OpenClaw Too Heavy? These 5 Lightweight AI Assistant Alternatives Run on $5 Hardware

OpenClaw is one of the most starred AI projects on GitHub with nearly 200,000 stars. It is powerful, feature-rich, and has a lobster mascot named Molty. It also needs over 1GB of RAM, takes 500 seconds to boot on low-end hardware, and has 52 modules most people will never fully read or audit.

If that sounds like overkill for what you need, you are not alone.

Five serious alternatives have emerged in the last few months, each built in a completely different language with a completely different philosophy. One runs on a $5 board. One compiles to a 678KB binary. One was built by NEAR AI with WASM sandboxing so tight your own tools cannot touch your credentials. One was built by a hardware company specifically for $10 embedded devices. And one is small enough to read and understand in 8 minutes.

All five are free. All five are open source. All five are actively maintained.

This guide covers every alternative with real install commands, real benchmark numbers, and real GitHub data, no filler, no fluff.

What Is OpenClaw and Why Are People Looking for Alternatives?

OpenClaw is a wildly popular personal AI assistant framework. With 196,000 stars on GitHub it is one of the most starred AI projects ever created. The tagline on the official website is simple: your own personal AI assistant, any OS, any platform, the lobster way. The lobster mascot is a red lobster named Molty, and the community slogan is EXFOLIATE! EXFOLIATE!

OpenClaw connects to messaging channels you already use including WhatsApp, Telegram, Slack, Discord, Google Chat, Signal, iMessage, Microsoft Teams, Matrix, Zalo, and a web chat interface. It is written in TypeScript and runs on Node.js version 22 or higher. You install it with a single npm command, run the onboarding wizard, and from that point your AI assistant is available on every channel you have set up. The full install experience involves the openclaw onboard command, which walks you through setting up a Gateway daemon, connecting your messaging accounts, and choosing your AI model. Recommended models are Anthropic Claude Opus or Sonnet through a paid subscription. OpenClaw also supports any OpenAI compatible endpoint, making it possible to connect local models through tools like Ollama.

So why are people searching for openclaw alternatives? The honest answer is that OpenClaw is a large and ambitious project. It has over 400 source files, 52 or more modules, and dozens of dependencies. For many users this complexity is exactly what they want because it gives them a powerful, complete assistant. But for others it raises questions about security, resource use, and the ability to understand and audit what the software is doing. Some users also find that openclaw can be slow to respond when the host machine is under load, because the TypeScript runtime adds overhead and the full system takes time to initialize.

This blog post looks at five projects that have emerged as serious alternatives, each with a completely different philosophy and completely different technical approach. We read every single GitHub repository directly so everything here is based on what the projects actually say about themselves.

A Real History of the Claw Ecosystem

OpenClaw was created by Peter Steinberger and a growing community of contributors. The project grew extremely fast, going from zero to nearly 200,000 stars in a remarkably short period. It sits at openclaw.ai and has a Discord server, an iOS app, an Android app, a macOS menu bar companion, and a skill registry called ClawHub at clawhub.com.

As OpenClaw grew, developers who admired the core idea but wanted something different started building alternatives. These were not simple forks that copied the code. They were reimaginings built in entirely different programming languages with entirely different architectures. The projects that emerged form a fascinating snapshot of where the AI assistant space is heading in 2025 and 2026. The five alternatives we cover in this article are IronClaw by NEAR AI, PicoClaw by Sipeed, ZeroClaw by ZeroClaw Labs, NanoClaw by qwibitai, and NullClaw. Each one is a completely real, actively maintained open source project with its own community.

IronClaw: The Security Focused Rust Reimplementation

Who Built It and Why

IronClaw is maintained by NEAR AI and lives at github.com/nearai/ironclaw. It has 8,500 stars and 892 forks, with 58 contributors and active releases, the most recent being version 0.16.1 released on March 6, 2026. The official website is ironclaw.com. The project description is direct: IronClaw is an OpenClaw inspired implementation in Rust focused on privacy and security. The philosophy section of the readme opens with a clear statement: your AI assistant should work for you, not against you. In a world where AI systems are increasingly opaque about data handling and aligned with corporate interests, IronClaw takes a different approach. Where OpenClaw is TypeScript running on Node.js, IronClaw is Rust with a single compiled binary. Where OpenClaw uses Docker-based sandboxing, IronClaw uses WebAssembly containers for tool isolation. Where OpenClaw stores session data in simple files, IronClaw uses PostgreSQL with the pgvector extension for production-ready persistence and hybrid vector plus full text search.

