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OpenClaw just became dramatically easier to deploy.
Chutes has announced ParaClaw, a one-command installer that configures OpenClaw with Chutes as its model provider in under 60 seconds. The setup exposes more than 60 open source models immediately, without API key management or manual configuration.
The entire installation process is executed with a single command:
curl -fsSL https://chutes.ai/openclaw/init | sh
One Command, 60+ Models
OpenClaw adoption has accelerated rapidly across the AI developer community. But many users have been relying on closed-source APIs such as Anthropic to power their agents, often paying between $15 and $75 per million tokens depending on model and usage tier.
ParaClaw changes that dynamic.
With one command, OpenClaw is configured to run against Chutes’ model infrastructure, unlocking access to more than 60 open source models, including:
- DeepSeek
- Qwen 3.5
- GLM-5
- Kimi K2.5
The goal is simplicity.
No API key juggling. No environment variable setup. No configuration file troubleshooting.
Kimi K2.5 and the Open Model Shift
One of the more notable inclusions is Kimi K2.5, a one-trillion-parameter open source model that supports native multimodal input and agent swarm capabilities.
According to Chutes, Kimi K2.5 is outperforming closed models like Opus on agentic benchmarks such as BrowseComp and LiveCodeBench, while operating at a fraction of the cost.
If those performance characteristics hold under broader testing, it reinforces a growing narrative that frontier capability is no longer exclusive to closed providers.
For OpenClaw users, this means the cost structure of running autonomous agents can shift significantly. Instead of renting intelligence from premium API vendors, developers can deploy against open models with dramatically lower token costs.
Chutes estimates savings exceeding 90% compared to leading closed-source APIs.
Hardware-Level Privacy With TEE
Cost reduction is only part of the story.
Chutes also runs inference within Trusted Execution Environments, or TEEs. These environments provide hardware-enforced isolation, meaning GPU operators cannot access the underlying data being processed during inference.
For agents handling sensitive inputs such as email, documents, credentials, or private datasets, this introduces a stronger privacy model than traditional cloud inference.
The operator running the hardware cannot inspect the data. The processing happens inside an isolated enclave at the hardware level.
For OpenClaw deployments that interact with personal or enterprise data, this closes a major security concern.
Infrastructure Maturing Around OpenClaw
OpenClaw’s rapid adoption exposed a broader issue in decentralized AI infrastructure. Developers could build powerful agents, but deployment often required stitching together model providers, APIs, configuration files, and security workarounds.
ParaClaw compresses that stack into a single initialization command.
The combination of:
- One-command setup
- Access to frontier open source models
- Hardware-level privacy via TEE
- Major cost reduction
creates a materially different deployment profile for autonomous agents.
Instead of relying on closed APIs with markup pricing and opaque infrastructure, developers can run agents on decentralized infrastructure with open models and verifiable execution environments.
Decentralized Agents, Simplified
Chutes positions ParaClaw as a step toward fully autonomous AI agents operating on decentralized infrastructure without the cost overhead of centralized model providers.
For OpenClaw users already experimenting with decentralized agent frameworks, ParaClaw reduces friction at the exact moment adoption is accelerating.
The install command is live:
curl -fsSL https://chutes.ai/openclaw/init | sh
Documentation is available via Chutes’ GitHub repository.
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