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1

Author prompts in a rich editor

Build prompts with typed variables, structured content, and Git-like versioning.
2

Generate type-safe models

Run codegen to get Pydantic models with autocomplete and validation for your LLM invocations.
3

Use prompts in your code

Fetch prompts, inject your context, get provider specific dialects prepared, no more shoe horning.
4

Observe in production

Log traces and review them in the same editor you authored in.

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Quick Example

from moxn import MoxnClient
from anthropic import Anthropic

async with MoxnClient() as client:
    # Fetch prompt and inject your data
    session = await client.create_prompt_session(
        prompt_id="support-agent",
        session_data=SupportInput(query="How do I reset my password?")
    )

    # Send to provider
    response = Anthropic().messages.create(
        **session.to_anthropic_invocation()
    )

    # Log for observability
    await client.log_telemetry_event_from_response(session, response)

Quick Start

Install and run your first prompt

Why Moxn?

The problems we solve
For AI tools: This documentation is available as llms.txt and llms-full.txt for LLM consumption.