Open Source BSD OpenClaw Tools
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OpenClaw Tools for BSD
Browse free open source OpenClaw Tools and projects for BSD below. Use the toggles on the left to filter open source OpenClaw Tools by OS, license, language, programming language, and project status.
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Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
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Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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llmfit
157 models, 30 providers, one command to find what runs on hardware
llmfit is a terminal-based utility that helps developers determine which large language models can realistically run on their local hardware by analyzing system resources and model requirements. The tool automatically detects CPU, RAM, GPU, and VRAM specifications, then ranks available models based on performance factors such as speed, quality, and memory fit. It provides both an interactive terminal user interface and a traditional CLI mode, enabling flexible workflows for different user preferences. llmfit also supports advanced configurations including multi-GPU setups, mixture-of-experts architectures, and dynamic quantization recommendations. By presenting clear performance estimates and compatibility guidance, the project reduces the trial-and-error typically involved in local LLM experimentation. Overall, llmfit serves as a practical decision assistant for developers who want to run language models efficiently on their own machines. -
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openclaw-kapso-whatsapp
Give your OpenClaw AI agent a WhatsApp number
openclaw-kapso-whatsapp is a plugin repository designed to extend the OpenClaw AI agent by giving it a dedicated WhatsApp phone number using the official Meta Cloud API via Kapso, enabling direct interaction through one of the most widely used messaging platforms. This integration allows the autonomous AI assistant to send and receive messages on WhatsApp, turning the agent into a real-world task performer accessible through text conversations. The plugin is built in Go and handles communication entirely through cloud APIs, avoiding the risk of bans that come with unofficial or reverse-engineered interfaces. Projects like this make it possible for OpenClaw users to automate tasks, interact with personal contacts, or provide AI-driven services without building a custom bot infrastructure from scratch. Because OpenClaw itself runs on the user’s own hardware and can access external services, this WhatsApp extension serves as a bridge between the AI agent and daily messaging workflows. -
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OpenClaw Office
OpenClaw Office is the visual monitoring and management frontend
OpenClaw Office is a visual monitoring and management interface designed for the OpenClaw multi-agent system, providing an immersive and interactive way to observe and control autonomous AI agents. It presents agent activity through a virtual office environment, where each agent is represented as an animated entity within a 2D or 3D workspace. The platform enables real-time visualization of agent states, interactions, and workflows, making complex multi-agent coordination easier to understand and debug. Users can observe communication flows between agents through visual connections, track token usage and operational costs, and analyze performance through integrated dashboards and charts. The system also includes live chat capabilities, allowing users to monitor conversations and tool calls as they occur. -
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Lossless Claw
LCM (Lossless Context Management) plugin for OpenClaw
Lossless Claw is an advanced context management plugin for the OpenClaw agent ecosystem that redefines how conversational memory is handled in large language model systems. Instead of relying on traditional sliding-window truncation or lossy summarization, it introduces a lossless architecture that preserves all historical messages while maintaining usable context within token limits. The system stores every interaction in a persistent database and incrementally summarizes older content into a hierarchical directed acyclic graph, allowing efficient compression without discarding information. This structure enables agents to dynamically reconstruct detailed context by expanding summaries when needed, effectively simulating perfect long-term memory. -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Skill Scanner
Security Scanner for Agent Skills
This repository is a public security-focused scanning tool intended to analyze and assess AI agent skills for potential issues, quality concerns, and vulnerabilities. It acts as a scanner that inspects Agent Skills packages to flag structural problems, inconsistencies, or security flaws before they are deployed or integrated into agent workflows. Because agent skills can contain executable instructions and logic, scanning them for risky patterns is essential to prevent inadvertent exploitation when used by intelligent systems. The tool supports maintainers and community contributors in automating quality checks and enforcing conventions across skill sets in a standardized way. While still evolving with contributions and issue discussions, it shows the community’s interest in building safer AI ecosystems around reusable capabilities. The scanner also serves as a foundation for more sophisticated vetting frameworks that might be incorporated into CI/CD pipelines. -
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DenchClaw
Fully Managed OpenClaw Framework for all knowledge work ever
DenchClaw is a local-first AI-powered CRM and productivity platform built on top of the OpenClaw framework, designed to transform a user’s entire computer into a programmable, agent-driven workspace. Unlike traditional cloud-based CRMs or AI tools, it runs entirely on the user’s machine and exposes a web interface locally, allowing full control over data, workflows, and automation without relying on external servers. The system combines database management, browser automation, and AI reasoning into a unified interface where users can interact with their data and tools using natural language commands. It can ingest data from sources such as Google Drive, Notion, Gmail, and CRM platforms, consolidating everything into a centralized workspace for analysis and action. One of its most distinctive capabilities is its ability to use the user’s existing browser session, enabling it to log into services, scrape data, and perform actions like outreach or research as if it were the user. -
