# EmberFlow: A Local-First Workflow Tracking Runtime for AI Agents

> EmberFlow is a Rust-based local-first runtime designed specifically for tracking multi-step tasks of AI agents. It exposes interfaces via SQLite persistent storage and the MCP protocol, enabling agents to record work progress across tool calls, conversations, and session restarts—without any cloud infrastructure.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-05T23:15:27.000Z
- 最近活动: 2026-04-05T23:24:32.306Z
- 热度: 161.8
- 关键词: AI智能体, 工作流追踪, MCP协议, 本地优先, SQLite, Rust, 状态管理, Claude Code, Codex
- 页面链接: https://www.zingnex.cn/en/forum/thread/emberflow-ai
- Canonical: https://www.zingnex.cn/forum/thread/emberflow-ai
- Markdown 来源: floors_fallback

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## Introduction / Main Floor: EmberFlow: A Local-First Workflow Tracking Runtime for AI Agents

EmberFlow is a Rust-based local-first runtime designed specifically for tracking multi-step tasks of AI agents. It exposes interfaces via SQLite persistent storage and the MCP protocol, enabling agents to record work progress across tool calls, conversations, and session restarts—without any cloud infrastructure.

## Background and Problems

As the capabilities of AI Agents continue to grow, they are taking on increasingly complex multi-step tasks. However, agents face a fundamental challenge during execution: **how to persistently record work states?** When an agent needs to work across multiple tool calls, conversations, or even resume after a session restart, it requires a reliable place to store its current phase, task assignments, and execution history. Traditional solutions often rely on cloud services, which introduce latency, privacy, and dependency issues.

## EmberFlow's Solution

EmberFlow is a Rust-written local-first runtime that provides AI agents with a **Single Source of Truth**. Its core design principles are:

- **Local-First**: All states are stored in a local SQLite database, no network connection required
- **MCP Protocol**: Exposes interfaces via the Model Context Protocol, seamlessly integrating with agent clients like Claude Code and Codex
- **Persistent and Recoverable**: Work progress can be saved across sessions and resumed at any time
- **Zero Cloud Infrastructure**: Runs completely offline; data never leaves the local machine

## Core Concept Explanation

EmberFlow builds its state model around several key concepts:

## Track

A Track is a persistent work unit representing the complete context of a feature, task, or investigation. It is the basic unit for an agent to resume work.

## Task

A Task is an execution unit attached to a Track, recording who is handling it and what is being done.

## Event

An Event is a read-only message appended to a Track or Task, forming the authoritative historical record.

## Projection

A Projection is a filesystem view derived from the authoritative state, used for human reading and debugging, but never as an authoritative source.