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What teams building AI features on OpenAI Agents SDK report, with every finding tied to its evidence.

Read the findings

What the evidence says

Whose sources count

Practitioner sentiment, all practitioners

−17 Mixed from 50 sources that take a side

How this figure is made

Mixed overall signal, net 17 points negative

From 46 authors in 46 discussions. Build one pilot agent to measure the glue code and debugging time the framework adds.

Analyzed
75 total sources
Top praise
A Small Set of Primitives (11 of 11 sources) See praise evidence
Top concern
Runtime Defects in Production (14 of 14 sources) See concern evidence

Mar 2025 to Oct 2026.

59 sources from practitioners, by month and sentiment.

Builders praise the small set of primitives more than any other point, and named teams from Harvey to Lark build on it. Runtime defects lead the concerns, most of them closed GitHub issues. Teams that chose the SDK for its size report building session storage, sandboxes, and trace export around it.

Positive, mixed and negative sources

The score counts them as 11 positive, 16 mixed and 19 negative votes, one per author. See How the numbers work.

See the evidence

Below: 2 praise themes, 5 concerns, 1 point of agreement and 1 disagreement.

Findings

Counted for All practitioners, from 59 sources. Change group

What practitioners praise

Every capability practitioners describe positively, ranked by how many sources say so. Open one to read who said it.

  1. A Small Set of PrimitivesAll 11 sources on it praise itAgents, tools, handoffs, guardrails, and sessions cover most agent designs without the layers heavier frameworks add.

    Counted for All practitioners.

    1. Positive

      silksong_when, Reddit poster who works at SigNoz

      Observing AI Agents with OpenTelemetryReddit post

      Built an NBA agent with input guardrails, server-backed sessions, and a tool, and picked the SDK for its documentation. OpenTelemetry libraries did not yet record its guardrails.

      Published Oct 22, 2026self-described: NBA question-answering agent instrumented with OpenTelemetry

    2. Positive

      Chetan Wadhwa, developer on X

      First hands-on experience with the OpenAI Agents SDKPost on X

      On a first build, found that tracing out of the box made agent handoffs and tool calls much easier to debug.

      Published Oct 3, 2026self-described: First hands-on build with the SDK

    3. Positive

      InsideDebt6345, Reddit poster in r/AI_Agents

      OpenAI Agents SDK: using tool descriptions to route search vs. live-page retrievalReddit post

      Routed an agent between WebSearchTool and a custom page fetcher by writing distinct tool descriptions, after which it searched first and fetched pages when it needed their content.

      Published Aug 31, 2026self-described: Two-tool agent with web search and a page fetcher

    4. Positive

      sibidharan, Hacker News commenter

      Comment on: Ask HK: How are you building AI apps today?Hacker News comment

      Finds handoffs and multi-agent orchestration intuitive, and values the sessions, chat history, and boilerplate the SDK adds over the Responses API.

      Published Jun 1, 2026self-described: EdTech apps with AI learning and evaluation tools

    5. Mixed

      suncemoje, Hacker News commenter

      Comment on: Claude Managed AgentsHacker News comment

      Largely happy with the SDK's handoffs, tools, guardrails, and structured output, but found building infrastructure and observability that scale the bigger pain, which explains the pull of managed agents.

      Published Apr 9, 2026self-described: Has run agents on the SDK for a while

    6. Positive

      stochtinkerer, Hacker News commenter

      Comment on: Ask HN: Learning resources for building AI agents?Hacker News comment

      Advises starting minimal with the OpenAI Agents SDK and calls most other frameworks overengineered.

      Published Apr 5, 2026self-described: Advice to a developer starting to build agents

    7. Positive

      quleap, Hacker News commenter

      Comment on: Getting a Gemini API key is an exercise in frustrationHacker News comment

      Calls Google's ADK over-abstracted and points to smolagents, Pydantic AI, or the simpler OpenAI Agents SDK for a lightweight framework.

      Published Dec 18, 2025self-described: Comparing Google's ADK with lighter agent frameworks

    8. Positive

      NeutralCrane, Hacker News commenter

      Comment on: Open SWE by LangChainHacker News comment

      Names OpenAI's Agents SDK and Pydantic AI as solid alternatives to LangGraph for agent and workflow orchestration.

      Published Aug 8, 2025self-described: Comparing LangChain's products with alternatives

    9. Mixed

      PHM-Audit, GitHub user filing an issue

      Follow-up to #5330: recommended guardrail pattern verified on 0.23.1 (results + one output_info papercut)GitHub issue

      Confirmed the shared-context guardrail pattern a maintainer suggested, and measured that under the default concurrent setting all 6 tool calls had started before the tripwire fired, in 30 of 30 runs.

      Published Oct 12, 2026self-described: Guardrails that block out-of-plan tool calls, on 0.23.1

    10. Positive

      sibidharan, Hacker News commenter

      Comment on: Ask HK: How are you building AI apps today?Hacker News comment

      Moved every older Completions and Responses API project to the SDK, finds it stable, and uses it with other inference services and local models in LM Studio.

