Tool audit log:
- Every orchestrator tool call logged to home/{user}/tool_audit/YYYY-MM-DD.jsonl
- Files panel sidebar: audit log group (collapsed), date-linked read-only table
- Admin endpoints: /api/audit/files, /api/audit/day, /api/audit/recent, /api/audit/stats
- Engine and model name recorded per entry
OpenAI orchestrator improvements:
- Context budget enforcement: 75% of model context_k (min 16k)
- Message compaction: truncates old tool results when approaching budget
- max_rounds respected per model config (intersected with server cap)
OpenRouter onboarding (setup.html, onboarding.py, app.js, settings.html):
- Step 3 of 3: /setup/model with curated model picker
- Chat banner for users on server-default model (informational, not alarmist)
- Settings quick-link card; /setup/model works standalone for existing users
Model registry + session store:
- set_role_config / get_role_config for per-role tool lists and system_append
- session_store: session rename, session name backfill endpoint
UI updates (app.js, index.html, style.css, local_llm.html):
- Role toggle in context panel
- Off-the-record mode
- Agent notes read-only viewer
- OPERATIONS.md loaded at T2+ in context
Documentation:
- HELP.md: full tool table, per-role tool sets, Agent Notes, usage tracking
- TOOLS.md: Agent Notes section, count corrected to 44
- ARCH__SYSTEM.md, ARCH__BACKENDS.md, MASTER.md updated to match reality
- CLAUDE.md: onboarding flow, documentation philosophy sections
- README.md: stack in practice, DeepSeek TUI mention, architecture diagram updated
- TODO__Agents.md: onboarding task completed with deviation notes
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
8.5 KiB
Architecture: System Overview
How the pieces fit together. Last updated: 2026-05-06
Architecture Diagram
┌─────────────────────────────────────────────────────────┐
│ INPUT CHANNELS │
│ │
│ Web UI ──────────────────────────────────────────┐ │
│ Nextcloud Talk ──── POST /webhook/nextcloud/{u} ─┤ │
│ Google Chat ─────── POST /channels/google-chat/{u}┤ │
│ Cron / Scheduler ─────────────────────────────────┤ │
│ Webhooks (future) ─────────────────────────────────┘ │
└─────────────────────────────┬───────────────────────────┘
↓
┌─────────────────────────────────────────────────────────┐
│ CORTEX DISPATCHER (FastAPI — cortex/) │
│ │
│ auth_middleware.py → validates JWT session cookie │
│ persona.py → resolves user + persona context │
│ context_loader.py → assembles system prompt (tier 1-4)│
│ │
│ POST /chat → direct LLM, streaming SSE │
│ POST /orchestrate → Gemini tool loop → Claude │
│ GET /orchestrate/{id} → poll job result │
└────────────┬───────────────────┬────────────────────────┘
↓ ↓
┌─────────────────┐ ┌──────────────────────────────────┐
│ LLM BACKENDS │ │ PERSONA DATA │
│ │ │ home/{user}/persona/{name}/ │
│ Claude CLI │ │ │
│ Gemini CLI │ │ IDENTITY.md SOUL.md │
│ Gemini API │ │ PROTOCOLS.md MEMORY_*.md │
│ Local (httpx) │ │ USER.md REMINDERS.md │
│ │ │ TASKS.json CRONS.json │
└─────────────────┘ │ sessions/ SCRATCH.md │
└──────────────────────────────────┘
Details: ARCH__BACKENDS.md | ARCH__PERSONA.md | ARCH__CHANNELS.md
Service Layout (cortex/)
| File | Purpose |
|---|---|
main.py |
App entry point, router registration |
config.py |
All settings (pydantic-settings, reads .env) |
persona.py |
User + persona path resolution, ContextVars |
context_loader.py |
Builds system prompt from persona files (tiers 1–4) |
llm_client.py |
All LLM backends — Claude, Gemini CLI, Local |
orchestrator_engine.py |
Gemini API ReAct tool loop → Claude handoff |
openai_orchestrator.py |
OpenAI-compatible ReAct tool loop (local models via Open WebUI/OpenRouter) |
model_registry.py |
Per-user model registry V2 — providers, hosts, models, role assignments |
session_store.py |
In-memory + file session persistence (session_data/{id}.json) |
session_logger.py |
Writes session turns to sessions/YYYY-MM-DD.md |
memory_distiller.py |
Short/mid/long distill jobs |
scheduler.py |
APScheduler — distill jobs + user crons |
cron_runner.py |
Cron job storage, schedule parsing, execution |
