OpenClaw Agent Templates★

Building an AI agent from scratch — one that can take on a specific role, follow a personality, and plug into your chat tools — usually means writing code and wiring together a framework. But what if you could skip all of that and simply copy a config file for the exact agent you want? That’s the promise behind OpenClaw agent templates, and specifically the open-source library that collects hundreds of them.

The awesome-openclaw-agents library is a free, community-curated collection of 200+ ready-to-use, no-code AI agent templates for the OpenClaw framework. Each one is a copy-paste-ready configuration that turns a blank setup into a working, specialized agent in minutes. In this guide we’ll explain what these templates are, what OpenClaw itself is, how the system works, what you get, and — honestly — where it stands as a newer entry in the AI agent space.

If you’re exploring the wider world of agent tooling, this pairs naturally with frameworks like AgentScope and local agents like AgenticSeek, which we cover elsewhere in the directory.

What is the OpenClaw agent templates library?

The OpenClaw agent templates library — officially the awesome-openclaw-agents repository — is a curated, open-source collection of production-ready AI agent configurations for the OpenClaw platform. Rather than being a framework or app itself, it’s a library of copy-paste-ready templates: pre-written configuration files, each defining a complete agent for a specific job.

The library contains 205 templates organized across 24 categories, spanning use cases from productivity and development to marketing, finance, healthcare, legal, HR, e-commerce, security, and more. The idea is simple and powerful: instead of designing an agent’s behavior from a blank slate, you browse the library, find one close to what you need, copy it, and deploy — no coding required.

It’s released under the permissive MIT license, maintained by the GitHub developer mergisi, and has earned thousands of stars, reflecting real community interest. Contributions are welcome, so the collection keeps growing.

Why no-code agent templates matter

To appreciate why a library like this exists, it helps to understand the friction it removes. Historically, creating a capable AI agent meant becoming, in effect, a small-scale software project. You chose a framework, learned its concepts, wrote code to define the agent’s behavior, connected it to a model and any tools, handled errors, and figured out deployment. For a developer this is doable; for everyone else, it’s a wall.

Templates flip that model. Instead of building an agent, you select one. The hard thinking about how a “customer-success agent” or a “DevOps assistant” should behave has already been done and encoded into a shareable file. Your job shrinks from engineering to curation and customization: pick the closest match, adjust it to your context, and deploy. This is the same shift that made website builders and no-code app platforms so popular — moving the starting line from a blank page to a working draft.

There’s a second, subtler benefit: consistency and quality. A community-curated, production-tested template embeds accumulated best practices that a first-time agent builder wouldn’t know. You inherit sensible defaults rather than repeating others’ early mistakes. And because the templates are open and shared, improvements made by one user can benefit everyone. In a field moving as fast as AI agents, that collective, copy-paste-forward approach is a real accelerant — which is exactly why a 200-template library resonates.

What is OpenClaw?

To understand the templates, you need to understand the platform they’re built for. OpenClaw is an open-source, “config-first” AI agent framework. Its defining idea is that you define agents through configuration files rather than by writing code.

This sets it apart from established frameworks like LangChain or CrewAI, which typically require you to write Python, chain components, or build workflow graphs. OpenClaw’s pitch is minimal setup: describe your agent in a simple file, and the framework runs it. It is self-hostable and integrates with popular messaging platforms — Telegram, Slack, and Discord — so your agents can live where you already chat.

In short, OpenClaw aims to make deploying a functional AI agent as approachable as editing a config file, and the templates library exists to make that even faster by giving you proven starting points.

What is a SOUL.md template?

The heart of the system is the SOUL.md file. This is OpenClaw’s configuration format for defining an agent — its behavior, role, and personality — in a single, readable file. It requires no programming: a SOUL.md is essentially a structured description of what an agent should be and do.

Each template in the awesome-openclaw-agents library is one of these SOUL.md files, tuned for a particular purpose. Want a marketing assistant, a DevOps helper, or a customer-success bot? There’s a SOUL.md for that. You copy the file, register it with a command like openclaw agents add, start the gateway, and the agent comes to life. This “configuration as the agent” approach is what makes the no-code promise real.

The 205 templates and 24 categories

The breadth of the library is one of its biggest selling points. Its 205 templates are organized across 24 categories, giving you a starting point for an enormous range of roles. The categories include:

• Productivity, Development, DevOps, Automation
• Marketing & Content, Creative, Voice
• Business, SaaS, E-Commerce, Freelance, Real Estate
• Finance, Data, Supply Chain, Compliance
• Personal, Education, Healthcare, Legal, HR
• Security, Customer Success

This category spread means the library isn’t just for developers — it covers agents for marketers, business operators, support teams, and everyday personal use. Whatever role you have in mind, there’s a good chance a template already exists that gets you 80% of the way there, ready to customize the rest.