Key Technical Features

• All untrusted tools run in isolated WebAssembly containers with capability-based permissions
• Credential protection: secrets are never exposed to tools, injected at the host boundary with leak detection
• Prompt injection defense with pattern detection, content sanitization, and policy enforcement
• HTTP endpoint allowlisting so tools can only reach explicitly approved hosts
• Dynamic tool building: describe what you need and IronClaw builds it as a WASM tool
• MCP protocol support for connecting to Model Context Protocol servers
• Routines engine for cron schedules, event triggers, and webhook handlers
• Web gateway with browser UI that supports real-time SSE and WebSocket streaming
• Self-repair capability for automatic detection and recovery of stuck operations

System Requirements

IronClaw requires Rust 1.85 or higher, PostgreSQL 15 or higher with the pgvector extension installed, and a NEAR AI account for the default authentication flow. This is a meaningfully higher bar than OpenClaw. You need a running PostgreSQL server and you need to understand how to set up a database and enable an extension. This is the right choice if you are deploying something serious, but it is not a beginner setup. The good news is that IronClaw offers alternative LLM providers so you are not locked into NEAR AI. You can use OpenRouter to access 300 or more models, Together AI, Fireworks AI, Ollama for local models, or any self-hosted server running vLLM or LiteLLM.

How to Install IronClaw

There are several install paths. On Windows you download the MSI installer from the releases page. On macOS and Linux there is a shell installer:

curl --proto '=https' --tlsv1.2 -LsSf https://github.com/nearai/ironclaw/releases/latest/download/ironclaw-installer.sh | sh

On macOS you can also use Homebrew:

brew install ironclaw

After installing you run the onboarding wizard:

ironclaw onboard

The wizard handles database connection, NEAR AI authentication via browser OAuth, and secrets encryption using your system keychain.

Where IronClaw Makes Sense

IronClaw is the right choice when security is the primary concern and you have the infrastructure to support it. It is well suited for developers who handle sensitive personal or professional data and want multiple defense layers between the AI and their information. The PostgreSQL requirement means it fits naturally into environments that already run a database server. If you are a developer who has been nervous about running OpenClaw because you cannot easily audit 52 modules, IronClaw’s security-first, auditable design directly addresses that concern.

PicoClaw: The Ultra Lightweight Alternative for $10 Hardware

Who Built It and Why

PicoClaw is built by Sipeed, a well-known hardware company in the embedded and RISC-V space, and lives at github.com/sipeed/picoclaw. It has 1,300 stars and launched in a single day on February 9, 2026. The tagline gives you everything you need to know: ultra-efficient AI assistant in Go, $10 hardware, 10MB RAM, 1 second boot.

The repo describes PicoClaw as inspired by NanoBot and then refactored from the ground up in Go through a self-bootstrapping process where the AI agent itself drove the architectural migration and code optimization. Ninety-five percent of the core was generated by the agent with human-in-the-loop refinement.

The comparison numbers in the readme are striking. OpenClaw uses more than 1 GB of RAM and takes over 500 seconds to start on a 0.8 GHz single core. PicoClaw uses less than 10 MB of RAM and boots in under 1 second on the same hardware. That is not a small difference. It means PicoClaw can run on a $9.90 Sipeed LicheeRV-Nano board, a $30 NanoKVM device for server maintenance, or a $50 MaixCAM smart camera.

Key Technical Features

• Written in Go and compiled to a single self-contained binary for x86, ARM64, and RISC-V
• Under 10 MB of RAM usage, 99 percent less than OpenClaw
• Boots in under 1 second even on a 0.6 GHz single core processor
• Runs on any Linux device regardless of architecture
• Configuration is done through a single JSON file at ~/.picoclaw/config.json
• Supports Telegram, Discord, QQ, DingTalk, and Feishu messaging channels
• Web search via optional Brave Search API with 2000 free queries per month
• Voice transcription via Groq Whisper for Telegram voice messages

System Requirements

The minimum hardware to run PicoClaw is any 64-bit processor, which includes x86, ARM64, and RISC-V. The RAM requirement is under 10 MB for the agent itself, though the AI model you connect to will obviously need its own resources if running locally. There is no runtime dependency. No Node.js, no Python, no database. You download the binary or build it from source with Go 1.21 or higher and run it.

