Open Executive

An AI system that acts as your company's virtual executive team — a senior advisor with Harvard MBA-level knowledge, customized for your specific business.

Demo

A walkthrough of Open Executive in action — watch on YouTube.

What It Does

Developed by sentelabs.ai Open Executive provides a single coherent executive voice backed by eight specialist AI agents:

• Chief Strategy Officer — competitive analysis, M&A, market positioning, OKRs
• Chief Financial Officer — financial modeling, fundraising, unit economics, cash flow
• Chief HR/People Officer — hiring, compensation, performance, culture
• General Counsel — contracts, IP, employment law basics, compliance
• Chief Operating Officer — process design, vendor management, operational scaling
• Chief Marketing Officer — GTM strategy, brand, communications, PR
• Chief Product Officer — roadmap, prioritization, product strategy
• Board Communications Director — board decks, investor relations, governance

All responses come from one consistent executive voice. The internal agent architecture is never exposed to the user. Beyond Q&A, the system maintains episodic memory of past decisions and initiatives across sessions, and a built-in scheduler can proactively surface follow-ups and time-sensitive actions.

Architecture

User message
    ↓
Executive Orchestrator (claude-sonnet-4-6)
    ↓ tool use → parallel specialist calls
CSO / CFO / CHRO / GC / COO / CMO / CPO / Board
    ↓ each specialist retrieves relevant context from ChromaDB
Built-in MBA knowledge + Your company documents
    ↓
Synthesized executive response

Knowledge — Two retrieval layers per specialist call: (1) built-in MBA-level Markdown (knowledge/builtin/, git-tracked) seeded into ChromaDB at startup, and (2) your uploaded company documents chunked and stored in a separate company_docs collection. RAG context is injected into the user turn, never the cached system prompt.

Episodic memory — After every response, a background claude-haiku-4-5 pass extracts key decisions, initiatives, and advice into SQLite. The next session opens with a <past_decisions> block so the Executive remembers what it recommended last month.

Scheduler — A built-in job runner claims due actions via UPDATE … RETURNING to prevent double-firing. The API must run as a single instance; do not horizontally scale it without gating the scheduler first.

Prompt caching — The system prompt is structured so the Executive persona, company profile, and knowledge index are cached separately (up to 85% cache hit rate after the first few turns). No dynamic content ever goes in a cached block.

See docs/architecture.md for the full design.

Tech Stack

Layer	Choice
LLM backbone	Anthropic Claude API
Default model	claude-sonnet-4-6 (Executive + most specialists)
Deep reasoning	claude-opus-4-7 (CSO, CFO, GC, Board — with extended thinking)
Backend	Python 3.11 + FastAPI
Package manager	uv
Vector store	ChromaDB (local, embedded)
Episodic memory	SQLite
Web UI	Next.js 15 (App Router) + Tailwind
License	Apache 2.0

Repo Layout

openexecutive/
├── packages/
│   ├── core/
│   │   └── openexecutive/
│   │       ├── orchestrator/     # Executive persona + routing loop
│   │       ├── agents/           # 8 specialist agents
│   │       ├── knowledge/        # ChromaDB store + RAG pipeline
│   │       ├── memory/           # Company profile + episodic memory
│   │       ├── onboarding/       # Wizard state machine + profile builder
│   │       ├── prompts/          # Persona + domain prompts + cache manager
│   │       ├── api/              # FastAPI app + routes
│   │       ├── integrations/     # Slack, Email, Telegram, Google Chat, Discord
│   │       ├── scheduler/        # Background job runner (single-instance)
│   │       ├── alerts/           # Proactive alert system
│   │       ├── audit/            # Audit logging
│   │       ├── architecture/     # Internal architecture utilities
│   │       ├── workflows/        # Multi-step workflow definitions
│   │       └── cli.py            # Click CLI
│   └── ui/                       # Next.js 15 web UI
├── evals/                        # Eval scenarios + LLM-as-judge runner
├── fixtures/                     # Demo company fixtures (profiles, docs, rosters)
├── scripts/                      # Operator scripts (Fly secrets, Google auth)
├── docker/                       # Dockerfile(s) + docker-compose.yml
├── fly.api.toml / fly.ui.toml    # Fly.io configs — dev API + UI apps
├── fly.api.qa.toml / fly.ui.qa.toml  # Fly.io configs — QA API + UI apps
├── fly.honcho.toml               # Fly.io config — Honcho memory app (optional)
└── docs/                         # Architecture + deployment docs

Quick Start

Clone the repo

git clone https://github.com/SenteLabsAI/OpenExecutive.git cd OpenExecutive

Set your Anthropic API key

cp .env.example .env

Edit .env and add ANTHROPIC_API_KEY=sk-ant-...

