atheon-codex 1.0.7

Python SDK for Atheon

Codex: Python SDK for Atheon

The Atheon Codex Python library provides convenient access to the Atheon Gateway from any Python 3.11+ application. It includes type definitions for all request params and response fields, and offers both synchronous and asynchronous clients powered by httpx.

Installation

pip install atheon-codex

Quick Start

import os
import atheon
 
# Initialise once at application startup
atheon.init(os.environ["ATHEON_API_KEY"])
 
# Track a completed interaction — non-blocking, enqueues in the background
interaction_id = atheon.track(
    provider="openai",
    model_name="gpt-4o",
    input="How can I write blogs for my website?",
    output="Start by identifying your target audience...",
    tokens_input=18,
    tokens_output=120,
    finish_reason="stop",
    status_code=200,
)
 
# Pass interaction_id to your frontend: <atheon-container interaction-id="...">
print(interaction_id)
 
# Flush and stop the background queue before process exit
atheon.shutdown()

Note: Get your API key from the Atheon Gateway Dashboard under Project Settings. We recommend storing it in a .env file using python-dotenv rather than hardcoding it in source.

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Usage

Initialisation

Call atheon.init() once at application startup. All subsequent calls share the global client automatically.

atheon.init(
    api_key=os.environ["ATHEON_API_KEY"],
    upload_size=10,        # events per HTTP batch (default 10)
    upload_interval=1.0,   # seconds between background flushes (default 1.0)
    max_queue_size=10_000, # max in-memory queue depth (default 10 000)
)

Streaming & Multi-Turn: begin() / finish()

Use begin() / finish() when the response spans time. Wall-clock latency is measured automatically.

interaction = atheon.begin(
    provider="anthropic",
    model_name="claude-sonnet-4-5",
    input="Summarise our Q3 report",
    properties={"agent": "rag-pipeline", "environment": "production"},
)
 
# ... stream response, call tools, run sub-agents ...
 
interaction.set_property("user_tier", "pro")  # enrich mid-flight
 
interaction_id = interaction.finish(
    output=final_text,
    tokens_input=80,
    tokens_output=220,
    finish_reason="stop",
    status_code=200,
)

Tool Tracking: @atheon.tool

Decorate any function (sync or async) to record its name, latency, and errors into the active interaction automatically — no explicit passing required.

@atheon.tool("vector-search")
def search(query: str) -> list[str]:
    return db.search(query)
 
@atheon.tool("reranker")
async def rerank(docs: list[str]) -> list[str]:
    return await model.rerank(docs)

@atheon.tool is a no-op if called outside an active begin() context — safe to use unconditionally.

Sub-Agent Tracking: @atheon.agent

Decorate LLM-backed sub-agent functions to nest their tool calls and token usage inside the root interaction. Everything ships in a single payload on finish().

@atheon.agent(
    "rag-pipeline",
    provider="anthropic",
    model_name="claude-haiku-4-5",
)
def rag_agent(query: str) -> str:
    chunks = search(query)
    response = llm.messages.create(...)
    atheon.set_result(
        tokens_input=response.usage.input_tokens,
        tokens_output=response.usage.output_tokens,
        finish_reason=response.stop_reason,
        status_code=response.status_code,
    )
    return response.content[0].text

Async Support

For async frameworks (FastAPI, Django async views, etc.), use the async API. async_track() and async_begin() are synchronous enqueues — call without await.

# FastAPI example
from contextlib import asynccontextmanager
from fastapi import FastAPI
 
@asynccontextmanager
async def lifespan(app: FastAPI):
    atheon.async_init(os.environ["ATHEON_API_KEY"])
    yield
    await atheon.async_shutdown()
 
app = FastAPI(lifespan=lifespan)
 
@app.post("/chat")
async def chat(req: ChatRequest):
    final_text = await llm.complete(req.message)
    interaction_id = atheon.async_track(   # no await
        provider="openai",
        model_name="gpt-4o",
        input=req.message,
        output=final_text,
        finish_reason="stop",
        status_code=200,
    )
    return {"reply": final_text, "interaction_id": str(interaction_id)}

Use either atheon.init() or atheon.async_init() — not both in the same process.

Direct Client Instantiation

The module-level helpers are recommended for most applications. For multiple isolated clients in the same process, instantiate directly:

from atheon import AtheonCodexClient, AsyncAtheonCodexClient
 
# Sync
with AtheonCodexClient(api_key=os.environ["ATHEON_API_KEY"]) as client:
    client.track(provider="openai", model_name="gpt-4o", input="...", output="...")
 
# Async
async with AsyncAtheonCodexClient(api_key=os.environ["ATHEON_API_KEY"]) as client:
    client.track(provider="openai", model_name="gpt-4o", input="...", output="...")

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Links

• Atheon Documentation
• Gateway Dashboard
• PyPI