Llimona 0.3.0b2

Open and modular framework for building observable LLM gateways with OpenAI-compatible APIs and pluggable providers.

Llimona

Llimona is an open and modular Python framework for building production-ready LLM gateways. It provides OpenAI-compatible APIs, provider-aware routing, and an extensible plugin model for integrating multiple backends behind a single interface.

By keeping providers as addons, Llimona stays lightweight at its core while enabling deployments to include only the integrations, policies, and observability components they actually need.

Key Features

• OpenAI-compatible service interfaces (currently Responses and Models).
• Provider routing using the provider_name/model_name naming convention.
• Addon-based extensibility through Python entry points (llimona.addon).
• Typed YAML configuration with Pydantic validation.
• Request Context propagation with actor/origin metadata, constraints, and sub-context trees.
• Sensor support for metrics such as request counters and elapsed time, making request execution observable.

Architecture

Architecture documentation

Requirements

• Python >= 3.14
• uv (recommended)

Installation

Install dependencies for local development

uv sync

Install the core package

uv pip install .

Install an addon package

uv pip install ./addons/llimona_azure_openai

Quick Start

1) Create an app config

Example (test_config/app.yaml):

provider_addons:
  - azure_openai
provider_loaders:
  - type: autodiscovery_dirs
    src: !path .

2) Create a provider directory with provider.yaml

Example (example_config/azure_1/provider.yaml):

type: azure_openai
name: azure_1
display_name: Azure Example 1
owner_id: 444444-222-333-222 # Not used, just for future purposes
base_url: !envvar AZURE_OPENAI_1_BASE_URL
credentials:
  api_key: !envvar AZURE_OPENAI_1_API_KEY
services:
- type: openai_responses
- type: openai_models
models:
- name: gpt-4o-mini
  allowed_services:
  - openai_responses

3) Run a request

uv run llimona app --config-file example_config/app.yaml openai responses create azure_1/gpt-4o-mini "Hello" --stream

4) Observe sensor metrics

After the request completes, Llimona prints sensor values that make execution observable:

Sensor value: elapsed_time=0.606314 (Elapsed time of the request.)
Sensor value: request_count=1 (Number of requests being processed for the sensor request_count.)
Sensor value: request_per_unit_of_time=1 (Number of requests in the last 0:01:00.)
Sensor value: request_per_window_of_time=1 (Number of requests until the next reset.)

CLI Usage

Top-level help

llimona --help

List discovered addons

llimona addons

Run commands with an app config

llimona app --config-file <path-to-app.yaml> <command>

Providers

# list all providers
llimona app --config-file <cfg> providers

# inspect one provider
llimona app --config-file <cfg> providers <provider_name>

# list models in one provider
llimona app --config-file <cfg> providers <provider_name> models

OpenAI-compatible interface commands

# create a response
llimona app --config-file <cfg> openai responses create <provider>/<model> "Prompt"

# streaming response
llimona app --config-file <cfg> openai responses create <provider>/<model> "Prompt" --stream

# list models (global or filtered by provider)
llimona app --config-file <cfg> openai models list
llimona app --config-file <cfg> openai models list <provider_name>

Configuration Overview

The app configuration supports these top-level fields:

• provider_addons: provider addons to register.
• provider_loader_addons: provider-loader addons to register.
• sensor_addons: sensor addons to register.
• id_builder: optional ID builder configuration.
• provider_loaders: loader definitions.

Built-in provider loader:

• autodiscovery_dirs: scans child directories under src, reads provider.yaml, and optionally merges definitions from models/*.yaml, services/*.yaml, and sensors/*.yaml.

Architecture Summary

Llimona receives OpenAI-compatible requests, decomposes model IDs, routes to the appropriate provider, and maps provider-specific responses back to interface models.

Every call flows through a Context object, which can carry:

• action metadata (provider, service, service_action, model)
• actor and origin information
• conversation metadata
• constraints
• collected sensor values

Routing strategies can create sub-contexts, enabling per-branch observability and post-execution failure inspection.

Sensors make the platform observable by exposing execution metrics across the full request context tree.

For full technical details, see docs/arch.md.

Addons in This Repository

• addons/llimona_azure_openai: Azure OpenAI provider addon.
• addons/llimona_smart_provider: smart/virtual provider routing addon.

Development

Install development tools

uv sync --group dev

Run tests

poe test

Lint and format

poe fix

Branching and Versioning

The repository follows a GitFlow-like model with:

• main as the default integration branch
• feat/*, fix/*, and chore/* working branches
• squash-merge pull requests
• SemVer/PEP 440 release semantics

See branching model document for the complete policy.

Security Notes

• Do not commit real API keys or secrets in provider files.
• Inject credentials at runtime through your deployment environment.

License

This project is licensed under the GNU AFFERO GENERAL PUBLIC LICENSE. See LICENSE for details.