Start everything

make dev

Open http://localhost:3000 to start chatting with your executive. The API runs on port 8000 and the UI on 3000.

First run: requires Python 3.11+ and Node 22+. The initial uv sync pulls heavy ML dependencies (ChromaDB + sentence-transformers/PyTorch), and the first boot downloads a small embedding model (~90 MB) to build the local vector index — so the first make dev takes a few minutes before the app is ready. Subsequent starts are fast.

For contributors not using make:

cd packages/core uv sync source .venv/bin/activate uvicorn openexecutive.api.main:app --reload --port 8000

In a second terminal

cd packages/ui && npm install && npm run dev

Run the Discord Bot

1. Create a Discord application at https://discord.com/developers/applications
2. Enable the Message Content privileged intent (Bot → Privileged Gateway Intents)
3. Invite the bot with bot + applications.commands scopes
4. Set env vars in .env: DISCORD_BOT_TOKEN, DISCORD_APP_ID, DISCORD_GUILD_IDS
5. Run the API normally — the bot starts as part of the FastAPI lifespan when DISCORD_BOT_TOKEN is set:

make dev

The bot is embedded in the API process (alongside the email poller, scheduler, and resumer) so it shares the same SQLite database and ChromaDB vector store under /data in production. Skip the token to disable.

For iterating on bot-only code without restarting the API, make discord runs the bot as a standalone process against the same local DB.

Users can DM the bot, @mention it in a channel (replies in a thread), or use /ask and /today slash commands. Slash commands sync to DISCORD_GUILD_IDS instantly on startup; leave blank for global registration (up to 1-hour propagation delay).

Deploying to production

Just set the secrets on the existing API app — no new Fly app required:

flyctl secrets set -a openexec-api-dev \ DISCORD_BOT_TOKEN=... \ DISCORD_APP_ID=... \ DISCORD_GUILD_IDS=...

Discord user access is managed via the /people UI — add a Person row with discord_user_id set.

The machine restarts and the bot starts on the next lifespan boot. To disable in prod: flyctl secrets unset -a openexec-api-dev DISCORD_BOT_TOKEN.

Onboarding Your Company

The first time you visit the app, you'll be guided through a wizard to set up your company profile:

• Company basics (name, industry, stage, team size)
• Business model and revenue
• Competitive landscape
• Strategic priorities
• Culture and values
• Optional: financial position, document upload

After onboarding, the Executive will reference your specific company context in every response.

Interfaces

Interface	How to Use
Web UI	http://localhost:3000
Slack	Mention @OpenExecutive or DM the app
Email	CC or email the configured address (IMAP/SMTP poller)
Telegram	Message the configured bot
Google Chat	Mention the app in a space
Discord	DM the bot, @mention it in a channel, or use /ask / /today slash commands
CLI	openexecutive chat

Document Upload

Upload your pitch deck, financial model, strategy docs, or any company documents via the web UI or API. The Executive will reference them when relevant.

Via CLI

openexecutive upload deck.pdf model.xlsx strategy.md

Via API

curl -X POST http://localhost:8000/documents \ -F "file=@deck.pdf" \ -F "domain=strategy"

Deployment (Fly.io)

Two environments, each a separate set of Fly apps, driven by branch:

Environment	Trigger	Workflow	Apps
dev	push/merge to main (continuous)	.github/workflows/deploy.yml	openexec-api-dev, openexec-ui-dev
qa	push/merge to qa (deliberate promotion)	.github/workflows/deploy-qa.yml	openexec-api-qa, openexec-ui-qa

Both workflows use dorny/paths-filter to deploy only the changed app (API, UI, or both). QA is a stable twin of dev — same image and runtime, only the app name differs (fly.api.qa.toml / fly.ui.qa.toml) — so it lags main and stays vetted. An optional Honcho memory app (fly.honcho.toml) deploys independently.

Topology

App	Purpose	State
openexec-api-{dev,qa}	FastAPI + scheduler	Persistent volume executive_data at /data
openexec-ui-{dev,qa}	Next.js 15	Stateless
openexec-honcho-dev	Honcho per-person memory (optional)	Postgres-backed

⚠️ Single-instance only: The scheduler claims rows via UPDATE … RETURNING. Running two API machines would double-fire scheduled actions. max_machines_running = 1 is set in fly.api.toml / fly.api.qa.toml — do not override it.

