# OpenAI Agents platform vs SOAT: Feature Comparison

> OpenAI Agents platform and SOAT compared on 15 agent capabilities, 8 vs 12 native, each rating linked to its source.

# SOAT vs OpenAI Agents platform

OpenAI Agents platform is a managed agent platform (MIT (Agents SDK); platform services are proprietary, managed). SOAT is agent infrastructure (Apache-2.0, self-hosted). Of the 15 capabilities the [benchmark](/benchmark) tracks, OpenAI Agents platform covers 8 natively and SOAT 12.

## At a glance

| | OpenAI Agents platform | SOAT |
| --- | --- | --- |
| Type | a managed agent platform | agent infrastructure |
| License | MIT (Agents SDK); platform services are proprietary | Apache-2.0 |
| Deployment | managed | self-hosted |
| Native capabilities | 8 of 15 | 12 of 15 |
| Last verified | 2026-08-14 | 2026-08-14 |

## Where each one leads

- **OpenAI Agents platform** is native where SOAT is not: code execution.
- **SOAT** is native where OpenAI Agents platform is not: identity & governance, data & secrets, evaluation, declarative deployment, versioning & rollout.

## What OpenAI Agents platform is

OpenAI's agent stack: the MIT-licensed Agents SDK (agent loop, sessions, handoffs, guardrails, tracing) plus the Responses API, OpenAI-hosted tools (web search, file search, code interpreter, hosted MCP), and a managed Traces dashboard. Execution and hosted tools depend on OpenAI's API; there is no self-hosted control plane. [OpenAI Agents platform documentation](https://developers.openai.com/).

## Capability by capability

### Agent runtime

_Agent loop, sessions, and generation lifecycle._

- **OpenAI Agents platform: Native.** The SDK ships a built-in agent loop (Runner) plus Sessions as a persistent working-context memory layer across turns. ([source](https://openai.github.io/openai-agents-python/))
- **SOAT: Native.** Agents, sessions, chats, and generations with full lifecycle tracking. ([source](https://soat.ttoss.dev/docs/modules/agents))

### Orchestration

_Multi-step pipelines, workflows, and triggers._

- **OpenAI Agents platform: Native.** Handoffs and agents-as-tools provide multi-agent delegation; Agent Builder adds a visual multi-step workflow canvas. ([source](https://developers.openai.com/api/docs/guides/agent-builder))
- **SOAT: Native.** DAG orchestrations, workflow state machines, durable tasks, and triggers (manual, webhook, schedule). ([source](https://soat.ttoss.dev/docs/modules/orchestrations))

### Knowledge & memory

_Documents, embeddings, semantic search, and recall._

- **OpenAI Agents platform: Native.** FileSearchTool retrieves from OpenAI-hosted vector stores with managed chunking, embeddings, and hybrid semantic plus keyword search. ([source](https://developers.openai.com/api/docs/guides/tools-file-search))
- **SOAT: Native.** Documents with per-chunk embeddings, named memories, unified semantic search, and ingestion rules for PDFs, images, and audio. ([source](https://soat.ttoss.dev/docs/modules/knowledge))

### Tools & integration

_Tool calling, MCP, providers, and webhooks._

- **OpenAI Agents platform: Native.** Function tools with automatic schema generation, hosted tools, and HostedMCPTool for remote MCP servers are built into the SDK. ([source](https://openai.github.io/openai-agents-python/tools/))
- **SOAT: Native.** HTTP, MCP, client, and agent-to-agent tools; model routes with failover; webhooks; any LLM provider. ([source](https://soat.ttoss.dev/docs/modules/tools))

### Identity & governance

_IAM, guardrails, approvals, quotas, and audit._

- **OpenAI Agents platform: Partial.** Input, output, and tool guardrails with tripwires plus human-in-the-loop exist, but there is no tenant IAM, per-project authorization, quota policy, or audit log. ([source](https://openai.github.io/openai-agents-python/guardrails/))
- **SOAT: Native.** AWS-inspired IAM, actors, scoped API keys, guardrails, human approvals, quotas, and an append-only audit log. ([source](https://soat.ttoss.dev/docs/modules/iam))

### Observability

_Traces, activity, usage, and failure queues._

- **OpenAI Agents platform: Native.** Tracing is on by default and records model generations, tool calls, handoffs, and guardrail evaluations to the OpenAI Traces dashboard. ([source](https://openai.github.io/openai-agents-python/tracing/))
- **SOAT: Native.** Traces of every tool call and model response, activity feed, usage/cost tracking, and an exceptions queue. ([source](https://soat.ttoss.dev/docs/modules/traces))