How to use OpenClaw templates

The library is designed to get you from zero to a running agent quickly. According to its quickstart, the typical workflow is:

1. Clone the repository from GitHub to get the full template collection.
2. Copy a SOUL.md template into your own project — pick the one that matches your goal.
3. Configure your environment by setting variables such as API keys in a .env file.
4. Register and run the agent with OpenClaw, launching it via Node.js or Docker.

The README includes a five-minute quickstart using a sample marketing agent, so newcomers can see the whole flow end to end before customizing. There’s also a mentioned deployment service, CrewClaw (crewclaw.com), which can generate a full deploy package — Dockerfile, docker-compose, bot, and README — to streamline getting a template into production.

A closer look: from template to running agent

To make the workflow concrete, imagine you want a support agent that lives in your team’s Telegram group. Here’s how the pieces fit together, based on the library’s documented flow.

You browse the collection and find a customer-success template in the relevant category — a SOUL.md file that already describes a helpful, on-brand support persona. You copy that file into your project. Because it’s a configuration rather than code, you can open it and read exactly how the agent is defined, then tweak it: adjust its tone, its name (which OpenClaw also uses as an identifier), and any role-specific instructions to match your product.

Next, you set up your environment. In a .env file you add the necessary keys — the LLM provider credentials the agent will use to think, and the Telegram binding so it can join your group. This is the step that connects the abstract template to the real services it needs. With that in place, you register the agent with OpenClaw and start the gateway, launching it via Node.js or Docker. Moments later, the agent is live in your chat, responding as configured.

The important thing this illustrates is how little stands between “I want an agent for X” and a working deployment. The heavy lifting — defining behavior — was pre-solved by the template; your effort goes into configuration and personalization. That compression of effort is the entire value proposition, and it’s why having 200+ vetted starting points matters so much: the closer a template is to your goal, the less you have to do.

Who maintains it, and what does it cost?

The library is maintained by the GitHub developer mergisi and is community-curated, meaning contributors submit and improve templates over time. It carries the permissive MIT license, and its thousands of GitHub stars indicate genuine, active usage.

On cost: the templates library and OpenClaw itself are open-source and free. There are no fees to use the templates. Your real costs come from the LLM or API usage your agents consume when they run, and any hosting you choose for self-deployment. Optional services like CrewClaw may offer paid convenience for deployment, but the core path — grab a template, self-host, run — is free.

Who is this for?

The OpenClaw templates library is aimed at people who want working AI agents fast, without building them from scratch. It’s a particularly good fit for:

• Makers and operators who want a functional agent for a specific role (support, marketing, ops) without coding it themselves.
• Developers who prefer to start from a proven config and customize, rather than begin with an empty framework.
• Teams using Telegram, Slack, or Discord who want agents living directly in their chat tools.
• Tinkerers and early adopters exploring the config-first approach to agents.

Because OpenClaw is self-hosted and involves environment configuration, some technical comfort helps — you’ll be handling files, API keys, and either Node.js or Docker. But the no-code SOUL.md approach lowers the barrier significantly compared to writing agent code by hand. You’ll find more agent tooling across our Developer Tools directory.

Common use cases

Given the breadth of categories, the templates support a wide array of applications:

• A marketing or content assistant that drafts and manages content workflows.
• A DevOps or infrastructure agent that helps monitor and manage systems.
• A customer-success or support bot living in your team’s chat platform.
• Personal productivity agents for planning, reminders, and daily tasks.
• Domain-specific helpers across finance, legal, HR, education, and more.

The unifying theme is speed: each template is a shortcut to a deployed, specialized agent, letting you skip the design-from-scratch stage entirely.

Advantages

• No-code, config-first. Define agents with a SOUL.md file instead of writing framework code — a genuinely lower barrier.
• Huge head start. 200+ ready-made templates across 24 categories mean you rarely start from zero.
• Free and open-source. MIT-licensed templates and an open framework, free to use and modify.
• Chat-native. Built-in integration with Telegram, Slack, and Discord puts agents where you work.
• Self-hosted control. Run it yourself, keeping deployment and data in your hands.
• Active community. Thousands of stars and open contributions keep the library growing.

Limitations and honest considerations

In keeping with a straight assessment, here’s what to weigh before adopting it.

