langchain-codex-oauth 1.0.0

Use ChatGPT Plus/Pro Codex (OAuth) as a LangChain/LangGraph chat model

langchain-codex-oauth

Use the Codex models included with a ChatGPT Plus/Pro subscription inside LangChain/LangGraph.

This project authenticates locally via OpenAI OAuth (no OPENAI_API_KEY) and talks to the ChatGPT/Codex backend.

What this is

• A dev-friendly adapter that makes Codex feel like a native-ish LangChain chat model.
• Local OAuth login (langchain-codex-oauth auth login) storing credentials under ~/.langchain-codex-oauth/.
• Streaming support via SSE (.stream() yields chunks as they arrive).
• Async support via .ainvoke() / .astream().
• Tool calling via .bind_tools(...) (useful for LangGraph agents).
• Streaming tool call chunks (tool_call_chunks) when available.
• Stop sequences (stop=[...]) for invoke/stream (best-effort).
• Response and usage metadata (response_metadata, usage_metadata) when available.

What this is not

• Not for sharing accounts/subscriptions.
• Not intended for production or multi-user hosting.

Stability (v1.0)

• The Python API (ChatCodexOAuth, CLI commands, and message/tool wiring) is considered stable.
• The upstream backend is a ChatGPT consumer endpoint and may change without notice. If that happens, new releases may be required to restore compatibility.
• Recommendation: pin a minor version in real projects (e.g. langchain-codex-oauth>=1.0,<1.1) and upgrade when needed.

Requirements

• Python >=3.10
• Active ChatGPT Plus/Pro subscription with Codex access

Install

python -m pip install langchain-codex-oauth

Authenticate (one time)

langchain-codex-oauth auth login
# If port 1455 is busy or you’re on a remote machine:
langchain-codex-oauth auth login --manual

Quickstart

from langchain_core.messages import HumanMessage, SystemMessage
from langchain_codex_oauth import ChatCodexOAuth

model = ChatCodexOAuth(model="gpt-5.2-codex")

messages = [
    SystemMessage(content="You are a concise assistant."),
    HumanMessage(content="Say hello and give a one-line coding tip."),
]

for chunk in model.stream(messages):
    print(chunk.content, end="", flush=True)

Or run the examples (see examples/README.md for details):

python examples/langchain/hello.py
python examples/langchain/tools.py
python examples/langchain/async_hello.py
python examples/langchain/chatopenai_compatibility.py
python examples/langchain/flags_and_params.py
python examples/langchain/usage_and_metadata.py
python examples/langchain/tool_call_chunks.py

# Optional (requires installing langgraph in a separate venv)
python examples/langgraph/system_prompt_drift.py --mode strict

Configuration knobs (ChatOpenAI-like)

ChatCodexOAuth supports a small set of common ChatOpenAI knobs:

• timeout (seconds) and max_retries
• temperature and max_tokens (best-effort passthrough)
• stop=[...] for .invoke/.stream/.ainvoke/.astream (best-effort)

Additional Codex-specific knob:

• system_prompt_mode ("strict" default, "default", "disabled") to reduce system-prompt drift on the consumer backend.

RAG / Embeddings

ChatCodexOAuth is a chat model, not an embedding model.

• For RAG, you can pair it with local embeddings (e.g. Ollama mxbai-embed-large) or OpenAI embeddings.
• The critical rule is: the embedding model used to index documents must match the embedding model used to embed queries.
• See examples/langchain/rag_chroma_ab.py for an A/B harness (Chroma + persisted local DB).

Notes

• The Codex backend requires validated instructions. By default the library uses cached prompts, attempts GitHub fetch if missing, and falls back to bundled prompts (override with LANGCHAIN_CODEX_OAUTH_INSTRUCTIONS_MODE).
• The adapter aims to be OpenAI-like: tool schemas from convert_to_openai_tool(...) and with_structured_output(...) are supported.
• If you hit ChatGPT usage limits, the library normalizes some backend “usage limit” errors to HTTP 429 semantics.
• Usage counts and finish reasons are best-effort (the backend is not a stable public API).