OpenAI Agents SDK vs AutoGen
OpenAI Agents SDK
OpenAI
OpenAI Agents SDK wins · 20–6 (19 drawn)
Agenticness — how well agents can access and operate the productAgenticness
How well agents can access and operate the product
Agent access
ai-native userPoint an agent at llms.txt or agent-oriented docs
weight 2 · round to OpenAI Agents SDKOpenAI's developer platform exposes a live llms.txt (HTTP 200) and markdown-formatted docs pages explicitly designed for agent consumption, which an agent built with the Agents SDK could be pointed at directly. missing for 10: no first-party doc or example showing the Agents SDK itself fetching/parsing llms.txt as a built-in feature, and no independent hands-on report confirming an agent successfully using it end-to-end.
- [probe] “PROBE llms.txt: HTTP 200 at https://developers.openai.com/llms.txt # OpenAI Developers > Complete documentation hub for OpenAI API, Ads, Pl…”
- [probe] “PROBE docs-md: HTTP 200 at https://developers.openai.com/api/docs/guides/agents.md # Agents > For the complete documentation index, see [ll…”
- [claimed-docs] “The WebSearchTool lets an agent search the web.”
- [claimed-docs] “The Agents Python SDK understands multiple MCP transports. This lets you reuse existing MCP servers or build your own to expose filesystem, …”
AutoGennone0/10Probes explicitly show no llms.txt (404) and no markdown-formatted docs endpoint (404) or OpenAPI spec, so there is no evidence AutoGen exposes agent-oriented docs formats; all other evidence is standard human-readable documentation.
- [probe] “PROBE llms.txt: HTTP 404 at https://microsoft.github.io/llms.txt”
- [probe] “PROBE docs-md: HTTP 404 at https://microsoft.github.io/autogen/stable/user-guide/agentchat-user-guide/index.html.md”
- [probe] “PROBE openapi: all candidate paths 404 (https://microsoft.github.io/openapi.json, https://microsoft.github.io/swagger.json, https://microsof…”
ai-native userRun the product headlessly / in CI for automation
weight 2 · round to OpenAI Agents SDKThe SDK is a pure Python/JS library invoked programmatically via Runner.run(), run_sync(), or run_streamed() with no UI dependency, making it inherently usable in scripts, servers, or CI pipelines; tracing/session features work headlessly too. Missing for 10: explicit CI/automation setup docs, example GitHub Actions/pipeline configs, or independent reports of production CI usage.
- [claimed-docs] “You can run agents via the Runner class. You have 3 options: Runner.run(), Runner.run_sync(), Runner.run_streamed()”
- [claimed-docs] “You have 3 options: Runner.run(), Runner.run_sync(), Runner.run_streamed()”
- [claimed-docs] “You can run agents via the `Runner` class. You have 3 options: `Runner.run()`... `Runner.run_sync()`... `Runner.run_streamed()`”
- [claimed-docs] “The Agents SDK includes built-in tracing, collecting a comprehensive record of events during an agent run: LLM generations, tool calls, hand…”
- [claimed-docs] “The Agents SDK provides built-in session memory to automatically maintain conversation history across multiple agent runs, eliminating the n…”
AutoGen ships as a pip-installable Python library (autogen-agentchat) with agents/teams fully scriptable and exportable to plain Python code, which implies it can run headlessly in CI pipelines without any GUI dependency; AutoGen Studio's 'export and run teams in python code' and Docker execution further support automatable, non-interactive runs. However, there is no explicit CI/CD documentation, GitHub Actions example, or headless-mode flag demonstrated in the evidence pack. missing for 10: explicit CI/automation docs or examples, confirmation of non-interactive/headless flags, independent report of someone running it in a CI pipeline.
- [github] “pip install -U "autogen-agentchat" "autogen-ext[openai]"”
- [claimed-docs] “pip install -U "autogen-agentchat"”
- [claimed-docs] “Export and run teams in python code”
- [claimed-docs] “Export and run teams in python code * Setup and test endpoints based on a team configuration * Run teams in a docker container”
- [claimed-docs] “Serialize Components: Serialize and deserialize components”
ai-native userPlug MCP servers into this product so it can use their tools
weight 3 · round to OpenAI Agents SDKDocs explicitly state the Agents SDK understands multiple MCP transports, letting users reuse existing MCP servers or build their own to expose filesystem, HTTP, or connector-backed tools to an agent, and also supports hosted MCP as a managed tool option. This directly matches the story of plugging in MCP servers to use their tools. Missing for 10: independent/hands-on corroboration beyond first-party docs.
- [claimed-docs] “The Agents Python SDK understands multiple MCP transports. This lets you reuse existing MCP servers or build your own to expose filesystem, …”
- [claimed-docs] “The Agents Python SDK understands multiple MCP transports. This lets you reuse existing MCP servers or build your own”
- [claimed-docs] “Use OpenAI-managed tools (web search, file search, code interpreter, hosted MCP, image generation)”
AutoGen's GitHub docs explicitly show creating an agent that uses the Playwright MCP server, confirming MCP server tool integration is supported, but the evidence pack lacks first-party documentation detailing a general-purpose MCP client/adapter API, configuration options, or broader ecosystem support beyond this single example. Missing for 10: dedicated MCP integration documentation, examples with multiple/varied MCP servers, independent hands-on corroboration of MCP tool usage reliability.
- [github] “Create a web browsing assistant agent that uses the Playwright MCP server.”
ai-native userUse an official CLI
weight 2 · round drawnOpenAI Agents SDKnone0/10The evidence pack is entirely about the Agents SDK library (Python/JS) — agent primitives, tools, tracing, sessions, MCP, streaming — with no mention of an official CLI tool for scaffolding, running, or managing agents; OpenAPI/CLI probes returned 404s.
- [probe] “PROBE openapi: all candidate paths 404 (https://developers.openai.com/openapi.json, https://developers.openai.com/swagger.json, https://deve…”
ai-native userDrive the product through a documented public API
weight 3 · round to OpenAI Agents SDKThe SDK is extensively documented as a Python/JS library with a public, well-documented API surface (Agents, Runner, Tools, Handoffs, Guardrails, Sessions, Tracing, MCP) that AI-native developers can drive programmatically, corroborated by GitHub docs and community usage reports. Missing for 10: a formal OpenAPI/machine-readable spec (probe found openapi.json 404) and deeper independent third-party validation of full API coverage.
- [claimed-docs] “You can run agents via the Runner class. You have 3 options: Runner.run(), Runner.run_sync(), Runner.run_streamed()”
- [claimed-docs] “An agent is a large language model (LLM) configured with instructions, tools, and optional runtime behavior such as handoffs, guardrails, an…”
- [claimed-docs] “FunctionTool instances: wrap any Python function as a tool.”
- [claimed-docs] “Handoffs allow an agent to delegate tasks to another agent. This is particularly useful in scenarios where different agents specialize in di…”
- [claimed-docs] “The Agents SDK includes built-in tracing, collecting a comprehensive record of events during an agent run: LLM generations, tool calls, hand…”
- [claimed-docs] “The Agents SDK provides built-in session memory to automatically maintain conversation history across multiple agent runs, eliminating the n…”
- [github] “It is provider-agnostic, supporting the OpenAI Responses and Chat Completions APIs, as well as 100+ other LLMs.”
