Automation & AI · Personal AI agent framework
OpenClaw
integration & API.
Open-source personal AI agent framework — we build custom skills to extend it for business systems.
O What is OpenClaw?
OpenClaw in plain English.
OpenClaw is an open-source AI agent framework released by Peter Steinberger. It runs locally on the user's machine, supports multiple AI models (Anthropic Claude, OpenAI, and various local models), and defines agents through an extension system. Status is currently Beta. The OpenClaw site at openclaw.ai is the authoritative source for current capabilities, licensing terms, and supported integrations.
What businesses use it for
Common OpenClaw
use cases.
- 01 Personal productivity automation (email triage, calendar, file management)
- 02 Browser automation for repeatable workflows
- 03 Multi-step task automation across personal services and tools
- 04 Custom skill development for individual or small-team workflows
- 05 Connections to chat platforms (WhatsApp, Telegram, Discord, Slack, Signal, iMessage) per the OpenClaw documentation
- 06 Self-hosted personal AI assistant with persistent context
Why custom?
Beyond the default OpenClaw integrations.
OpenClaw is an open-source personal AI assistant framework with a skills-based extension model and broad integration set. In our experience, OpenClaw fits the needs of individuals and teams wanting an extensible agent layer over their personal productivity tools. For business use — particularly in regulated industries — OpenClaw's skill system and supported model integrations are extensible, but the framework itself is positioned for personal productivity rather than enterprise compliance use cases. We build custom skills that extend OpenClaw to interact with business systems (CRMs, practice management platforms, internal APIs) and advise on where it does and does not fit a given workflow.
What we build with OpenClaw
Common OpenClaw integrations.
Custom skill development that connects OpenClaw to your business APIs
Workflow design and implementation for the specific automations you actually run
Architectural advice on where OpenClaw fits vs. where a different framework or pattern is the better choice
Integration layers between OpenClaw and an organisation's existing systems
Migration planning for moving manual workflows into agent-driven automation
Operational guardrails — logging, supervision points, audit trails — designed around the workflow being automated
Commonly paired with
OpenClaw works with the rest of your stack.
How we build
Production-grade.
Not Zapier in a trench coat.
Every OpenClaw integration we ship handles failure properly: idempotent jobs, retry logic, dead-letter queues for unrecoverable cases, and observability so you can see exactly where data is at any moment.
Tested
Real test suite, real edge cases. Not just "it worked once."
Observable
You see what is happening — events logged, errors surfaced.
Maintainable
Documented, version-controlled, handed over properly.
More Automation & AI
Other automation & ai platforms we connect.
Z Zapier
Zapier integrations and the moments when custom code is the cheaper answer.
M Make
Make (Integromat) integrations and migrations to more reliable architectures.
N n8n
n8n integrations and self-hosted automation for businesses that want control over their data.
OpenAI
OpenAI integrations engineered for production — not demo-ware that breaks at the second edge case.
FAQ
Common questions about OpenClaw integration.
Is OpenClaw production-ready for business use?
OpenClaw is currently in Beta per its own documentation. In our experience, it is well-suited to personal productivity automation and to teams who want to experiment with agent-based workflows. For mission-critical business workflows we recommend a careful pilot and a clear-eyed view of OpenClaw's current state before committing to a production rollout. The OpenClaw site is the authoritative source for current status.
Can OpenClaw be used in regulated industries (legal, healthcare, finance)?
In our experience, OpenClaw's skill system can be extended to support regulated use cases, but the framework itself is positioned for personal productivity rather than as a compliance-certified platform. Any deployment in a regulated industry should be assessed against the specific regulatory framework applying to that industry (for example, ASCR confidentiality obligations for Australian legal practitioners). We design custom builds with supervision points and audit logging where the workflow requires it; final assessment of regulatory fit is for the firm or organisation and their professional advisers.
What models does OpenClaw run?
Per the OpenClaw documentation, the framework supports Anthropic Claude (primary), OpenAI models, and various local models. Model choice affects cost, latency, and data-handling posture — local models keep data on the user's machine; remote API models route data through the provider. For confidentiality-sensitive workflows, local model inference is often the preferred starting point.
How does OpenClaw compare with Hermes Agent?
Both are open-source agent frameworks released in 2026. Per their respective documentation, OpenClaw is positioned as a personal AI assistant product with a consumer-style integration set, and Hermes Agent (by Nous Research) is positioned as an open-source agent runtime with documentation emphasising local data storage and container hardening. We work with both. The choice between them depends on the specifics of the workflow, and each project's documentation at openclaw.ai and hermes-agent.org is the authoritative source on current capabilities.
Related reading
Thinking we’ve published on this kind of work.
Agentive AI in Australian legal practice: what's possible, what's gated, and what isn't yet
A working overview of where autonomous AI agents fit in Australian law-firm operations today — the frameworks (OpenClaw, Hermes Agent), the model question, the LEAP API surface, and the licensing and regulatory constraints that shape what can actually be built.
AI digital marketing agencies in Adelaide: where AI actually helps (and where it doesn't, yet)
An honest look at what an AI-led digital agency in Adelaide can and can't do for Australian businesses right now — and the questions to ask before you sign anything.
What AI actually costs to run in production
AI demos are cheap. Production is not. Where the money actually goes when you ship an AI feature, and how to size the engineering investment around the model.
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