Required GitHub Actions secrets

Deploys authenticate with per-app Fly deploy tokens stored as repo (or org) Actions secrets. Generate each with flyctl tokens create deploy -a <app> -x 999999h:

Secret	App	Used by
FLY_API_TOKEN_API	openexec-api-dev	dev
FLY_API_TOKEN_UI	openexec-ui-dev	dev
FLY_API_TOKEN_HONCHO	openexec-honcho-dev	dev (honcho job)
FLY_API_TOKEN_API_QA	openexec-api-qa	qa
FLY_API_TOKEN_UI_QA	openexec-ui-qa	qa

Per-app runtime secrets (ANTHROPIC_API_KEY, BACKEND_SHARED_SECRET, the AUTH_* set, integration tokens) are set directly on each Fly app — see scripts/fly-secrets.sh.example.

One-time bootstrap (dev)

1. Create apps and volume

flyctl apps create openexec-api-dev flyctl apps create openexec-ui-dev flyctl volumes create executive_data --region iad --size 1 -a openexec-api-dev

2. Set the required secret

flyctl secrets set -a openexec-api-dev ANTHROPIC_API_KEY=sk-ant-...

3. Create deploy tokens and add as GitHub secrets FLY_API_TOKEN_API and FLY_API_TOKEN_UI

flyctl tokens create deploy -a openexec-api-dev -x 999999h flyctl tokens create deploy -a openexec-ui-dev -x 999999h

4. First deploy

gh workflow run "Deploy (dev)" -f target=both

QA bootstraps the same way against the -qa app names (push to the qa branch, or gh workflow run "Deploy (qa)"). See docs/deployment.md for the full runbook (operations, rollback, common failure modes, why .flycast isn't used).

Access control

The deployed UI is gated behind Google sign-in with an email allow-list, and the public API is protected by a shared-secret header between the UI proxy and the FastAPI backend. See docs/auth.md for the full setup (Google Cloud Console steps, required Fly secrets, adding/removing users, rotating secrets, and a debugging table).

Configuration

All settings via environment variables. Minimum required: ANTHROPIC_API_KEY — unless you configure a local or OpenRouter backend instead (see Running on Local Models). At least one provider must be set or the app refuses to start.

Variable	Required	Default	Description
ANTHROPIC_API_KEY	Yes¹	—	Anthropic API key
DEFAULT_MODEL	No	claude-sonnet-4-6	Executive + most specialists
DEEP_REASONING_MODEL	No	claude-opus-4-7	CSO, CFO, GC, Board
VECTOR_STORE_PATH	No	./chroma_db	ChromaDB directory
EPISODIC_DB_PATH	No	./episodic_memory.db	SQLite for episodic memory
COMPANY_PROFILE_PATH	No	./company/profile.yaml	Company profile
ENABLE_CACHING	No	true	Anthropic prompt caching
ROUTING_MODEL	No	claude-haiku-4-5-20251001	Model for intent routing
SLACK_BOT_TOKEN	No	—	Slack bot OAuth token
SLACK_APP_TOKEN	No	—	Slack socket mode token
EXEC_EMAIL_ADDRESS	No	—	Executive Gmail address (Gmail MCP OAuth)
EMAIL_POLL_INTERVAL_SECONDS	No	60	How often to poll for new email
TELEGRAM_BOT_TOKEN	No	—	Telegram bot token (from @BotFather)
TELEGRAM_WEBHOOK_SECRET	No	—	Random string for webhook validation
DISCORD_BOT_TOKEN	No	—	Discord bot token (Developer Portal → Bot tab)
DISCORD_APP_ID	No	—	Discord application ID (General Information tab)
DISCORD_GUILD_IDS	No	—	Comma-separated guild IDs for dev slash-command registration
DISCORD_NOTIFY_CHANNEL_ID	No	—	Default channel ID for outbound notifications
GOOGLE_CHAT_PROJECT_NUMBER	No	—	GCP project number for Google Chat
GOOGLE_CHAT_SERVICE_ACCOUNT_FILE	No	—	Path to service account JSON key
GOOGLE_OAUTH_CLIENT_ID	No	—	Google OAuth client ID (Gmail MCP)
GOOGLE_OAUTH_CLIENT_SECRET	No	—	Google OAuth client secret (Gmail MCP)
OPENROUTER_ENABLED	No	false	Route Claude calls through OpenRouter and unlock non-Anthropic models per-agent in the Council UI
OPENROUTER_API_KEY	No	—	Required when OPENROUTER_ENABLED=true
LOCAL_MODELS_ENABLED	No	false	Route selected slugs to a local OpenAI-compatible server (Ollama, LM Studio, vLLM, llama.cpp)
LOCAL_BASE_URL	No	—	Local server URL incl. version path, e.g. http://localhost:11434/v1. Required when LOCAL_MODELS_ENABLED=true
LOCAL_API_KEY	No	—	Optional bearer token (vLLM / gateways); Ollama & LM Studio need none
LOCAL_MODELS	No	—	Comma-separated local model slugs to surface in the Council UI and route locally, e.g. llama3.3,qwen2.5
LOCAL_TIMEOUT_S	No	300	Per-call timeout for local generation, in seconds
HONCHO_ENABLED	No	false	Per-person memory layer (honcho.dev) — a peer card shared across all channels
HONCHO_API_KEY	No	—	Required when HONCHO_ENABLED=true
HONCHO_BASE_URL	No	—	Self-hosted Honcho endpoint

