Reading
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.
Section 01
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.
Section 02
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.
Section 03
EmberFlow is a Rust-written local-first runtime that provides AI agents with a Single Source of Truth. Its core design principles are:
Section 04
EmberFlow builds its state model around several key concepts:
Section 05
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.
Section 06
A Task is an execution unit attached to a Track, recording who is handling it and what is being done.
Section 07
An Event is a read-only message appended to a Track or Task, forming the authoritative historical record.
Section 08
A Projection is a filesystem view derived from the authoritative state, used for human reading and debugging, but never as an authoritative source.
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