Orbital PAI vs Persnally

Orbital PAI and Persnally are both personal assistants 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.

Orbital PAI

Built on Elixir/Phoenix for low-latency local execution, it handles calendar management, email, reminders, weather queries, and web search through a tool-calling brain that chains steps without you scripting the handoffs. Persistent memory means it carries context across sessions rather than starting blank every conversation. The self-hosted path is real — there is a Dockerfile and docker-compose.yml in the repo. The warning the maintainers put at the top of the README is not decorative: the project is under heavy active development, which means APIs shift, documented behavior changes, and any deployment you build today is maintenance work tomorrow.

Persnally

The setup is a single npm install and an import command that reads your Claude exports, Claude Code sessions, and git repos, then builds a structured, evidence-linked profile in ~/.persnally. From there, a local daemon serves that profile over MCP so Claude, Cursor, Copilot, and other connected tools load your conventions and preferences at session start without you repeating them. Per-client scopes let you decide exactly what each tool can see. The architecture is the trust model — context never leaves your machine, deletions erase source events and rebuild all derived views with no residue. Where it breaks: any tool that does not speak MCP stays out of the loop, and teams using proprietary AI integrations without MCP support get nothing from this.

Attribute	Orbital PAI	Persnally
Pricing	Free	Free
Free trial	No	No
Open source	Yes	Yes
Has API	No	No
Self-hosted option	Yes	Yes
Platforms	Web (PWA), Self-hosted (Elixir/Phoenix, Docker)	macOS, Linux, Windows (Node 20+)
Pros	Self-hosted by design with a Dockerfile and docker-compose.yml included, so voice data never leaves your infrastructure — teams handling sensitive personal information avoid the compliance questions that come with cloud-dependent assistants. Tool-calling brain chains calendar writes, email sends, web searches, and reminders in a single spoken request, so you are not scripting multi-step workflows manually the way you would with a simple command-dispatch system. Persistent memory across sessions carries user context forward, meaning the assistant does not ask you to re-explain your preferences every conversation the way stateless assistants do. Elixir/Phoenix concurrency handles overlapping I/O stages in the voice pipeline — STT, tool execution, TTS — without blocking, which means response latency stays low even when a tool call takes a moment to return. MIT license with full source access means you can modify the core behavior, swap out underlying models, or strip out capabilities you do not need — without waiting for a vendor roadmap.	Learns your stack, conventions, and preferences from existing AI history on import, so you stop re-explaining the same context at the start of every new session across different tools. Serves context over MCP — the protocol already adopted by Claude, Cursor, Copilot, and others — which means no custom plugin per tool; one daemon serves all connected clients. Per-client scopes let you control exactly what each AI tool is allowed to read, so a scoped agent gets a narrower view than your primary coding assistant without manual prompt filtering. Provenance-complete profile means every inferred trait links to the source events that produced it, so you can audit what the tool thinks it knows and why rather than treating it as a black box. Deletion erases source events and rebuilds all derived views from zero — no soft deletes or hidden residue — which means removing a topic actually removes it from everything downstream.
Cons	The maintainers flag the project as under heavy active development at the top of the README, which means any integration you build against the current API surface is at risk of breaking on the next commit — teams that need a stable deployment skip this until a tagged stable release exists. No API surface is exposed, so if your use case requires another service to trigger the assistant or consume its output programmatically, there is no endpoint to call — teams with that requirement wire up a different tool or build the interface layer themselves from source. The Elixir/Phoenix stack is the whole runtime, not an optional layer — teams without BEAM experience face a steep operational learning curve for debugging, monitoring, and extending the assistant, and when something breaks at 2am the docs assume you already know how Elixir supervision trees work. Zero community pull requests and two stars on the repository at the time of scraping means the bug surface is whatever the maintainers have personally tested — teams evaluating this against a voice assistant with an active contributor base and documented issue resolutions will find nothing equivalent here, and if the project goes dormant, maintenance falls entirely to the fork.	Any AI tool that does not implement MCP cannot call get_context and receives no context at all — teams whose primary tools lack MCP support get zero benefit from the daemon, and at that point they are back to manual context pasting or switch to a tool with a native memory feature built into the vendor's platform. The profile is only as accurate as the history you can import; the page names Claude exports, Claude Code sessions, and git repos as sources, so workflows living in other tools — Slack threads, Jira tickets, proprietary IDEs without export — contribute nothing until an import path exists for them. Running a persistent local daemon adds a process to maintain across machine restarts and OS updates; teams on locked-down corporate machines or shared development environments without npm global install rights hit a setup wall before the tool does anything useful.

Bottom line

Orbital PAI and Persnally 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 Orbital PAI and Persnally?

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

Is Orbital PAI better than Persnally?

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.