A Python framework for building agentic AI applications with server-driven UI, native observability, OS-level sandboxing, pluggable model orchestration, and a strict extension boundary — designed for environments where reproducibility, auditability, and operational control matter.

Status: Beta 0.0.1b4 — public preview. The core architecture is implemented and actively tested, but APIs and operational behavior may still change before the first stable release. The software ships without warranty.

Links

• Website: democr.ai
• Documentation: democr.ai/docs/

Overview

Democr.ai is a complete runtime framework for AI applications. It provides:

• Server-driven UI for agentic contexts, implementing and extending Google's A2UI protocol
• Multi-client rendering of the same UI definition across web and desktop clients
• Native multi-tenancy enforced at write time
• Cross-platform OS-level sandboxing for engine and extension processes
• Triple-layer audit through SQLAlchemy hooks, with sensitive field redaction
• RBAC with declarative module manifests
• Pluggable AI engine orchestration across local, remote, and distributed runtimes
• Engine quotas for request and token governance by engine and subject scope
• Knowledge subsystem with vector and graph backends
• A strict modular architecture where everything — including authentication — is a module built against the public SDK

The framework is designed around a clear boundary between core runtime and extensions. The SDK is the only supported way to build on top.

What makes it different

Most AI application stacks combine a chat UI, an LLM client, a workflow library, and separate infrastructure for audit, permissions, sandboxing, and observability. Democr.ai puts those concerns in one runtime contract:

• UI is declared server-side and rendered by independent clients.
• Modules, engines, and extractors are installable extensions with a public SDK boundary.
• Model calls flow through an orchestrator that supports local providers, remote providers, process isolation, quotas, and multi-node execution.
• Security and audit are runtime primitives, not application conventions.
• Knowledge ingestion, retrieval, media handling, tools, MCP, and agent skills share the same request and observability context.

That combination is intentionally broad. The project is still beta, but the architecture is already aimed at production-grade constraints rather than demos or single-chatbot prototypes.

A2UI

Let agents build UI components that render as native application surfaces, not as screenshots or markup fragments. Democr.ai modules describe UI declaratively on the server, then stream structured updates as the agent works.

The short demo shows the important part: one backend-declared YAML surface reflected through client renderers, while the backend keeps ownership of behavior, actions, and runtime effects.

Watch the A2UI demo video

Who it's for

Democr.ai targets applications where:

• The deployment environment has compliance, regulatory, or operational constraints
• Observability and reproducibility are required from day one, not added later
• Avoiding vendor lock-in is a hard constraint: the framework is fully self-hostable, model-agnostic, and ships no proprietary cloud services
• Auditability across user actions, data mutations, and AI calls is required at the deployment level
• The team prefers an integrated runtime over assembling separate pieces (LLM library + UI framework + observability stack + audit + RBAC + workflow engine)

It is not a thin wrapper, a model router, or a chat UI library. It is a runtime where modules, engines, extractors, and clients compose into a single coherent system.

Core principles

Everything is a module

There are no privileged components. Authentication, system administration, and the demo applications shipped in this repository are all built as modules, using the same SDK that third-party developers use. There are no reserved APIs, no hidden hooks, and no internal-only services.

The modules included in this repository (auth, system, components) are primarily examples of how the framework works and how applications integrate with it. They provide a working baseline, but deployments are not required to use them as-is: teams can replace them, extend them, or build their own domain-specific implementations.

No vendor lock-in

• The runtime is fully self-hostable.
• AI engines are pluggable: local inference (vLLM, llama.cpp) and cloud providers (OpenAI, Anthropic, Google, and others), or custom integrations.
• Storage backends are pluggable for vector (sqlite-vec, pgvector, and others), knowledge graph, and core persistence (SQLite, PostgreSQL).
• No proprietary serialization formats. State is portable across deployments.

Observability and reproducibility as development guides

Observability is not added as later instrumentation — it is part of how the framework expects developers to build. Every action, AI call, and module operation flows through a correlation chain that allows step-by-step reconstruction of any request. Pipelines are observable. The audit subsystem captures mutations at three layers (request, ORM, AI invocation) automatically, with sensitive field redaction.

