Explore / OpenAI Agents SDK Template
Agent TemplateInstall-verifiedmedium risk

OpenAI Agents SDK Template

A structured starting point for tool-using agents built with OpenAI Agents SDK.

Preview · 94

Preview: the curated catalog plus the latest auto-checked entries. 47 newly auto-checked

74/100
Recommended
Confidence: medium
80/100
Benchmark
Confidence: medium
Needs human reviewInstall-verified · Seed profile normalized into AgentMaps schema.
Why
Recommendation 74/100, fits Automation, setup is medium.
Best for
Developers using ChatGPT for automation workflows.
Not for
Users expecting a fully managed marketplace install flow.
Boundary
Checked up to "Install-verified"; deeper install and interface testing isn't covered yet — confirm in your own environment.
Remaining risk
Risk appears manageable for personal developer workflows when configured narrowly.
Next step
Review tokens, write access, and network scope in a sandbox before team adoption.
Check before you adopt
This capability uses higher-risk permissions (tokens, write access, shell, and the like). Try it in an isolated environment and confirm what it actually does before using it for real work.

What it is good for

A structured starting point for tool-using agents built with OpenAI Agents SDK. AgentMaps treats this as a agent template candidate and scores it with a capped benchmark score plus a separate recommendation score that includes trust, platform fit, setup preference, and risk preference.

Use cases

  • - Run repeatable task flows
  • - Coordinate tool calls
  • - Escalate ambiguous cases
  • - Gather sources
  • - Compare claims

Best for

  • - Developers using ChatGPT for automation workflows.
  • - Teams that want visible setup, verification, and risk evidence before adoption.

Not for

  • - Users expecting a fully managed marketplace install flow.
  • - Users who cannot provide the required credentials.

Limitations

  • - Scenario-level L5-L7 benchmark testing is not part of the current MVP record.
How it runsAgentic workflow

How to use it & what to watch

AI may help filter, explain, and draft a trial plan, but must not perform write, shell, external side effects, or production publishing before review.

What you'll see

  • Start from your task and the platform you use
  • Review its setup, source, and permission scope
  • Try it first in an isolated or low-permission environment
  • For high-risk actions, wait for a review before continuing

Where you can step in

  • Adjust task/platform filters
  • Add to compare
  • Open the source to check
  • Stop a high-risk adoption
  • Add what you've learned

Actions needing approval

  • Token/API key scope approval
  • External network target approval
  • Login-required path not fully tested

If something goes wrong

  • Auth path needs manual reproduction or a constrained test account.

How to judge a trial

  • Can you find a task-fit candidate within two minutes
  • Can it explain why it's recommended and where it doesn't fit
  • Can you see the token, write, shell, network, or local-file risks
  • Can you tell 'checked the docs' apart from 'actually verified in a run'
  • If a trial fails, is there a fallback or a way to take over manually

Verification evidence

Below is the result of each check — passed, partial, skipped, or not yet tested. It shows how far checking has gone, not that the capability is cleared for production use.

Info checked

Source, docs, license, or package metadata exists.

passed
Risk reviewed

Static review assigns permission and risk boundaries.

passed
Install-verified

Install path can be checked, but not necessarily in your environment.

passed
Interface-verified

Interface or entrypoint parsed; not a production safety approval.

skipped
  • Seed profile normalized into AgentMaps schema.
  • Source and documentation fields are present.
  • Static risk flags are assigned.
  • Install path is represented for harness checks.
  • Interface parsing pending.
  • Benchmark score capped at 80 by L3 verification.

Evaluation summary

Quick estimate
Needs reviewStatic reviewMisfire risk: Medium

Risk findings

  • - Requires Token
  • - External Network

Verified evidence

  • - Seed profile normalized into AgentMaps schema.
  • - Source and documentation fields are present.
  • - Static risk flags are assigned.
  • - Install path is represented for harness checks.

Score breakdown

Safety & permissions70
How well it works85
Actively maintained87
Setup & integration82
Interface quality86
Follows standards83
Task fit85
Works across platforms86

Why it scores well

  • - Clear task fit for the selected scenario.
  • - Static verification evidence is available.
  • - Focused platform fit.

Watch outs

  • - Requires credentials before full testing.
  • - Production adoption still needs local validation.

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