI out of the box
npm install -g openclaw@latest && openclaw onboard- +Native MCP — Carryall plugin auto-discovers all tools on startup
- +20+ messaging channels (Telegram, Discord, WhatsApp, iMessage, Slack)
- +Built-in Control UI at port 18789
- +ClawHub marketplace with 3,200+ skills
- +Local-first: runs on any Mac/Linux hardware
- -Node.js — friction if you live in Python
- -Single-user trust model (not multi-tenant by default)
- -Newer project — active development, some rough edges
LangChain
Python-native framework for building custom agent pipelines. Excellent RAG and document retrieval patterns. Large ecosystem of vectorstores, document loaders, and chains. LangGraph adds complex stateful workflows.
Best for: Python developers building custom pipelines, RAG systems, or document processing workflows
pip install langchain langchain-community- +Python-native — fits existing ML/data science stacks
- +Excellent RAG/document retrieval patterns
- +Large ecosystem (vectorstores, loaders, chains)
- +LangGraph for complex agent workflows with state
- -No built-in UI or channel integrations
- -Telegram/Discord requires separate libraries
- -MCP support is add-on, not first-class
- -Carryall integration requires custom BaseTool wrapper
AutoGen
Microsoft-backed multi-agent framework where agents coordinate, debate, and verify each other's outputs. Native support for complex multi-agent research and verification workflows.
Best for: Complex tasks requiring multiple agents debating, researching, or verifying each other
pip install autogen- +Native multi-agent framework (agents talk to each other)
- +Good for research-style tasks (propose → critique → refine)
- +Microsoft-backed, active development
- +Works well with local models
- -Steeper learning curve
- -No built-in channel integrations
- -Overkill for simple personal assistant use cases
- -Carryall integration requires custom agent wrapper
Decision Guide
Do you want messaging channels out of the box?
Yes → OpenClaw
Are you primarily a Python developer building custom pipelines?
Yes → LangChain (simple pipelines) or AutoGen (multi-agent)
Do you need agents to verify each other's outputs?
Yes → AutoGen
Are you building a product for non-technical users?
Yes → OpenClaw (has a UI they can use)
Not sure?
Start with OpenClaw — easiest path to a working Carryall stack
Carryall works with any execution layer. The plugin format differs, but the core pattern is always the same: call carryall_compile_policy with your intent, use the returned envelope for vault access, and let the audit log do the rest.