      Published May 29, 2026self-described: EdTech apps with AI learning and evaluation tools

    11. Positive

      ag_21, Octagon AI (Show HN author)

      AI VC Simulations Built on OpenAI's Agents SDKHacker News comment

      Octagon AI open-sourced an MCP server that runs VC simulations on the SDK, citing multi-agent reasoning, context handoff, and tool use out of the box.

      Published Apr 26, 2025self-described: Octagon VC Agents, an open-source MCP server

    GILD's fact checks, which don't count as sources

    Checked Oct 28, 2026.OpenAI documents a lightweight package with very few abstractions: agents with tools, agents as tools and handoffs, guardrails, sessions, approvals, MCP tool calling, and tracing. OpenAI Agents SDK (Python) overview

    Open them in the record
  2. Agents Built and ShippedAll 9 sources on it praise itBuilders report products and internal agents running on the SDK, from multi-agent IDEs and research agents to lead generation.

    Counted for All practitioners.

    1. Mixed

      OkShirt9372, Reddit poster in r/AI_Agents

      Why I combined fetching and extraction in one agent toolReddit post

      A lead-generation agent overflowed the model's context when a 540K-character page passed between two tools, so the builder merged steps into compact tools. One directory page then yielded 78 companies.

      Published Oct 6, 2026self-described: Lead-generation agent built with the SDK and ZenRows

    2. Positive

      baalimago, Hacker News commenter

      Comment on: OpenAI Agents APIHacker News comment

      Has had great success with the SDK, building sandbox, Slack, and knowledge-base integrations separately and limiting what reaches OpenAI. Sees little point in the hosted Agents API, since hosting a Python service is easy.

      Published Sep 11, 2026self-described: Company agent with sandbox, Slack, and knowledge-base integrations

    3. Positive

      sibidharan, Hacker News commenter

      Comment on: Ask HK: How are you building AI apps today?Hacker News comment

      Moved every older Completions and Responses API project to the SDK, finds it stable, and uses it with other inference services and local models in LM Studio.

      Published May 29, 2026self-described: EdTech apps with AI learning and evaluation tools

    4. Positive

      Vijit and Jack, builders of Runtime Use and Lark (Show HN authors)

      Show HN: Runtime Use, run agents in any sandboxHacker News post

      Lark's team open-sourced the sandbox runtime it built for its test-generating agents, which by default runs an Agents SDK agent with shell and web search tools and can switch to a Claude Code agent.

      "By default it runs an OpenAI Agents SDK agent with shell + web search tools."
      Excerpt, Vijit and Jack

      Published Mar 18, 2026self-described: Lark's agents generate end-to-end tests inside sandboxes

    5. Positive

      ag_21, Octagon AI (Show HN author)

      AI VC Simulations Built on OpenAI's Agents SDKHacker News comment

      Octagon AI open-sourced an MCP server that runs VC simulations on the SDK, citing multi-agent reasoning, context handoff, and tool use out of the box.

      Published Apr 26, 2025self-described: Octagon VC Agents, an open-source MCP server

    6. Positive

      ramnique, Rowboat co-founder (Show HN author)

      Comment on: Show HN: RowboatHacker News comment

      Rowboat's agents runtime and copilot are Python apps built on the SDK, beside a Next.js dashboard.

      Published Apr 22, 2025self-described: Rowboat, an open-source IDE for multi-agent systems

    7. No opinion stated

      hackfather, Hacker News commenter

      Comment on: Open Deep ResearchHacker News comment

      Built an open-source deep research agent on the newly released SDK that plans a topic, splits it, and researches sub-topics in parallel. The limits the author lists concern the models' long-context output.

      Published Apr 2, 2025self-described: Open-source deep research agent built on the SDK at launch

    8. Mixed

      asdev, Hacker News commenter

      Comment on: Launch HN: Hoplite (YC S26)Hacker News comment

      Building cloud coding agents from the ground up on the SDK inside an existing in-house environment, and says the work to turn it into an agent is not small.

      Published Aug 3, 2026self-described: Team with its own in-house cloud development environment

    9. Mixed

      j0selit0, Hacker News commenter (author of openai-agents-redis)

      Comment on: OpenAI-agents-Redis: Native OpenAI Agents SDK session management using RedisHacker News comment

      Built a Redis session store for the SDK because the built-in SQLite sessions could not scale across instances or survive container restarts.

      Published Jul 22, 2025self-described: Production app whose agents run on several instances

    GILD's fact checks, which don't count as sources

    Checked Oct 28, 2026.The MIT-licensed Python repository had 30,355 GitHub stars on October 28, 2026, and its v0.24.0 release shipped on October 15, 2026. openai-agents-python on GitHub

    Open them in the record

What concerns them

Every concern practitioners raise, most raised first.

  1. Runtime Defects in ProductionAll 14 sources on it raise it as a concernSessions, streaming, approvals, voice, and memory misbehave under real load in reported issues, many fixed in later releases.

    Counted for All practitioners.

    1. Negative

      Tardfyou, GitHub user filing an issue

      Approval decision transfers across runs when call ID is reusedGitHub issue

      An approval given in one run can authorize the same protected tool call in a different run when the call ID and arguments repeat; the issue is still open.