See .env.example for the full list.

¹ ANTHROPIC_API_KEY is required only when you serve Claude models directly. It can be omitted entirely if you run on local models (LOCAL_MODELS_ENABLED) or route through OpenRouter (OPENROUTER_ENABLED).

Running on Local Models

Open Executive can run against any OpenAI-compatible local server — Ollama, LM Studio, vLLM, or llama.cpp — instead of (or alongside) the Anthropic API. Local model slugs route to your server through the same provider abstraction the hosted models use; no agent or orchestrator code changes.

1. Pull a capable, tool-use-friendly model (example: Ollama)

ollama pull llama3.3

2. In .env — point at the local server and list the slugs to expose

LOCAL_MODELS_ENABLED=true LOCAL_BASE_URL=http://localhost:11434/v1 # Ollama default LOCAL_MODELS=llama3.3

3. (Optional) run with NO Anthropic key — make local the default everywhere

DEFAULT_MODEL=llama3.3 DEEP_REASONING_MODEL=llama3.3 ROUTING_MODEL=llama3.3

...and leave ANTHROPIC_API_KEY unset

The listed slugs appear in the Council UI model dropdown, so you can also run a hybrid setup — keep the Executive on Claude while flipping individual specialists to a local model per-agent.

Caveats. Server-side web search (ENABLE_WEB_SEARCH) and Anthropic prompt caching / extended thinking have no local equivalent and are automatically disabled for local models. Multi-agent routing leans heavily on tool use, so pick a model that's strong at it (e.g. Llama 3.3 70B, Qwen2.5) — small models may route poorly. LOCAL_API_KEY is only needed if your server (vLLM, or a gateway) requires a bearer token; Ollama and LM Studio need none.

Adding a New Specialist Agent

1. Create packages/core/openexecutive/agents/your_agent.py extending BaseAgent
2. Add a system prompt constant in packages/core/openexecutive/prompts/domain_prompts.py
3. Register in packages/core/openexecutive/orchestrator/router.py — add to SPECIALIST_REGISTRY and the specialist enum in SPECIALIST_TOOLS
4. Add domain alias to DOMAIN_ALIASES in packages/core/openexecutive/knowledge/retriever.py
5. Add knowledge docs to knowledge/builtin/your_domain/
6. Add at least 2 eval scenarios to evals/scenarios/
7. Submit a PR — CI requires all of the above

Development

make dev # Start FastAPI + Next.js make test # Run Python tests make eval # Run eval suite make lint # Run ruff + mypy make docker # Build and run Docker stack

Unit tests only (no API calls required)

pytest packages/core/tests/unit/ -v

Evaluation System

evals/ contains 29 scenarios covering all 8 domains, scored by claude-opus-4-7 as an LLM-as-judge. Each scenario defines a query, simulated company context, expected topics, required specialist routing, and a domain-specific rubric. Five scoring dimensions (persona coherence, domain accuracy, company context utilization, routing quality, actionability) are each rated 1–5. The CI gate requires ≥ 3.5/5 average; any dimension dropping > 10% vs main fails the PR.

Privacy

Everything in company/ is gitignored — the profile YAML, uploaded documents, and the ChromaDB vector store. None of this leaves your local machine (or your own Fly volume in cloud deployments) except as part of prompts sent to the Anthropic API. Anthropic does not train on API data.

Contributing

See .github/CONTRIBUTING.md. All PRs must include:

• Working implementation (no stubs)
• Tests for new behavior
• Eval scenarios for new agents or prompt changes

License

Apache 2.0 — free to use commercially, requires attribution.