How to Install PicoClaw

Building from source with Go:

git clone https://github.com/sipeed/picoclaw.git
cd picoclaw && make deps && make build

After building you run the onboarding command:

picoclaw onboard

This creates a config file at ~/.picoclaw/config.json. You then add your API key from OpenRouter, Zhipu, Anthropic, or another provider, and optionally add a Brave Search API key for web search. The full setup from zero to working assistant takes about two minutes. Supported providers include OpenRouter, Zhipu, Anthropic, OpenAI, Gemini, DeepSeek, and Groq. The default model in the examples is glm-4.7 via Zhipu.

Where PicoClaw Makes Sense

PicoClaw is the right choice for anyone who wants to run an AI agent on minimal hardware. This includes Raspberry Pi and similar ARM single board computers, edge computing devices, smart cameras, KVM devices for server maintenance, and home automation setups. It is also excellent as a sidecar process in any environment where you need a small footprint. If you have been told that running an AI assistant requires a Mac mini or a powerful server, PicoClaw is the project that proves that assumption wrong.

ZeroClaw: Zero Overhead, 100 Percent Rust, 100 Percent Agnostic

Who Built It and Why

ZeroClaw is built by ZeroClaw Labs and lives at github.com/zeroclaw-labs/zeroclaw. It has 24,100 stars and 3,100 forks, making it by far the most popular of the alternatives covered in this article. The project was built by students and members of the Harvard, MIT, and Sundai Club communities. The official website is zeroclawlabs.ai. The description is: fast, small, and fully autonomous AI assistant infrastructure, deploy anywhere, swap anything. The mascot is a red crab emoji, contrasting with OpenClaw’s lobster. The tagline is zero overhead, zero compromise, 100 percent Rust, 100 percent agnostic.

ZeroClaw is not just a lighter version of OpenClaw. It is a full rethinking of what an AI assistant runtime should be. Every subsystem in ZeroClaw is a trait, which is Rust’s way of saying an interface. This means you can swap the AI model provider, the messaging channel, the memory backend, the security sandbox, the tunnel provider, and more just by changing a configuration value, with zero code changes needed.

The benchmark numbers from the readme are impressive. OpenClaw needs over 1 GB of RAM and takes over 500 seconds to start on a 0.8 GHz edge core. ZeroClaw uses under 5 MB of RAM and starts in under 10 milliseconds. The binary is 8.8 MB. The entire system runs on any Linux board that costs 10 dollars or more. Important note: ZeroClaw has been dealing with impersonation. The official repo is github.com/zeroclaw-labs/zeroclaw and the official website is zeroclawlabs.ai. The zeroclaw.org and zeroclaw.net domains point to an unauthorized fork. Do not use those sources.

Key Technical Features

• Written in Rust, compiling to a small single binary across ARM, x86, and RISC-V
• Under 5 MB RAM usage, startup in under 10 milliseconds
• 22 plus AI provider support including OpenRouter, Anthropic, OpenAI, Ollama, DeepSeek, Groq, Mistral, Gemini, and many more
• 18 channel implementations: Telegram, Discord, Slack, Mattermost, iMessage, Matrix, Signal, WhatsApp, Lark, DingTalk, QQ, Nostr, IRC, Email, Webhook, Line, and more
• Full memory system built custom with no external dependencies: SQLite with FTS5 full-text search and vector cosine similarity
• Optional PostgreSQL backend for remote memory storage
• Subscription-native auth profiles supporting OpenAI Codex and Claude Code OAuth
• Multi-layer security: gateway pairing, strict sandboxing, allowlists, encrypted secrets
• Tunnel support for Cloudflare, Tailscale, ngrok, and custom tunnel binaries
• Homebrew install on macOS and Linux
• Migration command for importing memory from OpenClaw
• Python companion package zeroclaw-tools for LLM providers with inconsistent tool calling

System Requirements

ZeroClaw requires Rust to build from source, but pre-built binaries are available for Linux x86_64, aarch64, and armv7, macOS x86_64 and aarch64, and Windows x86_64. Building from source needs 2 GB of RAM minimum and 6 GB of free disk space. Running the binary needs under 5 MB of RAM. On low RAM or low disk hosts you can skip compilation entirely with the prebuilt installer:

