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AI (oridecon-ai)

Agents, LLMs, RAG, Skills — wired together, no glue code.

PyPI version Python versions License

Oridecon AI demo

oridecon-ai is the AI layer of the oridecon framework: a thin coordinator that wires the oridecon-ai family — agents, LLMs, RAG, memory, skills, MCP, session, workers, observability, feedback, guard, governance, evaluation, prompt, relay — into the container through entry-point discovery. one install, one Application.boot, and the whole family is resolvable by contract. every backend is swappable: run it on your own infra or point it at an API.

  • wired, not glued. agents, llms, rag, memory — one container, one boot call.
  • async, end to end. the container, the modules, the controllers — concurrency-safe by construction.
  • contracts everywhere. every package talks through protocols, so swapping an implementation never ripples.
  • local-first. defaults point at any OpenAI-compatible server — Ollama, LM Studio, vLLM — hosted providers are a config change away.

→ full docs at docs.oridecon.dev

install

Section titled “install”
Terminal window
uv add "oridecon[ai,web]"    # framework + web + ai + server (what the example below uses)
uv add oridecon-ai           # just the coordinator
pip install "oridecon[ai,web]"

60 seconds, end to end

Section titled “60 seconds, end to end”
from oridecon import Application
from oridecon.web import Controller, get, WebModule
from oridecon.web.server import run_server
from oridecon.ai.llm import LLMModule, ClientConfig
from oridecon.contracts.ai import LLMClientProtocol, ChatMessage, Role




class ChatController(Controller):
    def __init__(self, llm: LLMClientProtocol):
        self.llm = llm


    @get("/chat")
    async def chat(self, q: str) -> dict:
        messages = [ChatMessage(role=Role.USER, content=q)]
        result = await self.llm.complete(messages)
        return {"reply": result.unwrap().content}




app = Application()
app.add_modules(
    [
        # Local-first. To talk to a hosted provider instead, set
        # `provider="openai"` (or "anthropic", "groq", ...) and supply
        # the matching API key.
        LLMModule.configure(
            ClientConfig(
                provider="ollama",
                model="llama3.2",
                api_base="http://localhost:11434",
                api_key="ollama",
            )
        ),
        WebModule.configure(controllers=[ChatController]),
    ]
)


run_server(app, port=8000)

→ http://localhost:8000/chat?q=hello

No API key needed if you’re pointing at a local model. To talk to a hosted provider instead, set provider="openai" (or "anthropic", "groq", …), drop api_base, and supply the matching API key — or let LLMModule.configure() read the whole block from ORI_AI_LLM__* env vars.

what just happened?

  • Application.boot assembled two modules — an LLM client and a web server — into one container and started them together.
  • LLMModule.configure(...) declared a provider, a model, and an endpoint. No SDK, no per-provider code.
  • ChatController resolved LLMClientProtocol by type from the container. Swap the provider; the controller never changes.
oridecon-ai
├── umbrella        entry point · discovers subsystems
├── llm             provider-agnostic clients
├── agents          tools, react, and beyond
├── rag             chunkers, embedders, retrieval pipelines
├── memory          working, episodic, semantic stores
├── skills          versioned agent capabilities
├── session         conversation state and resumption
├── mcp             model-context-protocol clients
├── workers         background AI jobs
├── observability   tracing and metrics
├── feedback        quality loops
├── guard           input/output safety gates
├── governance      policy, audit, budgets
├── evaluation      evals and quality gates
├── prompt          versioned prompt templates
├── relay           protocol conversion engine
└── relay-gateway   HTTP gateway for relay

what’s in the box

Section titled “what’s in the box”

the whole family lives in this repository under experimental/ai/ — experimental tier, API stability is not guaranteed between releases. same container, same contracts, same rules as the stable core.

  • oridecon-ai — the coordinator (this package)
  • oridecon-ai-llm — provider-agnostic clients for Ollama, OpenAI, Anthropic, Groq, Mistral, and more
  • oridecon-ai-agents — tools, react, and beyond
  • oridecon-ai-rag — chunkers, embedders, retrieval pipelines
  • oridecon-ai-memory — working, episodic, semantic stores
  • oridecon-ai-skills — versioned agent capabilities
  • oridecon-ai-session — conversation state and resumption
  • oridecon-ai-mcp — model-context-protocol clients
  • oridecon-ai-workers — background AI jobs
  • oridecon-ai-observability — tracing and metrics
  • oridecon-ai-feedback — quality loops
  • oridecon-ai-guard — input/output safety gates
  • oridecon-ai-governance — policy, audit, budgets
  • oridecon-ai-evaluation — evals and quality gates
  • oridecon-ai-prompt — versioned prompt templates
  • oridecon-ai-relay — protocol conversion engine
  • oridecon-ai-relay-gateway — HTTP gateway for relay

early on purpose

Section titled “early on purpose”

The AI layer is in 0.1 — which means you can still change it. APIs may shift before 1.0, so pin your versions, and tell us what feels wrong. Shaping a framework is more fun when it’s still soft.

pointers

Section titled “pointers”