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clawchief
Turn your OpenClaw into a Chief of Staff
clawchief is an agent orchestration and management layer designed to coordinate and control multiple AI agents within structured workflows, acting as a central authority that assigns tasks, monitors execution, and ensures coherence across complex operations. The system is built around the idea of hierarchical control, where a “chief” agent oversees subordinate agents and directs their activities based on high-level objectives. This approach allows for more predictable and organized multi-agent behavior compared to decentralized systems. The architecture likely includes task planning, delegation logic, and feedback loops that enable iterative refinement of outputs. It is particularly useful in scenarios where multiple agents must collaborate on interdependent tasks, such as coding, research, or automation pipelines. The system may also include monitoring tools to track agent performance and identify failures or inefficiencies. -
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Claw Compactor
14-stage Fusion Pipeline for LLM token compression
Claw Compactor is a utility designed to optimize and manage the context limitations inherent in AI agent systems, particularly those built on OpenClaw-like architectures. It addresses the challenge of finite context windows in language models by compressing or summarizing historical interactions while preserving essential information. The system works by transforming older conversation data into condensed representations that maintain continuity without exceeding token limits. This approach allows long-running agent sessions to continue operating efficiently without losing critical context. It is especially useful in autonomous workflows where agents accumulate large volumes of interaction history over time. The project aligns with broader strategies in AI systems that balance memory retention with computational constraints. Overall, claw-compactor functions as an infrastructure component that enhances scalability and stability in persistent AI agent environments. -
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birdclaw
Stores all your tweets nicely claw-able for agents
birdclaw is a local-first Twitter workspace designed for archiving, organizing, searching, and interacting with Twitter or X content in a structured environment. The project combines a web application and command-line interface to help users import personal archives, cache live content locally, and manage replies or engagement workflows from a centralized system. Its architecture prioritizes data ownership and offline accessibility, allowing users to retain searchable records of posts, likes, bookmarks, and conversations without depending entirely on external platforms. birdclaw also supports synchronization and filtering features that make large archives easier to navigate and analyze. The tool is intended for researchers, creators, developers, and heavy social media users who want more control over their Twitter history and workflows. By emphasizing local storage and flexible retrieval, the project transforms social media archives into a searchable personal knowledge system. -
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
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ClawRouter
Smart LLM router
ClawRouter is a flexible networking and routing framework designed to support AI-oriented distributed systems and agent ecosystems by managing how messages, requests, and responses are routed between components. It provides a programmable router abstraction that can handle complex traffic patterns, enabling dynamic message forwarding, load balancing, and custom routing logic based on content, context, or policy rules. Because distributed AI systems often involve many services, agents, and runtime components interacting with each other and with external APIs, ClawRouter helps ensure that communication paths remain clear, efficient, and adaptable as systems scale. The framework supports plugin-based extensions so developers can define custom protocols, transformation hooks, and monitoring handlers without modifying core routing logic. It also offers operational features like health checking, metrics reporting, and failure handling that make production deployments more reliable. -
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MimiClaw
Run OpenClaw on a $5 chip
MimiClaw (from the mimiclaw project) is an edge-AI personal assistant that runs directly on extremely low-cost hardware like an ESP32-S3 microcontroller without a full operating system, Node.js, or cloud backend. By running pure C on a bare-metal chip, MimiClaw brings AI interactions and persistent memory to a tiny USB-powered device you can carry in your pocket. You connect the device to Wi-Fi and chat with it using Telegram, making it a convenient always-on assistant for tasks like reminders, quick lookups, or custom AI interactions. Even though it’s running on minimal hardware, MimiClaw maintains local memory that persists across power cycles, enabling context continuity over time without relying on cloud services. Its architecture emphasizes privacy, low power, and portability, ideal for personal or hobbyist use cases where privacy and local control matter. -
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OpenClaw Opik Observability Plugin
Official plugin for OpenClaw that exports agent traces to Opik
OpenClaw Opik Observability Plugin is an open-source plugin designed to add observability and monitoring capabilities to OpenClaw autonomous AI agents by exporting operational traces to the Opik observability platform. The project integrates directly with OpenClaw’s plugin architecture so that developers can capture detailed runtime information about how their agents behave while executing tasks. Each time an AI agent performs an action—such as calling a large language model, invoking a tool, accessing memory, or delegating to a sub-agent—the plugin records the full interaction and sends it to Opik for analysis and visualization. This allows developers to inspect inputs, outputs, token usage, latency, and execution flow across complex multi-step agent workflows. The goal of the project is to provide transparency into the internal reasoning and operational pipeline of agent systems so developers can diagnose failures, control costs, and improve reliability. -
