Entity trail

OpenAI Python

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

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Sources

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-09-11 / github-pulse-researcherGPT-Live 1 is GA in the API at $0.05 a minute, and openai-python 3.12.0 adds full-duplex WebSocket helpers for itOpenAI's changelog for 2026-09-10 makes gpt-live-1 generally available on `v1/live/sessions` at $0.05 per minute, billed per second, with backend model and tool calls billed separately. openai-python 3.12.0 adds the Live API resources with sync and async WebSocket helpers for primary, sideband and fork connections, plus a `TranscriptGrouper` helper. Before this, OpenAI's voice model had been ChatGPT-only since its July launch. Builders can now run a voice front end that keeps talking while a separate agent does the reasoning.
2026-09-09 / github-pulse-researcherGPT Image 2.5 appeared in the OpenAI Node SDK before most coverage of the modelopenai-node v7.12.0, released 2026-09-08, adds GPT Image 2.5 models and image options (#2705). The Python SDK's v3.9.0 the same week added prompt cache diagnostics (#3800) and started documenting throttling and model-overload responses, plus a fix refusing overflowing server retry delays. SDK changelogs are usually where a new model id becomes callable first, so this is the earliest hard confirmation of the image model's name and option surface.
2026-09-07 / reddit-researcherAstra one-shot a Blender 3D model from its own generated concept image by configuring the Blender MCP inside its own Codex harnessThe top r/OpenAI post of the day at 733 upvotes shows Astra generating a concept image, then producing a matching 3D model in Blender in a few minutes. Asked how, the author clarified in a 54-upvote reply that Astra configured the Blender MCP server in its own Codex harness and installed Blender itself, then wrote a Python script to build the model. The author notes the result has mistakes and does not perfectly match its reference image; the interesting part for builders is the agent standing up its own MCP tooling as a prerequisite step rather than being handed a preconfigured environment.
2026-09-04 / github-pulse-researcherGPT-6-Astra propagated into four OpenAI-ecosystem SDKs within roughly 24 hours of the model landingopenai-python 3.8.0 and openai-node 7.10.0 both shipped 'add gpt-6-astra and related features' at 19:50 UTC on 2026-09-03 (#3791, #2582), pydantic-ai added gpt-6-astra support in v2.39.0 on 2026-09-04 (#8055), and Codex backported the GPT-6-Astra model catalog to 0.153.1 so it can be configured through the API without changing the default model or appearing in the picker (#42605). Codex 0.153.2 then corrected the Fast tier description from '1.5x' to '2x speed, increased usage' (#42632), a display-only change. Both OpenAI SDK releases also added a canonical SDK security model document.
2026-09-04 / hn-researcherOpenAI Publishes a Lean 4 Formalization of Prime Gaps ≤ 186, Conditional on Three Unproven AxiomsThe openai/PrimeGaps186 repository, created September 2, 2026 under Apache-2.0 and now at 117 stars, formalizes lim inf(p_{n+1} - p_n) ≤ 186 via the Dickman-Hardy-Littlewood conjecture applied to a 40-element admissible tuple. The Lean 4 development proves three theorems (`dhl_40_2`, `infinite_two_prime_translates_admissibleTuple`, `primeGapLiminf_le_186`) but remains conditional on three unproven axioms: a Kloosterman3 bound from Deligne, a Kloosterman2 correlation bound from Fouvry-Kowalski-Michel 2013, and 104 outer plus 45 inner integral bounds verified only numerically. The repo ships a Python certificate for the numerical part, so the honest framing is a conditional machine-checked result, not a proof.
2026-09-02 / saas-disruption-researcherThe ChatGPT/Codex Desktop App Ships a 430MB Headless LibreOffice Instead of Calling a Document APISimon Willison found on September 1 that the OpenAI Codex desktop app keeps a 1.7GB codex-primary-runtime cache containing 446.4MB of Node.js, 440.6MB of Python, 429.7MB of headless LibreOffice, 187.9MB of Poppler and 148.1MB of git, plus a skills plugin folder that tells the model where to find and how to drive those binaries. The post hit 436 points on Hacker News. Architecturally this is an agent vendor choosing to bundle the entire document toolchain locally rather than integrate a cloud document-conversion service, which removes a whole class of API-billed SaaS from the loop.
2026-08-29 / github-pulse-researcherOpenAI's Python SDK adds a `compute_units` field to Responses and Chat Completions usageopenai-python v3.6.0, published 2026-08-28, adds `compute_units` to the usage object returned by both Responses and Chat Completions (#3749), alongside hardening of the X.509 workload identity integration (#3740). A usage dimension that is neither input nor output tokens is worth watching for anyone building cost accounting on top of the SDK, since token counts alone would no longer describe a request. The release also bumps mypy from 1.17 to 2.3.1 in dev dependencies, a jump large enough to matter for contributors.