- [community] “OpenAI SDK is much simpler to use and understand than langchain etc. I havent deployed really complicated agents, but I have a number of sim…”
- [probe] “PROBE openapi: all candidate paths 404 (https://developers.openai.com/openapi.json, https://developers.openai.com/swagger.json, https://deve…”
AutoGen is a Python library/framework with an extensively documented public API (AssistantAgent, GroupChat variants, tool integration, memory, serialization) that AI-native users can directly script against via pip-installed packages. missing for 10: no formal OpenAPI/REST spec (probe shows 404s), no independent third-party corroboration of API stability beyond community sentiment.
- [claimed-docs] “AssistantAgent is a built-in agent that uses a language model and has the ability to use tools.”
- [claimed-docs] “RoundRobinGroupChat is a simple yet effective team configuration where all agents share the same context and take turns responding in a roun…”
- [claimed-docs] “The UserProxyAgent is a special built-in agent that acts as a proxy for a user to provide feedback to the team.”
- [claimed-docs] “Create your own agents with custom behaviors”
- [claimed-docs] “Multi-agent coordination through a shared context and centralized, customizable selector”
- [claimed-docs] “SelectorGroupChat: A team that selects the next speaker using a ChatCompletion model after each message.”
- [claimed-docs] “Swarm: A team that uses HandoffMessage to signal transitions between agents.”
- [claimed-docs] “GraphFlow: Multi-agent workflows through a directed graph of agents.”
- [github] “pip install -U "autogen-agentchat" "autogen-ext[openai]"”
- [github] “You can use `AgentTool` to create a basic multi-agent orchestration setup.”
- [probe] “PROBE openapi: all candidate paths 404 (https://microsoft.github.io/openapi.json, https://microsoft.github.io/swagger.json, https://microsof…”
ai-native userIssue scoped/least-privilege API credentials for an agent
weight 2 · round drawnOpenAI Agents SDKnone0/10No evidence of scoped or least-privilege API credential issuance for agents; the docs cover tools, handoffs, guardrails, tracing, sessions, MCP, and sandboxing but nothing about credential scoping or per-agent API key permissions. This is a plausible axis for an agent SDK (credential/permission management is a reasonable capability), but no evidence supports it. missing for 10: any mention of scoped/least-privilege credential issuance, API key scoping, or permission boundaries for agents.
ai-native userBuild against official SDKs
weight 2 · round to OpenAI Agents SDKOpenAI Agents SDK is itself an official first-party SDK (Python and JS) with extensive documentation covering core primitives (agents, tools, handoffs, guardrails, tracing, sessions, streaming, MCP support, voice/realtime), and is corroborated by GitHub repo docs and community usage reports confirming real-world adoption. Missing for 10: deeper independent third-party production case studies beyond a couple of HN threads.
- [claimed-docs] “An agent is a large language model (LLM) configured with instructions, tools, and optional runtime behavior such as handoffs, guardrails, an…”
- [claimed-docs] “You can run agents via the Runner class. You have 3 options: Runner.run(), Runner.run_sync(), Runner.run_streamed()”
- [claimed-docs] “The Agents SDK includes built-in tracing, collecting a comprehensive record of events during an agent run: LLM generations, tool calls, hand…”
- [claimed-docs] “The Agents Python SDK understands multiple MCP transports. This lets you reuse existing MCP servers or build your own to expose filesystem, …”
- [github] “It is provider-agnostic, supporting the OpenAI Responses and Chat Completions APIs, as well as 100+ other LLMs.”
- [community] “OpenAI SDK is much simpler to use and understand than langchain etc. I havent deployed really complicated agents, but I have a number of sim…”
AutoGen ships official Python SDKs (autogen-agentchat, autogen-ext) with pip install instructions, documented core classes (AssistantAgent, UserProxyAgent, teams, tools), and GitHub-hosted source, giving AI-native developers a genuine first-party SDK to build against. Missing for 10: no evidence of official SDKs in other languages, no machine-readable API reference (openapi probes 404), and no independent third-party validation of SDK stability/versioning beyond community sentiment.
- [github] “pip install -U "autogen-agentchat" "autogen-ext[openai]"”
- [claimed-docs] “pip install -U "autogen-agentchat"”
- [claimed-docs] “AssistantAgent is a built-in agent that uses a language model and has the ability to use tools.”
- [claimed-docs] “The UserProxyAgent is a special built-in agent that acts as a proxy for a user to provide feedback to the team.”
- [claimed-docs] “Create your own agents with custom behaviors”
- [claimed-docs] “How to migrate from AutoGen 0.2.x to 0.4.x.”
- [probe] “PROBE openapi: all candidate paths 404 (https://microsoft.github.io/openapi.json, https://microsoft.github.io/swagger.json, https://microsof…”
ai-native userSubscribe to events via webhooks
weight 2 · round drawnOpenAI Agents SDKnone0/10The evidence shows in-process streaming/tracing for observing agent run events, but nothing about registering webhook URLs or subscribing to events via HTTP callbacks. No mention of webhook endpoints, event subscriptions, or push notifications anywhere in the docs or GitHub pack.
Agentic features
ai-native userSet up automations that run autonomously in the background
weight 2 · round to AutoGenOpenAI Agents SDKnone0/10The docs describe how to run agents interactively (Runner.run/run_sync/run_streamed), track sessions, and trace runs, but there is no evidence of scheduling, triggers, or a hosting/orchestration layer that lets a user set up an automation to run autonomously in the background without manual invocation. missing for 10: scheduling/cron or event-trigger support, a deployment/hosting mechanism for unattended background execution, and any documentation of persistent autonomous operation outside a developer-invoked run.
- [claimed-docs] “You can run agents via the Runner class. You have 3 options: Runner.run(), Runner.run_sync(), Runner.run_streamed()”
- [claimed-docs] “The Agents SDK provides built-in session memory to automatically maintain conversation history across multiple agent runs, eliminating the n…”
- [claimed-docs] “The Agents SDK includes built-in tracing, collecting a comprehensive record of events during an agent run: LLM generations, tool calls, hand…”
AutoGen supports building agent teams (RoundRobinGroupChat, SelectorGroupChat, Swarm, GraphFlow) that execute autonomously without human intervention unless a UserProxyAgent is added, and AutoGen Studio lets you export teams to run as Docker containers or set up endpoints, which enables non-interactive/background execution. However, there is no explicit documentation of scheduling, triggers, persistent background daemons, or always-on automation management — the framework is oriented toward orchestrated agent conversations/workflows rather than dedicated 'set-and-forget' background automation tooling. Missing for 10: scheduling/trigger mechanisms, persistent background service management, and independent evidence of long-running unattended automations in production.
- [claimed-docs] “AssistantAgent is a built-in agent that uses a language model and has the ability to use tools.”
- [claimed-docs] “The UserProxyAgent is a special built-in agent that acts as a proxy for a user to provide feedback to the team.”
- [claimed-docs] “Multi-agent coordination through a shared context and centralized, customizable selector”
- [claimed-docs] “SelectorGroupChat: A team that selects the next speaker using a ChatCompletion model after each message.”
- [claimed-docs] “Swarm: A team that uses HandoffMessage to signal transitions between agents.”
- [claimed-docs] “GraphFlow: Multi-agent workflows through a directed graph of agents.”
