chatnificent 0.0.13

LLM chat app framework - Minimally complete. Maximally hackable

Chatnificent

LLM chat app framework. Minimally complete. Maximally hackable.

Pre-built chat UIs give you a working app but almost no way to customize it. Building from scratch gives you full control but means wiring up a UI, LLM client, message store, streaming, auth, and tool calling yourself.

Chatnificent is a Python framework where each of those concerns is an independent, swappable component. You get a working app immediately. When you need to change something — the LLM provider, the database, the entire UI — you swap one component, instead of rewriting the whole app.

Quickstart

pip install chatnificent

import chatnificent as chat

app = chat.Chatnificent()
app.run()  # http://127.0.0.1:8050

No API keys, no extras, no configuration. You get a working chat UI with the built-in Echo LLM, a stdlib HTTP server, and an HTML/JS frontend — all with zero dependencies.

One Install Away from Real LLM Responses

pip install openai
export OPENAI_API_KEY="sk-..."

Run the same code. Chatnificent auto-detects the installed OpenAI SDK and your API key — no code change needed.

Swap Anything

Every component is a pillar you can swap independently:

import chatnificent as chat

# Different LLM providers
app = chat.Chatnificent(llm=chat.llm.Anthropic())   # pip install anthropic
app = chat.Chatnificent(llm=chat.llm.Gemini())       # pip install google-genai
app = chat.Chatnificent(llm=chat.llm.Ollama())       # pip install ollama (local)

# Persistent storage
app = chat.Chatnificent(store=chat.store.SQLite(db_path="chats.db"))
app = chat.Chatnificent(store=chat.store.File(directory="./conversations"))

# Mix and match
app = chat.Chatnificent(
    llm=chat.llm.Anthropic(),
    store=chat.store.SQLite(db_path="conversations.db"),
    layout=chat.layout.Bootstrap(),  # Requires: pip install "chatnificent[dash]"
)

Streaming by Default

All LLM providers stream by default — token-by-token delivery via Server-Sent Events. Opt out with stream=False:

app = chat.Chatnificent(llm=chat.llm.OpenAI(stream=False))

The Architecture: 9 Pillars

Every major function is handled by an independent pillar with an abstract interface:

Pillar	Purpose	Default	Implementations
Server	HTTP transport	DevServer (stdlib)	DevServer, DashServer
Layout	UI rendering	DefaultLayout (HTML/JS)	DefaultLayout, Bootstrap, Mantine, Minimal
LLM	LLM API calls	OpenAI / Echo	OpenAI, Anthropic, Gemini, OpenRouter, DeepSeek, Ollama, Echo
Store	Persistence	InMemory	InMemory, File, SQLite
Engine	Orchestration	Orchestrator	Orchestrator
Auth	User identification	Anonymous	Anonymous, SingleUser
Tools	Function calling	NoTool	PythonTool, NoTool
Retrieval	RAG / context	NoRetrieval	NoRetrieval
URL	Route parsing	PathBased	PathBased, QueryParams

Dash-based layouts (Bootstrap, Mantine, Minimal) require pip install "chatnificent[dash]" and the DashServer.

Customize the Engine

The Orchestrator manages the full request lifecycle: conversation resolution, RAG retrieval, the agentic tool-calling loop, and persistence. Override hooks (for monitoring) and seams (for logic):

import chatnificent as chat
from typing import Any, Optional

class CustomEngine(chat.engine.Orchestrator):

    def _after_llm_call(self, llm_response: Any) -> None:
        tokens = getattr(llm_response, 'usage', 'N/A')
        print(f"Tokens: {tokens}")

    def _prepare_llm_payload(self, conversation, retrieval_context: Optional[str]):
        payload = super()._prepare_llm_payload(conversation, retrieval_context)
        if not any(m['role'] == 'system' for m in payload):
            payload.insert(0, {"role": "system", "content": "Be concise."})
        return payload

app = chat.Chatnificent(engine=CustomEngine())

Build Your Own Pillars

Implement the abstract interface and inject it:

import chatnificent as chat
from chatnificent.models import Conversation

class MongoStore(chat.store.Store):
    def save_conversation(self, user_id, conversation): ...
    def load_conversation(self, user_id, convo_id): ...
    def list_conversations(self, user_id): ...
    def get_next_conversation_id(self, user_id): ...

app = chat.Chatnificent(store=MongoStore())

Every pillar works the same way: subclass the ABC, implement the required methods, pass it in.