      Published Oct 24, 2026self-described: Approval-gated tools, tested on 0.23.1

    2. Negative

      sushantsimpplr, GitHub user filing an issue

      Streamed run keeps calling the model and tools after its only stream_events() consumer stops outside the queue waitGitHub issue

      When the only stream consumer stopped while handling an event, the run kept calling the model and tools in the background with nobody reading its events.

      Published Oct 17, 2026self-described: Streamed runs whose consumer stops early

    3. Mixed

      PHM-Audit, GitHub user filing an issue

      Follow-up to #5330: recommended guardrail pattern verified on 0.23.1 (results + one output_info papercut)GitHub issue

      Confirmed the shared-context guardrail pattern a maintainer suggested, and measured that under the default concurrent setting all 6 tool calls had started before the tripwire fired, in 30 of 30 runs.

      Published Oct 12, 2026self-described: Guardrails that block out-of-plan tool calls, on 0.23.1

    4. Negative

      BlueX888, GitHub profile lists Beijing University of Technology

      ShellTool: empty ShellResult fabricates stdout "(no output)" in the model-visible shell_call_outputGitHub issue

      When a shell command succeeded and printed nothing, the SDK told the model the command's stdout was "(no output)" and stored the same text as the command's output, so the model could quote output that never existed. The issue is closed.

      "…the SDK fabricates stdout that the executor never produced:…"
      Excerpt, BlueX888

      Published Oct 11, 2026self-described: Shell tool runs for commands that succeed and print nothing

    5. Mixed

      Loïc Minaudier, GitHub profile lists Datadog

      GPT-6 models get no default reasoning settings leading to 400 on /v1/chat/completions with function toolsGitHub issue

      Agents on GPT-6 Sol with any function tool failed with a 400 on Chat Completions, because the SDK sent no default reasoning setting for GPT-6 models while the same code worked on gpt-5.6-luna. The issue is closed.

      Published Oct 8, 2026self-described: An agent on GPT-6 Sol with function tools, called through Chat Completions behind an OpenAI-compatible gateway

    6. Negative

      sushantsimpplr, GitHub user filing an issue

      RedisSession fails on Redis Cluster: add_items/clear_session raise AttributeError ('ClusterPipeline' has no 'raise_first_error')GitHub issue

      RedisSession fails on a Redis Cluster client because two session methods call a pipeline method the cluster client lacks; the issue is still open.

      Published Oct 6, 2026self-described: Sessions stored on a Redis Cluster

    7. Negative

      kosesena, GitHub user filing an issue

      Voice: add_audio(None) doesn't end the STT session, so the pipeline waits ~1000s for EVENT_INACTIVITY_TIMEOUTGitHub issue

      After the documented end-of-stream signal, the voice pipeline kept waiting about 16.7 minutes with the speech-to-text socket open.

      Published Sep 27, 2026self-described: Voice pipeline with streaming speech-to-text

    8. Negative

      RongJie G (CorgiBoyG), GitHub profile lists Xidian University

      Responses terminal failures drop provider-reported usageGitHub issue

      When the Responses API ended a call incomplete or failed and still reported its usage, the SDK raised the right error but recorded zero requests and zero tokens for it, so failed calls vanished from the run's usage. The issue is closed.

      Published Sep 7, 2026self-described: Token accounting on runs whose Responses call ends incomplete or failed

    9. Negative

      Julio de la Calle (dixso), GitHub user filing an issue

      Deferred interrupted-turn session items are never persisted when an approval resume goes through next_step_run_againGitHub issue

      On main at commit 89c02c8, after #4613, an approval resume could write a tool output without its tool call into the session, after which every later turn of that conversation failed with a 400 and the conversation was permanently dead. In the reproducer 0.21.1 was unaffected and 0.22.0 failed earlier, during the resume itself. The issue is closed.

      Published Sep 2, 2026self-described: An agent with output guardrails and a tool that needs approval, resumed after the decision

    10. Negative

      ruskaruma, GitHub user filing an issue

      Memory leak: Agent references not released between turnsGitHub issue

      Agent objects stayed in memory across runs in long workflows because run items kept strong references to them.

      Published Dec 20, 2025self-described: Long-running workflows that run an agent in a loop

    11. Negative

      Dhanush Kumar Shankar (shankar-dh), GitHub user filing an issue

      Item with id rs_ not foundGitHub issue

      A triage agent on GPT-5 with minimal reasoning stored an empty reasoning item in its SQLite session, and once control came back to it after two handoffs every new message failed with a 404 for that item. The issue is closed.

      Published Oct 31, 2025self-described: Three agents on GPT-5 with handoffs and the default SQLiteSession

    12. Negative

      markns, GitHub user filing an issue

      Agent run with previous_response_id fails - No tool output found for function callGitHub issue

      Runs chained with previous_response_id failed sporadically after tool calls with a missing tool output error; the issue drew 24 comments before it closed.

      Published Jul 11, 2025self-described: Multi-turn runs chained with previous_response_id

    13. Mixed

      stavros, Hacker News commenter

      Comment on: Show HN: My LLM CLI tool can run tools now, from Python code or pluginsHacker News comment

      Found tool calls hit-and-miss in a calendar assistant on the SDK, with GPT-4.1 claiming it had added events without calling the tool.

