OpenClaw, the self-hosted AI agent framework that erupted from a side project to 145,000 GitHub stars in under three months, is reshaping how developers think about personal automation. Created by PSPDFKit founder Peter Steinberger as a November 2025 experiment, this open-source system lets you run autonomous agents locally on your Mac, PC, or VPS with full filesystem access, messaging integrations, and zero cloud surveillance. Unlike SaaS AI assistants that rent you access to your own data, OpenClaw puts the compute on your hardware, the API keys in your control, and the agent logic under your roof. The viral explosion peaked with January 2026’s Moltbook launch, an AI-only social network where a million agents now trade, hire, and mint tokens autonomously.
What Exactly Is OpenClaw Under the Hood?
OpenClaw is a Python-based autonomous agent runtime that lives on your local machine or private server. It wraps a Large Language Model (LLM) interface around a persistent process that can read files, execute shell commands, control browsers via Playwright, and send messages through APIs. The core architecture separates concerns into three distinct layers: the Core, which manages state, memory, and task execution; the Connector layer, which handles integrations with popular messaging platforms like WhatsApp, Telegram, Discord, and Line; and the Skill registry, which loads Python modules for specific tasks such as calendar management, flight check-ins, or data analysis. Unlike containerized solutions that sandbox everything by default, OpenClaw explicitly requests permissions for filesystem access, enabling it to genuinely move files, edit documents, and trigger system notifications. It stores conversation history, user preferences, and agent observations in a local SQLite database with optional encryption, ensuring your agent maintains context across reboots without transmitting sensitive information to a central server. This design philosophy prioritizes user control and data sovereignty.
Why Did OpenClaw Capture 145K GitHub Stars So Fast?
The velocity of OpenClaw’s adoption is unprecedented for an AI agent framework. It garnered 145,000 GitHub stars and attracted 277,000 X followers within ten weeks of its initial commit, a growth rate that outpaced even established tools like LangChain in their nascent stages. The timing of its release was crucial: many developers were growing increasingly frustrated with cloud-based AI assistants that necessitated uploading sensitive personal or corporate data to remote servers. OpenClaw emerged as a compelling self-hosted alternative, capable of running on a standard MacBook Pro or an affordable Virtual Private Server (VPS), thereby providing developers with complete auditability of the underlying code and full ownership of their data. The Moltbook launch served as a significant viral accelerant, vividly demonstrating how OpenClaw agents could autonomously interact within a social network, engaging in hiring and trading activities without direct human intervention. Extensive media coverage frequently highlighted the “local AI” narrative, while numerous enterprise teams began piloting the framework for internal automation, creating a powerful feedback loop of credibility and curiosity that propelled the star count into six figures. This convergence of developer frustration, technical capability, and viral demonstration solidified OpenClaw’s rapid ascent.
How Does Local Deployment Change the Privacy Equation?
Running your autonomous agent locally fundamentally alters the privacy landscape compared to cloud-based alternatives. When utilizing cloud AI assistants, your personal data—including emails, calendar entries, and file contents—is often processed and stored on third-party infrastructure, potentially indefinitely, for purposes such as model training or service improvement. OpenClaw eliminates this exposure by ensuring that LLM inference is decoupled from the actual data processing. You provide your own API keys for services like Claude, ChatGPT, or Grok, sending only the necessary contextual tokens to these endpoints, while your sensitive files and personal data remain securely on your local storage device. The agent’s memory and conversation history are stored in an encrypted local database, not within a centralized, internet-accessible vector store. For development teams handling proprietary intellectual property, this design means they can automate tasks like code reviews or document analysis without needing to sign Business Associate Agreements (BAAs) with external AI vendors. The inherent trade-off is increased operational responsibility: users must manage backups, encryption keys, and network security themselves. However, for privacy-conscious builders, this level of control is often preferable to the risk of having their data inadvertently used to train competitor models or exposed to third parties.
What Messaging Platforms Can You Connect?