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Obsidian Skills
Agent skills for Obsidian
Obsidian-Skills is a repository of agent skills tailored for use with Obsidian and any Claude-compatible agent that follows the standard Agent Skills specification, enabling AI assistants to better understand and interact with Obsidian content. These skills are markdown-driven specifications that teach Claude Code (or similar agents) how to perform context-aware tasks within Obsidian’s unique environment, such as interpreting different file types and workflows, automating workflows tied to notes, or enhancing agent responses with structured knowledge. By providing formal descriptions of patterns, conventions, and workflows common to Obsidian users, the skills empower AI tools to give more relevant suggestions, generate content that adheres to user conventions, or execute complex multi-step operations that respect the knowledge graph and file relationships. -
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Build Your Own OpenClaw
A step-by-step guide to build your own AI agent
Build Your Own OpenClaw is a step-by-step educational framework that teaches developers how to construct a fully functional AI agent system from scratch, gradually evolving from a simple chat loop into a multi-agent, production-ready architecture. The project is structured into 18 progressive stages, each introducing a new concept such as tool usage, memory persistence, event-driven design, and multi-agent coordination, with each step including both explanatory documentation and runnable code. It begins with foundational concepts like conversational loops and tool integration, then expands into more advanced capabilities such as dynamic skill loading, web interaction, and context management. As the tutorial progresses, it introduces architectural improvements including event-driven systems, WebSocket communication, and configuration hot-reloading to support scalability and real-time interaction. -
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ClawHost
Deploy OpenClaw with one click
ClawHost is an open-source, self-hostable cloud hosting platform designed to simplify the deployment of OpenClaw onto a dedicated VPS in minutes, giving users full control over their AI infrastructure without relying on shared or managed services. It automates server provisioning, DNS configuration, SSL certificates, and firewall setup, so developers can focus on running their AI workloads rather than configuring infrastructure manually. The platform includes a user-friendly web dashboard and REST API that let users select regions, manage servers, and configure SSH keys with minimal friction. ClawHost supports automatic domain and subdomain management through Cloudflare integration and uses services like Hetzner for cloud provisioning, making it scalable across geographies. With built-in billing support via integrations like Polar.sh, users can manage subscriptions and invoicing directly from the platform, enabling it to serve both personal projects and small business use cases. -
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LabClaw
Operating Layer for LabOS (Stanford-Princeton AI Co-Scientists)
LabClaw is an open-source AI experimentation and agent orchestration platform designed to help developers build, test, and iterate on complex autonomous workflows in a controlled and modular environment. It provides a framework for composing multiple tools, prompts, and execution steps into structured pipelines that can be reused and evaluated across different scenarios. The system emphasizes experimentation, allowing users to run multiple variations of agent workflows, compare outputs, and refine performance over time. LabClaw is designed to integrate with various large language models and external tools, enabling flexible configurations that adapt to different use cases such as automation, research, and product prototyping. It also includes logging and observability features that help developers track agent decisions and debug behavior during execution. -
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MemOS
AI memory OS for LLM and Agent systems
MemOS is an experimental operating system and runtime built around the concept of memory-centric computing, where memory objects are first-class citizens and program execution is organized around efficient, persistent memory access rather than traditional process and file system boundaries. The project explores rethinking system abstractions by tightly coupling computation with memory objects so that programs can operate on large datasets without expensive serialization or context switching. It aims to support advanced workflows like persistent in-memory data structures, crash-resilient state handling, and seamless sharing of data across tasks without copying. By abandoning some of the historical assumptions of Unix-style operating systems, MemOS attempts to unlock new performance and scalability tradeoffs for applications that need high throughput and low latency on memory-intensive workloads. -
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Build with Claude
A single hub to find Claude Skills, Agents, Commands, Hooks, Plugins
Build with Claude is an open-source plugin marketplace and discovery hub for the Claude Code ecosystem that centralizes hundreds of plugins, agents, commands, hooks, skills, and marketplaces to enhance developer workflows with autonomous AI functionality. It serves as a one-stop index where users can browse curated agent modules for tasks like blockchain development, code analysis, DevOps, documentation generation, and much more — all designed to be installed directly into Claude Code using a simple plugin system. The repository includes an organized collection of community-maintained plugins, searchable by category, and offers clear instructions on how to add and install marketplace content within Claude Code environments. Alongside agents, Build with Claude features hooks that can trigger actions on events, slash commands that automate developer tasks, and skill packages that bundle reusable AI behaviors for common problems. -