      Published May 27, 2025self-described: Personal assistant with access to a calendar

    14. Negative

      Filimoa, GitHub user filing an issue

      Compaction introduces post-output latency: stream_events() doesn't terminate after streaming output completesGitHub issue

      With a compaction session on a streamed run, the stream stopped showing output and did not end until compaction finished, adding a wait after the answer.

      Published Jan 20, 2026self-described: Streamed runs with a compaction session

    GILD's fact checks, which don't count as sources

    Checked Oct 28, 2026.The SDK stays on 0.Y.Z versions, and OpenAI says a minor version may include breaking changes to public interfaces that are not marked beta. Agents SDK (Python): Release process

    Open them in the record
  2. Latency & Run OverheadAll 7 sources on it raise it as a concernAgent calls slow down in the Responses API, long tool loops grow slower, and nested agents add prompt tokens or miss the cache.

    Counted for All practitioners.

    1. Mixed

      deadstrobe5, GitHub user filing an issue

      Tool-calling runs get quadratically slower: completed tool calls are re-fingerprinted every turnGitHub issue

      Run time grew quadratically with tool-calling turns: a 300-turn run with a fake model took 2.69 seconds, about 1.2 of them spent re-fingerprinting earlier tool calls.

      Published Oct 16, 2026self-described: Long tool-calling runs

    2. Negative

      prajwal-bik-ai, GitHub user filing an issue

      Agent.as_tool() nested subagent runs never hit prompt cache, even with byte-identical instructions/tools/prompt_cache_keyGitHub issue

      Across 42 nested Agent.as_tool calls above the 1,024-token minimum, none returned cached tokens, so the cached-input discount never applied.

      Published Sep 18, 2026self-described: Top-level agent with nested subagents attached as tools

    3. Mixed

      code-with-rashid, author of the open-source agentic-arena benchmark

      Measured findings - agentic-arenaOpen-source benchmark site

      With a scripted model, the SDK sent 856.9 prompt tokens per tool-use item, 1.14 times a hand-rolled loop, and its handoffs added about 251 characters of schema per delegate to every request.

      Published Sep 8, 2026self-described: Scripted-model comparison of agent frameworks against a hand-rolled loop

    4. Negative

      Filimoa, GitHub user filing an issue

      Compaction introduces post-output latency: stream_events() doesn't terminate after streaming output completesGitHub issue

      With a compaction session on a streamed run, the stream stopped showing output and did not end until compaction finished, adding a wait after the answer.

      Published Jan 20, 2026self-described: Streamed runs with a compaction session

    5. Negative

      sibblegp, GitHub user filing an issue

      Unacceptable latency with 4.1GitHub issue

      After a tool call, GPT-4.1 often took up to 30 seconds to answer with few tokens, and the issue questions whether businesses can adopt the SDK at that speed.

      Published Sep 4, 2025self-described: Client apps that run agents on GPT-4.1

    6. Negative

      sibblegp, GitHub user filing an issue

      Why have agents (or GPT 4.1) slowed down so much?GitHub issue

      With no code change, one agent call on GPT-4.1 went from under 4 seconds to over 11 seconds, all of it spent in the Responses API.

      Published Jul 25, 2025self-described: Client apps that run agents on GPT-4.1

    7. Negative

      PaulSGit1, GitHub user filing an issue

      Long response times when using multiple agents with agents_as_toolsGitHub issue

      Single agent calls in a seven-agent orchestrator took about 90 seconds in traces, most of it in the Responses API.

      Published Apr 2, 2025self-described: Orchestrator on GPT-4.5 with seven agents as tools

    GILD's fact checks, which don't count as sources

    Checked Oct 28, 2026.With no model set, agents run gpt-5.6-luna with reasoning effort none and low verbosity, which OpenAI positions for cost-sensitive, high-volume workflows. Agents SDK (Python): Models

    Open them in the record
  3. OpenAI-First Design & Adapter Gaps7 of 8 sources on it raise it as a concernThe SDK is built around OpenAI's APIs, and builders running other models through adapters report crashes, silent truncation, and parallel tool-call failures.

    Counted for All practitioners.

    Raised as a concern, 7

    1. Negative

      igorbereza, GitHub user filing an issue

      aisdk(): parallel tool results become separate tool messages, which breaks GeminiGitHub issue

      The AI SDK adapter sends parallel tool results as separate messages, so Gemini rejects the next request after two or more parallel tool calls; the issue is still open.

      Published Oct 8, 2026self-described: Gemini through the TypeScript AI SDK adapter

    2. Negative

      Cheolhee Lee, AI full-stack developer at a B2B AI software company

      Non-streaming Chat Completions run fails when provider usage has null token countsGitHub issue

      A non-streaming run against an OpenAI-compatible server failed with a validation error whenever the provider sent a null token count, even though the model had returned a valid answer; streaming runs already handled it. The issue is closed.