- [claimed-docs] “Export and run teams in python code * Setup and test endpoints based on a team configuration * Run teams in a docker container”
- [community] “i noticed autogen creates a new docker container each time code is executed by agents (default behaviour, can be turned off), so it's safe a…”
ai-native userDelegate tasks to a built-in AI assistant inside the product
weight 3 · round drawnThe SDK's core primitives (Agent, Runner, Handoffs, Agents-as-tools) let a developer delegate tasks to an LLM-backed agent and even chain delegation between specialized agents, which is the closest analog to 'delegating tasks to a built-in assistant.' However, this is a developer framework rather than an end-user product with a ready-made assistant UI — the 'assistant' must be built and wired up by the developer, not delegated to out-of-the-box by an end user. Missing for 10: evidence of a ready-to-use, no-code assistant interface for non-developer end users, and independent hands-on confirmation of smooth task delegation in production.
- [claimed-docs] “Handoffs allow an agent to delegate tasks to another agent. This is particularly useful in scenarios where different agents specialize in di…”
- [claimed-docs] “You can run agents via the Runner class. You have 3 options: Runner.run(), Runner.run_sync(), Runner.run_streamed()”
- [claimed-docs] “Agents as tools: expose an agent as a callable tool without a full handoff.”
- [claimed-docs] “An agent is a large language model (LLM) configured with instructions, tools, and optional runtime behavior such as handoffs, guardrails, an…”
AutoGen ships a built-in AssistantAgent (LLM + tool use) and a UserProxyAgent that lets a human delegate/oversee tasks, plus AutoGen Studio gives an interactive UI to run agent teams without writing full code. However, this is fundamentally a developer framework requiring setup/config rather than a ready-made assistant embedded in an end-user product experience. Missing for 10: evidence of a zero-setup, end-user-facing 'assistant' experience (vs. code/Studio configuration), and independent hands-on confirmation of ease of delegation.
- [claimed-docs] “AssistantAgent is a built-in agent that uses a language model and has the ability to use tools.”
- [claimed-docs] “The UserProxyAgent is a special built-in agent that acts as a proxy for a user to provide feedback to the team.”
- [claimed-docs] “Interactive environment for testing and running agent teams”
- [claimed-docs] “A visual interface for creating agent teams through declarative specification (JSON) or drag-and-drop”
ai-native userOperate the product with natural-language commands
weight 2 · round to OpenAI Agents SDKThe SDK's core primitive is an LLM agent configured with natural-language instructions that processes natural-language input via Runner.run(), with tools, handoffs, and streaming built around this NL-driven interaction model. This is the central, well-documented capability of the product across many docs pages. missing for 10: no evidence of a dedicated end-user-facing NL interface (e.g., CLI/chat UI) shipped with the SDK—interaction is at the API/code level rather than an out-of-the-box NL command surface, and no independent hands-on account of NL command usage.
- [claimed-docs] “An agent is a large language model (LLM) configured with instructions, tools, and optional runtime behavior such as handoffs, guardrails, an…”
- [claimed-docs] “You can run agents via the Runner class. You have 3 options: Runner.run(), Runner.run_sync(), Runner.run_streamed()”
- [claimed-docs] “You can run agents via the `Runner` class. You have 3 options: `Runner.run()`... `Runner.run_sync()`... `Runner.run_streamed()`”
- [claimed-docs] “Agents, which are LLMs equipped with instructions and tools”
- [claimed-docs] “Streaming lets you subscribe to updates of the agent run as it proceeds.”
AutoGen's agents (AssistantAgent, UserProxyAgent) communicate and are steered via natural-language messages, and UserProxyAgent explicitly lets a human give feedback in natural language during human-in-the-loop workflows; AutoGen Studio also provides an interactive environment for running/testing teams. However, this evidence centers on agent-to-agent conversation and a GUI/declarative builder rather than a dedicated natural-language 'command' interface for the whole product, and there's no first-party doc or hands-on example showing a user simply typing commands to control the system end-to-end. Missing for 10: explicit documentation of a natural-language command/control layer for the overall product (vs. per-agent chat), and independent hands-on confirmation that NL commands reliably drive product behavior.
- [claimed-docs] “AssistantAgent is a built-in agent that uses a language model and has the ability to use tools.”
- [claimed-docs] “The UserProxyAgent is a special built-in agent that acts as a proxy for a user to provide feedback to the team.”
- [claimed-docs] “Interactive environment for testing and running agent teams”
- [claimed-docs] “A visual interface for creating agent teams through declarative specification (JSON) or drag-and-drop”
Api quality
ai-native userExplore an interactive API reference with runnable examples
weight 2 · round drawnOpenAI Agents SDKnone0/10The evidence pack shows only static documentation pages describing SDK concepts (agents, tools, handoffs, tracing) with no interactive API reference, no runnable code sandbox, and the OpenAPI/swagger probe explicitly returned 404 on all candidate paths, indicating no machine-readable interactive reference exists.
- [probe] “PROBE openapi: all candidate paths 404 (https://developers.openai.com/openapi.json, https://developers.openai.com/swagger.json, https://deve…”
- [probe] “PROBE llms.txt: HTTP 200 at https://developers.openai.com/llms.txt # OpenAI Developers > Complete documentation hub for OpenAI API, Ads, Pl…”
- [probe] “PROBE docs-md: HTTP 200 at https://developers.openai.com/api/docs/guides/agents.md # Agents > For the complete documentation index, see [ll…”
AutoGennone0/10AutoGen ships conventional static docs and tutorials (autogen-docs-1..22) but no evidence of an interactive, runnable API reference (e.g., embedded live code execution, Jupyter-style sandbox tied to reference pages); probes confirm no llms.txt, no markdown-served docs, and no OpenAPI spec (autogen-probe-1,2,3), indicating the docs are not AI-native/interactive in the way the story describes.
- [probe] “PROBE llms.txt: HTTP 404 at https://microsoft.github.io/llms.txt”
- [probe] “PROBE docs-md: HTTP 404 at https://microsoft.github.io/autogen/stable/user-guide/agentchat-user-guide/index.html.md”
- [probe] “PROBE openapi: all candidate paths 404 (https://microsoft.github.io/openapi.json, https://microsoft.github.io/swagger.json, https://microsof…”
- [claimed-docs] “How to migrate from AutoGen 0.2.x to 0.4.x.”
- [claimed-docs] “Migration Guide: How to migrate from AutoGen 0.2.x to 0.4.x.”
ai-native userDownload a machine-readable API spec (OpenAPI or equivalent)
weight 2 · round drawnOpenAI Agents SDKnone0/10The probe explicitly checked common OpenAPI spec locations and all returned 404, and no other evidence shows a machine-readable API spec being published for the Agents SDK.
- [probe] “PROBE openapi: all candidate paths 404 (https://developers.openai.com/openapi.json, https://developers.openai.com/swagger.json, https://deve…”
AutoGennone0/10Probes for OpenAPI/Swagger endpoints and llms.txt all returned 404, and no documentation mentions a machine-readable API spec for AutoGen; AutoGen is a Python framework/library, not a hosted API service, but a downloadable spec is still a fair question for its SDK surface and none is provided.
- [probe] “PROBE llms.txt: HTTP 404 at https://microsoft.github.io/llms.txt”
- [probe] “PROBE docs-md: HTTP 404 at https://microsoft.github.io/autogen/stable/user-guide/agentchat-user-guide/index.html.md”
- [probe] “PROBE openapi: all candidate paths 404 (https://microsoft.github.io/openapi.json, https://microsoft.github.io/swagger.json, https://microsof…”
ai-native userTest against a sandbox environment without touching production data
weight 1 · round to OpenAI Agents SDKThe SDK documents 'Sandbox agents' that run specialists inside real isolated workspaces with manifest-defined files and sandbox-native capabilities, plus a CodeInterpreterTool that executes code in a sandboxed environment — directly supporting isolated, non-production testing. missing for 10: independent/hands-on verification that isolation prevents production data access, and explicit guidance on how to configure sandbox vs production environments.