### Data & secrets

_File storage and encrypted secrets._

- **OpenAI Agents platform: Partial.** The Files API and hosted vector stores provide managed file storage, but there is no encrypted secrets manager for agent credentials. ([source](https://developers.openai.com/api/docs/guides/tools-file-search))
- **SOAT: Native.** File storage over local/S3/GCS backends and encrypted secrets storage. ([source](https://soat.ttoss.dev/docs/modules/files))

### Evaluation

_Datasets, scorers, and eval-gated improvement._

- **OpenAI Agents platform: Partial.** The Evals API supports JSONL datasets, graders, and eval runs, but it is scheduled to become read-only and then shut down. ([source](https://developers.openai.com/api/docs/guides/evals))
- **SOAT: Native.** Datasets, scorers, and scored runs; eval-gated promotion of agent versions. ([source](https://soat.ttoss.dev/docs/modules/evaluations))

### Channels

_Built-in gateways to messaging platforms and UIs._

- **OpenAI Agents platform: Partial.** ChatKit provides an embeddable web chat UI, but no gateways to Slack, WhatsApp, Telegram, or Discord are documented. ([source](https://developers.openai.com/api/docs/guides/chatkit))
- **SOAT: Absent.** Surfaces are REST, MCP, CLI, and SDK — no built-in gateways to messaging platforms like Slack, WhatsApp, or Telegram.

### Code execution

_Sandboxed code or terminal runtimes for agents._

- **OpenAI Agents platform: Native.** CodeInterpreterTool is a hosted tool that executes model-written code in an OpenAI-managed sandbox. ([source](https://openai.github.io/openai-agents-python/tools/))
- **SOAT: Absent.** Tool types are HTTP, MCP, client, and agent-to-agent — no built-in sandboxed code or terminal runtime.

### Human-in-the-loop

_Pausing a run for human approval, then resuming it._

- **OpenAI Agents platform: Native.** Tools marked needs_approval pause the run and surface approval items in RunResult.interruptions, resolved with approve/reject and resumed from serialized RunState. ([source](https://openai.github.io/openai-agents-python/human_in_the_loop/))
- **SOAT: Native.** Approvals are a first-class human-decision queue with frozen evidence, and guardrails classify each tool call so risky actions pause for a decision. ([source](https://soat.ttoss.dev/docs/modules/approvals))

### Multi-tenancy

_Projects or workspaces scoping resources and permissions._

- **OpenAI Agents platform: Native.** Organizations contain projects that scope keys, files, and resources, with roles assignable at organization or project level. ([source](https://developers.openai.com/api/docs/guides/rbac))
- **SOAT: Native.** Projects are multi-tenant namespaces scoping every resource, with IAM policies and quotas enforced per project. ([source](https://soat.ttoss.dev/docs/modules/projects))

### Declarative deployment

_Templates that provision agent resources as one stack._

- **OpenAI Agents platform: Absent.** No template or infrastructure-as-code format provisions agent resources; agents are created imperatively through the API or SDK.
- **SOAT: Native.** Formations provision providers, memories, tools, agents, orchestrations, and webhooks from one template, resolving refs into a dependency graph with tracked operations. ([source](https://soat.ttoss.dev/docs/modules/formations))

### Versioning & rollout

_Agent versions, canary rollout, gated promotion._

- **OpenAI Agents platform: Partial.** Agent Builder publishes a workflow as a versioned snapshot that can be pinned by ID, but there is no canary rollout or eval-gated promotion. ([source](https://developers.openai.com/api/docs/guides/agent-builder))
- **SOAT: Native.** Every config change is archived as an append-only agent version, with canary rollout to a slice of traffic and eval-gated promotion. ([source](https://soat.ttoss.dev/docs/tutorials/agent-versioning-and-canary-rollout))

### Skill learning

_Agents authoring reusable skills from their own experience._

- **OpenAI Agents platform: Absent.** Sessions persist conversation state, but agents do not author or refine reusable skills from their own experience.
- **SOAT: Absent.** Memories and knowledge store retrievable context, but agents do not author or refine reusable skills from their own experience.

## How this is rated

Each rating comes from the public documentation linked beside it: **Native** is built in, **Via plugin** needs an add-on, **Partial** covers part of the capability, **Absent** is not offered. Every solution side by side is on the [benchmark](/benchmark). A rating that is out of date is a bug: [open an issue](https://github.com/ttoss/soat/issues).