      Published Sep 28, 2026self-described: Agents on an OpenAI-compatible server through the Chat Completions model

    3. Negative

      CoralGarden52, GitHub user filing an issue

      AnyLLM and LiteLLM return successful empty output for truncated responsesGitHub issue

      The Any-LLM and LiteLLM adapters returned an empty, truncated response marked as success.

      Published Sep 6, 2026self-described: Chat Completions models through the Any-LLM and LiteLLM adapters

    4. Mixed

      Elshad Karimov, builds production agents for clients

      Claude Agent SDK vs OpenAI Agents SDK: I Built the Same Agent in BothPersonal newsletter (Substack)

      Built one lead research agent in both SDKs. The OpenAI version cost US$0.08 a run with GPT-5, took 22 seconds and 125 lines, and hallucinated where the Claude version refused; the author finds the SDK built around OpenAI.

      Published Jun 13, 2026self-described: Same lead research agent built in the Claude Agent SDK and the OpenAI Agents SDK

    5. No opinion stated

      senko, Hacker News commenter

      Comment on: OpenAI's unit economicsHacker News comment

      Reads the Agents SDK, the Assistants API, and ChatGPT Apps as OpenAI's lock-in layer on top of a commodity Responses API.

      Published Jan 29, 2026self-described: OpenAI's product strategy and API lock-in

    6. Mixed

      dast-draftwise, GitHub user filing an issue

      Resilience mechanism Openai/ LiteLLMGitHub issue

      Could not get retries to work after moving an agent to Claude through LitellmModel, and found no documentation on resilience settings.

      Published Nov 6, 2025self-described: Moving an agent from OpenAI models to Claude through LiteLLM

    7. Negative

      ruidazeng, GitHub user filing an issue

      LiteLLM + Gemini 2.5 Pro: cached_tokens=None crashes Agents SDK with Pydantic int-validation errorGitHub issue

      Runs with Gemini 2.5 Pro through LiteLLM crashed during cost calculation because the model returned no cached-token count.

      Published May 26, 2025self-described: Gemini 2.5 Pro through the LiteLLM adapter

    Discussed without concern, 1

    1. Mixed

      baalimago, Hacker News commenter

      Comment on: OpenAI Agents APIHacker News comment

      Uses the SDK because tracking OpenAI's API changes by hand is more trouble than it is worth, and says a portable multi-model agent would skip the OpenAI SDK entirely.

      Published Sep 11, 2026self-described: Company agent with sandbox, Slack, and knowledge-base integrations

    GILD's fact checks, which don't count as sources

    Checked Oct 28, 2026.OpenAI calls LiteLLM and Any-LLM support best-effort beta, and notes that many other models lack structured outputs, multimodal input, and hosted file and web search. Agents SDK (Python): Models

    Checked Oct 28, 2026.OpenAI lists enterprise-level authentication through SAML SSO among the controls for its business products, the API Platform among them, and says it does not train its models on business data by default. OpenAI: Enterprise privacy

    Open them in the record
  4. Hosting & Storage Left to the TeamAll 5 sources on it raise it as a concernThe SDK runs in the team's own service, so builders add shared session storage, sandboxes, start-up timeouts, and scaling themselves.

    Counted for All practitioners.

    1. Mixed

      MuneebMalik244535, GitHub user filing an issue

      docs(mcp): clarify client_session_timeout_seconds default (5s), npx/cli cold-start timeout, and document missing constructor optionsGitHub issue

      MCPServerStdio defaults to a 5-second session timeout, while an uncached npx or uvx server often takes 10 to 25 seconds to start.

      Published Oct 21, 2026self-described: MCP servers started with npx

    2. Mixed

      asdev, Hacker News commenter

      Comment on: Launch HN: Hoplite (YC S26)Hacker News comment

      Building cloud coding agents from the ground up on the SDK inside an existing in-house environment, and says the work to turn it into an agent is not small.

      Published Aug 3, 2026self-described: Team with its own in-house cloud development environment

    3. Mixed

      j0selit0, Hacker News commenter (author of openai-agents-redis)

      Comment on: OpenAI-agents-Redis: Native OpenAI Agents SDK session management using RedisHacker News comment

      Built a Redis session store for the SDK because the built-in SQLite sessions could not scale across instances or survive container restarts.

      Published Jul 22, 2025self-described: Production app whose agents run on several instances

    4. Negative

      sushantsimpplr, GitHub user filing an issue

      RedisSession fails on Redis Cluster: add_items/clear_session raise AttributeError ('ClusterPipeline' has no 'raise_first_error')GitHub issue

      RedisSession fails on a Redis Cluster client because two session methods call a pipeline method the cluster client lacks; the issue is still open.

      Published Oct 6, 2026self-described: Sessions stored on a Redis Cluster

    5. Mixed

      suncemoje, Hacker News commenter

      Comment on: Claude Managed AgentsHacker News comment

      Largely happy with the SDK's handoffs, tools, guardrails, and structured output, but found building infrastructure and observability that scale the bigger pain, which explains the pull of managed agents.

