← Back to LLM Wiki
LLM Wiki · Agent Runtime · Claw Ecosystem

ZeroClaw

Minimal footprint, maximum provider flexibility. 3.4MB Rust binary, sub-10ms startup, under 5MB runtime RAM, native support for 22 AI providers.
ZeroClaw is the Claw runtime optimized for one metric: not being in the way. A 3.4MB static binary that starts in under 10ms, uses less than 5MB of RAM, and speaks to any of 22 AI providers through a single abstraction. The typical deployment is a sidecar or serverless function where every megabyte and millisecond is billed.
Agent Runtime Rust 3.4MB Binary Sub-10ms Start 22 Providers

Quick Facts

Category
Agent runtime (not a model)
Language
Rust
Binary size
3.4MB (static, stripped)
Startup
Sub-10ms cold start
Runtime RAM
Under 5MB
Providers
22 native — Anthropic, OpenAI, Bedrock, Vertex, Azure, Groq, Together, Fireworks, DeepSeek, Moonshot, Mistral, Cohere, Ollama, vLLM, etc.
Dependencies
Zero runtime dependencies
Target
Serverless (Lambda, Workers, Fly), sidecars, CLI embeds, low-spec hosts
License
Open source (see Claw ecosystem on GitHub)

Summary

ZeroClaw exists for the places where every constraint matters: Lambda cold starts billed in milliseconds, Cloudflare Workers capped at a few MB, Alpine-based sidecars that sit next to every microservice, CLI tools where a 200MB Node runtime is a non-starter. The engineering discipline is aggressive — no runtime reflection, no dynamic loading, no async runtime overhead it doesn't need, no features that can't be justified against the binary-size budget.

The feature set is deliberately narrow: an agent loop, a tool protocol, a provider abstraction, and basic observability. Everything else — memory, sandboxing, channel adapters, integrations — lives upstream or elsewhere in the architecture. ZeroClaw is a lean core that other runtimes (OpenClaw, MicroClaw, ExoClaw) use internally for hot paths.

Architecture

When to Choose ZeroClaw

ZeroClaw is the right choice when: the deployment surface is constrained (serverless, edge, embedded); cold-start time is billed; the binary has to drop into a distroless or scratch container; or you want a consistent agent-loop implementation across many small services that each need agent capability without the operational weight of a full runtime. It's also the typical pick for CLI tools that embed an agent — terminal utilities, CI steps, local dev tools.

Tradeoffs

Related

References

  1. Organized AI on GitHub
  2. The Agent Infrastructure Stack — Organized AI
  3. Edge Compute Economics — Organized AI