Kernel-first OSS for self-hosted AI agents.
The kernel is the product; the WebUI is an optional add-on.

There are already large open-source projects (for example, Open WebUI). AIgentOS is intentionally a lightweight baseline for running a local multi-turn chatbot in Docker, with a focus on simplicity and transparency.

This project is built to provide:

• A small, understandable codebase with clear backend behavior
• Practical visibility into prompt/runtime logic and performance data
• A kernel foundation for future agent systems, including alternatives to tool-calling workflows in later versions

This baseline comes from building multiple agents and needing an abstracted kernel to reuse across future agent experiences.

• People who want a highly personalized and consistent chatbot experience, and prefer stable, bounded behavior over constantly changing frontier features
• People who care about security/privacy and want practical local capabilities (summarization, rubber-ducking, open-model experimentation) without heavy infrastructure

This repository currently ships:

• A FastAPI-based kernel (/kernel) with Ollama chat integration
• Prompt component/profile management (/agent-prompts)
• Local SQLite conversation storage in /models-local/chat.db
• Performance/debug API surfaces for recent exchanges
• A Vite/React WebUI (/agent-webui) as an optional interface

We keep kernel and WebUI in one repo right now for simpler OSS onboarding, versioning, and CI.

• The kernel remains independently useful without the WebUI.
• The WebUI is treated as a reference client and bonus add-on.
• If ecosystem usage grows, splitting into -kernel and -webui repos later is straightforward.

Default prompt configs are centered around SmolLM3-class usage because the model family is:

• Open, with strong visibility into training methodology/data disclosures
• Practical for local/self-hosted inference footprints
• Intended to be commercially usable (Apache-2.0)

Always validate model and dataset terms for your own deployment and jurisdiction.

• https://huggingface.co/HuggingFaceTB/SmolLM3-3B
• https://ollama.com/alibayram/smollm3

.
├── kernel/          # Kernel API and orchestration logic
├── agent-prompts/   # Prompt bundle + components (default agent profile)
├── agent-webui/     # Optional WebUI client
├── models-local/    # Local runtime data (e.g., SQLite chat DB)
├── docker-compose.yml
└── LICENSE

• Docker + Docker Compose
• Ollama running on the host

docker compose up -d

Default endpoints:

• WebUI: http://localhost:5500
• Kernel API: http://localhost:5501

docker compose up -d kernel

Current kernel endpoints include:

• GET /health
• POST /api/chat
• POST /api/llm/warmup
• Conversation CRUD under /api/conversations
• Prompt settings/profiles/components under /api/prompts/*
• Performance/debug surfaces under /api/performance/* and /api/debug/logs
• Admin export/reset endpoints under /api/admin/*

Baseline runs are tracked in /baseline/perf/ (timestamped markdown reports).

Current dataset snapshot:

• 3 runs on alibayram/smollm3
• 34 calls per run across: simple Q/A, summarization, 20-turn chat, structured extraction, and system-prompt pressure
• Total run duration observed: 141.62s to 235.97s

Observed ranges from those runs:

• 20-turn conversation average latency: 2.65s to 6.25s per turn
• 20-turn conversation max single-turn latency: up to 16.23s
• System prompt pressure at ~10k system tokens: 14.40s to 22.80s
• 2000-token summarization latency: 3.93s to 10.51s

Baseline reports:

Benchmark environment:

• Apple M3 Pro
• 18 GB RAM
• macOS Tahoe 26.3

Interpretation:

• Latency is most sensitive to effective prompt size and completion length.
• The 20-turn test shows context growth impact over time.

AIgentOS uses a split-license model:

• kernel/ -> MPL-2.0
• agent-webui/ -> Apache-2.0
• agent-prompts/ -> Apache-2.0

See /LICENSE for the directory-level map and rationale, and the license files inside each directory for full legal text.