      Published Apr 9, 2026self-described: Has run agents on the SDK for a while

    GILD's fact checks, which don't count as sources

    Checked Oct 28, 2026.OpenAI documents session stores for SQLite, Redis, SQLAlchemy, MongoDB, Dapr, and OpenAI Conversations, which the team chooses and operates. Agents SDK (Python): Sessions

    Checked Oct 28, 2026.OpenAI reports an independent SOC 2 Type 2 examination of controls for Security, Availability, Confidentiality, and Privacy covering its API and ChatGPT business services. OpenAI: Security and privacy

    Open them in the record
  5. Tracing Defaults & Export Gaps4 of 5 sources on it raise it as a concernTraces go to OpenAI unless the exporter is replaced, and builders report traces lost from workers, dropped batches, and duplicates in other tools.

    Counted for All practitioners.

    Raised as a concern, 4

    1. Mixed

      Andrew (gocronk), heavy AI user on X

      Adding your own trace collector to the OpenAI Agents SDKPost on X

      Warns that adding a trace processor leaves OpenAI's exporter running, and only set_trace_processors replaces it.

      Published Oct 7, 2026self-described: Keeping agent traces in the team's own system

    2. Negative

      coderdailyone, GitHub user filing an issue

      BackendSpanExporter drops the whole batch on HTTP 429 instead of retrying with Retry-AfterGitHub issue

      The tracing exporter dropped a whole batch of spans on an HTTP 429 and skipped retrying after the Retry-After delay.

      Published Sep 14, 2026self-described: Trace export under rate limiting

    3. Negative

      kiansdi, GitHub user filing an issue

      Traces are silently dropped in long-running workersGitHub issue

      Traces created inside long-running background workers never reached OpenAI's Traces dashboard, with no error.

      Published Nov 29, 2025self-described: Agents run in Celery and FastAPI background workers

    4. Mixed

      Karolis Mariunas (kmariunas), Developer (issue author)

      Duplicate traces through OpenAI agents SDKGitHub issue

      Each Agents SDK trace appeared three times in LangSmith with different ids, which made the team's dashboards untrustworthy. A suggested pre-release then broke handoffs and was reverted.

      Published Mar 31, 2025self-described: Agents SDK app with handoffs, traced to LangSmith

    Discussed without concern, 1

    1. Positive

      silksong_when, Reddit poster who works at SigNoz

      Observing AI Agents with OpenTelemetryReddit post

      Built an NBA agent with input guardrails, server-backed sessions, and a tool, and picked the SDK for its documentation. OpenTelemetry libraries did not yet record its guardrails.

      Published Oct 22, 2026self-described: NBA question-answering agent instrumented with OpenTelemetry

    GILD's fact checks, which don't count as sources

    Checked Oct 28, 2026.Tracing is on by default and exports to OpenAI's backend. add_trace_processor() keeps that exporter, set_trace_processors() replaces it, and organizations under Zero Data Retention cannot use it. Agents SDK (Python): Tracing

    Checked Oct 28, 2026.Traces land in the OpenAI API organization whose key the SDK uses. The Admin API audit logs list user actions and configuration changes within that organization. OpenAI API reference: Audit logs

    Open them in the record

Where the evidence agrees

Statements this group's sources agree on, then the ones close to agreement.

  1. Recurring concern

    The SDK's speed, state, and tracing depend on OpenAI's APIs, and other models work less well through its adapters.

    13 supporting sources, 1 contradicting

Close to agreement: each needs 12 supporting sources

  1. Below threshold

    The SDK's few primitives are enough to build working agents without a heavier framework.

    10 supporting sources
  2. Below threshold

    Session storage, sandboxes, trace export, and scaling around the SDK take engineering the SDK does not supply.

    7 supporting sources, 1 contradicting

Where it disagrees

Questions the sources split on, with the count on each side.

  • Evidence remains split

    Should a team run the SDK in its own service or move to a hosted agent runtime?

    • Run it in our own service, 3 sources

      Builders who host the SDK themselves say a Python service is easy to run and keeps what reaches OpenAI under their control.

    • A hosted runtime fits better, 2 sources

      Others found infrastructure and observability the bigger pain, or a hosted harness such as Codex's app-server more flexible.

The evidence

Whose experience this is

The three groups, then every practitioner by the context they work in, and how emphasis changes with scale.

Three groups drawn from the same sources. Choose a group to select whose experience counts. Within that group, each author counts once in the practitioner sentiment, with their latest view. Signals open on All practitioners. Teams and leaders have 7 accounts; GILD prints their sentiment from 20.

  • Teams and technical leaders

    12 sources. Company engineering teams and founders describe products and evals built on the SDK and the hosting work around it.

  • Individual builders

    47 sources. Individual builders describe agents they shipped, the defects they filed, and the models they ran.

  • All practitioners

    59 sources. These accounts combine founders and small company teams with solo developers and GitHub issue authors.

59 sources from practitioners, by the context they work in. Filled rows count for this group.

