Xpersona

Crawler Summary

developing-openai-agents-sdk-agents answer-first brief

Build, create, debug, review, implement, and optimize agentic AI applications using the OpenAI Agents SDK for TypeScript. Use when creating new agents, defining tools, implementing handoffs between agents, adding guardrails, debugging agent behavior, reviewing agent code, or orchestrating multi-agent systems with the @openai/agents package. --- name: developing-openai-agents-sdk-agents description: Build, create, debug, review, implement, and optimize agentic AI applications using the OpenAI Agents SDK for TypeScript. Use when creating new agents, defining tools, implementing handoffs between agents, adding guardrails, debugging agent behavior, reviewing agent code, or orchestrating multi-agent systems with the @openai/agents package. --- Developing Ope Capability contract not published. No trust telemetry is available yet. 2 GitHub stars reported by the source. Last updated 4/15/2026.

Freshness

Last checked 4/15/2026

Best For

developing-openai-agents-sdk-agents is best for call, validate workflows where MCP compatibility matters.

Not Ideal For

Contract metadata is missing or unavailable for deterministic execution.

Evidence Sources Checked

editorial-content, GITHUB OPENCLEW, runtime-metrics, public facts pack

CardFactsSnapshotContractTrust
Claim this agent
Agent DossierGitHubSafety: 80/100

developing-openai-agents-sdk-agents

Build, create, debug, review, implement, and optimize agentic AI applications using the OpenAI Agents SDK for TypeScript. Use when creating new agents, defining tools, implementing handoffs between agents, adding guardrails, debugging agent behavior, reviewing agent code, or orchestrating multi-agent systems with the @openai/agents package. --- name: developing-openai-agents-sdk-agents description: Build, create, debug, review, implement, and optimize agentic AI applications using the OpenAI Agents SDK for TypeScript. Use when creating new agents, defining tools, implementing handoffs between agents, adding guardrails, debugging agent behavior, reviewing agent code, or orchestrating multi-agent systems with the @openai/agents package. --- Developing Ope

MCPself-declared

Public facts

5

Change events

1

Artifacts

0

Freshness

Apr 15, 2026

Verifiededitorial-contentNo verified compatibility signals2 GitHub stars

Capability contract not published. No trust telemetry is available yet. 2 GitHub stars reported by the source. Last updated 4/15/2026.

2 GitHub starsTrust evidence available

Trust score

Unknown

Compatibility

MCP

Freshness

Apr 15, 2026

Vendor

Mikekelly

Artifacts

0

Benchmarks

0

Last release

Unpublished

Executive Summary

Key links, install path, and a quick operational read before the deeper crawl record.

Verifiededitorial-content

Summary

Capability contract not published. No trust telemetry is available yet. 2 GitHub stars reported by the source. Last updated 4/15/2026.

View Source

Setup snapshot

git clone https://github.com/mikekelly/developing-openai-agents-sdk-agents.git
  1. 1

    Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.

  2. 2

    Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Evidence Ledger

Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.

Verifiededitorial-content
Vendor (1)

Vendor

Mikekelly

profilemedium
Observed Apr 15, 2026Source linkProvenance
Compatibility (1)

Protocol compatibility

MCP

contractmedium
Observed Apr 15, 2026Source linkProvenance
Adoption (1)

Adoption signal

2 GitHub stars

profilemedium
Observed Apr 15, 2026Source linkProvenance
Security (1)

Handshake status

UNKNOWN

trustmedium
Observed unknownSource linkProvenance
Integration (1)

Crawlable docs

6 indexed pages on the official domain

search_documentmedium
Observed Apr 15, 2026Source linkProvenance

Release & Crawl Timeline

Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.

Self-declaredagent-index

Docs Update

Docs refreshed: Sign in to GitHub · GitHub

search_documentmedium

Fresh crawlable documentation was indexed for the official domain.

Observed Apr 15, 2026

Artifacts Archive

Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.

Self-declaredGITHUB OPENCLEW

Extracted files

0

Examples

0

Snippets

0

Languages

typescript

Parameters

Docs & README

Full documentation captured from public sources, including the complete README when available.

Self-declaredGITHUB OPENCLEW

Docs source

GITHUB OPENCLEW

Editorial quality

ready

Build, create, debug, review, implement, and optimize agentic AI applications using the OpenAI Agents SDK for TypeScript. Use when creating new agents, defining tools, implementing handoffs between agents, adding guardrails, debugging agent behavior, reviewing agent code, or orchestrating multi-agent systems with the @openai/agents package. --- name: developing-openai-agents-sdk-agents description: Build, create, debug, review, implement, and optimize agentic AI applications using the OpenAI Agents SDK for TypeScript. Use when creating new agents, defining tools, implementing handoffs between agents, adding guardrails, debugging agent behavior, reviewing agent code, or orchestrating multi-agent systems with the @openai/agents package. --- Developing Ope

Full README

name: developing-openai-agents-sdk-agents description: Build, create, debug, review, implement, and optimize agentic AI applications using the OpenAI Agents SDK for TypeScript. Use when creating new agents, defining tools, implementing handoffs between agents, adding guardrails, debugging agent behavior, reviewing agent code, or orchestrating multi-agent systems with the @openai/agents package.

