# ibl.ai vs OpenAI Agents SDK

> Source: https://ibl.ai/resources/comparisons/self-hosted-ai-vs-openai-agents-sdk
> Last updated: 2026-10-20


*A lightweight, MIT-licensed SDK that is at its best on OpenAI's Responses API, or an owned platform that runs any model and keeps traces, files and conversations inside your perimeter*

**On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network.**

## What's the difference between ibl.ai and OpenAI Agents SDK?

The OpenAI Agents SDK is MIT-licensed, so the SDK code is yours either way. With ibl.ai you own all the code and the data: the platform source, the traces, the files and the conversations all stay on infrastructure you control.

That data path is where the two differ. The SDK's hosted tools — web search, file search over OpenAI vector stores, code interpreter, hosted MCP — work only with OpenAI Responses models, and traces go to OpenAI's dashboard by default.

The Responses API keeps application state for 30 days by default, per OpenAI's data controls. You can turn tracing off, add your own trace processors, and reach 100+ other models through beta LiteLLM and any-llm adapters.

ibl.ai is model-agnostic from the start, has no per-seat pricing, and deploys anywhere: your cloud, a VPC, on-premise or air-gapped. The SDK leads on handoffs and realtime voice with OpenAI models. ibl.ai is family-owned and operated from New York, NY.

## How do ibl.ai and the OpenAI Agents SDK compare feature by feature?

### Ownership & Data Path

| Criteria | ibl.ai | OpenAI Agents SDK |
|----------|--------------------|--------------------|
| Source code you receive | The platform source under license, running on your infrastructure. | The SDK is MIT (Python and JavaScript); the Responses API and hosted tools behind it are OpenAI's service. |
| Where traces, files and state live | In your deployment: conversations, datasets, memories and eval traces. | Traces default to OpenAI's dashboard; Responses keep state 30 days by default; ZDR orgs lose tracing. |
| Model choice | Provider cards include Amazon, Anthropic, Google, Microsoft, NVIDIA, OpenAI and xAI; switch per agent. | Provider-agnostic via Chat Completions and beta LiteLLM/any-llm adapters (100+ models); hosted tools stay OpenAI-only. |
| Pricing shape | Usage-based with no per-seat pricing, against caps you set. | SDK free; you pay OpenAI per token — also usage-based, on one vendor's price list. |

### Agent Engineering

| Criteria | ibl.ai | OpenAI Agents SDK |
|----------|--------------------|--------------------|
| Agent-to-agent handoffs | Workflows route between Agent nodes; Claw deployments run an orchestrator with specialist subagents. | A first-class primitive: an agent hands the conversation to another with one declaration. |
| Tracing & debugging in development | Per-conversation transcripts with retrieved documents and tool calls; cost, token and latency analytics. | Built-in tracing of every run, viewable in OpenAI's Traces dashboard with no setup. |
| Realtime voice agents | Live voice calls over WebRTC (LiveKit) with OpenAI, Google or Anthropic as the call provider. | RealtimeAgent and VoicePipeline for low-latency speech agents. |
| Guardrails | Moderation and safety prompts, PII redact/mask/block across 13 entity types, a Guardrails workflow node. | Input and output guardrails you write as functions that can halt a run. |

### What Ships Around the Agent

| Criteria | ibl.ai | OpenAI Agents SDK |
|----------|--------------------|--------------------|
| RAG over your documents | Datasets from files, URLs, crawls, Drive, OneDrive and GitHub, retrained on a schedule, in your store. | FileSearchTool over OpenAI vector stores, or a retrieval tool you build. |
| Evals | Benchmarks up to 10,000 rows, LLM-as-Judge with any configured model, human scores, CSV export. | Not part of the SDK; teams use OpenAI's platform evals or a third-party tool. |
| Users, RBAC & interface | SSO, RBAC, SCIM, spend caps and a chat UI with web, desktop and mobile builds. | Out of scope for an SDK; you build the app, auth and permissions. |

## Where does the OpenAI Agents SDK win, and where does ibl.ai?

### Is the OpenAI Agents SDK locked to OpenAI models?

**ibl.ai:** ibl.ai treats every provider the same: OpenAI is one provider card among Anthropic, Google, Amazon and others, and an agent switches between them without code changes.