  1. Individual builderDevelopers building on the platform on their own47 sources, counted here

    Emphasizes Primitives, runtime defects, and adapters for other models

  2. Small product team2 to 10 engineers, or an unnamed startup team6 sources, counted here

    Emphasizes Startup products on the SDK and the latency they measured

  3. Product or platform team11 to 50 engineers, or a named company's team3 sources, counted here

    Emphasizes Harvey's evals on the SDK's traces, Coinbase's AgentKit extension, and GPT-6 Sol agents with tools that failed on Chat Completions. Pilot one bounded agent and record latency, cost, and where its traces go

  4. Technical leadershipLeaders setting model and platform choices across teams3 sources, counted here

    Emphasizes Founders building products and sandbox infrastructure around the SDK

  5. Organization-wideEnterprise or company-wide AI platformNo sources from this setting yet

What changes with scale. Individual builders praise the small set of primitives and report runtime defects in GitHub issues. Small companies build products on it: Rowboat Labs runs its agents runtime on it, Octagon AI ships an MCP server, and Lark's sandbox runtime defaults to an SDK agent, while Aurelio Labs replaced it with its own framework. At company scale, Harvey evaluates its legal search agent on the SDK's traces and Coinbase ships an AgentKit extension. Builders who keep it describe adding session storage, sandboxes, and trace export themselves.

Where the sources come from

Where this group's sources were published.

Counted for All practitioners, from 59 sources. Change group

75 sources for this group, 16 of them documentation

  • Developer Communities55, 73% of 75 sources
  • Product Documentation16, 21% of 75 sources
  • Engineering Blogs & Benchmarks4, 5% of 75 sources

Earlier evidence remains when it still applies. Records tied to closed issues or to GPT-4.1 latency carry Needs a recheck.

See sources by origin

The record

Who each source is, how it knows, whether it still applies and what it counts toward.

Newest first, on its own page. Every count above opens its sources there.

Read all 75 sources

About this read

What changed, and how we count

What changed

  • Nov 1, 2026: since Oct 30 the page prints one vote per author, and a figure given on the weighted reading is the second view. Individual builders read −25 and all practitioners −17; teams and leaders are unscored.
  • Every record links to a public source and names its author and publication date.
  • A page shows a finding when at least 5 sources from 3 different authors discuss it. A disagreement also requires at least 2 sources on each side.
  • Tracing, model, session, and pricing findings are checked against OpenAI's documentation and platform pages as of October 28, 2026.
  • Oct 28, 2026: adds 6 Operator Insights from named teams (Harvey's evals on the SDK's traces, Aurelio Labs leaving it for its own framework, Lark's sandbox runtime, Coinbase's AgentKit extension, Selfmade Ninja Academy's migration and Octagon AI), ledger quotes on 3 insights, and 15 records: 7 from GitHub, 3 from Hacker News, 3 dev.to posts, Harvey's engineering blog and a post on X. Two OpenAI pages join the documentation: the April 2026 sandbox release and Agent Builder's retirement at the end of November 2026.
  • Oct 30, 2026: a second-model check removed five records: eCorpIT's post, which markets its agent-building services, two dev.to framework comparisons whose feature claims rest on no vendor page, a Hacker News expectation with no use and an X job-application reply. A latency issue now carries an unchecked affiliation and no stance, and an approval-resume issue names the commit its reproducer failed on. Lark's builders and an EdTech builder's migration read as their pages say, and Coinbase's AgentKit card keeps to its pull request, with one example chatbot. On the weighted reading, all practitioners move from -16 to -15 on 51 counted sources, and individual builders stay at -21 on 43. The graphai maintainer's Hacker News comment then lists as a competitor's and counts toward no figure, so on the weighted reading, all practitioners move back to -16 on 50.

Method

A running review of what teams building AI features on OpenAI Agents SDK report, from individual builders to technical leaders. Each finding links to its sources, and each conflict shows both sides.

Counts cover only the sources GILD reviewed.

Who counts

Teams and technical leaders
Small product team, Product or platform team, Technical leadership, Organization-wide
Individual builders
Individual builder
All practitioners
Everyone in the teams and leaders and individual builders groups

Choose a group to select whose experience counts. Within that group, each author counts once in the practitioner sentiment, with their latest view. GILD files each source in a group from how the account describes its author. A role or employer marked self-described is what the source states about themselves. Documentation checks facts for every group and never counts toward sentiment.