./bootstrap.sh --prefer-prebuilt

How to Install ZeroClaw

Using Homebrew on macOS or Linux:

brew install zeroclaw

Using the one-click bootstrap script:

git clone https://github.com/zeroclaw-labs/zeroclaw.git
cd zeroclaw && ./bootstrap.sh

The bootstrap script can also install system dependencies, Rust, and run onboarding in one flow:

./bootstrap.sh --install-system-deps --install-rust --onboard --api-key "sk-..." --provider openrouter

After installing, the onboarding wizard handles channel configuration:

zeroclaw onboard --interactive

Configuration Highlights

ZeroClaw uses a TOML configuration file at ~/.zeroclaw/config.toml. Configuration is extremely flexible. You set your default provider and model, configure memory backend, add channels with allowlists, set autonomy level from readonly to supervised to full, configure sandbox settings including Docker containerization, and define tunnel settings. Hot-reload is supported for many configuration values.

Where ZeroClaw Makes Sense

ZeroClaw is the right choice for developers who want the full capability of OpenClaw in a system they can deploy to minimal hardware, understand deeply, and extend through its trait-based architecture. Its massive community, 24,000 stars, and extensive documentation make it the most mature and production-ready of the alternatives. If you want to run a capable AI agent on a 10 dollar board or a minimal VPS while retaining the ability to connect to 18 different messaging channels and 22 different AI providers, ZeroClaw is the clear answer.

NanoClaw: The Security Through Isolation Alternative

Who Built It and Why

NanoClaw is maintained by qwibitai and lives at github.com/qwibitai/nanoclaw. It has 7,200 stars and 871 forks with 10 contributors. The description is precise: a lightweight alternative to Clawdbot and OpenClaw that runs in Apple containers for security, connects to WhatsApp, has memory, scheduled jobs, and runs directly on Anthropic’s Agents SDK.

The developer who built NanoClaw explains the motivation clearly in the readme. OpenClaw has 52 or more modules, 8 config management files, 45 or more dependencies, and abstractions for 15 channel providers. Security is application level, meaning allowlists and pairing codes, rather than OS isolation. Everything runs in one Node process with shared memory. The developer says: I cannot sleep well running software I do not understand with access to my life. NanoClaw gives you the same core functionality in a codebase you can understand in 8 minutes. One process. A handful of files. Agents run in actual Linux containers with filesystem isolation, not behind permission checks.

One important distinction from the previous article: NanoClaw is TypeScript, not Go. It runs on Node.js 20 or higher. It is not about being smaller in terms of memory footprint. It is about being smaller in terms of codebase, so you can actually read and understand everything it does. NanoClaw was also first to support Agent Swarms, the ability to spin up teams of specialized AI agents that collaborate on complex tasks through Anthropic’s Agents SDK.

Key Technical Features

• Written in TypeScript running on Node.js 20 plus
• Agents run in isolated Linux containers using Apple Container on macOS or Docker on Linux
• WhatsApp integration via Baileys for phone-based messaging
• Isolated group context: each group has its own CLAUDE.md memory, isolated filesystem, and its own container sandbox
• Scheduled tasks using a cron-like scheduler that can message you back with results
• Agent Swarms for spinning up teams of collaborating specialized agents
• No configuration files by design. You just tell Claude Code what you want to change and it changes the code
• Skills-based extension model: contributors add skills like /add-telegram rather than features to the core codebase
• Runs on Anthropic’s Agents SDK directly, meaning you run Claude Code as the execution harness

System Requirements

NanoClaw requires macOS or Linux, Node.js version 20 or higher, Claude Code installed, and either Apple Container on macOS or Docker on macOS and Linux. The use of Claude Code as a required component is unique among these alternatives. Setup is done by running the /setup command inside Claude Code, which handles everything automatically.

How to Install NanoClaw

git clone https://github.com/gavrielc/nanoclaw.git
cd nanoclaw
claude

Then run /setup inside Claude Code. Claude Code handles dependencies, authentication, container setup, and service configuration automatically. This is a genuinely different approach to installation. There is no traditional install script or configuration file. You talk to Claude Code and it does the setup for you.