- [claimed-docs] “Sandbox agents: Run specialists inside real isolated workspaces. Sandbox agents support manifest-defined files, sandbox client selection, an…”
- [claimed-docs] “If the agent should run inside an isolated workspace with manifest-defined files and sandbox-native capabilities, read Sandbox agent concept…”
- [claimed-docs] “OpenAI offers a few built-in tools when using the OpenAIResponsesModel... The WebSearchTool lets an agent search the web. The FileSearchTool…”
AutoGen isolates code execution in per-run Docker containers by default (autogen-comm-2), which provides a sandbox for agent-executed code and reduces risk to the host system, and AutoGen Studio offers an 'interactive environment for testing and running agent teams' (autogen-docs-7) and can 'run teams in a docker container' (autogen-docs-17). However, there is no explicit documentation of a dedicated sandbox vs production-data separation, test data isolation, or staging environment concept for AI-native testing workflows. Missing for 10: explicit sandbox/production data separation, dedicated test-environment tooling, first-party guidance on avoiding production data during agent testing.
- [community] “i noticed autogen creates a new docker container each time code is executed by agents (default behaviour, can be turned off), so it's safe a…”
- [claimed-docs] “Interactive environment for testing and running agent teams”
- [claimed-docs] “Export and run teams in python code * Setup and test endpoints based on a team configuration * Run teams in a docker container”
ai-native userRely on versioned APIs with a documented deprecation policy
weight 2 · round to AutoGenOpenAI Agents SDKnone0/10The evidence pack documents SDK features (agents, tools, tracing, handoffs) but contains no mention of API versioning scheme or a documented deprecation policy; the OpenAPI probe even returned 404s, and no versioning/deprecation docs are cited.
- [probe] “PROBE openapi: all candidate paths 404 (https://developers.openai.com/openapi.json, https://developers.openai.com/swagger.json, https://deve…”
AutoGen provides a migration guide for moving from 0.2.x to 0.4.x, showing awareness of versioning and breaking changes, but there is no documented deprecation policy, semver commitments, or API stability guarantees in the evidence. missing for 10: explicit deprecation policy statement, semantic versioning guarantees, timelines for deprecating old APIs.
- [claimed-docs] “How to migrate from AutoGen 0.2.x to 0.4.x.”
- [claimed-docs] “Migration Guide: How to migrate from AutoGen 0.2.x to 0.4.x.”
Agents tools — stories about agents tools in this arenaAgents tools
Stories about agents tools in this arena
Agent authoring
developerDefine an agent with typed custom tools in a few lines of code
weight 3 · round to OpenAI Agents SDKDocs show agents defined with instructions/tools in a few lines, with FunctionTool wrapping any Python function via automatic schema generation and Pydantic-powered validation, giving typed custom tools with minimal boilerplate; community feedback corroborates the SDK's simplicity relative to alternatives. Missing for 10: no direct hands-on code snippet in evidence showing the exact few-line agent+tool definition, only docs descriptions.
- [claimed-docs] “An agent is a large language model (LLM) configured with instructions, tools, and optional runtime behavior such as handoffs, guardrails, an…”
- [claimed-docs] “FunctionTool instances: wrap any Python function as a tool.”
- [claimed-docs] “Function tools: Turn any Python function into a tool with automatic schema generation and Pydantic-powered validation.”
- [claimed-docs] “`FunctionTool` instances: wrap any Python function as a tool.”
- [community] “OpenAI SDK is much simpler to use and understand than langchain etc. I havent deployed really complicated agents, but I have a number of sim…”
AutoGen's AssistantAgent supports tool use and custom agent creation, and AgentTool is documented for building agents with tools, indicating typed tool integration is possible in relatively few lines. However, the evidence pack lacks a concrete code example showing typed tool schemas (e.g., function signatures/Pydantic typing) wired directly into an agent definition. missing for 10: a first-party minimal code snippet demonstrating typed tool definition and attachment to an agent, independent hands-on confirmation of the 'few lines of code' ergonomics.
- [claimed-docs] “AssistantAgent is a built-in agent that uses a language model and has the ability to use tools.”
- [claimed-docs] “Create your own agents with custom behaviors”
- [github] “You can use `AgentTool` to create a basic multi-agent orchestration setup.”
Ai buildability
ai-native userHave a coding agent scaffold a new agent project from an official CLI or template in one command
weight 2 · round drawnOpenAI Agents SDKnone0/10The evidence pack covers agent primitives, tools, handoffs, tracing, and MCP support, but there is no mention of an official CLI or project scaffolding/template command to bootstrap a new agent project in one step.
AutoGennone0/10AutoGen provides pip install and a Python library/AutoGen Studio for building agents, but there is no evidence of a scaffolding CLI or project template command for one-command project generation. missing for 10: an official CLI scaffold/init command, project template generation, evidence of one-command bootstrap workflow.
ai-native userRun the framework's example agents headlessly from a terminal so an agent can verify what it just built
weight 2 · round to OpenAI Agents SDKRunner.run_sync() and run() enable headless CLI execution of agents, and tracing lets an agent's own run be verified programmatically, but there is no documented example agent or CLI harness specifically for an agent to verify what it just built. missing for 10: a ready-made example agent script for self-verification, CLI-specific documentation/tutorial, and independent confirmation of headless terminal usage for this exact workflow.
- [claimed-docs] “You can run agents via the Runner class. You have 3 options: Runner.run(), Runner.run_sync(), Runner.run_streamed()”
- [claimed-docs] “You can run agents via the `Runner` class. You have 3 options: `Runner.run()`... `Runner.run_sync()`... `Runner.run_streamed()`”
- [claimed-docs] “The Agents SDK includes built-in tracing, collecting a comprehensive record of events during an agent run: LLM generations, tool calls, hand…”
- [claimed-docs] “Using the Traces dashboard, you can debug, visualize, and monitor your workflows during development and in production.”
AutoGen agents are plain Python objects installed via pip and run as scripts, and community evidence confirms code execution happens in isolated Docker containers by default, which supports a terminal/headless workflow. However, there is no documented CLI or explicit 'run example agents headlessly to verify a build' feature — examples are shown as notebooks, and AutoGen Studio (the interactive runner) is UI-first with only python-code export, not a described headless verification loop. missing for 10: a documented CLI/headless example-runner, explicit self-verification workflow, independent confirmation of headless terminal use for build-verification purposes.
- [github] “pip install -U "autogen-agentchat" "autogen-ext[openai]"”
- [community] “i noticed autogen creates a new docker container each time code is executed by agents (default behaviour, can be turned off), so it's safe a…”
- [community] “FWIW the 'group research' and 'chess' examples from the notebooks folder in their repo have been the best for explaining the utility of this…”
- [claimed-docs] “Export and run teams in python code”
- [claimed-docs] “Export and run teams in python code * Setup and test endpoints based on a team configuration * Run teams in a docker container”
ai-native userRely on strict typing and schema validation so a coding agent catches its own mistakes at build time
weight 2 · round to OpenAI Agents SDKDocs confirm automatic schema generation and Pydantic-powered validation for function tools and structured outputs, which supports catching malformed tool calls early, but this is runtime validation rather than true build-time/compile-time type checking, and no independent evidence corroborates catching agent mistakes at build time. missing for 10: evidence of compile-time/static type checking (e.g., TypeScript strict mode enforcement), independent hands-on validation of build-time error catching, and confirmation this applies uniformly across JS/Python SDKs.