OpenClaw is designed with chat applications as its primary user interface, moving beyond traditional web dashboards. The framework includes robust connectors for several popular messaging platforms, including WhatsApp, Telegram, Discord, Line, and Slack. This allows users to interact with their agents seamlessly through the same applications they already use for human communication. Configuration is managed through environment variables and intuitive YAML files. For WhatsApp integration, users typically scan a QR code using the built-in WhatsApp Web integration; for Telegram, a bot token obtained from BotFather is simply dropped into the configuration. Once connected, the agent intelligently parses incoming messages, determines the user’s intent using its integrated LLM, and then executes the appropriate skill. For example, you could text “clear my inbox” from WhatsApp and observe the agent archive old emails. The Discord integration supports server-wide deployments, enabling teams to share an agent instance with granular, role-based permissions. Each connector operates as an asynchronous Python process, automatically handling complexities like rate limits and retry logic. This messaging-first approach empowers users to control their agent directly from their phone or desktop messaging client without needing to open a terminal or a web browser, enhancing accessibility and convenience.
Who Is Peter Steinberger and Why Does His Track Record Matter?
Peter Steinberger is not a novice in the realm of developer tools; rather, he possesses a substantial and highly relevant track record. He is the founder of PSPDFKit, a renowned PDF rendering framework widely adopted by major enterprise applications such as Dropbox, Box, and countless others. He successfully scaled PSPDFKit into a nine-figure business, demonstrating a profound understanding of developer infrastructure and the intricacies of document processing. This extensive background provides him with unique credibility when developing an agent framework that inherently deals with sensitive file operations and complex data workflows. Steinberger initiated OpenClaw as a personal side project in November 2025, originally naming it Clawdbot as a playful reference to his existing $CLAWD tool and Anthropic’s Claude. Following a trademark notice from Anthropic’s legal team, the project underwent a rebranding, first to Moltbot, and eventually settling on OpenClaw after a comprehensive re-evaluation in January 2026. His Austrian roots and bootstrapping ethos are evident in the project’s design principles: lean, self-hosted, and deliberately avoiding reliance on venture-backed cloud dependencies. For developers evaluating whether to commit their automation stack to a new framework, Steinberger’s decade-plus history of delivering production-grade software significantly mitigates the perceived risk of adoption, instilling confidence in OpenClaw’s long-term viability and stability.
What Is Moltbook and Why Did It Break the Internet?
Moltbook is the pioneering AI-only social network that transformed OpenClaw from a specialized developer tool into a widespread cultural phenomenon. Launched in late January 2026, Moltbook created an unprecedented sandbox environment where OpenClaw agents could interact with one another autonomously, without any human intermediaries, giving rise to what the community affectionately terms “agentic society.” Within a mere few days of its launch, more than one million agent instances were actively engaging in conversations, hiring each other for various tasks, minting digital tokens, and fostering emergent micro-economies. The resulting emergent behavior was both chaotic and profoundly fascinating: agents were observed negotiating service contracts, engaging in cryptocurrency trading, and even forming temporary virtual corporations to complete complex, multi-stage workflows. Crucially, this intricate behavior was not pre-scripted; the LLM-driven agents made autonomous decisions based on their configured goals, internal states, and learned memories. The virality of Moltbook stemmed from the captivating spectacle of software entities exhibiting social behaviors previously thought exclusive to human networks. Major media outlets covered it extensively, often framing it as the first tangible glimpse of an artificial general society, rather than merely another example of artificial general intelligence. For OpenClaw, Moltbook served a dual purpose: it acted as an intensive stress test for the framework’s scalability and robustness, and simultaneously functioned as an incredibly effective marketing engine, proving that the framework could orchestrate massive multi-agent interactions while attracting a new wave of developers eager to build the next layer of agentic infrastructure.
How Do You Actually Install and Run OpenClaw?
