data-ai MCP Server
AI agent runtime for Rust — type-safe state, multi-protocol serving, plugin extensibility.
Discovered via github-topic:mcp and last synced 3mo ago.
Install instructions not detected yet
Check the source repository for the latest setup steps.
`POST /v1/a2a/message:send`
Route / transport
Start with
What it does
What it does
`POST /v1/ag-ui/run`
`POST /v1/mcp`
Start with
What it shows
stdio via `serve_stdio`
Write your own with `ToolGateHook` or `BeforeToolExecute` — same trait signatures the built-ins use. ## Architecture <p align="center"> <img src="./docs/assets/demo.svg" alt="Awaken demo — managed agent run with tool calls, approval, and trace" width="800"> </p> ```text awaken Facade crate with feature flags ├─ awaken-runtime-contract Runtime contracts: specs, tools, events, state, commit coordinator ├─ awaken-server-contract Server/store contracts: queries, scoped stores, mailbox/outbox, staged commits ├─ awaken-runtime Resolver, phase engine, loop runner, runtime control ├─ awaken-server HTTP routes, SSE replay, mailbox dispatch, protocol adapters ├─ awaken-stores Thread + run + config + mailbox + profile stores ├─ awaken-tool-pattern Glob/regex matching used by extensions └─ awaken-ext-* Optional extensions and companion plugins ``` For details, start with [Architecture](https://awakenworks.github.io/awaken/explanation/architecture/) and [Run Lifecycle and Phases](https://awakenworks.github.io/awaken/explanation/run-lifecycle-and-phases/). ## When this fits - You want a **Rust backend** for AI agents with compile-time guarantees. - You need to serve **AI SDK, CopilotKit, A2A, MCP, and/or ACP** from a single backend. - Tools need to **share state safely** during concurrent execution, and runs need auditable history with checkpoints and resume. - Operators need to tune prompts, models, permissions, skills, traces, datasets, and evals without changing code. ## When it does not - You need **built-in file/shell/web tools** out of the box — consider OpenAI Agents SDK, Dify, or CrewAI. - You want a **visual workflow builder** — consider Dify or LangGraph Studio. - You want **Python** and rapid prototyping — consider LangGraph, AG2, or PydanticAI. - You need an **LLM-managed memory** subsystem where the agent decides what to remember — consider Letta. ## Examples and learning paths