Pyrana vs OpenAI Agents SDK

Architecture

The OpenAI Agents SDK is a library that runs inside your application. It gives you agents (a model with instructions and tools), handoffs between agents, guardrails that validate inputs and outputs, and a built-in run loop. Pyrana is the application itself: a platform where identity, authorization, storage, orchestration, approvals, and audit already exist, and each application is configuration on top.

OpenAI's own guide now frames three ways to build. The Agents SDK runs in your application and, in OpenAI's words, gives it control over deployment, storage, approvals, and runtime integration. The Responses API is for working directly with model responses. The Agents API runs a managed Codex harness, where OpenAI manages the agent and saves its progress. ChatKit adds an embedded chat experience.

What happened to AgentKit and Agent Builder?

OpenAI introduced AgentKit at DevDay on October 6, 2025: Agent Builder (a visual canvas), ChatKit, the Connector Registry, and new Evals capabilities. On June 3, 2026, OpenAI notified developers that Agent Builder and the Evals platform are deprecated. Both shut down on November 30, 2026. ChatKit remains available. OpenAI's recommended destinations for Agent Builder workflows are the Agents SDK or ChatGPT Workspace Agents.

The October 2025 view on this site was that a visual agent builder produces agents, not applications. The shift to a code-first SDK keeps that question open: an agent still needs an application around it.

Context and knowledge

The Agents SDK connects agents to knowledge through tools: MCP servers and functions you write. Sessions keep conversation history. What counts as company knowledge, how it is curated, and how a run proves which facts it used are design decisions left to your team. Pyrana ships cortIQ, a context engine with typed, classified, evidence-backed knowledge.

Each Context Unit carries a claim, its evidence passage with source and location, a classification that governs how it may change, and a content-hash identity. Every run counts what was retrieved, injected, and cited, so the exact business context behind an output is provable.

Agent capabilities

The SDK is capable and moving quickly. Beyond agents, handoffs, and guardrails, it includes sessions, MCP tools, realtime voice agents, and sandbox agents: specialists that work in an isolated workspace (Unix-local, Docker, or hosted providers), edit files, run commands, and resume from saved state. It supports non-OpenAI models through LiteLLM and Any-LLM adapters.

Pyrana's Harness provides native tool calling, planning, subagents, checkpoints, and resume, plus a code interpreter with deterministic scripts for data shaping. Workflows fan out to specialized agents per entity and group in parallel, and every output publishes back to the App as a versioned resource. Tools are job-specific: the agent passes the entity and period, and the tool holds the query.

Governed actions and approvals

The SDK pauses a run when a tool marked needs_approval is called, and resumes when your code approves or rejects it. Pyrana decides with a policy, freezes the exact action, routes it to a named lane, and checks reviewer eligibility at decision time. The SDK supplies the pause. Routing, eligibility, durable storage of the pending state, and exactly-once execution are yours.

Does the OpenAI Agents SDK have human approval?

Yes. needs_approval can be True or a function that decides per call. A paused run returns interruptions; you call state.approve(...) or state.reject(...) and resume. For approvals that wait hours or days, the SDK serializes RunState to JSON and asks you to store it in a database or queue. Its docs are direct about the boundary: the loaders do not authenticate the snapshot or the person submitting it, so your application must authorize the reviewer and prevent duplicate or replayed resumes.

In Pyrana, those controls are the platform: frozen invocation under a hash, named lanes, eligibility checked at decision time, revalidation before dispatch, idempotent dispatch that reuses the receipt on retry.

Building the application

With the SDK, you build the application around the agent: frontend, API, users and roles, storage for outputs, an approval inbox, and the release process. Pyrana provides the App shell, declared pages that hand exact page context to the agent, a copilot, personal pages, and versioned resources. With clean data available, an application typically takes about a week to stand up.

Migrating from Agent Builder

OpenAI's migration guide exports a workflow as Agents SDK code in Python or TypeScript. The guide states the export does not convert the workflow graph or guarantee every behavior transfers unchanged, and that on the SDK path deployment, auth, and permissions are on you. The ChatGPT Workspace Agent path needs a Business, Enterprise, or Edu workspace, and the guide notes strongly deterministic workflows may not migrate faithfully. Teams moving off Agent Builder are choosing an application layer either way. For an approval-heavy business process, Pyrana is one option; its workflows are declarative and its approvals are policies.

Governance and security

The SDK leaves identity and authorization to the host application. Tracing is on by default and exports to OpenAI's backend; it can be disabled or redirected to other processors, and it is unavailable to organizations under Zero Data Retention. Pyrana enforces one fail-closed gate on every call, with an audit record written before dispatch.

What is OpenAI Frontier?

OpenAI announced Frontier in February 2026 as an enterprise platform to build, deploy, and manage agents, reportedly with per-agent identity, permissions, shared business context, and evaluation, and it reportedly launched to select customers first. Frontier is aimed at managing a fleet of agents across vendors. Pyrana's view is that this is a control plane over agents, while Pyrana is a platform for the applications those agents work inside. Pyrana is SOC 2 aligned and runs in the client's own cloud subscription or a dedicated private tenant.

Operations

The SDK's built-in tracing supports visualization, debugging, and monitoring, with external processors for Weights & Biases, Arize Phoenix, Langfuse, LangSmith, Datadog, and others. Durable orchestration comes from your own infrastructure or Temporal's integration. Pyrana captures traces, tokens, and tool errors per run as JSON and runs workflows on Temporal workers by default.

Can the OpenAI Agents SDK run durably on Temporal?

Yes. Temporal's integration became generally available on March 23, 2026. Every agent invocation runs as a Temporal Activity, so Temporal retries failed calls and a restarted application resumes where it left off. Pyrana's Harness runs on Temporal too: a run waiting on an approval survives a restart or deploy and resumes.

Cost structure

The Agents SDK is free under the MIT license. You pay OpenAI for model tokens and hosted tools at API rates, plus your own infrastructure and the engineering time to build the application layer. Pyrana is enterprise platform pricing, with identity, authorization, approvals, storage, context, and audit included, and models billed to the client's own keys.

What it takes to ship the same application

Reference application: an approval-gated procurement request. A person or agent drafts a purchase request; amounts above a threshold need a finance approver; the request waits durably for the decision; everything is recorded.

Using them together

Pyrana uses OpenAI and Azure OpenAI models through its provider adapters, assigned per agent with the client's own keys. An agent built with the Agents SDK can work with a Pyrana App through the App MCP server, as the user, with every call passing the same gate. In the other direction, an SDK agent exposed as an MCP server or REST endpoint can be a Pyrana tool source.

Teams that already run Temporal for Agents SDK workloads will recognize the durability model under Pyrana's Harness. Existing SDK code with custom business logic can live in a client-owned extension, reachable only through the platform.

When not to choose Pyrana

• You are building an agent into your own software product, which already owns users, permissions, and UI.
• You want the thinnest possible layer over OpenAI models and plan to build the rest yourself.
• You need a ChatGPT-native experience for employees, where Workspace Agents fit better.
• The work is a prototype where approvals, audit, and access control do not yet matter.