Where NanoClaw Makes Sense

NanoClaw is the right choice for developers who want an AI assistant they can actually read and understand, who primarily use WhatsApp as their messaging platform, who are running on macOS with Apple Silicon and want to take advantage of Apple Container, and who want to run directly on Claude and Anthropic’s Agents SDK rather than abstracting over multiple providers. It is also excellent for anyone who has been uncomfortable running large complex software with access to their personal accounts and data. The small codebase is a deliberate security feature.

NullClaw: Fastest and Smallest, Written in Zig

Who Built It and Why

NullClaw lives at github.com/nullclaw/nullclaw. It has 4,800 stars, 539 forks, and the most recent release is v2026.3.2 from March 2, 2026. The description is: fastest, smallest, and fully autonomous AI assistant infrastructure written in Zig.

The headline numbers are extraordinary. The binary is 678 kilobytes. Peak RAM usage is about 1 megabyte. Startup time is under 2 milliseconds on Apple Silicon and under 8 milliseconds on a 0.8 GHz edge core. It has zero external dependencies besides libc and optional SQLite. It has 3,230 or more tests. It supports 22 or more AI providers, 18 messaging channels, and 18 or more tools. The tagline: null overhead, null compromise, 100 percent Zig, 100 percent agnostic.

NullClaw describes itself as the smallest fully autonomous AI assistant infrastructure, a static Zig binary that fits on any 5 dollar board, boots in milliseconds, and requires nothing but libc. NullClaw is not a minimal no-features system. It is a complete AI agent runtime that just happens to be extraordinarily compact because it is written in Zig, a modern systems programming language that compiles to native code with no garbage collector, no runtime overhead, and no allocator overhead.

Key Technical Features

• Written in Zig 0.15.2, compiling to a 678 KB static binary
• Under 1 MB peak RAM usage on release builds
• Starts in under 2 milliseconds on Apple Silicon
• Supports 22 plus AI providers through vtable interfaces
• Supports 18 messaging channels including Telegram, Signal, Discord, Slack, iMessage, Matrix, WhatsApp, IRC, Lark and Feishu, OneBot, Line, DingTalk, Email, Nostr, QQ, MaixCam, and Mattermost
• Full memory system: hybrid vector plus FTS5 keyword search in SQLite with no external dependencies
• Multi-layer sandbox: auto-detects Landlock, Firejail, Bubblewrap, or Docker based on what is available
• Secrets encrypted with ChaCha20-Poly1305
• Hardware peripheral support: serial, Arduino, Raspberry Pi GPIO, STM32/Nucleo
• Edge deployment example using Cloudflare Workers with Zig WASM module
• 3,230 plus tests across the codebase
• OpenClaw-compatible config structure for easy migration
• Migration command for importing from OpenClaw
• CalVer versioning in the format YYYY.M.D

System Requirements

To build NullClaw you need Zig 0.15.2 exactly. The project is explicit that 0.16.0 development builds and other versions are unsupported and may fail to build. Once built, the binary requires only libc. You can run NullClaw on any hardware with a CPU, including 5 dollar ARM boards.

How to Install NullClaw

git clone https://github.com/nullclaw/nullclaw.git
cd nullclaw
zig build -Doptimize=ReleaseSmall

Then run onboarding:

nullclaw onboard --api-key sk-... --provider openrouter

Or use the interactive wizard:

nullclaw onboard --interactive

Everyday commands follow the same pattern as other claw projects:

nullclaw agent -m "Hello, nullclaw!"
nullclaw gateway
nullclaw service install

Security Model

NullClaw enforces security at every layer. The gateway binds to 127.0.0.1 by default and refuses to bind to 0.0.0.0 without a tunnel or explicit override. Pairing requires a six digit one-time code. Filesystem access is scoped to the workspace by default with null byte injection blocking and symlink escape detection. API keys are encrypted with ChaCha20-Poly1305. The sandbox backend is auto-detected from what is available on the host: Landlock, Firejail, Bubblewrap, or Docker. NullClaw also enforces a deny-by-default channel policy. An empty allowlist means no messages are processed. You must explicitly add users or set the allowlist to an asterisk to allow everyone in.

Where NullClaw Makes Sense

NullClaw is the right choice for developers who want the most capable AI agent runtime in the smallest possible package, for edge computing and embedded deployments on 5 dollar boards, for projects where binary size and startup time are hard requirements, and for researchers who want to study how a full-featured AI agent runtime can be implemented in under 45,000 lines of Zig code with 3,230 tests. It is also the choice for hardware hackers who want native peripheral support for Arduino, Raspberry Pi GPIO, and STM32.