**OpenAI Agents SDK:** Not strictly. The SDK reaches 100+ models through beta adapters, but hosted tools work only with OpenAI Responses models, and OpenAI's docs suggest disabling tracing or adding processors for non-OpenAI users.

**Verdict:** Model choice is real in the SDK, but its best features assume OpenAI. If multi-provider is a requirement rather than an option, that asymmetry matters.

### Where does my data go with each one?

**ibl.ai:** On ibl.ai conversations, retrieved documents, memories and eval traces sit in the database you operate, and calls go to whichever model endpoint you configure.

**OpenAI Agents SDK:** With the default setup, Responses state is stored for 30 days, traces go to OpenAI's dashboard and files live in OpenAI vector stores. Each default can be changed.

**Verdict:** Both can be configured for privacy. The difference is which way the defaults point, and how many settings an audit has to prove you changed.

### Which one is faster to a working prototype?

**ibl.ai:** ibl.ai gives a configured agent with a chat UI, memory and datasets in an afternoon, but a custom code path takes longer than a script.

**OpenAI Agents SDK:** The SDK is a few lines of Python to a running multi-agent prototype with handoffs and traces — genuinely excellent developer ergonomics.

**Verdict:** The SDK wins the first day. ibl.ai wins the day you need users, permissions, audits and a second model provider.

## Should my team choose the OpenAI Agents SDK or ibl.ai?

### Teams standardised on OpenAI models — OpenAI Agents SDK

Handoffs, hosted tools, tracing and realtime voice all work best together on the Responses API, with very little code.

### Organizations that must run several model providers — ibl.ai

Every ibl.ai feature works with every configured provider, so switching models never removes a tool or a trace.

### Deployments under zero-data-retention or residency rules — ibl.ai

Traces, files and conversations stay in your deployment by default, rather than being opted out of a vendor's cloud one setting at a time.

### Engineering teams prototyping multi-agent patterns — Either

Prototype in the SDK, then move agents your organization will rely on to a platform with RBAC, evals and audit.

## How do I migrate from the OpenAI Agents SDK to ibl.ai?

### OpenAI Agents SDK → ibl.ai (low difficulty)

Timeline: Two to six weeks, mostly re-ingesting documents and rebuilding evaluation sets

- Agents: each Agent's instructions and model become an ibl.ai agent's system prompt and LLM setting — you can stay on OpenAI models.
- Tools: function tools become MCP servers or platform tools; hosted file search becomes an ibl.ai dataset ingested from your own copies of the documents.
- Handoffs: route between agents with Workflow Agent and If/else nodes, or run a Claw orchestrator that delegates to specialist subagents.
- Memory: SDK sessions map to ibl.ai chat sessions; durable facts become global or agent-specific memories.
- Guardrails: input and output guardrails map to the Safety tab's moderation and safety prompts, plus PII redaction.
- Eval & deployment: rebuild test sets as benchmarks with LLM-as-Judge, then deploy with infra-cli on AWS, GCP or an existing server.
- Raw Responses calls: point completion code at ibl.ai's OpenAI-compatible /v1/chat/completions endpoint, routed to any provider/model.

Docs and skills for this move:

- [Skill: iblai-api-agent-create](/developer/skills/iblai-api-agent-create)
- [Skill: iblai-api-agent-mcp](/developer/skills/iblai-api-agent-mcp)
- [Workflows](/docs/os/chat-canvas/workflows)
- [Multi-agent Claw orchestrators](/developer/guides/claw-agents)
- [Agent memory](/developer/platform/agents/memory)
- [Safety settings](/docs/os/agent-settings/safety)
- [Evaluations](/developer/platform/agents/evaluations)
- [Skill: iblai-api-inference](/developer/skills/iblai-api-inference)

### ibl.ai → OpenAI Agents SDK (medium difficulty)

Timeline: Days for the agent, longer for users, retrieval and evals

- Prompts and settings read out over the REST API; chat history and evaluation results export as files.
- Upload documents to OpenAI vector stores if you want hosted file search, or build your own retrieval tool.
- Plan where traces, users and permissions will live, since the SDK does not provide them.
- Reasonable for a single OpenAI-only agent embedded in a product your team already operates.