Developing OpenAI Agents SDK Agents

Comprehensive workflow-driven skill for building production-ready agentic AI applications with the OpenAI Agents SDK.

<essential_principles>

Core Concepts

Agents are LLMs with structure: An agent combines an LLM with instructions (system prompt), tools (functions it can call), handoffs (delegation targets), and optional guardrails (validators).

Minimal abstractions: The SDK provides primitives (Agent, tool, run) rather than heavyweight frameworks. You compose behavior through code, not configuration.

Context injection: Tools and instructions receive RunContext, enabling dependency injection of user data, database connections, or other runtime context without global state.

Handoffs transfer ownership: When one agent hands off to another, the target agent becomes the active conversational participant. This differs from tools (manager pattern) where the calling agent maintains control.

Guardrails run in parallel: Input guardrails can validate user input concurrently with the LLM call, reducing latency. Output guardrails check responses before returning them.

Structured output is typed: Using Zod schemas for outputType gives you compile-time type safety and runtime validation of agent responses.

Human-in-the-loop is first-class: Tools with needsApproval create interruptions that your code handles explicitly, enabling approval workflows without special infrastructure.

Design Principles

Start simple, add complexity as needed: Begin with a single agent and basic tools. Add handoffs, guardrails, and orchestration only when requirements justify them.

Test with real LLM calls: Mocking LLMs hides emergent behavior. Use small models (gpt-4.1-mini) or cached prompts for fast iteration, but always test end-to-end.

Make instructions specific: Vague prompts ("be helpful") produce vague behavior. Specify the agent's role, available information, decision criteria, and output format.

Tools are for actions, not data: Don't create tools just to return static information. Put reference data in instructions or context. Tools should execute side effects or retrieve dynamic data.

Fail explicitly: Return error strings from tools rather than throwing exceptions. This lets the LLM see what went wrong and potentially retry with different parameters.

Trace everything: Enable tracing in development to understand agent decision-making. The SDK's built-in tracing shows tool calls, handoffs, and model reasoning.

</essential_principles>

<intake>

What would you like to do with OpenAI Agents SDK?

Common activities:

  • Build a new agent or multi-agent system
  • Add tools (functions) to an existing agent
  • Implement agent handoffs (delegation)
  • Add guardrails (validation)
  • Debug agent behavior (unexpected actions, loops, errors)
  • Review or optimize existing agent code
  • Set up tracing and observability
  • Implement human-in-the-loop approval flows
  • Integrate with MCP servers
  • Structure agent output with Zod schemas
</intake> <routing>

| User wants to... | Route to workflow | |-----------------|-------------------| | Create a new agent from scratch | workflows/build-new-agent.md | | Add a tool (function) to an agent | workflows/add-tool.md | | Set up handoffs between agents | workflows/implement-handoff.md | | Add validation (guardrails) | workflows/add-guardrails.md | | Debug agent behavior | workflows/debug-agent.md | | Review agent code quality | workflows/review-agent-code.md | | Set up structured output | workflows/add-structured-output.md | | Implement approval workflows | workflows/implement-human-approval.md | | Add tracing/observability | workflows/enable-tracing.md | | Choose orchestration pattern | workflows/choose-orchestration.md | | Integrate MCP servers | workflows/integrate-mcp.md | | Optimize agent performance | workflows/optimize-agent.md |

</routing>

<reference_index>

Domain Knowledge References

</reference_index>

<workflows_index>

Step-by-Step Workflows

Building

Architecting

Operating

</workflows_index>

Contract & API

Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.

MissingGITHUB OPENCLEW

Endpoints

Dossier APISnapshot APIContract APITrust API

Contract coverage

Status

missing

Auth

None

Streaming

No

Data region

Unspecified

Protocol support

MCP: self-declared

Requires: none

Forbidden: none

Guardrails

Operational confidence: low

No positive guardrails captured.
Invocation examples
curl -s "https://xpersona.co/api/v1/agents/mikekelly-developing-openai-agents-sdk-agents/snapshot"
curl -s "https://xpersona.co/api/v1/agents/mikekelly-developing-openai-agents-sdk-agents/contract"
curl -s "https://xpersona.co/api/v1/agents/mikekelly-developing-openai-agents-sdk-agents/trust"

Reliability & Benchmarks

Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.

Missingruntime-metrics

Trust signals

Handshake

UNKNOWN

Confidence

unknown

Attempts 30d

unknown

Fallback rate

unknown

Runtime metrics

Observed P50

unknown

Observed P95

unknown

Rate limit

unknown

Estimated cost

unknown

Do not use if

Contract metadata is missing or unavailable for deterministic execution.
No benchmark suites or observed failure patterns are available.

Media & Demo

Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.

Missingno-media
No screenshots, media assets, or demo links are available.

Related Agents

Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.

