What is AMS and its primary purpose?

AMS is an audit-first operating layer for AI agents, enabling durable project scope, task control, long-term memory, and orchestration across isolated chat sessions, transforming them into managed projects.

How does AMS provide durable project management for AI agents?

It offers persistent project and task state that survives client restarts, roadmap orchestration for planned multi-step execution, and memory/retrieval flows, ensuring continuous progress and context retention for long-running work.

Does AMS integrate with note-taking apps or specific AI models?

Yes, AMS features an Obsidian plugin for integrating AI projects and sessions directly into your managed vault notes. It also provides Claude-specific capabilities for advanced file, vision, streaming, and conversation management.

What does 'audit-first' mean for AI agent execution in AMS?

Audit-first means AMS generates immutable execution trails with Merkle-backed proof material. This allows users to transparently review, verify, and understand every step an AI agent takes, enhancing trust and control.

AMS

Name: AMS
Author: LastEld

byLastEld

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Provides an audit-first operating layer for AI agents, enabling durable project scope, task control, and long-term memory management across isolated chat sessions.

AMS transforms how AI agents interact with tasks by establishing an audit-first operating layer that provides durable project scope, granular task control, and persistent memory, effectively moving beyond the limitations of isolated chat states. It fills the gap left by most MCP clients by offering context snapshots, verifiable execution trails, and roadmap-driven orchestration for planned work. With native integrations for Obsidian and Claude, AMS allows for sophisticated codebase archaeology, counterfactual analysis, and cross-version comparisons, making it an indispensable tool for managing long-running, complex AI projects.

Key Features

018 GitHub stars

02Audit-first execution trails with Merkle-backed proof material for review.

03Durable project and task state with long-term memory and retrieval flows.

04Roadmap and GSD orchestration for planned, multi-step agent execution.

05Obsidian plugin for integrating AI projects and sessions into managed vault notes.

06Claude-specific capabilities for files, vision, streaming, and conversation management.

Use Cases

01Managing and orchestrating complex, long-running AI agent projects with persistent state.

02Generating auditable and verifiable proof trails for AI agent task execution and completion.

03Integrating AI agent memory and project management workflows with Obsidian for enhanced note-taking and documentation.