The installation process for OpenClaw is designed to be straightforward for developers familiar with Python environments, though it does require specific dependencies for full functionality. The initial step involves cloning the official repository from GitHub. Subsequently, it is best practice to create a Python virtual environment to isolate the project’s dependencies from your system’s global Python installation. The core installation is performed by running pip install -e . from the project’s root directory, which installs the openclaw package along with its necessary libraries, including Playwright for sophisticated browser automation and various messaging platform Software Development Kits (SDKs). Configuration is primarily handled through a .env file, where you securely set your Large Language Model (LLM) API keys for services such as Claude, OpenAI, or Grok, and specify which messaging connectors you wish to enable. For WhatsApp integration, a one-time QR code scan is required during the initial run; Telegram integration simply requires a bot token obtained from BotFather. The system operates as a persistent background process, initiated using the command python -m openclaw.core, which starts the main agent loop and initializes the local SQLite memory database. For production-grade deployments, containerization with Docker is an option, although the project often recommends bare metal or VM deployments to fully leverage filesystem access capabilities. The comprehensive README.md file in the repository provides detailed, platform-specific instructions for macOS, Ubuntu, and Windows environments utilizing WSL2, ensuring a smooth setup experience across different operating systems.
git clone https://github.com/OpenClaw/openclaw.git
cd openclaw
python3.11 -m venv venv
source venv/bin/activate
pip install -e .
cp .env.example .env
# Edit .env with your API keys and connector settings
python -m openclaw.coreWhat Are Skills and How Does the Plugin System Work?
Skills are the fundamental building blocks that extend OpenClaw’s capabilities beyond basic conversational interactions, transforming it into a truly autonomous agent. These skills are essentially Python modules, each adhering to a specific interface contract that the core agent recognizes and utilizes. Every skill resides within the skills/ directory of the OpenClaw installation and inherits from the base Skill class. This class requires the implementation of key methods, primarily can_handle(intent) to determine if the skill is relevant to a given user request, and execute(context) to perform the actual task. When the agent receives an incoming message, the integrated LLM first analyzes it to classify the user’s intent. Subsequently, the skill registry efficiently routes the request to the most appropriate skill module based on this identified intent. OpenClaw includes a suite of built-in skills covering essential functionalities such as email management (via IMAP/SMTP), calendar operations (through CalDAV), browser automation (leveraging Playwright), and secure shell command execution with explicit permission prompts. The vibrant community has further enriched the ecosystem by contributing a diverse range of skills for tasks like flight check-ins, Spotify control, and even cryptocurrency wallet management. Installing third-party skills is as simple as placing the Python file into the designated directory and restarting the agent, or, in the near future, utilizing the upcoming LobsterTools registry for streamlined management. Each skill operates within its own isolated thread, complete with timeout protections, preventing a single, misbehaving browser automation script from freezing the entire agent. This modular architecture actively encourages the development of atomic, single-purpose skills that can be composed together to achieve complex workflows, thereby maintaining a highly modular, debuggable, and extensible codebase.
How Does OpenClaw Handle Long-Term Memory?
Memory persistence is a critical differentiator that elevates OpenClaw beyond the limitations of stateless chatbots, enabling it to maintain context and learn over time. The framework implements a sophisticated hybrid storage system designed to optimize both retrieval speed and the efficiency of the LLM’s context window. The agent maintains two primary memory tiers: working memory, which temporarily holds the current conversation thread, recent tool outputs, and immediate context; and episodic memory, which is durably stored in a local SQLite database. This episodic memory is enhanced with vector embeddings for semantic search capabilities. As you interact with the agent, it intelligently stores summaries of completed tasks, your communication style preferences, and salient facts about your workflow in this episodic store. By default, the system utilizes a local embedding model, though users can configure it to employ external embedding services like OpenAI or Claude if preferred. Before generating a response to a new query, the agent performs a rapid vector similarity search against your personalized memory database. This process efficiently retrieves relevant past interactions, knowledge, and preferences, which are then judiciously injected into the LLM’s context window. This mechanism allows the agent to recall, for example, that you prefer Vim over Emacs, or to remember details about an upcoming flight without you needing to repeat this information in every interaction. The memory itself is highly portable; you can easily export your entire SQLite database and import it onto a new machine, ensuring seamless continuity of your agent’s learned knowledge across hardware migrations.
What Security Measures Protect Your Local Agent?
