Control plane for production AI-agent work: plan, govern, and verify agent runs.
Project description
AI Runtime Supervisor / Veille Console
Control plane for production AI-agent work. The Supervisor plans, contextualizes, routes, governs, and verifies each agent run—reducing wasted spend and unreliable outcomes without requiring teams to rebuild their applications.
Current release: 0.2.0 — Phases 1–5 + Local Integration Console. Event schema 0.2.0. All capabilities are opt-in and off by default.
What exists today
- Versioned data contracts (task, events, plan, policy, validation)
- Python SDK (
Supervisor+RunCollector) for zero-touch event emission - LangGraph adapter with automatic callback-based instrumentation
- OpenAI Agents SDK adapter and OpenAI Responses API adapter (skeleton)
- Model provider port with 8 provider drivers (LiteLLM, OpenAI, Anthropic, Gemini, OpenRouter, Ollama, LM Studio, OpenAI-compatible)
- Synthetic cited market-research LangGraph demo workflow with mock tools
- Representative trace fixtures for success, expensive, and failed-validation runs
- Observe-only policy engine (duplicate detection, retry budget, cost overrun, validation)
- Opt-in enforcement (Phase 2): block / retry / pause / stop with full audit trail
- Budget tracking (
BudgetTracker, in-memory default, Redis backend port) - Run-explorer CLI and OpenTelemetry export (Console + OTLP)
- Advisory planning (Phase 3): tier selection, per-step context manifests, capability+tier model routing
- Adaptive optimization (Phase 4): semantic near-duplicate detection + idempotent result caching (
SUPERVISOR_OPTIMIZE, dry-run default) - Memory governance (Phase 5): memory store + scoring + governor (
memory.retrieved/memory.expiredmanifest) with audited expiry, no automatic deletion (SUPERVISOR_MEMORY) - Local Integration Console (
veilleCLI + FastAPI + React web UI) — register workflows, connect providers, run live, inspect execution - Local development environment (Docker Compose + pytest)
Quickstart
Prerequisites
- Python 3.12+
- Docker Desktop (optional, for Postgres/Redis/MinIO scaffold)
Setup (Windows)
.\scripts\dev.ps1
.\.venv\Scripts\Activate.ps1
Run tests
pytest -v
Use the console
# Doctor — environment + safe-config report
veille doctor
# List provider connections
veille connections
veille connections validate openai
# List registered workflows
veille workflows
# Run the mock demo
veille demo mock
# Run the real-world demo
veille demo real-world
# List saved runs
veille runs
# Run an arbitrary registered workflow
veille run cited_market_research --input '{"scenario":"success"}'
# Start the web UI (then open http://127.0.0.1:8000)
veille serve
Run the classic demo workflow
# Successful run
python -m examples.cited_market_research.agent --scenario success
# All scenarios + write trace fixtures
python -m examples.cited_market_research.agent --scenario all --write-fixtures
# Phase 1 run explorer (inspect a captured run)
python -m supervisor.cli explore --run fixtures/traces/expensive_run.json --policy
# Live run with policy observations and OTel export
python -m supervisor.cli explore --live --scenario expensive --policy --otel
No API keys required. Mock models and tools are used by default.
Repository layout
src/supervisor/ Core contracts, SDK, adapters, analytics, policy, telemetry, CLI, console
examples/ Runnable demo workflows
fixtures/traces/ Synthetic trace JSON for tests and baselines
docs/ Architecture, contracts, roadmap, ADRs
docs/development/ Source master prompt, blueprint, phase plans
ui/ React+TypeScript+Vite web UI (veille console frontend)
templates/ Baseline measurement templates
Documentation
| Document | Description |
|---|---|
| Architecture | System components and boundaries |
| Runtime chain | Stage-by-stage runtime flow |
| Data contracts | Schema reference |
| Roadmap | Phase status and deferrals |
| Integrations | Adapter contracts + provider drivers |
| Operations | Local dev, commands, runbooks |
| Policy engine | Policy modes and Phase 1 observe policies |
| ADR-013 | Local Integration Console design |
Setup guides
| Guide | Description |
|---|---|
| Safe local setup | Running with mock providers (default) |
| Mock demo walkthrough | End-to-end mock demo |
| Real provider setup | Setting up real model providers |
| OpenRouter integration | Using OpenRouter as a gateway |
| OpenAI Agents SDK | Running an OpenAI Agents SDK workflow |
| LiteLLM integration | Using LiteLLM for multi-provider access |
License
MIT
Project details
Release history Release notifications | RSS feed
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