NexusOS/CLAUDE.md

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

What is NexusOS

NexusOS is a local AI assistant platform. It runs a Python/FastAPI backend (Synapse) that interfaces with a locally bundled Ollama instance, a dedicated memory microservice, and a React/Vite frontend. All AI inference runs through Ollama on localhost; no external AI provider is configured or called.

Running the Project

NexusOS runs single-process: the Synapse backend on port 8000 serves the built web UI (interface/web/dist) itself, so there is no separate Vite server at runtime. Ollama is not started on backend process startup itself, but ncp start/start -b bring it up right after via the /ollama/start endpoint; the sidebar Start/Stop AI button and ncp start --ai remain for toggling it independently once the backend is already up.

Windows (recommended):

powershell -ExecutionPolicy Bypass -File .\install-windows.ps1   # one-time native install
ncp web                                                          # backend :8000 + UI

ncp comes from management\ncp.cmd, which the installer puts on the machine PATH — open a NEW shell after installing. .\launch_nexus.ps1 is what the desktop shortcut runs and still works directly.

Linux install / update:

./install.sh              # wrapper over `python3 bin/sync.py restore`
./install.sh --check      # dry run
./install.sh --no-desktop # skip the XFCE wiring (test clones, non-XFCE boxes)

Linux full stack (dev):

./launch_nexus.sh

This activates the Promethean venv and starts the Synapse backend on port 8000 (which also serves the built UI). It additionally starts a Vite dev server for frontend hot-reload — a Linux-dev convenience, unlike the single-process Windows/production path where the backend serves dist/ alone. It does not start Ollama.

Individual services via CLI:

# From nexus-core/ with Promethean venv active:
source Promethean/bin/activate

# Backend (serves the built UI at :8000 too)
uvicorn synapse.main:sio_app --host 127.0.0.1 --port 8000 --reload

# Frontend DEV server (hot-reload) — only when editing the UI; production is the
# built dist/ served by the backend. Run `npm run build` to refresh dist/.
cd interface/web && npm run dev

Management CLI (nexus / ncp) — start/stop services with PID tracking, plus terminal access to the same features as the web UI (REST API on :8000):

./management/nexus-cli.sh start   # starts backend + frontend
./management/nexus-cli.sh stop
./management/nexus-cli.sh start --backend|-b / --frontend|-f / --memory|-m

# Interactive TUI (Hermes/OpenClaw-style; needs pip install 'nexusos-ai[tui]'):
nexus                             # bare command opens the Textual chat TUI
nexus tui                         # same, explicit

# Feature one-shots (dispatch to nexusos_cli/nexus_api.py — httpx):
nexus chat send "<message>"       # stream a reply (POST /chat/stream)
nexus memory list|add <text>|rm <id>
nexus playbook list|show <id>     # first playbook (*) is the active system prompt
nexus history [query]             # recent conversations
nexus monitor                     # ASCII status dashboard (no prompt)

The interactive TUI lives in nexusos_cli/tui_app.py (Textual, optional extra). One-shot subcommands and nexus monitor remain for scripts. The CLI package is nexusos_cli/ (what the wheel ships); management/ keeps desktop-only pieces — shell wrappers, Tk control panel, XFCE panel wiring. management/controlpanel.py (tkinter GUI, wired into the XFCE panel via bin/panel/nexus-popup.py) stays.

Checks (the release gate):

./bin/check.sh          # pytest + eslint + frontend tests + .ps1/.sh parse + wheel build

There is no hosted CI — the remote is self-hosted Gitea with no act_runner — so this script is the gate. Run it before tagging a release.

Frontend lint only:

cd interface/web && npm run lint

Frontend build:

cd interface/web && npm run build

Architecture

Python venv

All Python code runs inside Promethean/ (a local venv). Always activate it before running backend commands: source Promethean/bin/activate. Dependencies are layered: requirements-base.txt holds the GPU-agnostic core (nothing in it needs a GPU or imports torch), and a thin overlay per platform sets the right PyTorch package index — requirements-amd.txt (ROCm), requirements-nvidia.txt (CUDA, generated by bin/gen-nvidia-reqs.py), or requirements-windows.txt (CPU-only, standalone). bin/sync.py (requirements()) selects NVIDIA, AMD, or CPU/Windows requirements from the host and installs that alone by default — fast, no multi-GB downloads.

requirements-ml.txt is a separate, opt-in overlay for local ML inference (transformers/accelerate/bitsandbytes + torch/torchaudio/torchvision) — nothing in synapse/ imports any of it; Ollama does all inference over HTTP. Only pull it in for local model work outside Ollama: pip install -r requirements-amd.txt -r requirements-ml.txt (or -nvidia, or alone for CPU-only torch). Not installed by bin/sync.py/the installers.