- [claimed-docs] “Function tools: Turn any Python function into a tool with automatic schema generation and Pydantic-powered validation.”
- [claimed-docs] “An agent is a large language model (LLM) configured with instructions, tools, and optional runtime behavior such as handoffs, guardrails, an…”
- [claimed-docs] “FunctionTool instances: wrap any Python function as a tool.”
AutoGennone0/10The evidence pack covers AutoGen's multi-agent orchestration, teams, memory, and studio UI, but contains no mention of strict typing, schema validation, or build-time error catching for a coding agent's own output. This is a fair question for an agent framework (e.g., via Pydantic-typed messages or structured outputs) but no such capability is documented here.
Automation depth — how much of the product can run unattendedAutomation depth
How much of the product can run unattended
ai-native userPerform bulk operations across many items at once
weight 2 · round drawnOpenAI Agents SDKnone0/10The evidence describes agent orchestration primitives (tools, handoffs, streaming, tracing, sessions) but nothing documents built-in support for bulk/batch operations across many items at once; that would need to be custom-built by a developer using function tools.
AutoGennone0/10The evidence pack covers AutoGen's multi-agent orchestration (teams, group chats, graph flows) but contains no mention of bulk/batch processing of many items at once (e.g., batch task queues, mass data operations). This is a fair axis for an automation framework, but no documentation or community evidence shows such a capability.
ai-native userDefine rules that trigger actions automatically on events
weight 3 · round drawnThe SDK supports guardrails (validation checks that can block/alter agent behavior), human-in-the-loop interruptions when a tool call requires approval, and streaming to subscribe to run events — these act as limited event-triggered mechanisms within an agent run. However, there is no documented general-purpose rule engine or event/webhook trigger system for arbitrary external events. Missing for 10: explicit event-trigger/rule definition API (e.g., on-event listeners tied to external triggers), documentation of custom event-driven automation beyond guardrails/interruptions, and independent evidence of this pattern in practice.
- [claimed-docs] “Guardrails enable you to do checks and validations of user input and agent output.”
- [claimed-docs] “When a tool call requires approval, the SDK pauses the run, returns interruptions, and lets you resume later from the same RunState.”
- [claimed-docs] “When a tool call requires approval, the SDK pauses the run, returns `interruptions`, and lets you resume later from the same `RunState`.”
- [claimed-docs] “Streaming lets you subscribe to updates of the agent run as it proceeds. This can be useful for showing the end-user progress updates and pa…”
AutoGen provides some conditional/event-driven orchestration primitives — Swarm's HandoffMessage triggers transitions between agents, and GraphFlow defines directed-graph workflows that route execution based on conditions — which can be used to build reactive, event-triggered behavior. However, there is no dedicated declarative 'rule' definition system (e.g., event-condition-action rules, triggers/webhooks) documented; the automation is implemented via developer code (agents, handoffs, selectors) rather than a rules engine an AI-native user configures directly. Missing for 10: explicit rule/trigger definition API or UI, event-listener/webhook mechanism, and independent evidence of this being used for automated event-driven actions outside code-defined agent handoffs.
- [claimed-docs] “Swarm: A team that uses HandoffMessage to signal transitions between agents.”
- [claimed-docs] “Multi-agent workflows through a directed graph of agents.”
- [claimed-docs] “GraphFlow: Multi-agent workflows through a directed graph of agents.”
- [claimed-docs] “SelectorGroupChat: A team that selects the next speaker using a ChatCompletion model after each message.”
ai-native userSchedule recurring jobs or workflows
weight 2 · round drawnOpenAI Agents SDKnone0/10The Agents SDK provides primitives for running agents (Runner, streaming, sessions, tracing) but no evidence of a scheduler, cron-like trigger, or built-in mechanism for recurring/periodic job execution; scheduling would need to be built externally.
ai-native userVersion, review, and roll back my automations
weight 1 · round drawnOpenAI Agents SDKnone0/10The evidence covers agent orchestration, tracing, tools, memory, and MCP support, but there is no mention of versioning automations, review workflows, or rollback capabilities anywhere in the docs or community discussion. Tracing/debugging is observability, not version control or rollback.
AutoGennone0/10AutoGen docs mention serializing/deserializing components and exporting team configs as JSON/Python, but there is no evidence of built-in versioning, review workflows, or rollback of automations/agent teams. Missing for 10: version history tracking, diff/review UI, rollback mechanism, audit trail for changes.
- [claimed-docs] “Serialize Components: Serialize and deserialize components”
- [claimed-docs] “Export and run teams in python code”
- [claimed-docs] “A visual interface for creating agent teams through declarative specification (JSON) or drag-and-drop”
Deployment portability — stories about deployment portability in this arenaDeployment portability
Stories about deployment portability in this arena
Deployment
engineering-leadDeploy an agent to a managed runtime and call it as an API endpoint
weight 2 · round to AutoGenOpenAI Agents SDKnone0/10The Agents SDK is a code framework for building and running agents locally/self-hosted (Runner class, sessions, tracing), but there is no evidence of a managed runtime/hosting service that deploys an agent and exposes it as a callable API endpoint; OpenAPI probes even 404. Sandbox agents run isolated workspaces for tool execution, not deployment-as-API.
- [claimed-docs] “You can run agents via the Runner class. You have 3 options: Runner.run(), Runner.run_sync(), Runner.run_streamed()”
- [claimed-docs] “Sandbox agents: Run specialists inside real isolated workspaces. Sandbox agents support manifest-defined files, sandbox client selection, an…”
- [probe] “PROBE openapi: all candidate paths 404 (https://developers.openai.com/openapi.json, https://developers.openai.com/swagger.json, https://deve…”
AutoGen Studio docs mention the ability to 'setup and test endpoints based on a team configuration' and 'run teams in a docker container,' which shows some API-endpoint and containerization support, but this is self-managed docker, not a Microsoft-managed runtime/PaaS. Missing for 10: evidence of an actual managed/hosted runtime service, deployment guides, or cloud endpoint provisioning beyond local docker export.
- [claimed-docs] “Export and run teams in python code * Setup and test endpoints based on a team configuration * Run teams in a docker container”
- [claimed-docs] “A visual interface for creating agent teams through declarative specification (JSON) or drag-and-drop”
engineering-leadRun my agents entirely on my own infrastructure with no dependence on the vendor's platform
weight 2 · round to AutoGenThe SDK is an open-source Python/JS library you run yourself, and it's explicitly provider-agnostic (100+ LLMs beyond OpenAI), so an engineering lead can host the orchestration layer entirely on their own infra. However, defaults point to OpenAI models/env vars, and built-in tracing pushes data to OpenAI's hosted Traces dashboard, meaning some optional but promoted features still tie back to the vendor platform. missing for 10: explicit documentation on disabling all OpenAI-hosted dependencies (tracing, hosted tools) for a fully vendor-free deployment, and independent confirmation of successful fully self-hosted production use.
- [github] “It is provider-agnostic, supporting the OpenAI Responses and Chat Completions APIs, as well as 100+ other LLMs.”
- [claimed-docs] “The Agents SDK includes built-in tracing, collecting a comprehensive record of events during an agent run: LLM generations, tool calls, hand…”
- [claimed-docs] “Using the Traces dashboard, you can debug, visualize, and monitor your workflows during development and in production.”
