Not surprised OpenAI is not there, I struggled so much with telemetry in Codex that I had to build my own one Codex Plus (https://github.com/aperoc/codex-plus) -_-
Seems like it can be a standard the project can adopt.
These can also be observed through OTEL telemetries.
I use headless codex exec a lot, but struggles with its built-in telemetry support, which is insufficient for debugging and optimization.
Thus I made codex-plus (https://github.com/aperoc/codex-plus) for myself which provides a CLI entry point that mirrors the codex exec interface but is implemented on top of the TypeScript SDK (@openai/codex-sdk).
It exports the full session log to a remote OpenTelemetry collector after each run which can then be debugged and optimized through codex-plus-log-viewer.
Sad that a lot of these are for Claude Code and not Codex which I uses more, so I started https://github.com/aperoc/codex-plus which has telemetry built-in, now moving to build a Ralph loop on top of it.
I think both Cursor and Cognition and going in the same direction of SWE-grep[0].
SWE-grep was able to hit ~700tokens/s and Cursor ~300token/s, hard to compare the precision/recall and cost effectiveness though, considering SWE-grep also adopted a "hack" of running it on Cerebras.
I'm trying to kickstart a RL-based code search project called "op-grep" here[1], still pretty early, but looking for collaborators!
I recommend reading Shopify CEO Tobi's try[0] for good example of how Ruby's block behavior and meta-programming makes it easy to create a single file, shell wrapper.
I've drafted an architecture, with the steps mainly as so:
1. Collect actions (grep/glob/read) policies either from usage logs or open datasets
2. Optimize by removing redundant actions or parallelization
3. Train model on optimized action policy
4. Release model as a single file, MCP tool
(Refer to repo for visual diagram of the architecture)
I've just released the base model and added `openai_forwarder.py` to start collecting action policies.
Looking for more eyes and contributors to make this a reality, thanks!