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ami
Pricing
- Model
- Free
Summary
Cloud-based AI agents drag your personal tokens, private repos, and internal tool credentials through someone else's infrastructure — and you lose control the moment you authorize the first OAuth grant. Ami is a local agent harness that runs entirely on your machine, connecting to your tools with your own credentials and keeping everything under ~/.ami/.
Ami builds a context graph in SQLite and markdown on disk — tracking entities, relationships, your past decisions, and your writing style — so the agent gets less hand-holdy the more you use it. It maintains a live to-do list and executes recurring busy work by learning how you handle tasks, not by following a static playbook. The self-hosting story is real: no data leaves your machine, no org-level sharing. Where it breaks is scale and surface area — the repo has 3 commits and 4 stars at time of writing, which means production edge cases are yours to discover and debug, not documented anywhere. Teams that need multi-user workflows, audit logs, or a managed API surface will hit the ceiling fast.
Bottom line: Pick Ami if you want a private, local agent that learns your personal work patterns without any cloud dependency — but plan for a full rebuild if your use case grows to team-level coordination or requires a stable API contract.
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Pros
Sign in to edit- Fully local execution with data stored under ~/.ami/ in SQLite and markdown, so your credentials and task history never leave your machine — which means you can connect personal tokens to internal tools without authorizing a third-party cloud service.
- Context graph memory that tracks entities, relationships, decisions, and writing style across sessions, so the agent improves its accuracy on your specific tasks over time rather than treating every run as a cold start.
- MIT license with self-hosted deployment, so you own the full stack and can audit, fork, or extend any part of the system without a vendor relationship gating you.
- Workflow learning from observation rather than manual configuration, so you avoid the setup tax of explicitly scripting every automation — the agent encodes patterns from how you already work.
Cons
Sign in to edit- The project has 3 commits and 4 stars at the time the source page was scraped, which means production edge cases, integration failures, and memory corruption scenarios have no community documentation and no issue tracker history to search — you are debugging from scratch.
- No API surface is exposed, so any team or tool that needs to call the agent programmatically or integrate it into a pipeline hits a hard wall immediately; teams with that requirement move to an agent framework that exposes an API endpoint.
- Memory and state are scoped to a single local user under ~/.ami/, with no mechanism described for shared state or multi-user coordination — teams that need a shared task queue or collaborative agent context have to abandon Ami for a hosted alternative before the second team member needs access.
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About
- Platforms
- Local machine (Node.js)
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- 2026-07-23T02:37:38.367Z
Best For
Who it's for
- Users seeking fully local AI agents
- Developers integrating personal tools
- Task automation without cloud dependency
What it does well
- Personal task automation
- Tool integration and execution
- Workflow learning and replication
- Local memory management for agents
Integrations
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Frequently Asked Questions
- Is ami free?
- Yes — ami is fully free to use. There is no paid tier.
- Is ami open source?
- Yes. ami is open source.
- Can I self-host ami?
- Yes. ami supports self-hosting on your own infrastructure.
- What platforms does ami support?
- ami is available on: Local machine (Node.js).
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Curated lists that include this category
Most personal automation tools either require you to hand credentials to a cloud service or demand you wire up every workflow by hand every time. Ami approaches this differently: it acts as a local agent harness that connects to your apps, repositories, and tools using your own personal tokens, then builds a live context graph — stored in SQLite and markdown under ~/.ami/ — that it updates continuously as you work. The core loop is: connect a tool, let Ami observe how you handle a task, watch it encode that pattern as memory, and then have it execute the same class of task autonomously going forward.
The standout architectural decision is the context graph memory model. Rather than storing flat conversation history, Ami tracks named entities, relationships between them, your decisions, feedback you’ve given, and even your writing style. The vendor describes this as making the agent ‘more autonomous the more you use it’ — meaning the agent’s behavior adapts to your specific patterns rather than starting from a generic baseline on every session.
Ami fits solo developers and power users who handle repetitive coordination work — triaging issues, drafting responses, managing to-dos across tools — and who are unwilling to route personal credentials through a third-party service. It does not fit teams that need shared agent state, role-based access, or a stable programmatic API: there is no API surface exposed, and the data model is explicitly personal and local. The project is early-stage by any measure, which means missing integrations and undocumented failure modes are the expected experience, not exceptions.
The project is MIT-licensed, built in TypeScript with a pnpm monorepo structure, and is self-hostable on any machine that can run Node. The docs describe tool integration via personal tokens, and the memory store lives entirely on-disk — nothing is transmitted externally by design.