Introduction
Akshi is a Rust-based agent runtime that sandboxes AI agents in WebAssembly, routes inference between local and cloud models, and connects agents via mesh networking.
What Akshi does
- Declarative agents – Define agent behavior in TOML: goal, file pattern, LLM prompt, output schema. No code needed for most use cases.
- Sandboxed execution – Agents run as WebAssembly modules with explicit capability grants. No ambient filesystem or network access unless configured.
- Inference routing – A built-in router dispatches LLM calls to local models (via Ollama) or cloud providers (Anthropic, OpenRouter) based on prompt complexity.
- Mesh networking – Agents discover and communicate with each other over a lightweight peer-to-peer mesh, enabling multi-agent workflows without a central orchestrator.
- Developer CLI – The
akshibinary handles scaffolding, running, monitoring, and deploying agents from a single command.
Who this is for
Akshi is aimed at developers who want to run autonomous AI agents with strong isolation guarantees and flexible model routing, whether on a single laptop or across a cluster of machines.
How to use this book
| Section | What you will find |
|---|---|
| Getting Started | Installation, quickstart, building your first agent |
| Architecture | Runtime internals, sandbox model, broker design |
| Configuration | Reference for runtime.toml and CLI flags |
| SDK Reference | Agent-side API (logging, inference, database, MCP tools) |
| Operations | Deployment, monitoring, troubleshooting |
If you are new to Akshi, start with Getting Started. It takes about five minutes to go from zero to a running agent.