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ClawPort
Open-source AI agent command center for Claude Code agent teams
ClawPort is an open-source web-based dashboard designed to act as a centralized command center for managing and interacting with OpenClaw AI agent systems. It provides a visual interface that replaces fragmented terminal-based workflows, allowing users to monitor, control, and communicate with multiple agents from a single location. The platform is built to improve visibility into multi-agent environments, offering insights into agent activity, task execution, and system performance in real time. It supports direct interaction with agents through chat interfaces, enabling users to guide or override automated workflows when necessary. The system also incorporates organizational tools such as task boards and scheduling mechanisms, helping structure complex agent operations. By consolidating logs, metrics, and memory access into one interface, it reduces operational complexity and improves productivity. -
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Clawra
Openclaw as your girlfriend
Clawra is a creative open-source AI persona built as a “skill” or extension for the OpenClaw agent framework that exemplifies how flexible autonomous AI agents can become companions or interactive characters you run yourself. Rather than being a static chatbot tied to a corporate ecosystem, Clawra runs locally or on a private server, giving users full control over the software and data that back her behavior. She is designed not just to answer questions but to maintain a persistent character with memory, backstory, and the ability to present visual outputs like generated selfies through integrated image tools, blending conversational AI with a playful persona. Clawra has captured attention as an experimental project showcasing how far open-source agents can be pushed in creating engaging and personalized interactions, with community interest spiking around her capabilities. -
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Moltworker
Run OpenClaw on Cloudflare Workers
Moltworker is an experimental Cloudflare Workers-based project that allows users to run OpenClaw (previously known as Moltbot or Clawdbot), a self-hosted personal AI agent, within the Cloudflare Developer Platform rather than on dedicated hardware. Acting as a middleware Worker and script adaptor, moltworker packages the OpenClaw agent and its dependencies into a Cloudflare Sandbox container that can operate at scale on the global edge network, offering an always-on deployment without users needing their own servers. With this setup, developers can host and interact with a personal AI assistant that maintains persistent conversations, integrates with chat platforms, and provides a control UI protected by Cloudflare Access. The project includes web UIs, Cloudflare configuration files, and scripts to authenticate devices, manage environment secrets, and enable optional services like persistent R2 storage for chat history. -
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OpenClaw-RL
Train any agents simply by 'talking'
OpenClaw-RL is an open-source reinforcement learning framework designed to train and personalize AI agents built on the OpenClaw ecosystem. The project focuses on enabling agents to improve their behavior through interactive learning rather than relying solely on static prompts or predefined skills. One of its key ideas is allowing users to train an AI agent simply by interacting with it conversationally, using natural language feedback to guide the learning process. The system incorporates reinforcement learning techniques to refine the agent’s policies for tool use, decision making, and task completion over time. It also explores approaches such as online policy distillation and hindsight feedback signals to strengthen training signals from real interactions. The framework operates asynchronously and does not require external API keys, making it easier to experiment with local agent training workflows. -
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mcp-use
A solution to build and deploy MCP agents and applications
mcp-use is an open source development platform offering SDKs, cloud infrastructure, and a developer-friendly control plane for building, managing, and deploying AI agents that leverage the Model Context Protocol (MCP). It enables connection to multiple MCP servers, each exposing specific tool capabilities like browsing, file operations, or specialized integrations, through a unified MCPClient. Developers can create custom agents (via MCPAgent) that dynamically select the most appropriate server for each task using configurable pipelines or a built-in server manager. It simplifies authentication, access control, audit logging, observability, sandboxed runtime environments, and deployment workflows, whether self-hosted or managed, making MCP development production-ready. With integrations for popular frameworks like LangChain (Python) and LangChain.js (TypeScript), mcp-use accelerates the creation of tool-enabled AI agents. -
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xiaohongshu-ops
Turn Openclaw into a Xiaohongshu operations assistant
xiaohongshu-ops-skill is a curated repository focused on operational strategies, growth techniques, and content optimization practices for the Xiaohongshu platform, also known as RED, which is widely used for social commerce and influencer marketing. Unlike traditional software projects, it functions as a structured knowledge base that compiles actionable insights for content creators, marketers, and businesses aiming to grow their presence on the platform. The repository includes guides on algorithm behavior, content positioning, audience targeting, and engagement strategies tailored to Xiaohongshu’s ecosystem. It also provides practical frameworks for increasing visibility, improving content performance, and leveraging trends effectively. The content is organized to support both beginners and experienced operators, offering step-by-step strategies as well as advanced growth tactics.
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