Reproducibility is achieved through deterministic configuration, structured event logs with stable IDs, and an immutable audit trail.

Security as a primitive, not a feature

The framework provides security as foundational primitives, not as opt-in features. See Threat model for the precise boundary between framework guarantees and deployer responsibilities.

Extending Google's A2UI protocol

The UI layer is an extended implementation of Google's A2UI protocol for server-driven UI in agentic contexts. The same UI definition renders consistently across multiple clients with no client-side framework code required from module authors. New clients can be added without changes to existing modules.

Architecture overview

Democr.ai is composed of distinct subsystems:

• Core runtime — bootstrap, lifecycle, request handling, dependency graph
• Network — WebSocket, IPC, HTTP under a unified protocol layer
• AI engine orchestrator — job-based scheduling, batching, gRPC process isolation, HITL support
• Knowledge — ingestion queue, projection workers, vector + graph retrieval
• Sandbox — OS-level isolation (Landlock + seccomp + iptables + helper process | seatbelt | Windows Low Integrity + WFP)
• Multi-tenancy — write-time enforcement with materialized scope filters
• RBAC — declarative access policies in module manifests
• Modules — framework extension surface, via SDK
• Engines — pluggable AI inference providers
• Extractors — pluggable knowledge extraction providers
• Clients — independent rendering surfaces for web, desktop, and mobile-oriented clients
• Platform — agents, skills, tools, MCP, UI builder

Detailed subsystem documentation is available at democr.ai/docs/.

Feature matrix

Capability	Status	Notes
Server-driven UI (extended A2UI)	Implemented	websocket and ipc transport
Multi-client rendering	Implemented	Web, Qt desktop, Tauri, React variants
Multi-tenancy	Implemented	Write-time enforcement
RBAC	Implemented	Declarative manifests
AI engine orchestration	Implemented	Job-based, gRPC isolated
Knowledge subsystem (vector + KG)	Implemented	Hybrid retrieval
Audit (triple layer)	Implemented	Sensitive field redaction
OS sandbox (Landlock + seccomp + iptables)	Implemented
OS sandbox on macOS	Implemented	Seatbelt
OS sandbox on Windows	Implemented	Low Integrity + WFP; egress enforcement uses an elevated helper
Local AI inference	Implemented	vLLM, llama.cpp, ONNX, Ollama, and others
Cloud AI providers	Implemented	OpenAI, Anthropic, Google, and others
Multi-node engine orchestration	Implemented	Shared queue, node registry, Redis response streams
mTLS for internal gRPC services	Roadmap	Server-side TLS + service JWT first
Context budget guard	Implemented	Heuristic guard for oversized prompt messages and tool outputs, scaled by model context settings
Multi-node tuning	Roadmap	Distributed fine-tuning workflows
Mobile clients	Experimental	React Native client exists but is not certified
Engine quotas	Implemented	Request and token limits by engine and subject scope
Request observability	Implemented	Correlation chain across layers

Stress benchmark snapshot

The following numbers are local stress-test snapshots for the authenticated request path: login, session handling, and server-side rendering of the server-driven UI route /components/_effects/yaml. They document observed behavior under concurrent load on one machine; they are not universal performance claims or comparisons with other frameworks.

Environment:

• CPU: Intel Core i9-14900K
• RAM: 64 GB
• GPU: NVIDIA RTX 3090 Suprim, not used by this benchmark path
• OS: Ubuntu 24
• Python: 3.12.3
• Providers: remote providers

Instances	Workers / instance	Clients / worker	Duration	Requests	Errors	Throughput	Avg latency	p95 latency
4	4	4	303.69s	468,840	0	1,543.83 req/s	33.28ms	47.05ms
8	4	4	302.39s	718,050	0	2,374.55 req/s	43.30ms	47.94ms

Command shape:

PYTHONPATH=. .venv/bin/python scripts/multi_core_benchmark.py \
  --instances 4 \
  --workers 4 \
  --clients 4 \
  --duration-seconds 300 \
  --profile custom \
  --profile-log-summary

For the 4-instance run, aggregate resource usage was:

Metric	Value
Avg total CPU	1326.9%
Peak RSS	7092.1 MB
Peak USS	5936.8 MB
Peak PSS	5986.8 MB

RSS can overstate standalone memory use because shared pages are counted more than once.