## FAQ

**Q: Is the OpenAI Agents SDK open source?**

Yes. The Python and JavaScript SDKs are MIT-licensed on GitHub. The Responses API, hosted tools and Traces dashboard they connect to are OpenAI's paid or hosted services.

**Q: Can the OpenAI Agents SDK use non-OpenAI models?**

Yes, through Chat Completions-compatible endpoints and beta LiteLLM and any-llm adapters. Hosted tools such as file search and web search still require OpenAI Responses models.

**Q: Can ibl.ai run OpenAI models?**

Yes. OpenAI is one of the provider cards in ibl.ai's LLM configuration, alongside Anthropic, Google, Microsoft, Amazon, NVIDIA and others, and an agent can switch providers at any time.

**Q: Does ibl.ai have agent handoffs?**

Not as a one-line SDK primitive. ibl.ai routes between agents with Workflow nodes, and Claw deployments run a parent orchestrator that delegates to specialist subagents. The SDK is simpler here.

**Q: Does ibl.ai charge per seat?**

No. ibl.ai has no per-seat pricing; you pay by usage against caps you set. The Agents SDK is also usage-based, through OpenAI's per-token API pricing.

**Q: How does ibl.ai fit alongside the OpenAI Agents SDK?**

On ibl.ai you own all the code and the data, run any model including OpenAI's, and deploy anywhere. Some teams prototype in the SDK and run production agents on the platform.


## Where do these facts come from?

1. The OpenAI Agents SDK is MIT-licensed, provider-agnostic across the Responses and Chat Completions APIs and 100+ other LLMs, and includes handoffs, guardrails, sessions, tracing and realtime voice agents. [openai/openai-agents-python on GitHub](https://github.com/openai/openai-agents-python)
2. Hosted tools — web search, file search over OpenAI vector stores, code interpreter, hosted MCP — are available only with OpenAI Responses models. [OpenAI Agents SDK docs: Tools](https://openai.github.io/openai-agents-python/tools/)
3. Traces go to OpenAI's Traces dashboard by default, tracing is unavailable to Zero Data Retention organizations, and custom trace processors can replace the exporter. [OpenAI Agents SDK docs: Tracing](https://openai.github.io/openai-agents-python/tracing/)
4. Non-OpenAI models are reached through beta LiteLLM and any-llm adapters, with documented limits on hosted tools and tracing. [OpenAI Agents SDK docs: Models](https://openai.github.io/openai-agents-python/models/)
5. The Responses API has a 30-day application state retention period by default, or when store is true. [OpenAI API docs: Data controls](https://developers.openai.com/api/docs/guides/your-data)


## Where does ibl.ai fit alongside OpenAI Agents SDK?

**ibl.ai is the agentic AI platform where you own all the code and the data. You self-host the entire stack inside your own perimeter, run it model-agnostic across any LLM and switch anytime, and pay by usage with no per-seat pricing — so you can deploy anywhere: your cloud, on-premise, GovCloud, or fully air-gapped.**

ibl.ai keeps what an OpenAI-first stack sends out — traces, files, conversation state — inside your deployment, and makes every provider a first-class citizen rather than an adapter.

Agentic OS is the platform, with an OpenAI-compatible completions endpoint for code that already speaks that API. Agentic Vibe gives coding agents 157 skills to build on it. You own all the code and the data and can keep using OpenAI models wherever they are the best fit.

- **You own all the code and the data.** Full source code under a perpetual license, running on your infrastructure. Not API access to someone else's platform — the stack itself is yours.
- **Model-agnostic.** Run any LLM — Claude, GPT, Gemini, Llama, Command, or your own fine-tune — and switch providers without rewriting the platform.
- **No per-seat pricing.** Usage-based billing against a budget cap you set. Cost tracks what your organization actually uses, not how many people you employ.
- **Deploy anywhere.** Your cloud, your VPC, on-premise, GovCloud, or a fully air-gapped network with no outbound connectivity.

1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.

ibl.ai is family-owned and operated from New York, NY — a U.S.-headquartered, domestically-owned long-term partner, not a vendor that sells licenses and moves on.