Synapse Backend (synapse/)

FastAPI app at synapse/main.py. Key responsibilities:

• /chat/stream — chat with Ollama; streaming uses SSE (the only chat endpoint — the non-stream /chat was removed). When a conversation goes idle the stream endpoint hands it to the in-process curator, which extracts persistent facts.
• /playbooks — CRUD for playbooks stored as YAML files in data/playbooks/ via synapse/playbooks/store.py.
• /memory — CRUD for persistent facts, backed by the same SQLite store the curator writes to.
• /models — lists, pulls, and deletes Ollama models by proxying Ollama's HTTP API.
• /settings and /ollama — persist runtime settings and control Ollama lifecycle.
• /conversations — persists, retrieves, edits, deletes, and exports full chat history from SQLite.
• /icons — lists local application icons and applies NexusOS branding.

System prompt assembly (in main.py chat_stream_endpoint): the final system prompt is built by layering the active playbook instructions → reference playbook context → persistent memory facts → relevant past conversation snippets retrieved by store.search_conversations.

Memory (synapse/memory/)

Runs in-process — there is no separate service and no second model. curator.py reads the messages a conversation has added since its watermark, extractor.py asks the chat model which permanent facts they contain, and store.py merges the results into memory.db. The backend schedules it when a conversation goes idle (_pending_extractions in main.py), so a half-said fact is never persisted mid-thought. The old :8001 FastAPI app held a second, smaller model that could not share the GPU with the chat model; the chat model is already resident, so the extra hop bought nothing.

Playbook System (synapse/playbooks/ + synapse/playbook_manager.py)

Playbooks are ordered records (title, goal, instructions, tags), each persisted as a {id}.yaml file in data/playbooks/ by PlaybookFileStore (the dir is PLAYBOOK_DIR in nexus_config.py). The first playbook by order is the active system prompt; all subsequent playbooks are injected as reference context. PlaybookManager is the thin class the backend uses to retrieve them and assemble the system prompt.

Ollama (ollama/bin/ollama)

A bundled Ollama binary lives at ollama/bin/ollama. OllamaManager in synapse/ollama_manager.py manages its lifecycle (start/stop/health-check) and selects the best available model. GPU detection uses Vulkan (vulkaninfo) to prefer discrete AMD/NVIDIA GPUs. The Ollama HTTP API is at http://127.0.0.1:11434 (overridable via OLLAMA_HOST env var).

Frontend (interface/web/)

React 19 + Vite. No routing library — App.jsx manages page state in a single currentPage useState. All API calls hit http://localhost:8000 (configured in src/config.js). Built to dist/ (gitignored) via npm run build and served by the backend at :8000 — the mount is in synapse/main.py (_DIST at /, guarded by is_dir()), so dist/ must be built for the UI to appear. Pages: Chatbot, Playbook editor, Conversation History, Models, Memory, Settings, Logs.

Persistent Storage

Most data lands in synapse/memory/memory.db (SQLite, WAL mode). Tables: memory facts, conversations, messages, app settings. synapse/memory/store.py (PersistentMemoryStore) owns the schema and all queries. Playbooks are the exception — they live as YAML files in data/playbooks/ (see Playbook System). nexus_config.py defines all paths; it also ensures all required directories exist on import.

Curry (synapse/curry_core.py + synapse/curry_store.py)

curry_core.py is vendored, unmodified-except-for-one-fix, from Athena-Pro/Curry — an immutable, versioned fact store (constants, functions, model registrations, inference provenance) backed by its own SQLite file (CURRY_DB in nexus_config.py, separate from memory.db). curry_store.py opens it into a module-level singleton (curry_db) at import time — the same pattern as memory.store.store / playbooks.store.playbook_store — so it's preloaded and callable (curry_db.declare_constant(...), curry_db.call_function(...), etc.) from anywhere in the backend without extra setup. It ships inside the wheel (bin/check.sh's packaging gate asserts this) and has no external dependencies of its own. Nothing currently wires chat/model-authored content into it — it's available, not yet exposed as an action tool. Re-sync curry_core.py from upstream by hand, not by script; see the file's own docstring for what changed and why.

Logs & Runtime State

• runtime/backend.log, runtime/frontend.log, runtime/memory.log — service stdout
• runtime/logs/ollama.log, runtime/logs/chat.log
• runtime/pids/backend.pid, runtime/pids/frontend.pid — used by the management CLI

Key Config

Concern	Location
Ollama host	OLLAMA_HOST env var (default http://127.0.0.1:11434)
All filesystem paths	synapse/nexus_config.py Settings class
Frontend API base URL	interface/web/src/config.js
Default chat/memory models	synapse/nexus_config.py DEFAULT_CHAT_MODEL / DEFAULT_MEMORY_MODEL
Python dependencies (base)	requirements-base.txt
Python dependencies (AMD/ROCm)	requirements-amd.txt
Python dependencies (NVIDIA/CUDA)	requirements-nvidia.txt (generated by bin/gen-nvidia-reqs.py)
Python dependencies (Windows/CPU)	requirements-windows.txt
Python dependencies (optional local ML/torch)	requirements-ml.txt (opt-in, not installed by default)