Threat model

The framework offers security as foundational primitives: OS-level sandboxing, immutable audit, RBAC, scoped data access, and at-rest encryption of secrets stored in the database. Modules operate within these primitives according to the policies they declare (sandbox allow-lists, RBAC permissions, access scope).

The threat model covered by the framework is accidental supply-chain compromise — a third-party module with a bug or compromised dependency must not be able to harm the system beyond its declared scope.

The threat model not covered is a deliberately malicious module installed consciously by the deployer. The selection of modules to install and the configuration of security policies are deployer responsibilities.

The framework's config.yaml is plaintext and is generated locally by the setup wizard. It is not designed to be deployed. Secrets are encrypted at rest only when stored in the database.

Quick start

Requirements

• Python >=3.12,<3.14
• Linux is the most tested environment for full security features
• Optional: NVIDIA GPU with CUDA for local inference

Install

From the repository root:

./setup_venv.sh

# alternatively, install and run desktop mode with ./run.sh

For the React web clients and the Tauri desktop client, install frontend dependencies before running them. Tauri also requires Cargo.

yarn --cwd clients/webclient install
yarn --cwd clients/reactbootstrap install
yarn --cwd clients/reactfluent install
yarn --cwd clients/tauri install

Run

Default startup is desktop mode with the Qt client:

python main.py

Equivalent explicit form:

python main.py --mode desktop --client qtdesktop

Server mode (HTTP + WebSocket exposed)

python main.py --mode server --host 127.0.0.1 --port 8000

Desktop with HTTP also exposed

Desktop mode uses IPC by default. Add --http to also expose HTTP/WebSocket:

python main.py --mode desktop --http --client qtdesktop

Web client

Web clients are independent React applications. The runner launches them with yarn dev:

python main.py --mode server --client webclient
python main.py --mode server --client reactbootstrap

Web client in desktop mode with tauri

Web clients are independent React applications. The runner launches them with yarn dev:

python main.py --client tauri

First-time setup

On first run, the framework starts a setup wizard via the connected client. The wizard configures the database, AI providers, and the initial administrator user. The resulting configuration is generated locally and not designed to be deployed.

Initial setup can also run from the core CLI:

python main.py setup path/to/setup.yaml

The setup YAML contains admin credentials and the runtime config payload. If admin.password is omitted, the command prompts for it interactively; in non-interactive runs use an environment variable such as ${DEMOCRAI_ADMIN_PASSWORD}. The command only runs before an application config exists and will not overwrite an existing installation.

Commands

Command	Purpose
python main.py	Default desktop mode
python main.py --mode server	Server mode (HTTP + WebSocket)
python main.py --mode desktop	Desktop mode (IPC transport)
python main.py --http	Also expose HTTP in desktop mode
python main.py migrate all	Run all pending database migrations
python main.py migration-status all	Show migration status
python main.py create-migration db -m "desc" --autogenerate	Create a new core DB migration
python main.py create-migration data --module <name> -m "desc" --autogenerate	Create a new module data migration
python main.py rollback data --steps 1	Roll back one data migration step
python main.py validate-config	Validate current configuration
python main.py setup examples/setup.example.yaml	Run initial setup from YAML
python main.py install-engines examples/engine-install.example.yaml	Install and activate engines/models from YAML
python main.py install-extractors examples/extractor-install.example.yaml	Install and activate extractors from YAML
python main.py reset-install	Reset the installation state
python main.py module-status	Show module discovery and load status
python main.py knowledge-rebuild --all --dry-run	Rebuild knowledge projections (dry run)

Engine installation from YAML

The core CLI can install and activate engine instances from a YAML file:

python main.py install-engines path/to/engines.yaml

The command starts the application runtime, uses the core SDK context, and follows the same engine lifecycle used by the runtime: registry upsert, runtime config check, install, activation, model download when requested, and model binding activation. It does not require the system module to be installed.