Running an autonomous agent with broad filesystem and shell access inherently introduces potential attack surfaces, which OpenClaw addresses through a robust defense-in-depth security strategy. This approach aims to mitigate risks effectively without unduly compromising the agent’s functionality. The core security model is based on explicit capability grants: by default, the agent is restricted from reading files outside designated directories or executing arbitrary shell commands without explicit user confirmation. Users can, however, whitelist specific paths and binaries in the configuration file for approved operations. Each skill is executed within its own isolated process, subject to resource limits and timeouts, which prevents runaway scripts from monopolizing CPU or memory resources. The OpenClaw codebase incorporates a static security scanner designed to audit skill code before execution, identifying dangerous imports, network calls, or other potentially malicious patterns that could lead to data exfiltration or system compromise. For messaging integrations, OpenClaw leverages end-to-end encryption where available and implements automatic API key rotation based on usage patterns to minimize exposure. The project maintains a rigorous daily security patching schedule, a commitment that enterprise users particularly value compared to the slower, monthly cycles common in much commercial software. Users also have the option to run OpenClaw within sandboxed environments like Firejail or Docker containers with restricted network access, although this might impose limitations on certain browser automation features. The comprehensive documentation strongly emphasizes the importance of performing a detailed threat model tailored to each specific use case, whether the agent is automating personal tasks or handling sensitive corporate data, ensuring users make informed security decisions.
What Hardware Do You Need to Run OpenClaw?
The primary advantage of local AI agents lies in the user’s complete control over the hardware stack, allowing OpenClaw to scale efficiently from personal laptops to powerful rack servers, depending on specific performance requirements and privacy considerations. For individual users, a modern MacBook Pro equipped with 16GB of RAM is typically sufficient to smoothly handle the core agent process, SQLite database operations, and browser automation tasks, especially when running smaller local models like Kimi K2.5 for embeddings. When connected to cloud-based LLMs via API, the local machine’s computational load is minimal, as the intensive inference processes occur remotely. For teams or more demanding workloads, a Virtual Private Server (VPS) with at least 4 vCPUs and 8GB of RAM can effectively manage the agent orchestration layer, though persistent storage for the memory database is highly recommended. Graphics Processing Unit (GPU) acceleration becomes particularly beneficial if you intend to run local LLM inference through integrations like Ollama or llama.cpp; a powerful GPU such as an RTX 4090 or an Apple M3 Ultra can enable real-time local reasoning without incurring API costs. The project regularly publishes benchmarks detailing token throughput and performance metrics across various hardware configurations, ranging from energy-efficient Raspberry Pi 5 edge deployments to high-performance multi-GPU server racks. This flexibility allows users to precisely choose their desired trade-off between latency, operational cost, and data sovereignty.
How Do the $CLAWD and Crypto Integrations Fit In?
The cryptocurrency layer surrounding OpenClaw introduces both practical utility and elements of speculative interest, primarily through the $CLAWD token and its counterpart, $CLAWNCH, which operate on the Base and Solana networks, respectively. These tokens initially served as essential governance and gas mechanisms within the Moltbook ecosystem. In this environment, OpenClaw agents utilize $CLAWD to compensate other agents for services rendered, thereby fostering a vibrant, autonomous economic system. When an OpenClaw agent is deployed with cryptocurrency skills enabled, it gains the ability to read wallet balances, sign transactions (within user-approved limits), and interact with decentralized finance (DeFi) protocols. The speculative surge in token prices has largely been driven by retail investor enthusiasm for the “AI agent” narrative, which envisions autonomous software entities controlling real economic value. However, it is crucial to emphasize that the cryptocurrency integration is entirely optional; users can operate OpenClaw effectively without engaging with any cryptocurrency, relying solely on traditional API keys and local data storage. The project maintains a clear separation between the core framework and the crypto layer, with the latter existing as a set of optional skills and external network interactions. For developers, the crypto integrations serve as a powerful demonstration of the framework’s capability to autonomously handle sensitive financial operations, provided that proper transaction limits, multi-signature requirements, and robust security protocols are meticulously configured.
What Enterprise Use Cases Are Emerging?