How the numbers work

  • Overall signal. Positive votes minus negative ones, per 100 votes from the sources that take a side. Each author counts once, with their latest view, whoever they are and wherever they posted. The older weighted reading, where accounts GILD matched to a named person count three times when GILD lists the author among technical leaders and twice otherwise, is shown beside it as a second view. Where an author wrote more than one source, the line beside the split gives the votes, one per author, so the score follows from it. When one author's sources from the same day disagree, that author counts as mixed, and a half point rounds away from zero. Sources with no opinion are listed and not counted. Strong positive is +50 or higher, positive +20 to +49, mixed −19 to +19, negative −20 to −49, strong negative −50 or lower.
  • Weighted reading, second view. Counted the older way, where an account GILD matched to a named person counts two or three times, all practitioners read −16 mixed (method sentiment/S1). GILD printed that number as "GILD score" until October 29, 2026; most of those matches rest on a name or a self-described role.
  • Method change, Oct 30, 2026. GILD changed how practitioner sentiment counts, to one vote per author. Weighted, it read −9 Mixed; with one vote per author it reads −10 Mixed. No evidence changed.
  • Unscored. A group gets a score only with 20 sources from 15 authors. Below that, one source moves the score about 5 points, so the read says "Unscored until 20 sources from 15 authors" and shows its counts.
  • Independence. A source whose author works for the vendor, for one of its partners or programs, or for a competitor, and a page the vendor published or ran, is listed with that tie and counts toward nothing. One author counts on at most two records per product. The line under the score says how many authors and how many separate discussions the counted sources come from: comments in one thread are one discussion.
  • Praise and concerns. How many of the sources that discuss a theme praise it or raise it.
  • Consensus. A statement joins when 20% of the group's sources support it and fewer contradict it. Contradicting sources are counted separately. Below that line a statement shows as building. At 40% or more it reads Strong agreement; below, Recurring point.
  • Disagreements. The count on each side. GILD names no winner and gives no share of all users.
  • Minimum evidence. A finding needs 5 sources from 3 authors; a disagreement also needs 2 on each side. Smaller counts stay in the table below, marked under the minimum.

Labels on a source

A source's date and whether it still applies are separate: a recent post can describe an old version, and an old one can still hold.

Documented
Stated in official documentation.
Reported
A practitioner's account of their own use.
Measured
Backed by the source's own measurements, such as a comparative test or tracked team metrics.
GILD analysis
GILD's check of a claim against official documentation.
Current
Describes the product as it is now.
Still applicable
Older, and the behavior or lesson still holds.
Needs a recheck
May depend on a plan, version or setting that changed since it was written. It keeps counting until GILD rechecks it, since the rest of the account usually holds, and its card says so.
Out of date
No longer accurate. Kept in the record; counts toward nothing current.

Flagged for a recheck, of the sources that take a side: teams and leaders 3 of 7, individual builders 22 of 43, all practitioners 25 of 50.

Limits

  • The counts cover 59 public sources: 21 Hacker News posts and comments, 28 GitHub issues and 1 pull request, 3 Reddit posts, 2 posts on X, and 4 blog, engineering and benchmark pages. Harvey's engineering post and Coinbase's AgentKit pull request state no stance and count toward no figure, and so does a latency issue whose author's GitHub profile names Google and Elastic, since his ties to vendors are unchecked. They do not represent all SDK users. GitHub issues are bug reports and feature requests, which tilt the GitHub share toward problems. Reddit refused direct access, so its posts were read from the Arctic Shift archive. OpenAI's own blog and customer stories are left out.
  • Sources run from the SDK's launch in March 2025 to Python 0.23.x and TypeScript 0.21.x in October 2026, and each record keeps the version or model it states. OpenAI has since documented RedisSession, flush_traces for long-running workers, and a blocking guardrail mode. Closed issues on those topics carry Needs a recheck, as do latency reports on GPT-4.1 and the 0.22.x defects in approval resume, usage on failed calls and shell output, each fixed in a later release.

Every count, and the sources behind it

13 findings for 3 groups

Each row opens its sources for the group you are reading.

FindingTeams and technical leadersIndividual buildersAll practitionersSources
Current readCurrent sentiment3 positive, 2 mixed, 2 negative10 positive, 14 mixed, 19 negative13 positive, 16 mixed, 21 negativeSources
PraiseA Small Set of Primitives1 of 1 positive (under the minimum)10 of 10 positive11 of 11 positiveSources
PraiseAgents Built and Shipped4 of 4 positive (under the minimum)5 of 5 positive9 of 9 positiveSources
ConcernRuntime Defects in Production1 of 1 raise it (under the minimum)13 of 13 raise it14 of 14 raise itSources
ConcernLatency & Run Overhead2 of 2 raise it (under the minimum)5 of 5 raise it7 of 7 raise itSources
ConcernOpenAI-First Design & Adapter Gaps0 of 0 raise it (under the minimum)7 of 8 raise it7 of 8 raise itSources
ConcernHosting & Storage Left to the Team1 of 1 raise it (under the minimum)4 of 4 raise it (under the minimum)5 of 5 raise itSources
ConcernTracing Defaults & Export Gaps0 of 0 raise it (under the minimum)4 of 5 raise it4 of 5 raise itSources
ConsensusSmall enough to learn quickly1 supporting (under the minimum)9 supporting (below threshold)10 supporting (below threshold)Sources
ConsensusTied to OpenAI's platform2 supporting (under the minimum)11 supporting, 1 contradicting13 supporting, 1 contradictingSources
ConsensusThe production work sits around it2 supporting (under the minimum)5 supporting, 1 contradicting (below threshold)7 supporting, 1 contradicting (below threshold)Sources
DisagreementShould a team run the SDK in its own service or move to a hosted agent runtime?Run it in our own service 1, A hosted runtime fits better 0 (under the minimum)Run it in our own service 2, A hosted runtime fits better 2 (under the minimum)Run it in our own service 3, A hosted runtime fits better 2Sources
Current readEvidence mix28 sources63 sources75 sourcesSources

Fact checks

GILD's checks of claims against official documentation, kept apart from the source counts.