Supported options:

python main.py install-engines path/to/engines.yaml --reset-mode keep
python main.py install-engines path/to/engines.yaml --reset-mode selected
python main.py install-engines path/to/engines.yaml --reset-mode full --yes
python main.py install-engines path/to/engines.yaml --json

Reset modes:

Mode	Behavior
keep	Keep existing engines and models; update declared items and skip already available downloads
selected	Reset only engines/models declared in the YAML
full	Reset all engine registry rows, model bindings, and downloaded model artifacts; requires --yes outside interactive confirmation

YAML shape:

engines:
  - provider: openai
    name: openai-main
    config:
      api_key: "${OPENAI_API_KEY}"
    models:
      - id: gpt-5.5
        activate: true

  - provider: gemini
    instances:
      - name: gemini-main
        config:
          api_key: "${GEMINI_API_KEY}"
        models:
          - id: gemini-2.5-pro
            activate: true
      - name: gemini-fast
        config:
          api_key: "${GEMINI_API_KEY}"
        models:
          - id: gemini-2.5-flash
            activate: true

  - provider: ollama
    instances:
      - name: ollama-local
        config:
          base_url: http://localhost:11434
        models:
          - id: llama3.2
            activate: true

  - provider: llamacpp
    name: llamacpp-local
    models:
      - id: tiny-llama3-test-q2-k
        download: true
        activate: true

Sensitive config values can reference environment variables with ${NAME}. When multiple instances use the same provider, provider installation runs once and each instance is activated separately. A complete provider-oriented example is available at examples/engine-install.example.yaml.

Extractor installation from YAML

The core CLI can install and activate knowledge extractors, then configure MIME routing:

python main.py install-extractors path/to/extractors.yaml

Supported options mirror install-engines:

python main.py install-extractors path/to/extractors.yaml --reset-mode keep
python main.py install-extractors path/to/extractors.yaml --reset-mode selected
python main.py install-extractors path/to/extractors.yaml --reset-mode full --yes
python main.py install-extractors path/to/extractors.yaml --json

YAML shape:

extractors:
  - id: docling
    install_config:
      ocr_engine: rapidocr
    runtime_config:
      ocr_enabled: true
      chunk_size: 1200
    mime_bindings:
      - application/pdf
      - text/plain

  - id: ai_audio
    runtime_config:
      model_registry_id: 12
    mime_bindings:
      - audio/mpeg

ai_audio and ai_image require a real model_registry_id for an already configured model with the required capability. If model_registry_id is omitted, the command can still install and activate the extractor, but the runtime configuration is incomplete and extraction will not be usable until that field is saved. A complete example is available at examples/extractor-install.example.yaml.

Runtime extension paths

The runner sets default extension path variables if they are not already configured:

DEMOCRAI_MODULES_PATH=/path/to/modules
DEMOCRAI_ENGINES_PATH=/path/to/engines
DEMOCRAI_EXTRACTORS_PATH=/path/to/extractors

Each variable can contain one or more existing directories. Multiple directories use the platform path separator (: on Linux/macOS, ; on Windows).

At startup, the core runtime discovers and registers extension packages from these paths. Modules, engines, and extractors are all extension types: modules add application behavior, engines add AI provider/runtime integrations, and extractors add document or media ingestion capabilities.

Tested components

The components listed below have working implementations covered by development or integration tests. The list will be refined as engines, extractors, and specific model combinations are certified for stable releases.

Note: a complete and versioned list of tested models, engines, and extractors will accompany the first stable release. Provider implementations may exist in the repository before they are certified. Components not listed here should be treated as available implementation work, not as a stable support promise.

AI engines

Engine	CPU/GPU	Status
onnx	CPU	Implemented
llamacpp	CPU/GPU	Implemented
vllm	GPU	Implemented
parler	GPU	Implemented
qwen_tts	GPU	Implemented
whisper	GPU	Implemented
espeak	CPU	Implemented
rebel	GPU	Implemented
gliner+glirel	GPU	Implemented
ollama	GPU (local)	Implemented
openai compatible	-	Implemented
openai	-	Implemented
gemini	-	Implemented
anthropic	-	Implemented
openai whisper	-	Implemented
openai tts	-	Implemented
edge tts	-	Implemented
nvidia_nim	-	Implemented