Self-declaredprotocol-neighbors
GITLAB_AI_CATALOGgitlab-mcp

Rank

83

A Model Context Protocol (MCP) server for GitLab

Traction

No public download signal

Freshness

Updated 2d ago

MCP
GITLAB_PUBLIC_PROJECTSgitlab-mcp

Rank

80

A Model Context Protocol (MCP) server for GitLab

Traction

No public download signal

Freshness

Updated 2d ago

MCP
GITLAB_AI_CATALOGrmcp-openapi

Rank

74

Expose OpenAPI definition endpoints as MCP tools using the official Rust SDK for the Model Context Protocol (https://github.com/modelcontextprotocol/rust-sdk)

Traction

No public download signal

Freshness

Updated 2d ago

MCP
GITLAB_AI_CATALOGrmcp-actix-web

Rank

72

An actix_web backend for the official Rust SDK for the Model Context Protocol (https://github.com/modelcontextprotocol/rust-sdk)

Traction

No public download signal

Freshness

Updated 2d ago

MCP
Agent hubMore from this sourceMCP agents
Machine Appendix

Contract JSON

{
  "contractStatus": "missing",
  "authModes": [],
  "requires": [],
  "forbidden": [],
  "supportsMcp": false,
  "supportsA2a": false,
  "supportsStreaming": false,
  "inputSchemaRef": null,
  "outputSchemaRef": null,
  "dataRegion": null,
  "contractUpdatedAt": null,
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Invocation Guide

{
  "preferredApi": {
    "snapshotUrl": "https://xpersona.co/api/v1/agents/mikekelly-developing-openai-agents-sdk-agents/snapshot",
    "contractUrl": "https://xpersona.co/api/v1/agents/mikekelly-developing-openai-agents-sdk-agents/contract",
    "trustUrl": "https://xpersona.co/api/v1/agents/mikekelly-developing-openai-agents-sdk-agents/trust"
  },
  "curlExamples": [
    "curl -s \"https://xpersona.co/api/v1/agents/mikekelly-developing-openai-agents-sdk-agents/snapshot\"",
    "curl -s \"https://xpersona.co/api/v1/agents/mikekelly-developing-openai-agents-sdk-agents/contract\"",
    "curl -s \"https://xpersona.co/api/v1/agents/mikekelly-developing-openai-agents-sdk-agents/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "MCP"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_OPENCLEW",
      "generatedAt": "2026-04-16T23:40:52.153Z"
    }
  },
  "retryPolicy": {
    "maxAttempts": 3,
    "backoffMs": [
      500,
      1500,
      3500
    ],
    "retryableConditions": [
      "HTTP_429",
      "HTTP_503",
      "NETWORK_TIMEOUT"
    ]
  }
}

Trust JSON

{
  "status": "unavailable",
  "handshakeStatus": "UNKNOWN",
  "verificationFreshnessHours": null,
  "reputationScore": null,
  "p95LatencyMs": null,
  "successRate30d": null,
  "fallbackRate": null,
  "attempts30d": null,
  "trustUpdatedAt": null,
  "trustConfidence": "unknown",
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Capability Matrix

{
  "rows": [
    {
      "key": "MCP",
      "type": "protocol",
      "support": "unknown",
      "confidenceSource": "profile",
      "notes": "Listed on profile"
    },
    {
      "key": "call",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    },
    {
      "key": "validate",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    }
  ],
  "flattenedTokens": "protocol:MCP|unknown|profile capability:call|supported|profile capability:validate|supported|profile"
}

Facts JSON

[
  {
    "factKey": "docs_crawl",
    "category": "integration",
    "label": "Crawlable docs",
    "value": "6 indexed pages on the official domain",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  },
  {
    "factKey": "vendor",
    "category": "vendor",
    "label": "Vendor",
    "value": "Mikekelly",
    "href": "https://github.com/mikekelly/developing-openai-agents-sdk-agents",
    "sourceUrl": "https://github.com/mikekelly/developing-openai-agents-sdk-agents",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-04-15T04:13:05.065Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "MCP",
    "href": "https://xpersona.co/api/v1/agents/mikekelly-developing-openai-agents-sdk-agents/contract",
    "sourceUrl": "https://xpersona.co/api/v1/agents/mikekelly-developing-openai-agents-sdk-agents/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-04-15T04:13:05.065Z",
    "isPublic": true
  },
  {
    "factKey": "traction",
    "category": "adoption",
    "label": "Adoption signal",
    "value": "2 GitHub stars",
    "href": "https://github.com/mikekelly/developing-openai-agents-sdk-agents",
    "sourceUrl": "https://github.com/mikekelly/developing-openai-agents-sdk-agents",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-04-15T04:13:05.065Z",
    "isPublic": true
  },
  {
    "factKey": "handshake_status",
    "category": "security",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "href": "https://xpersona.co/api/v1/agents/mikekelly-developing-openai-agents-sdk-agents/trust",
    "sourceUrl": "https://xpersona.co/api/v1/agents/mikekelly-developing-openai-agents-sdk-agents/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true
  }
]

Change Events JSON

[
  {
    "eventType": "docs_update",
    "title": "Docs refreshed: Sign in to GitHub · GitHub",
    "description": "Fresh crawlable documentation was indexed for the official domain.",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  }
]

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