OpenClaw vs LangGraph
One is a self-hosted assistant you reach from your chat apps; the other is a framework for building agent workflows inside your own backend. Different jobs — here's which fits yours.
OpenClaw vs LangGraph at a glance
Different tools
OpenClaw is a self-hosted assistant and gateway; LangGraph is a framework for building agent workflows. Different jobs.
Choose OpenClaw
For an assistant you reach from WhatsApp, Telegram, or Slack that acts on your files, browser, and scripts.
Both model-agnostic
Neither ships a model — both connect to cloud APIs or local servers like Ollama.
Choose LangGraph
For durable, stateful agent workflows embedded in your own backend, alongside databases and services.
Can combine
OpenClaw as the front-door assistant, LangGraph orchestrating complex workflows behind an API it calls.
Sizing
OpenClaw on cloud models fits 2–8 GB; add a local LLM or a full LangGraph stack (Postgres + Redis) and you're at 8–16 GB.
OpenClaw vs LangGraph: full comparison
An assistant runtime and an orchestration framework, side by side — across type, interfaces, tools, deployment, and who each is for. Based on the official docs and 2026 deployment guides.
| Dimension | OpenClaw | LangGraph |
|---|---|---|
| Type | Self-hosted AI assistant runtime and multi-channel gateway. | Orchestration framework/library for building stateful, long-running agents. |
| Main goal | A personal assistant that reads/writes files, controls a browser, runs shell commands, and coordinates tools via chat channels. | Durable, resilient agent workflows built from graphs of nodes, with persistence and memory. |
| Stack / language | Long-running Node.js Gateway plus plugins; installs on macOS, Linux, WSL2, remote Linux. | Python package deployed inside your app stack, often alongside Postgres and Redis. |
| Interface surfaces | Messaging channels, WebChat, voice, Canvas, and an HTTP gateway. | Library/API — you expose your own HTTP, gRPC, or app interfaces on top. |
| Orchestration | Sessions, memory, tools, and multi-agent routing centred on assistant behaviour. | Graph-based orchestration with branching, retries, streaming, and human-in-the-loop. |
| Tools & actions | Built-in browser control, filesystem access, shell execution, scheduled jobs, MCP, email, calendars. | Integrates tools through your own code; orchestrates tool calls and state, not OS/browser primitives. |
| Channels | 20+ messaging platforms; one Gateway serves several at once. | No built-in channels; wire it into whichever UIs you choose. |
| Models Both agnostic | Connects to cloud LLM APIs and local servers (Ollama, LM Studio). | Model-agnostic; configured in code via LangChain and provider SDKs. |
| Deployment | Long-lived Gateway on laptops, homelabs, VPSs, or AWS AMIs; secured via firewalls, SSH, VPN. | Runs inside your Python app stack; typically app container + Postgres + Redis on a VPS/VM. |
| Target users | Developers and power users wanting a practical assistant reachable from their chat apps. | Teams building production agent apps needing strong orchestration, state, and backend integration. |
What OpenClaw is best at
OpenClaw is best when you want a self-hosted assistant that operates in real chat channels and can take actions on your system:
- A multi-channel assistant: one Gateway serves WhatsApp, Telegram, Slack, Discord, Signal, iMessage, and more.
- Action-centric automation: read and write files, run shell commands and scripts, and control a browser.
- Practical jobs like inbox triage, calendar management, QA workflows, and small personal automations.
- Self-hosted privacy: runs on your own hardware or VPS, keeping data local to the workspace.
- Flexible models: cloud LLM APIs or local servers (Ollama, LM Studio) by cost and privacy preference.
What LangGraph is best at
LangGraph excels when you need durable, stateful agent workflows embedded in your own applications:
- Long-running, stateful agents: graphs of nodes, memory, and persistence across many steps.
- Resilient orchestration: retries, branching, streaming, and human-in-the-loop control.
- Production agent backends embedded in your own app stack with databases and services.
- Model-agnostic orchestration, configured entirely in code via LangChain or provider SDKs.
- Proven in production by teams at companies such as Klarna, Replit, and Elastic.
OpenClaw vs LangGraph: VPS requirements
Neither project publishes official hardware specs, so these are documented patterns from deployment guides. As always, the model and supporting services (database, vector store) drive the size more than the runtime itself.
| Scenario | OpenClaw | LangGraph |
|---|---|---|
| Cloud models only | 2–8 GB RAM, 2 vCPU (assistant + cloud LLM API) | 2 vCPU / 8 GB / ~100 GB (app + Postgres + Redis + proxy) |
| + local LLM on same VPS Local model | 4 vCPU / 8–16 GB / 40–80 GB (one 7–8B model on CPU) | 4 vCPU / 16 GB+ once a local model is added |
| Heavier multi-service | Larger instances for serious, long-running browser automation | 4 vCPU / 16 GB / 200 GB comfortable small-platform tier |
Read these figures carefully: neither OpenClaw nor LangGraph publishes an official VPS sizing table — these are documented patterns from deployment docs and third-party guides. For both tools, hardware needs are dominated by model choice and external services (local LLM, database, vector store), not the runtime or library itself. Benchmark your actual workload before committing.
Frequently asked questions
Is OpenClaw similar to LangGraph?
They solve related but different problems. OpenClaw is a self-hosted personal assistant runtime and gateway that connects chat channels, a browser, and tools to LLMs. LangGraph is a framework for building long-running, stateful agent workflows inside your own backend — it has no assistant UI or multi-channel gateway of its own.
Do OpenClaw or LangGraph include their own models?
No. Both are model-agnostic. OpenClaw connects to external providers (Anthropic, OpenAI, Google, Bedrock, OpenRouter) and local servers like Ollama or LM Studio. LangGraph expects you to configure model providers in code via LangChain or provider SDKs.
Can I use OpenClaw and LangGraph together?
Yes, in principle. OpenClaw can act as the front-door assistant and channel gateway, while LangGraph orchestrates complex workflows behind an API that OpenClaw calls as a tool. There’s no official integration documented, but their roles — assistant gateway vs orchestration library — are complementary.
Which is better for "one assistant on my VPS"?
OpenClaw. It’s designed for exactly that: install the Gateway on a VPS, connect one or more chat channels, and configure an LLM provider. Beginner setups run it on small VPS plans (2–8 GB RAM) with cloud models. LangGraph alone is overkill for a single assistant unless you also need a custom backend and complex orchestration.
Which is better for a production agent backend with many services?
LangGraph. It’s built for long-running agents with persistent state, branching, and integration with databases and external APIs, and is used in production by teams at companies such as Klarna, Replit, and Elastic. OpenClaw can run automation on a VPS, but its core is a personal, multi-channel assistant — not a general orchestration framework.
How much VPS do I need if I just want to try this?
Documented beginner setups: OpenClaw with cloud models only on 2–8 GB RAM, 2 vCPU, and ~20–40 GB disk; a small LangGraph app (no local model) on about 2 vCPU / 8 GB RAM / ~100 GB disk. These aren’t official minimums, but they’re realistic starting points. Adding a local LLM or complex backend quickly pushes you into 8–16 GB tiers.
Capabilities and VPS figures here are drawn from the official OpenClaw and LangGraph documentation and third-party deployment guides, and may change between releases. Neither project publishes official hardware minimums — measure your own workload before committing.