- [claimed-docs] “if you want to consistently use a specific model for all agents that do not set a custom model, set the `OPENAI_DEFAULT_MODEL` environment v…”
AutoGen is an open-source Python framework (pip installable) that runs locally, supports Docker-based code execution, and has no required vendor SaaS backend for agent execution; AutoGen Studio can also run teams in a docker container fully self-hosted. missing for 10: explicit vendor statement on air-gapped/offline deployment and independent case studies of fully on-prem production use.
- [github] “pip install -U "autogen-agentchat" "autogen-ext[openai]"”
- [claimed-docs] “pip install -U "autogen-agentchat"”
- [claimed-docs] “Export and run teams in python code * Setup and test endpoints based on a team configuration * Run teams in a docker container”
- [community] “i noticed autogen creates a new docker container each time code is executed by agents (default behaviour, can be turned off), so it's safe a…”
Portability
developerSwap the underlying LLM provider or model without rewriting my agent
weight 3 · round to OpenAI Agents SDKDocs confirm provider-agnostic design supporting OpenAI Responses/Chat Completions APIs plus 100+ other LLMs, with built-in OpenAI model flavors and an environment-variable/config mechanism (OPENAI_DEFAULT_MODEL) to swap default models without code rewrites, implying a model-abstraction layer for swapping providers. Missing for 10: independent hands-on verification of swapping to a non-OpenAI provider and clearer first-party documentation of the LiteLLM/custom-provider integration mechanism.
- [github] “It is provider-agnostic, supporting the OpenAI Responses and Chat Completions APIs, as well as 100+ other LLMs.”
- [claimed-docs] “The Agents SDK comes with out-of-the-box support for OpenAI models in two flavors”
- [claimed-docs] “if you want to consistently use a specific model for all agents that do not set a custom model, set the `OPENAI_DEFAULT_MODEL` environment v…”
AutoGen's model client abstraction (autogen-ext[openai] and similar model-client packages) and AssistantAgent design imply pluggable LLM providers, and community mentions confirm pointing agents at different LLMs (e.g., GPT-4 and others) without rearchitecting agent logic. However, the evidence pack lacks explicit documentation of a unified model-client interface listing multiple supported providers or a concrete swap example. Missing for 10: explicit docs enumerating supported model providers/backends, a documented config-only swap example, and independent confirmation of zero-code-change provider switching.
- [github] “pip install -U "autogen-agentchat" "autogen-ext[openai]"”
- [claimed-docs] “AssistantAgent is a built-in agent that uses a language model and has the ability to use tools.”
- [community] “However from his examples (and his own admission) it seems that AutoGen isn't benefitting from full GPT4-level performance even tho he's poi…”
Evals observability — stories about evals observability in this arenaEvals observability
Stories about evals observability in this arena
Evals
engineering-leadScore agent quality with built-in evals and run them as part of CI
weight 2 · round drawnOpenAI Agents SDKnone0/10The evidence pack shows tracing/observability (traces dashboard, tool/handoff logs) but no mention of built-in evals, scoring/grading of agent outputs, or CI integration for automated quality checks.
Testing
developerUnit-test agents with mocked models and tools
weight 2 · round drawnOpenAI Agents SDKnone0/10The evidence pack covers agents, tools, handoffs, tracing, sessions, and runners, but nothing mentions unit testing, mocking models/tools, or any test framework support for the SDK.
Tracing
developerTrace every LLM call and tool invocation of an agent run in an observability UI
weight 3 · round to OpenAI Agents SDKDocs explicitly describe built-in tracing that records LLM generations, tool calls, handoffs, guardrails, and custom events, viewable/debuggable in the Traces dashboard for development and production monitoring. This directly matches the story of tracing every LLM call and tool invocation in an observability UI. Missing for 10: independent/hands-on corroboration of the Traces UI beyond vendor docs.
- [claimed-docs] “The Agents SDK includes built-in tracing, collecting a comprehensive record of events during an agent run: LLM generations, tool calls, hand…”
- [claimed-docs] “Using the Traces dashboard, you can debug, visualize, and monitor your workflows during development and in production.”
- [claimed-docs] “The Agents SDK includes built-in tracing, collecting a comprehensive record of events during an agent run: LLM generations, tool calls, hand…”
- [claimed-docs] “The Agents SDK includes built-in tracing, collecting a comprehensive record of events during an agent run: LLM generations, tool calls, hand…”
- [claimed-docs] “The Agents SDK includes built-in tracing, collecting a comprehensive record of events during an agent run”
Docs confirm AutoGen has a logging/tracing feature for 'traces and internal messages' and a separate AutoGen Studio UI for building/testing/running teams, but no evidence explicitly ties these together into a UI that visualizes per-call LLM/tool traces for a given run. Missing for 10: explicit documentation or screenshots of an observability/tracing UI showing individual LLM calls and tool invocations, and any independent corroboration of this capability.
- [claimed-docs] “Logging: Log traces and internal messages”
- [claimed-docs] “Interactive environment for testing and running agent teams”
- [claimed-docs] “A visual interface for creating agent teams through declarative specification (JSON) or drag-and-drop”
- [claimed-docs] “Export and run teams in python code * Setup and test endpoints based on a team configuration * Run teams in a docker container”
Guardrails safety — stories about guardrails safety in this arenaGuardrails safety
Stories about guardrails safety in this arena
Guardrails
developerAttach input/output guardrails that validate, transform, or block unsafe content
weight 3 · round to OpenAI Agents SDKDocs directly state guardrails enable checks/validations of user input and agent output, and guardrails are a first-class runtime concept alongside handoffs/tools with tracing integration. Missing for 10: no independent/hands-on corroboration of guardrail blocking/transforming behavior in practice, and no detail on transform capability beyond validation/blocking.
- [claimed-docs] “Guardrails enable you to do checks and validations of user input and agent output.”
- [claimed-docs] “An agent is a large language model (LLM) configured with instructions, tools, and optional runtime behavior such as handoffs, guardrails, an…”
- [claimed-docs] “The Agents SDK includes built-in tracing, collecting a comprehensive record of events during an agent run: LLM generations, tool calls, hand…”
AutoGennone0/10No evidence of built-in input/output guardrails, content validation/transformation, or blocking mechanisms; docs cover agents, teams, memory, logging, serialization but nothing on safety/guardrail features. Community discussion touches on code-execution sandboxing (docker) but not content guardrails. missing for 10: any documentation of guardrail/validation hooks, content moderation APIs, or examples of blocking/transforming unsafe outputs.
- [claimed-docs] “Create your own agents with custom behaviors”
- [claimed-docs] “Add memory capabilities to your agents”
- [claimed-docs] “Logging: Log traces and internal messages”
- [community] “i noticed autogen creates a new docker container each time code is executed by agents (default behaviour, can be turned off), so it's safe a…”
engineering-leadRestrict what an agent may do with fine-grained tool permissions and sandboxed execution
weight 2 · round to OpenAI Agents SDKDocs show real guardrail mechanisms: input/output validation guardrails, human-in-the-loop tool-call approval that pauses runs pending explicit permission, and dedicated 'Sandbox agents' that execute in isolated, manifest-defined workspaces, plus a sandboxed CodeInterpreterTool. Together these give an engineering lead meaningful control over tool use and execution isolation, though there's no first-party doc on granular per-tool ACL/permission policies beyond approval gating, and no independent/hands-on security audit corroborating sandbox robustness. Missing for 10: explicit fine-grained per-tool permission/policy configuration docs, independent verification of sandbox isolation guarantees.
