ami vs OnBuzz

ami and OnBuzz are both ai agent apps tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

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.

OnBuzz

OnBuzz, built by Loxia, lets you spin up multiple autonomous agents that collaborate on tasks, connect directly to LLM providers, and execute work without a cloud intermediary. The Apache-2.0 license and self-hosted design mean your data and your agents stay where you put them. It ships as an Electron app with installers and binaries, so setup does not require hand-rolling a container stack. The tool-use and inter-agent collaboration model is genuinely capable — agents can hand off tasks, run in parallel, and schedule work without you babysitting. Where it strains: the community repository has modest GitHub traction (33 stars at time of indexing), which means documentation gaps surface quickly and community debugging support is thin.

Attribute	ami	OnBuzz
Pricing	Free	Free
Free trial	No	No
Open source	Yes	Yes
Has API	No	No
Self-hosted option	Yes	Yes
Platforms	Local machine (Node.js)	Windows, macOS, Linux (Electron desktop app, web UI, terminal UI)
Pros	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.	Fully local execution with no cloud dependency, so sensitive workloads — regulated data, proprietary models, air-gapped environments — never leave your machine. Direct LLM provider connections, which means you control API routing and can swap providers without changing platform configuration when costs or availability shift. Ships as a packaged Electron app with installers and binaries, so the time between download and first running agent is measured in minutes, not a multi-hour container setup. Apache-2.0 license with an open contribution model and public roadmap, so you can audit exactly what the agents are doing, fork freely, and patch the gaps that matter to your use case. Inter-agent collaboration built into the architecture — agents can work in parallel and hand tasks between each other — so complex multi-step jobs do not require you to serialize everything through a single prompt chain.
Cons	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.	The community is early-stage: 33 GitHub stars and a small contributor base at time of indexing means that when your agent workflow hits an undocumented edge case, the support path is reading source code, not finding a Stack Overflow thread or a Discord answer. Teams with a production deadline switch to a platform with a larger ecosystem — LangGraph, CrewAI, or a hosted alternative — precisely at this moment. There is no API surface described in the available documentation, which means OnBuzz cannot be embedded inside a larger application pipeline or triggered programmatically from external systems. Teams that need to call agent workflows from their own backend code hit this wall immediately and must either wrap the Electron app in a fragile subprocess layer or abandon the tool for something that exposes an API. As a local-only platform, horizontal scaling requires you to run multiple instances manually across machines. There is no built-in cluster management or work queue that distributes load — when agent throughput needs to grow beyond a single machine, you are building the scaling layer yourself.

Bottom line

ami and OnBuzz are closely matched on pricing model, openness, and API availability — pick by feature set and platform support in the table above.

Frequently asked questions

What is the difference between ami and OnBuzz?

ami is Free and open source, while OnBuzz is Free and open source. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is ami better than OnBuzz?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.

ami vs OnBuzz: which should I pick?

Pick ami if its pricing model, openness, or platform fit matches your constraints; pick OnBuzz otherwise. Check free-trial availability on each listing if you want to test before committing.

Comparison data is sourced and verified by the AIDiveForge data pipeline. AIDiveForge is editorially independent.