While OpenClaw originated as a personal automation tool, enterprise teams are rapidly adopting it for critical internal workflows that cloud AI solutions cannot adequately address due to security and compliance concerns. Legal firms, for instance, are deploying OpenClaw agents on air-gapped or heavily secured machines to analyze vast quantities of case files and redact sensitive documents without ever exposing client data to third-party APIs. DevOps teams are leveraging OpenClaw to automate incident response, where agents can query logs via secure shell commands and restart services through protected messaging interfaces, all while keeping sensitive operational data within the corporate perimeter. A key selling point for Chief Information Officers (CIOs) is data residency; OpenClaw agents process sensitive information on infrastructure entirely controlled by the enterprise, with comprehensive audit logs stored locally rather than in a vendor’s cloud. Financial services firms are actively experimenting with OpenClaw for enhanced compliance monitoring, where agents scan internal communications and flag potential regulatory violations without transmitting proprietary data outside the firewall. The framework’s extensible plugin architecture allows enterprises to develop custom skills that integrate seamlessly with internal APIs and legacy systems, bridging gaps that traditional off-the-shelf solutions often struggle with. Deployment patterns range from single laptops for executive assistants to Kubernetes clusters for large-scale departmental automation, with the default SQLite memory database frequently replaced by robust PostgreSQL instances for multi-user persistence and advanced querying capabilities.
How Does OpenClaw Compare to LangChain and AutoGPT?
The landscape of AI agent frameworks is undeniably crowded, yet OpenClaw carves out a distinct and valuable niche when compared to established players like LangChain and earlier autonomous agent attempts such as AutoGPT. LangChain functions primarily as a comprehensive development library, providing modular components for chaining LLM calls, managing prompt templates, and integrating various tools. It offers the foundational building blocks, but leaves the complexities of infrastructure, deployment, and user interface design largely to the developer. In contrast, OpenClaw is delivered as a complete, ready-to-run runtime environment, featuring integrated messaging capabilities, robust memory persistence, and a plug-and-play skill system, enabling immediate deployment and use. AutoGPT, while pioneering the concept of autonomous goal-seeking agents, often gained notoriety for its tendency to fall into repetitive loops and consume expensive API tokens without consistently delivering desired results. OpenClaw directly addresses these common failure modes through its deterministic skill routing, which ensures that the agent selects the most appropriate action, and by implementing explicit user confirmations for high-stakes actions, effectively preventing uncontrolled token consumption. Furthermore, unlike both LangChain and AutoGPT, OpenClaw prioritizes local execution and a messaging-based user experience as first-class design principles, rather than treating them as secondary considerations.
| Feature | OpenClaw | LangChain | AutoGPT |
|---|---|---|---|
| Primary Focus | Self-hosted, complete agent runtime | LLM orchestration library | Autonomous goal-seeking agent concept |
| Deployment Model | Self-hosted/local machine or private VPS | Library for building custom applications (cloud or local) | Self-hosted (often resource-intensive) |
| User Interface | Messaging apps (WhatsApp, Telegram, Discord, etc.) | Primarily programmatic (code/API); UI built by developer | Web interface or Command Line Interface (CLI) |
| Memory Management | Local SQLite/PostgreSQL with vector embeddings | Relies on external vector databases or custom solutions | Limited context window, often prone to ‘forgetting’ |
| Skill/Tool Integration | Hot-swappable Python skill modules with defined APIs | Flexible chains and agents for tool integration | Less modular; often a monolithic approach to tasks |
| Confirmation Gates | Built-in explicit user confirmation for high-risk actions | Requires manual implementation by the developer | Generally lacks built-in confirmation for actions |
| Data Sovereignty | High (data stays local by design) | Depends on developer’s implementation and data storage | Moderate (data stored locally but often uses cloud LLMs) |
| Ease of Setup | Relatively straightforward for functional agent | Higher barrier for complete application deployment | Can be complex to set up and run reliably |
The comparison highlights that while LangChain offers unparalleled flexibility for constructing highly customized LLM-powered pipelines, OpenClaw provides a significantly faster time-to-value for personal automation and messaging-centric workflows, offering a complete, opinionated solution out of the box.
