OpenClaw
Run OpenClaw with the infrastructure it needs for tools, credentials, models, data, and persistence.
The open platform for running AI agents
Host OpenClaw, NanoClaw, Anton, Hermes, and other open-source agents with the infrastructure they need to work with real tools, data, credentials, and models.
Routes each step to the right LLM by cost, latency, and capability.
Protected reproducible execution environment.
Long-term, cross-run recall — facts, preferences, prior outcomes.
MindsHub is built as a home for open agents and the builders who want control, inspectability, portability, and model choice. Run agents like OpenClaw, NanoClaw, Anton, and Hermes with shared infrastructure instead of stitching it together yourself.
Run OpenClaw with the infrastructure it needs for tools, credentials, models, data, and persistence.
Move lightweight agent experiments from local runs to managed, reliable execution.
The open-source AI coworker. Describe the outcome you need and get a finished artifact back.
Host Hermes alongside your other open agents and connect it to the systems it needs to work.
MindsHub gives open agents a Model Router they can actually work with: use proprietary and open models side by side, route tasks to the best provider, control cost, and avoid locking every workflow into one LLM.
Agents pick the model that fits the job: reasoning, coding, vision, long context, fast drafts, or cost-sensitive background work.
Real work needs real systems. MindsHub gives open agents access to the infrastructure layer they need to operate safely, usefully, and reliably.
Use the right model for each task. Switch providers, control cost, use open models, and avoid being locked into one LLM.
Connect agents to real apps and services without pasting secrets into prompts, notebooks, or one-off scripts.
Let agents work with files, apps, databases, warehouses, SaaS tools, and knowledge sources.
Turn local experiments into ongoing workers that keep running after the terminal closes.
Inspect plans, steps, code, outputs, and intermediate state so agent work is understandable and reusable.
Run recurring jobs, daily digests, ongoing research, and operational workflows without babysitting a local process.
Start with OpenClaw, NanoClaw, Anton, Hermes, or another open-source agent your team wants to run.
Pick the LLM that fits each task. Switch providers, mix open and proprietary models, and keep keys managed in one place.
Give the agent access to the apps, files, databases, and knowledge sources it needs — with credentials handled for you.
Move beyond the laptop with hosted execution, persistence, scheduling, logs, and operational control.
MindsHub is built for open agents. For a practical first run, start with Anton: an open-source AI coworker you can hand work to, inspect while it runs, and use with the models, tools, and credentials your workflow needs.
Describe the outcome in plain language. Anton plans the steps, works in a secure scratchpad, and returns useful artifacts — instead of making you copy, format, and stitch the result together yourself.
Hand off the task in plain language. Anton plans the work and runs the steps instead of requiring step-by-step supervision.
Reports, spreadsheets, summaries, organized folders, daily digests, reproducible analyses, documents, and dashboards.
Bring model choice into the workflow so each task can use the right provider, cost profile, or open model.
A credentials vault lets the agent work with apps, files, data, and tools without pasting secrets into prompts or scripts.
Review plans, steps, code, outputs, and intermediate state so the work is visible, reusable, and easier to trust.
Anton can use memory to preserve context from prior work, learn from repeated workflows, and make each handoff easier to continue.
Open-source agents are powerful, but running them reliably should not turn every developer into an agent infrastructure team. MindsHub gives agents a safe place to run — hosting, credentials, model access, tool access, data access, and persistent execution beyond a local experiment.
Start with an open agent. Connect the systems it needs. Choose the model that fits the task. Then run it as an always-on worker with the control, visibility, and reliability technical teams expect.