• OpenClaw is a newer ecosystem. Unlike well-established frameworks such as LangChain or CrewAI, OpenClaw is relatively new. That means faster change, a smaller (if enthusiastic) community, and less battle-tested tooling. This isn’t a knock on safety — it’s simply the reality of an emerging platform.
• Self-hosting requires discipline. Security depends on your configuration: binding gateways to localhost, protecting API keys in your .env, and not leaving autonomous agents unattended. The library helpfully includes a security best-practices section, but the responsibility is yours.
• Some technical setup is needed. While no coding is required to define an agent, you still handle cloning, environment variables, and running via Node.js or Docker.
• Templates are starting points. They get you most of the way, but real-world use usually means customizing a template to your exact needs and data.

None of these should deter the right user — they’re the normal trade-offs of a young, self-hosted, open-source ecosystem.

Security and privacy

Because OpenClaw is self-hosted, you keep control of where your agents run and what they can access — an advantage over fully cloud-bound platforms. The template library is transparent and open under MIT, so anyone can review it.

Encouragingly, the repository includes a dedicated security best-practices section that addresses real risks: exposed gateways, leaked API keys, and unattended autonomous agents. Following that guidance — restricting network exposure, safeguarding secrets, and supervising powerful agents — is essential. As always, whatever LLM provider your agents connect to will apply its own data policies, which you should review for sensitive work. Handled responsibly, the self-hosted model gives you meaningful control over privacy.

Best practices for deploying template agents safely

Because these templates deploy real, autonomous agents that can act and connect to your chat tools, a little discipline goes a long way. The following habits — several echoed by the repository’s own security guidance — help you use OpenClaw templates responsibly.

• Keep gateways private. Bind your agent’s gateway to localhost or a protected network rather than exposing it to the open internet. An exposed endpoint is one of the most common and serious mistakes.
• Guard your secrets. Treat your .env file as sensitive. Never commit API keys to a public repository, rotate them if they leak, and give each agent only the credentials it truly needs.
• Supervise powerful agents. An autonomous agent left completely unattended can take actions you didn’t intend. Start with human oversight, especially for anything that sends messages or touches important systems, until you trust its behavior.
• Start with one agent. Resist deploying a whole team at once. Get a single template working and understood before expanding, so you can reason about what each agent does.
• Read the template before you run it. Because a SOUL.md is human-readable, take a minute to understand exactly what an agent is configured to do before you deploy it live.
• Review the model provider’s terms. Whatever LLM your agent uses will process its inputs; check that provider’s data policy for anything sensitive.

Following these steps turns a quick template deployment into a genuinely dependable one, which is the difference between a fun experiment and something you can rely on.

OpenClaw templates vs. building agents yourself

It helps to see where this approach fits relative to the alternatives.

Approach	Effort	Flexibility	Best for
OpenClaw templates	Low (copy a config)	Medium (customize SOUL.md)	Deploying a role-specific agent fast
Agent frameworks (AgentScope, CrewAI, LangChain)	Higher (write code)	High (full control)	Custom, complex agent systems
Cloud agent products	Lowest (sign up)	Low (as offered)	Non-technical, hands-off use

Compared to coding an agent on a framework like AgentScope, OpenClaw templates trade some deep flexibility for dramatic speed and simplicity. Compared to a closed cloud product, they offer self-hosting and openness at the cost of doing your own setup. For anyone who wants a specialized agent running quickly — without writing framework code — the templates hit a genuinely useful middle ground.

Getting started

If you’d like to try it, the path is approachable:

1. Browse the library on GitHub and pick a template close to your goal from the 24 categories.
2. Clone the repository and copy your chosen SOUL.md into a project.
3. Set up your environment — add your API keys and settings to a .env file.
4. Run it with Node.js or Docker, or use a deploy helper like CrewClaw to generate a package.
5. Customize and iterate — tweak the SOUL.md to match your exact role, data, and tone.

Start with the quickstart example, confirm it works, then adapt a template to your real use case.

The bottom line

The OpenClaw agent templates library turns “build an AI agent” into “copy the right config file.” With 200+ free, MIT-licensed, no-code templates across two dozen categories, it offers one of the fastest routes to a working, specialized agent — especially for teams living in Telegram, Slack, or Discord. The trade-off is honest: OpenClaw is a newer ecosystem than the big-name frameworks, and self-hosting asks for some setup and security discipline. But for makers who value speed, openness, and control, that’s a fair deal.

If you want a specialized agent up and running without writing framework code, this library is well worth exploring. And to keep discovering tools shaping the AI-agent era, browse our AI Tools and Developer Tools categories, read our roundup of the best AI tools in 2026, or explore the full bla5k directory.

Official resource: the awesome-openclaw-agents GitHub repository.