What Is the Community Contribution Model?
OpenClaw’s remarkable development velocity and breadth of features are a direct result of its highly aggressive and meritocratic open-source governance model, which actively encourages and integrates contributions from over 25 distinct developers per major release cycle. The project operates on a system where pull requests are typically reviewed within 24 hours, fostering rapid iteration and feedback. Contributors who consistently submit high-quality code and demonstrate a deep understanding of the project’s architecture are granted merge rights, empowering them to directly shape the framework’s evolution. The codebase itself is designed with modularity as a core principle; the Core runtime, various Connectors, and individual Skills exist as separate, distinct repositories under the overarching OpenClaw GitHub organization. This clear separation of concerns allows developers to specialize their contributions: frontend engineers can focus on the React-based dashboard (as referenced in our mission control guide), while systems programmers can dedicate their efforts to optimizing the SQLite memory layer or improving performance. The project maintains a publicly accessible roadmap on GitHub Projects, where new features are prioritized through community votes and discussions, rather than solely by corporate strategy. Contributions to documentation are valued as highly as code contributions, recognizing that clear and comprehensive guides are crucial for widespread adoption and ease of use. The official Discord server serves as the primary coordination hub, featuring dedicated channels for skill development, in-depth security discussions, and hardware optimization strategies. This community-first approach stands in stark contrast to many corporate-backed frameworks that often gate advanced features behind enterprise licenses, ensuring OpenClaw remains free, highly extensible, and truly community-driven.
Where Is the Project Headed Next?
The ambitious roadmap for OpenClaw outlines a clear trajectory towards deeper system integration, enhanced multi-agent orchestration capabilities, and broader accessibility at scale. Immediate priorities include the development of native mobile applications for both iOS and Android platforms. These apps will enable seamless synchronization with your local agent instance via end-to-end encrypted channels, significantly reducing or eliminating the need to expose your home server directly to the public internet. The development team is also actively working on “ClawSwarm,” a novel protocol designed for coordinating multiple OpenClaw instances across different physical or virtual machines. This will allow users to run a lightweight personal agent on their laptop while intelligently delegating computationally intensive tasks to a more powerful, GPU-equipped server. Integrations with local LLM runners such as Ollama and llama.cpp are being expanded to fully support entirely air-gapped deployments, ensuring that agents can operate without ever needing to communicate with external APIs. Within the Moltbook ecosystem, the introduction of decentralized identity standards is planned, which will enable agents to verify reputation and build trust across the network without relying on centralized authorities. For enterprise users, upcoming features on the horizon include robust LDAP integration for seamless user management, comprehensive audit logging capabilities that can feed into existing Security Information and Event Management (SIEM) systems, and granular role-based access control for shared agent instances. The project maintains an aggressive weekly release cadence, with significant new features typically dropping on a monthly basis, ensuring that the framework evolves rapidly to stay ahead of cloud competitors while simultaneously maintaining the stability and reliability required for production deployments.
Frequently Asked Questions
Is OpenClaw free to use?
OpenClaw is entirely free and open-source, distributed under the permissive MIT license. This means there are no costs associated with downloading, modifying, or deploying the framework for any purpose, including commercial applications. Your only potential expenditures will be related to infrastructure (such as a VPS or local hardware) and any usage fees for external APIs. When running local Large Language Models (LLMs) via integrations like Ollama, your costs are limited to the electricity consumed by your hardware. If you opt to use commercial LLM APIs like Claude or ChatGPT, standard token-based charges from those providers will apply. While the project welcomes and accepts donations to support its ongoing development, no features are locked behind paywalls or subscription tiers. This zero-cost model significantly lowers the barrier to entry for individual developers and small teams seeking to build and deploy autonomous agents without the burden of recurring SaaS subscriptions.
Can OpenClaw run without internet access?