notification.py |
Outbound channel messages (distill alerts, cron proactive) |
auth_utils.py |
bcrypt passwords, JWT, invite tokens, channel config |
auth_middleware.py |
JWT cookie validation on all routes |
tool_audit.py |
JSONL audit log for every orchestrator tool invocation |
usage_tracker.py |
Per-user token usage tracking (daily buckets → usage.json) |
event_bus.py |
Internal SSE pub/sub (NC Talk → browser mirror) |
email_utils.py |
SMTP invite emails |
persona_template.py |
Bootstrap a new persona directory from templates |
routers/ |
One file per endpoint group — chat, orchestrator, auth, files, ui, settings, local_llm, distill, audit, usage, push, help, onboarding, auth_google, nextcloud_talk, google_chat |
tools/ |
Orchestrator tool implementations — web, tasks, scratch, reminders, cron, system, notify, ae_journals, ae_tasks, agent_notes |
static/ |
Web UI — index.html, app.js, style.css, login.html, setup.html, HELP.md, local_llm.html, settings.html |
tests/ |
pytest suite |
Key Design Decisions
Two-brain pattern (Gemini orchestrator) — Gemini API handles tool use (function calling, planning, web search). Claude CLI handles all user-facing responses. Direct chat bypasses the orchestrator entirely.
Single-model pattern (local orchestrator) — When the orchestrator role resolves to a local_openai model, openai_orchestrator.py runs the full ReAct loop and produces the final response itself. No Claude handoff — the local model does both reasoning and response.
Subprocess backends — Claude and Gemini run as CLI subprocesses (claude --print, gemini -p). This keeps auth transparent (Claude Code manages tokens) and avoids API costs on the Pro subscription path.
Local backend via httpx — Open WebUI's OpenAI-compatible API (/api/chat/completions). No CLI wrapper. Per-user host + model config in local_llm.json.
ContextVars for async isolation — persona.py uses Python contextvars.ContextVar so concurrent requests each see their own user/persona without thread-local hacks.
Per-user filesystem layout — home/{user}/persona/{name}/ mirrors Linux home directories. Each persona is a directory of markdown files and JSON. No database. Easy to inspect, edit, and back up.
No single point of coupling — tools live in cortex/tools/, separate from ae_* MCP tools. Channels live in cortex/routers/, each self-contained. Adding a channel or tool doesn't touch other subsystems.
Agent private notes — AGENT_NOTES.md per persona, writable only by the orchestrator via agent_notes_* tools. Never loaded into user-facing context. Three rolling backups (bak1–bak3) are visible read-only in the Files panel. Declared in tools/agent_notes.py; usage guidance in PROTOCOLS.md.
No black boxes — Every component, flow, and design decision is documented. Documentation is updated before implementation of significant changes and verified after. HELP.md is the user-facing contract; ARCH__*.md files are the developer contract; PROTOCOLS.md is the agent contract. If any of these drift from reality, that is a bug.
Onboarding Flow
New users are invited via a one-time token and complete a three-step setup before reaching the chat:
1. /setup/{token} → Set password (POST creates session cookie, consumes token)
2. /setup/persona → Create persona (slug, display name, emoji, description)
3. /setup/model → Connect a model — OpenRouter recommended
(skip link goes straight to /{user}/{persona})
Step 3 is the planned addition (see TODO__Agents.md § Guided onboarding). Before it exists,
users land in the chat with no model configured and must navigate Settings → Model Registry
manually — which is confusing for non-technical users.
After Step 3:
save_host()adds OpenRouter (https://openrouter.ai/api/v1, typeopenai)save_model()creates a model entry for the chosen modelset_role(chat, primary, model_id)assigns it as the chat role primary- Redirect to
/{user}/{persona}
Existing users with no model configured — a dismissable banner is shown in the chat on
load, linking to /setup/model (the Step 3 form works standalone, without step labels).