Deploy AI agents with a system prompt, agent card, and tool config — no code changes needed. A2A-compatible, multi-provider, ~10 MiB per pod.
brew install pavelanni/tap/docsclaw
Single static binary, ~10 MiB per pod on OpenShift. No Python or TypeScript runtime, no heavy dependencies. Starts in milliseconds.
Built on the Agent-to-Agent protocol. Agents discover, delegate, and orchestrate work with any A2A-compatible agent.
Agent behavior defined by a config directory: system prompt, agent card, tool config. Change personality by swapping a ConfigMap — no rebuild needed.
Mount skills securely into the agent container from OCI images via skillimage.dev. Sigstore-signed, versioned, and delivered as Kubernetes image volumes.
Anthropic, OpenAI, and any OpenAI-compatible API (LiteLLM, vLLM, Ollama). Switch providers with a config change. Token usage tracking built in.
Parallel tool execution via goroutines. Shell commands, file operations, web fetch, document service. Extensible tool registry with before/after hooks.
Multi-turn conversations with in-memory session management. The server maintains conversation history — no client-side state needed.
DocsClaw is a universal agent harness. You provide the personality via configuration files; it provides the runtime, protocol handling, and tool execution.
system-prompt.txt — agent personality
agent-card.json — A2A discovery metadata
agent-config.yaml — tools and loop settings
A2A protocol server
LLM provider abstraction
Agentic tool-use loop
Session management
Metrics and logging