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82 lines
4.3 KiB
Markdown
82 lines
4.3 KiB
Markdown
---
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description: >-
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High-level shape of the OpenHuman system (desktop shell, Rust core, Memory
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Tree, agent loop). Pointer to the deep developer architecture in the repo.
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icon: code-branch
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---
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# Architecture
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OpenHuman is open-sourced under GNU GPL3. This page is the high-level shape of the system; the deep developer architecture lives in [deep architecture reference](../architecture.md) in the repo.
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## The shape
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OpenHuman is a **React + Tauri v2 desktop app** with a **Rust core** that does the heavy lifting.
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```
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┌──────────────────────────────────────────────────┐
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│ Tauri shell (app/src-tauri/) │
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│ • windowing, OS integration, sidecar lifecycle │
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│ • CEF child webviews for integration providers │
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└──────────────────────────────────────────────────┘
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│ JSON-RPC (HTTP) ↕
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┌──────────────────────────────────────────────────┐
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│ Rust core (`openhuman` binary, `src/`) │
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│ • Memory Tree pipeline │
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│ • Integration adapters + auto-fetch scheduler │
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│ • Provider router (model routing) │
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│ • TokenJuice compression │
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│ • Native tools (search, fetch, fs, git, …) │
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│ • Voice (STT in, TTS out, Meet agent) │
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└──────────────────────────────────────────────────┘
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│
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┌──────────────────────────────────────────────────┐
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│ React frontend (app/src/) │
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│ • Screens, navigation │
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│ • Talks to core over `coreRpcClient` │
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│ • No business logic - presentation only │
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└──────────────────────────────────────────────────┘
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```
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**Where logic lives:**
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* **Rust core**. all business logic. Memory Tree, integrations, model routing, tools, voice. Authoritative.
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* **Tauri shell**. windowing, process lifecycle, IPC. A delivery vehicle, not where features live.
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* **React frontend**. UI and orchestration. Calls into core via JSON-RPC.
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## Data flow
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1. **Connect**. OAuth into a [integration](../../features/integrations/README.md). Backend stores the token; core never sees it in plaintext.
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2. **Auto-fetch**. Every twenty minutes the [scheduler](../../features/obsidian-wiki/auto-fetch.md) walks every active connection and asks each native provider to sync.
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3. **Canonicalize**. Provider output (an email page, a GitHub diff, a Slack channel dump) is normalized into provenance-tagged Markdown.
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4. **Chunk**. Markdown is split into ≤3k-token deterministic chunks.
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5. **Store**. Chunks land in SQLite (`<workspace>/memory_tree/chunks.db`) and as `.md` files in `<workspace>/wiki/`.
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6. **Score**. Background workers run embeddings, entity extraction, hotness scoring.
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7. **Summarize**. Source / topic / global summary trees are built and refreshed from the chunk pool.
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8. **Retrieve**. When you ask a question, the agent queries the Memory Tree (search / drill down / topic / global / fetch).
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9. **Compress**. Tool output and large source data go through [TokenJuice](../../features/token-compression.md) before entering LLM context.
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10. **Route**. The [router](../../features/model-routing/) picks the right provider+model for the task hint.
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## Privacy boundary
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Stays on your machine:
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* The Memory Tree SQLite DB.
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* The Obsidian Markdown vault.
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* Audio capture buffers and any local model state.
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Goes through the OpenHuman backend (under one subscription):
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* LLM calls (model providers).
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* Web search proxy.
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* Integration OAuth and tool proxying.
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* TTS streaming.
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See [Privacy & Security](../../features/privacy-and-security.md) for the full picture.
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## Open source
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* **Repo:** [github.com/tinyhumansai/openhuman](https://github.com/tinyhumansai/openhuman). GNU GPL3.
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* **Issues and PRs** are welcome. The project is in early beta.
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* For contributors, the canonical developer guide is [deep architecture reference](../architecture.md).
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