Full Comparison Table: All Six Projects Side by Side

Benchmark Numbers at a Glance

The readme files for PicoClaw, ZeroClaw, and NullClaw all publish comparable benchmarks normalized for a 0.8 GHz single-core processor, which is representative of low-cost edge hardware. Here is what they show, all measured consistently.

• OpenClaw: more than 1 GB RAM, more than 500 seconds startup time, 28 MB distribution size, requires a Mac mini level hardware to run comfortably at 599 dollars.
• PicoClaw: under 10 MB RAM, under 1 second startup, 8 MB binary, runs on any Linux board as cheap as 10 dollars.
• ZeroClaw: under 5 MB RAM, under 10 milliseconds startup, 8.8 MB binary, runs on any 10 dollar hardware.
• NullClaw: approximately 1 MB RAM, under 8 milliseconds startup on a 0.8 GHz core, 678 KB binary, runs on any 5 dollar board.

These are real numbers from the actual project documentation, not estimates. The performance difference between OpenClaw and NullClaw at the low end of hardware is approximately 1,000 times better RAM usage and over 60,000 times faster startup.

How to Choose the Right Alternative

You need the most complete and popular personal assistant

Stick with OpenClaw. With 196,000 stars, a macOS app, an iOS app, an Android app, a skill registry, a Discord community, and first-class support for 13 plus channels, it is the most complete option by far. Install it with npm and run openclaw onboard.

You need security above everything else and have a PostgreSQL server

Choose IronClaw. The WASM sandbox, credential injection at the host boundary, prompt injection defense, and the explicit principle that your data stays yours make it the most security-conscious choice. Be prepared to set up PostgreSQL with pgvector.

You want to run an AI agent on cheap or embedded hardware

Choose PicoClaw for simplicity on hardware down to 10 dollars. Choose ZeroClaw if you want more channel and provider options on the same hardware. Choose NullClaw if you need to go all the way down to 5 dollar boards or require absolute minimum binary size.

You want the most capable alternative with the largest community

Choose ZeroClaw. With 24,100 stars, 18 channels, 22 plus providers, a full memory system, subscription OAuth auth, migration from OpenClaw, and a one-click bootstrap, it is the most mature alternative. The Homebrew install makes getting started trivial.

You use WhatsApp, run macOS, and want a codebase you can actually read

Choose NanoClaw. It runs directly on Anthropic’s Agents SDK, uses real OS container isolation instead of application-level permission checks, and the entire codebase fits in a small number of files. The developer specifically says you can understand it in 8 minutes.

You need the absolute minimum binary size or want hardware peripheral support

Choose NullClaw. At 678 KB it is the smallest. It also has native hardware support for Arduino, Raspberry Pi GPIO, and STM32 boards, making it uniquely useful for AI-powered physical computing projects.

FAQs

Final Thoughts

The claw family of AI agent frameworks is a remarkable snapshot of open source software in early 2026. A single TypeScript project with a lobster mascot inspired Go, Rust, and Zig reimplementations that collectively cover everything from 5-dollar embedded boards to production servers with PostgreSQL. Each project has a genuine philosophy and a real user base.

OpenClaw remains the original and most complete option with nearly 200,000 GitHub stars. IronClaw is the choice for people who cannot trust software they cannot audit, offering WASM sandboxing and credential protection at the cost of PostgreSQL setup complexity. PicoClaw is the choice for hardware that most people would not even think to run software on, proving that a Go binary can do what used to require 1 GB of RAM in under 10 megabytes. ZeroClaw is arguably the most impressive overall package with 24,000 stars, 18 channels, 22 providers, and a memory system built entirely from scratch with no external dependencies. NanoClaw is the honest choice, a codebase small enough to actually read, running directly on Claude with real OS isolation. And NullClaw is the extreme answer to the question of how small you can go while still being fully capable, delivering 678 kilobytes that boot in under 2 milliseconds with 3,230 tests and hardware peripheral support.

All six are free. All six are open source. You can try ZeroClaw in minutes with a Homebrew install. You can try NanoClaw by cloning and running claude then /setup. You can try PicoClaw in two minutes with a single Go install. The best way to find your preference is to try the one that matches your situation and see how it feels.