OpenClaw AI Agents: Build Autonomous AI Employees in 7 days

Choose Claude for structured, reliable reasoning and long-context handling, then deploy GPT for creative, flexible interactions; connect Claude to OpenCLR to build autonomous AI employees.

Connect the Claude API to OpenClaw to power a thinking agent, securely manage the API key, configure model settings like Claude 3.5 Sonnet, and test with a simple prompt.

Configure models inside OpenClaw to transform a raw AI model into a reliable system by tuning model selection, temperature, max tokens, top P, and system prompts for consistent, task-focused behavior.

Master the agent lifecycle as a continuous loop—input, thinking, decision, action, memory—driving autonomous AI employees through automation, consistency, and scalability.

Define an AI's identity through the sole file, uniting role, tone, rules, and goals to ensure consistent, reliable behavior across tasks and workflows. Build AI employees rather than tools.

Define the identity file as the execution layer that turns the soul file into a persistent system-level AI personality, with role, tone, rules, goals, and output format guiding every interaction.

Explore how a user file personalizes AI behavior by storing tone, format, and detail preferences, coordinating with the identity file to deliver efficient, tailored responses.

OpenClaw AI agents show how the agent file enables real-world action by turning identity and personalization into execution, via input handling, decision logic, tool usage, and output execution.

Explore how tools enable AI agents to act in the real world, using APIs, databases, and services, and how reusable skills turn these tools into automated, measurable outcomes.

Connect AI agents to real-world systems by learning how APIs enable sending and receiving data, authenticating with API keys or OAuth, and turning responses into automated actions.

Harness file system access to enable persistent AI automation via reading, writing, updating, and deleting data across local and cloud storage. Secure data with least-privilege access, encryption, and logging.

Running commands empowers an AI to execute terminal commands, Python or Bash scripts, and direct system control, enabling automation and turning it from advisor into a real-world system operator.

Context files act as the working memory that gives AI awareness over time, storing past interactions, user preferences, and task states to build continuity across sessions.

Understand session memory as the AI's short-term brain that maintains coherence and stores user messages, AI responses, task state, and intermediate steps for follow-ups and multi-step workflows.

Discover how long-term memory provides persistent, contextual knowledge that lets AI remember user preferences, past interactions, and domain-specific knowledge to create continuity across sessions. Pair long-term memory with session memory, and apply storage and retrieval techniques—query-based search, embedding-based search, top-k filtering, and recency weighting—to build adaptive, intelligent AI.

Learn how to prevent hallucinations in ai by designing clear prompts, grounding outputs with real data, and validating results within robust, system-level architectures like retrieval augmented generation and tools.

The heartbeat system is a continuous trigger mechanism that powers autonomous AI agents to run on defined intervals, enabling 24-7 execution, proactive action, and automation without user prompts.

Understand hooks and triggers as the activation layer of event-driven AI systems, enabling real-time, efficient responses by listening for signals from APIs, databases, file systems, and user actions.

Cron jobs provide time-based automation in Unix-like systems by scheduling tasks with cron expressions, powered by the cron daemon in the cron tab, ensuring predictable, automated execution.

Understand how background execution runs tasks behind the scenes to stay responsive and non-blocking. Learn how queues, workers, and brokers enable scalable, automated AI inference and data processing.

Learn to deploy AI agents on a virtual private server, an always-on production home; set up Ubuntu, SSH keys, and run code with PM2 or Systemd.

Learn how SSH secures server access with encryption and port management, and how firewalls and port states enable layered security.

Run a 24-7 ai agent that stays on, autonomously processing tasks with continuous execution. Build it with infrastructure, process managers, auto-restart, logging, monitoring, and scaling to create a digital employee.

Olimar runs language models locally on your machine, eliminating cloud dependency and API costs; it provides a simple CLI, local REST API, and supports multiple models for private, offline AI.

Discover how routing and caching cut AI costs by matching tasks to the right model and reusing results, while managing token usage and cache strategies.

Understand how voice input converts speech to text through a capture, convert, interact pipeline powered by Whisper, delivering accurate multilingual transcription for real-time AI interactions.

Explore AI image generation, turning prompts into visuals via diffusion, using tools like DALL-E and Midjourney; master prompt engineering, styles, and real world applications.

Learn how multi-agent setups form cooperative AI teams where planners, executors, researchers, and reviewers collaborate. Understand communication, workflow patterns, and orchestration to scale automation and handle real-world tasks.