lingo-ai 1.3

A minimal, async-native, and unopinionated toolkit for modern LLM applications.

A minimal, async-native, and unopinionated toolkit for modern LLM applications.

Stop wiring graphs. Start modeling conversations.

Lingo is a lightweight, type-safe Python framework for building LLM-powered applications. It moves beyond generic "agents" and "chains" to focus on Conversational Modeling—the discipline of defining exactly how a system perceives, processes, and advances a dialogue state.

It unifies three powerful paradigms in a single, typed architecture:

1. Procedural Skills (Linear, script-like flows)
2. Symbolic States (Deterministic FSMs)
3. Reflexive Patterns (Event-driven guardrails)

⚡ Features

• 💾 Stateful by Default: The Python stack is your state machine. Use await engine.ask() to pause execution and wait for user input naturally.
• 🧠 Cognitive Architecture: Mix rigid business rules (States) with flexible reasoning (Skills).
• 🛡️ Type-Safe: Built on Pydantic. All inputs, outputs, and tool calls are validated schemas.
• 🌊 Low-Level Flow Control: Direct access to the underlying Flow graph for complex orchestration (Fork/Join, Retry, Loops).

🚀 Quickstart

Installation

pip install lingo-ai

The "Hello World" (Stateful Wizard)

Lingo allows you to model conversations as linear scripts. You don't need to manage session IDs or database steps manually—variables persist in memory across turns.

import asyncio
from lingo import Lingo

# Initialize the application
app = Lingo("Wizard", description="A helpful setup wizard")

@app.skill
async def onboarding(ctx, eng):
    # 1. Output a message
    await eng.reply(ctx, "Welcome to the system.")

    # 2. PAUSE execution and wait for user input
    # Lingo automatically suspends the stack here.
    # The variable 'name' is preserved in memory when the user replies!
    name = await eng.ask(ctx, "What is your name?")

    # 3. Resume and use context from previous turns
    email = await eng.ask(ctx, f"Hi {name}, what is your email?")

    # 4. Use structured decision making (LLM is forced to return bool)
    if await eng.decide(ctx, f"Is {email} a valid corporate email address?"):
        await eng.reply(ctx, "Registration complete.")
    else:
        await eng.reply(ctx, "Personal emails are not allowed.")

if __name__ == "__main__":
    from lingo.cli import loop
    loop(app)

---

🧠 The Three Modeling Paradigms

Lingo gives you the right abstraction for every type of logic.

1. Symbolic States (Finite State Machine)

Best for: Business Logic, Security Boundaries, Multi-Step Workflows.

Use the StateMachine to enforce strict rules about allowed transitions.

from lingo.fsm import StateMachine

# 1. Initialize the FSM with the bot's registry
fsm = StateMachine(app.registry)

@fsm.state
async def login(ctx, eng):
    await eng.reply(ctx, "Please log in.")
    # Deterministic transition to the next state
    fsm.goto(dashboard, restart=True)

@fsm.state
async def dashboard(ctx, eng):
    await eng.reply(ctx, "Welcome to your dashboard.")
    # Logic restricted to this state...

# Register the FSM as a skill
@app.skill
async def run_workflow(ctx, eng):
    await fsm.execute(ctx, eng)

2. Reflexive Patterns (Event-Driven)

Best for: Guardrails, Interruptions, Global Commands.

Use @app.when to define high-priority listeners that intercept messages before they reach skills.

@app.when("User wants to quit or cancel the operation")
async def emergency_stop(ctx, eng):
    await eng.reply(ctx, "Stopping immediately.")
    eng.stop() # Terminates the flow and clears the stack

3. Structured Flows (Low-Level Graph)

Best for: Parallel Processing, Retries, Complex Orchestration.

You can drop down to the Flow API to build complex execution graphs explicitly.

from lingo import Flow

# Define two sub-flows
research = Flow("Research").reply("Searching for info...")
draft = Flow("Draft").reply("Drafting content...")

# Build a flow that runs them in parallel (Fork)
# and summarizes the result
complex_flow = (
    Flow("ParallelWorker")
    .fork(
        research,
        draft,
        aggregator="Combine the research and draft into a final report."
    )
)

📦 Architecture

• Context: Mutable ledger of the conversation history.
• Engine: The "actuator" that drives the LLM. It exposes methods like .ask(), .decide(), .choose(), and .create().
• Flow: The underlying graph representation of all skills.

🤝 Contribution

We welcome contributions! Please see CONTRIBUTING.md for details on how to set up your development environment and submit pull requests.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.