Knowledge extractors

Extractor	Tested with	Status
docling	Tesseract/RapidOCR	Implemented
ai_audio	Whisper (faster whisper)	Implemented
ai_image	Gemma 4 26B A3B	Implemented

Storage backends

• SQLite (default, recommended for development)
• PostgreSQL (recommended for production multi-tenant deployments)
• sqlite-vec (vector, default)
• pgvector (vector, when PostgreSQL is in use)

Implemented, not yet certified

• Pinecone
• ClickHouse

Clients

• qtdesktop — PySide6-based desktop client
• webclient — React web client
• reactbootstrap — React web client with Bootstrap/AGID theme
• tauri — Tauri-based desktop client

Additional client implementations exist in the repository, including reactfluent, electron, and reactnativeclient, but are not part of the certified client set yet.

Repository layout

• main.py — runner for local development and packaged launcher flows
• pyproject.toml — build metadata for the democrai Python package
• democrai/core/ — internal runtime: bootstrap, storage, network, sandbox, orchestration, registries
• democrai/sdk/ — public SDK boundary for extensions (the only supported import surface for modules)
• democrai/third_party/ — bundled runtime assets (e.g., libmagic)
• modules/ — bundled demo modules (auth, system, components)
• engines/ — AI engine implementations and manifests
• extractors/ — knowledge and media extractor implementations
• clients/qtdesktop/ — Qt desktop client
• clients/webclient/ — React web client
• clients/reactbootstrap/ — React web client with Bootstrap/AGID theme
• clients/reactfluent/ — React web client variant
• clients/tauri/ — Tauri-based desktop client
• clients/electron/ — Electron wrapper for web clients
• clients/reactnativeclient/ — React Native client
• docs_site/ — MkDocs documentation pipeline
• tests/ — Python test suites

Modules may import only from democrai.sdk. Imports from democrai.core are not part of the supported boundary and locked by module runtime.

Documentation

Full documentation is built with MkDocs:

source .venv/bin/activate
.venv/bin/mkdocs build -f docs_site/mkdocs.yml --strict

Generated output is in docs_site/docs_html/.

Node tooling for documentation post-processing is managed inside docs_site/:

cd docs_site
npm install

Roadmap

The items below are planned for upcoming releases, in approximate order:

• Multi-node tuning — distributed fine-tuning workflows
• Client certification — broaden the certified set beyond webclient, reactbootstrap, qtdesktop, and Tauri-based desktop
• Mobile clients — complete and certify the React Native client
• Knowledge subsystem improvements — additional projection backends, refined retrieval, expanded extractor coverage
• And many more — see the issue tracker for the full backlog and active proposals

Tests

Run the full framework test suite:

.venv/bin/python -m pytest -c tests/pytest.ini

Run the runtime subset:

.venv/bin/python -m pytest -c tests/pytest.ini tests/runtime

With coverage:

.venv/bin/python -m pytest -c tests/pytest.ini --cov-config=tests/.coveragerc --cov=democrai tests/runtime
.venv/bin/coverage html --rcfile=tests/.coveragerc

Some tests depend on external engines or services. Verify the local environment before running broader suites.

License

Democr.ai uses a split licensing model:

• democrai/core/ is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0). Modifications to the core that are made available over a network must be released under the same license.
• democrai/sdk/, engines/, extractors/, modules/, and clients/ are licensed under the Apache License 2.0. You can build, ship, integrate, and commercialize extensions, engines, extractors, modules, and clients freely, subject to attribution.

If your use case requires a commercial license for the core (for example, proprietary modifications without source disclosure), contact license@democr.ai.

The repository root LICENSE file describes the split and lists the directory-specific licenses. Full license texts are in the corresponding directories (democrai/core/LICENSE for AGPL-3.0, democrai/sdk/LICENSE for Apache-2.0).

Acknowledgments

The A2UI protocol is a Google open specification for agentic UI. Democr.ai extends it to make the whole application server-driven and potentially controllable by agents.

This project builds on the work of the open-source Python ecosystem, including SQLAlchemy, FastAPI, Alembic, Pydantic, gRPC, Qt, and many others.