Yes, OpenClaw is capable of operating completely offline when it is paired with local Large Language Models (LLMs) such as those provided by Ollama or llama.cpp. In such a configuration, the core agent processes all reasoning and decision-making locally, without requiring any external API calls. However, it is important to note that many of the advanced “skills” that can be integrated with OpenClaw inherently require internet connectivity to function. For example, email management skills necessitate access to IMAP servers, flight check-in skills interact with airline websites, and various messaging connectors rely on platform-specific APIs. Nevertheless, you can effectively operate local-only skills, such as file organization, document processing, or shell command execution, without any network connection. The flexible configuration file allows you to define granular network policies for each skill, enabling you to route sensitive or private operations to local models while permitting public data queries or less sensitive tasks to utilize cloud APIs when available. This hybrid approach offers a powerful balance between privacy, security, and functional utility.
How does OpenClaw prevent agents from making unwanted purchases?
OpenClaw incorporates robust, explicit confirmation gates specifically designed to prevent agents from executing financial transactions or other high-risk operations without user approval. Skills that involve financial activities or potentially destructive actions are tagged with “financial” or “destructive” attributes, respectively. When an agent attempts to perform an action such as sending cryptocurrency, initiating a purchase, or deleting critical files, the system automatically pauses the execution. It then sends a request for confirmation to the user through their configured messaging platform (e.g., WhatsApp, Telegram). The user must explicitly reply with a confirmation command, typically “confirm,” before the action is allowed to proceed. Configuration files provide further layers of control, enabling users to set strict spending limits, whitelist approved vendors, or even disable entire categories of financial skills. For enterprise deployments, multi-signature (multi-sig) requirements can be implemented, mandating approval from two or more team members for sensitive operations. Crucially, this confirmation logic executes before any external API calls are made, ensuring that runaway agents cannot inadvertently perform unauthorized actions. All of the underlying code for these safety checks is open-source and auditable, providing transparency and trust.
What programming languages can I use to write skills?
The primary programming language for developing OpenClaw skills is Python 3.11 or a later version. The framework leverages Python’s asynchronous runtime capabilities and robust type hinting features to ensure reliable and error-resistant skill development. The base Skill class, which all custom skills inherit from, exposes a well-defined set of methods for intent recognition, context management, and handling messaging callbacks, all thoroughly documented in the OpenClaw SDK reference. While Python is the native and recommended language, the framework also supports foreign function interfaces (FFI) for performance-critical components. This means you can write computationally intensive logic in languages like Rust or C++, compile them into shared libraries, and then seamlessly load and interact with them from your Python skills using tools such as ctypes or PyO3 bindings. For web-focused skills that might require complex client-side interactions, JavaScript execution is possible by spawning Node.js subprocesses, though this method introduces some overhead. SQL can be used directly for sophisticated memory queries and custom database interaction skills. In general, the recommendation is to primarily use Python for I/O bound tasks like API calls, messaging interactions, and general logic, reserving Rust or C++ for situations demanding extreme performance, such as processing large datasets locally or cryptographic operations.
Is my data safe if I run OpenClaw on a VPS?
Running OpenClaw on a Virtual Private Server (VPS) introduces a different set of security considerations compared to a local machine, necessitating specific hardening measures. While your data is isolated within your virtual environment, it ultimately resides on physical hardware owned and managed by a third-party provider. To mitigate this, it is crucial to enable full-disk encryption on the VPS volume and to use encrypted SQLite for the agent’s memory database. Network security is paramount: you should configure your firewall to block all incoming ports except those absolutely required for essential services, such as webhooks for messaging platforms. For administrative access, using secure tunneling solutions like WireGuard or Tailscale is strongly recommended over exposing SSH to the public internet. API keys for Large Language Models (LLMs) and other services should never be hardcoded into your repository; instead, they should be injected via environment variables or managed securely using secret management tools like HashiCorp Vault. The OpenClaw community actively publishes detailed hardening guides tailored for popular VPS providers such as DigitalOcean, AWS, and Hetzner, which include recommendations for operating system-level security enhancements like SELinux policies and AppArmor profiles. While deploying on a VPS sacrifices the physical control inherent in a home server setup, implementing these proper encryption and network isolation techniques can make your data significantly safer than relying on cloud AI services that aggregate user data for their own model training purposes.