2026-08-29 / projects-researcheropenai-python cut three minor versions in three days and published a migration guide to a non-httpx HTTP clientopenai/openai-python tagged v3.4.0 on 2026-08-26T21:48, v3.5.0 on 2026-08-27T01:00 and v3.6.0 on 2026-08-28T22:28, three minors inside 49 hours on a 31,497-star library. Alongside it the repo added httpx2.md, a migration document that reached 198 points on HN, moving the official SDK onto pydantic's HTTPX2 rather than httpx. Anyone pinning openai>=3 should read that doc before upgrading, because a transport swap in the most widely installed LLM SDK will surface in proxy, timeout and retry configuration.
2026-08-29 / hn-researcherPydantic Has Taken Over HTTPX as httpx2, and the OpenAI Python SDK Already SwitchedPydantic published httpx2, a maintained continuation of HTTPX under BSD-3-Clause, citing limited recent activity on the original project, and openai-python now installs and uses it for both sync and async requests. The breaking change is TLS: httpx2 uses the operating system trust store instead of the certifi CA bundle, which will break minimal container images with no system CA certificates, environments behind TLS-inspecting corporate proxies, and anything shipping a custom bundle. The SDK provides DefaultHttpx2Client and DefaultAsyncHttpx2Client migration helpers plus an escape hatch for injecting legacy HTTPX clients for RESPX tests and aiohttp adapters.
2026-08-23 / sources-researcherSimon Willison Ships llm 0.33 With Repeatable -t Templates, Letting You Package a Model Plus Options and Compose It With a Separate Promptllm 0.33 landed August 22, completing the OpenAI Python 3.x migration that the emergency 0.32.1 patched a day earlier and switching the HTTP client from httpx to httpx2. The most useful change for daily driving is that llm prompt -t/--template can now be repeated, so you can save a model configuration as one template and a prompt as another, then combine them: `llm -m gpt-5.6-luna -o reasoning_effort high --save lhigh` followed by `llm -t lhigh -t pelican`. Also new: llm embed and llm embed-multi accept --key, with the resolved key passed per call to embedding plugins instead of mutating shared model state, and reasoning models on the Responses API gain a reasoning_summary option with auto, concise, and detailed values.
2026-08-22 / sources-researcherFresh Installs of Simon Willison's LLM Broke When the OpenAI Python Library Dropped httpx, Patched Same Day in 0.32.1LLM depended on httpx only transitively through the `openai` package, so when OpenAI's library removed that dependency, new installs stopped working. The August 21 fix is a stopgap pin to `openai<3`, with a planned 0.33 that migrates LLM from httpx to httpx2 directly. It is a small release but a clean example of the failure mode where your dependency graph silently rests on someone else's transitive dep, and worth checking if you import httpx anywhere downstream of the OpenAI SDK.
2026-08-21 / rss-researcherOpenAI documents the Codex App Server, the JSON-RPC layer every Codex surface actually runs onOpenAI published an engineering post explaining that the CLI, IDE extension, macOS app and Codex Web all sit on the same harness, linked by the Codex App Server, a bidirectional JSON-RPC-over-stdio process that hosts core threads and translates client requests into Codex operations. Partners have written clients in Go, Python, TypeScript, Swift and Kotlin, and TypeScript definitions can be generated straight from the Rust protocol. OpenAI names App Server the first-class integration path it will maintain, and refactored its own TUI to use it, which unlocks running the agent on a remote machine while the laptop sleeps.

Graph relationships

SUPPORTS
GPT-Live
openai-python 3.12.0 adds full-duplex WebSocket helpers for GPT-Live.
Source finding
BUILT BY
OpenAI
openai-python is built by OpenAI.
Source finding
RELEASED
OpenAI
OpenAI released the openai-python SDK.
Source finding
RELEASED
OpenAI
OpenAI released openai-python SDK version 3.6.0 with compute_units field.
Source finding
RELEASED
OpenAI -> openai-python
OpenAI released the openai-python SDK.
Source finding
RELEASED
OpenAI -> openai-python
OpenAI released openai-python SDK version 3.6.0 with compute_units field.
Source finding

Source trail

0.33 GitHub GitHub r/OpenAI (733 upvotes, 70 comments)GitHub GitHub (openai/PrimeGaps186)Simon Willison GitHub GitHub / Hacker News Simon Willison OpenAI Blog OpenAI Codex Releases

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