- [claimed-docs] “Guardrails enable you to do checks and validations of user input and agent output.”
- [claimed-docs] “When a tool call requires approval, the SDK pauses the run, returns interruptions, and lets you resume later from the same RunState.”
- [claimed-docs] “When a tool call requires approval, the SDK pauses the run, returns `interruptions`, and lets you resume later from the same `RunState`.”
- [claimed-docs] “Sandbox agents: Run specialists inside real isolated workspaces. Sandbox agents support manifest-defined files, sandbox client selection, an…”
- [claimed-docs] “If the agent should run inside an isolated workspace with manifest-defined files and sandbox-native capabilities, read Sandbox agent concept…”
- [claimed-docs] “OpenAI offers a few built-in tools when using the OpenAIResponsesModel... The WebSearchTool lets an agent search the web. The FileSearchTool…”
Community evidence confirms code execution runs in ephemeral Docker containers by default (sandboxed execution), and AutoGen Studio can run teams in a docker container, giving real sandboxing support corroborated by hands-on users. However, there is no documented fine-grained, per-tool permission system (e.g., allow/deny lists, scoped capabilities) for agents — only that agents 'have the ability to use tools' and can create custom agents. Missing for 10: explicit fine-grained tool-permission/allow-list mechanism, first-party docs on restricting specific tool access per agent, and independent verification of sandbox robustness beyond one HN thread noting it 'can be turned off'.
- [community] “i noticed autogen creates a new docker container each time code is executed by agents (default behaviour, can be turned off), so it's safe a…”
- [claimed-docs] “Export and run teams in python code * Setup and test endpoints based on a team configuration * Run teams in a docker container”
- [claimed-docs] “AssistantAgent is a built-in agent that uses a language model and has the ability to use tools.”
- [claimed-docs] “Create your own agents with custom behaviors”
Human in the loop — stories about human in the loop in this arenaHuman in the loop
Stories about human in the loop in this arena
Approval flows
developerPause an agent mid-run for human input or approval and resume with the human's decision
weight 3 · round to OpenAI Agents SDKDocs explicitly describe a human-in-the-loop flow where a tool call requiring approval pauses the run, returns interruptions, and lets the developer resume later from the same RunState with the human's decision. This directly matches the story's pause/resume-for-approval pattern. Missing for 10: independent/hands-on corroboration beyond first-party docs, and Python-specific (vs JS) documentation detail on the same mechanism.
- [claimed-docs] “When a tool call requires approval, the SDK pauses the run, returns interruptions, and lets you resume later from the same RunState.”
- [claimed-docs] “When a tool call requires approval, the SDK pauses the run, returns `interruptions`, and lets you resume later from the same `RunState`.”
AutoGen has a documented UserProxyAgent that provides human-in-the-loop feedback to the team, supporting pausing for human input, but the evidence pack doesn't detail explicit pause/resume with persisted state or approval gating mid-run (e.g., checkpoint/resume semantics). missing for 10: explicit resume-from-interrupt mechanics, evidence of approval gating on specific actions, independent hands-on confirmation of pause/resume behavior.
- [claimed-docs] “The UserProxyAgent is a special built-in agent that acts as a proxy for a user to provide feedback to the team.”
engineering-leadRequire human approval before specific sensitive tool calls execute
weight 2 · round to OpenAI Agents SDKDocs explicitly describe a needsApproval/interruption mechanism: when a tool call requires approval, the SDK pauses the run, returns interruptions, and lets you resume later from the same RunState, enabling human-in-the-loop gating for specific sensitive tools. Missing for 10: independent/hands-on corroboration of this workflow and Python-side (vs JS) doc citation for the same feature.
- [claimed-docs] “When a tool call requires approval, the SDK pauses the run, returns interruptions, and lets you resume later from the same RunState.”
- [claimed-docs] “When a tool call requires approval, the SDK pauses the run, returns `interruptions`, and lets you resume later from the same `RunState`.”
AutoGen's UserProxyAgent is documented as enabling human-in-the-loop feedback within a team, which could be used to pause and approve steps, but the evidence never shows a mechanism to gate specific sensitive tool calls (e.g., per-tool approval hooks) rather than general conversational feedback. missing for 10: documentation of tool-call-level approval/interrupt hooks, examples of selectively requiring approval only for sensitive tools, and independent confirmation this works as described.
- [claimed-docs] “The UserProxyAgent is a special built-in agent that acts as a proxy for a user to provide feedback to the team.”
- [claimed-docs] “AssistantAgent is a built-in agent that uses a language model and has the ability to use tools.”
Memory context — stories about memory context in this arenaMemory context
Stories about memory context in this arena
Memory
developerTrim, summarize, or filter conversation history to keep an agent inside its context window
weight 2 · round drawnOpenAI Agents SDKnone0/10Evidence shows built-in session memory that automatically persists conversation history across runs (docs-8, docs-33), but nothing describes trimming, summarizing, or filtering that history to manage context window size. No documentation mentions truncation, summarization tools, or history-pruning APIs.
- [claimed-docs] “The Agents SDK provides built-in session memory to automatically maintain conversation history across multiple agent runs, eliminating the n…”
- [claimed-docs] “The Agents SDK provides built-in session memory to automatically maintain conversation history across multiple agent runs”
AutoGennone0/10The evidence pack mentions general 'Memory' and 'Logging' capabilities but never documents any mechanism for trimming, summarizing, or filtering conversation history to manage context window size — no mention of context buffering, truncation, or summarization utilities.
- [claimed-docs] “Add memory capabilities to your agents”
- [claimed-docs] “Memory: Add memory capabilities to your agents”
- [claimed-docs] “Logging: Log traces and internal messages”
developerGive agents long-term memory that persists across sessions and threads
weight 2 · round drawnDocs describe built-in 'session memory' that automatically maintains conversation history across multiple agent runs, removing manual history management (openai-agents-docs-8/33), which covers persistence within a session. However, there's no evidence of explicit long-term memory that persists across different threads/sessions (e.g., a durable memory store, vector-based long-term recall, or cross-thread continuity beyond a single session object) — FileSearchTool/Vector Stores are mentioned only as retrieval tools, not as an automatic long-term memory mechanism tied to sessions. Missing for 10: documented cross-session/cross-thread persistent memory store, examples of custom long-term memory backends, and independent confirmation that memory survives beyond a single Session object.
- [claimed-docs] “The Agents SDK provides built-in session memory to automatically maintain conversation history across multiple agent runs, eliminating the n…”
- [claimed-docs] “The Agents SDK provides built-in session memory to automatically maintain conversation history across multiple agent runs”
- [claimed-docs] “OpenAI offers a few built-in tools when using the OpenAIResponsesModel... The WebSearchTool lets an agent search the web. The FileSearchTool…”
AutoGen's docs advertise a 'Memory' component ('Add memory capabilities to your agents') as part of AgentChat, indicating some support for giving agents memory, but the evidence pack gives no detail on how this memory persists across sessions/threads (e.g., storage backend, serialization, retrieval across conversations). missing for 10: explicit documentation of session/thread-persistent memory implementation, examples of memory surviving across separate runs, independent/hands-on confirmation.
- [claimed-docs] “Add memory capabilities to your agents”
- [claimed-docs] “Memory: Add memory capabilities to your agents”
Openness — open source, data portability, and self-hosting storiesOpenness
Open source, data portability, and self-hosting stories
ai-native userRead the product's source under an open license
weight 2 · round to AutoGenThe evidence confirms the SDK's source code is hosted publicly on GitHub (openai/openai-agents-python) and is actively discussed by the community, implying open access to the code, but no evidence pack item explicitly states or cites a license (e.g., MIT/Apache) or license file. Missing for 10: explicit license text/confirmation, documentation citing the specific open-source license terms.
- [github] “It is provider-agnostic, supporting the OpenAI Responses and Chat Completions APIs, as well as 100+ other LLMs.”
- [github] “Voice agents: Build voice pipelines that combine speech-to-text, an agent workflow, and text-to-speech”
- [github] “Realtime agents: Build powerful voice agents with `gpt-realtime-2.1` and full agent features”
- [community] “OpenAI SDK is much simpler to use and understand than langchain etc. I havent deployed really complicated agents, but I have a number of sim…”
The evidence pack shows AutoGen is distributed via a public GitHub repository (autogen-gh-1,2,3) and pip-installable packages, indicating its source is publicly readable, but no evidence explicitly states or confirms an open-source license (e.g., MIT/Apache) in the pack. missing for 10: explicit license file/badge citation, confirmation of open-license terms, independent verification of license compliance.
ai-native userSelf-host the core product
weight 3 · round drawnThe Agents SDK is an open-source Python/JS library (github.com/openai/openai-agents-python) that you install and run yourself via the Runner class (Runner.run/run_sync/run_streamed) with no required hosted backend, and it's provider-agnostic (100+ LLMs), meaning the core execution loop runs entirely in your own infrastructure by design. Missing for 10: no explicit self-hosting guide/deployment docs or independent report confirming production self-hosted deployments beyond code examples.
- [github] “It is provider-agnostic, supporting the OpenAI Responses and Chat Completions APIs, as well as 100+ other LLMs.”
- [claimed-docs] “You can run agents via the Runner class. You have 3 options: Runner.run(), Runner.run_sync(), Runner.run_streamed()”
- [claimed-docs] “You can run agents via the `Runner` class. You have 3 options: `Runner.run()`... `Runner.run_sync()`... `Runner.run_streamed()`”
- [claimed-docs] “if you want to consistently use a specific model for all agents that do not set a custom model, set the `OPENAI_DEFAULT_MODEL` environment v…”
AutoGen is an open-source Python framework installed via pip (autogen-agentchat) and available on GitHub, meaning the core agent/team runtime is self-hosted by design; AutoGen Studio can also be run locally or in a Docker container. Missing for 10: dedicated self-hosting/deployment guide or infrastructure requirements documentation beyond pip install and docker run mentions.
- [github] “pip install -U "autogen-agentchat" "autogen-ext[openai]"”
- [claimed-docs] “pip install -U "autogen-agentchat"”
- [claimed-docs] “Export and run teams in python code * Setup and test endpoints based on a team configuration * Run teams in a docker container”
- [community] “i noticed autogen creates a new docker container each time code is executed by agents (default behaviour, can be turned off), so it's safe a…”
Orchestration multi agent — stories about orchestration multi agent in this arenaOrchestration multi agent
Stories about orchestration multi agent in this arena
Multi agent
developerOrchestrate multiple agents — handoffs, subagents, or crews — inside one workflow
weight 3 · round drawnDocs explicitly describe handoffs for delegating tasks between specialized agents, agents-as-tools for subagent-style composition without full handoff, and a Runner to orchestrate multi-agent workflows with tracing, streaming, and session memory across runs. Community feedback confirms real-world use for orchestration, though notes complexity concerns for advanced cases. Missing for 10: independent/hands-on benchmark of complex multi-agent crews at scale beyond simple handoff examples.
- [claimed-docs] “Handoffs allow an agent to delegate tasks to another agent. This is particularly useful in scenarios where different agents specialize in di…”
- [claimed-docs] “Agents as tools: expose an agent as a callable tool without a full handoff.”
- [claimed-docs] “You can run agents via the Runner class. You have 3 options: Runner.run(), Runner.run_sync(), Runner.run_streamed()”
- [claimed-docs] “The Agents SDK includes built-in tracing, collecting a comprehensive record of events during an agent run: LLM generations, tool calls, hand…”
- [community] “OpenAI SDK is much simpler to use and understand than langchain etc. I havent deployed really complicated agents, but I have a number of sim…”
AutoGen provides extensive first-party documentation of multi-agent orchestration patterns: RoundRobinGroupChat, SelectorGroupChat, Swarm (handoff-based), and GraphFlow (directed-graph workflows), plus AgentTool for nesting agents as tools, all within a single workflow. Community feedback corroborates real-world use of multi-agent conversation control. Missing for 10: independent hands-on benchmark of complex multi-agent handoff reliability beyond the HN thread's mixed performance comments.
- [claimed-docs] “Multi-agent coordination through a shared context and centralized, customizable selector”
- [claimed-docs] “Multi-agent coordination through a shared context and localized, tool-based selector”
- [claimed-docs] “Multi-agent workflows through a directed graph of agents.”
- [claimed-docs] “SelectorGroupChat: A team that selects the next speaker using a ChatCompletion model after each message.”
- [claimed-docs] “Swarm: A team that uses HandoffMessage to signal transitions between agents.”
- [claimed-docs] “GraphFlow: Multi-agent workflows through a directed graph of agents.”
- [github] “You can use `AgentTool` to create a basic multi-agent orchestration setup.”
- [community] “Have been working with this and very impressed so far - it's a step ahead of LangChain agents and seems to be receiving more attention/devel…”
- [community] “The breakthrough I've had is realizing how important it is to control the conversation between agents. Just like in our work environments an…”
Workflow control
developerCompose agents into an explicit graph or workflow with branching, loops, and parallel steps
weight 2 · round to AutoGenThe SDK provides composable primitives—handoffs for delegation/branching, agents-as-tools for parallel-style composition, and Runner.run for orchestrated execution—but these are implemented via plain Python control flow rather than an explicit graph/workflow DSL with declared branching, loops, and parallel steps as first-class constructs (unlike graph-based orchestrators). Community commentary notes users often reimplement custom logic for more complex flows rather than relying on a built-in graph abstraction. missing for 10: explicit graph/DSL construct for defining workflows, first-class loop/parallel-step primitives, independent hands-on evidence of complex branching/looping workflows.
- [claimed-docs] “Handoffs allow an agent to delegate tasks to another agent. This is particularly useful in scenarios where different agents specialize in di…”
- [claimed-docs] “Agents as tools: expose an agent as a callable tool without a full handoff.”
- [claimed-docs] “You can run agents via the Runner class. You have 3 options: Runner.run(), Runner.run_sync(), Runner.run_streamed()”
- [community] “you're better off just implementing the logic yourself as it is more flexible.”
AutoGen explicitly ships GraphFlow, described as enabling 'multi-agent workflows through a directed graph of agents,' which directly supports the story's core ask of graph-based orchestration alongside other team patterns (RoundRobin, Selector, Swarm) for different coordination styles. However, the evidence pack never details how branching conditions, loop constructs, or parallel step execution are configured within GraphFlow, so the specific mechanics of the story are only partially substantiated. Missing for 10: explicit documentation/examples of conditional branching syntax, loop/cycle handling, and parallel step execution within GraphFlow, plus independent hands-on confirmation of these features working as described.
- [claimed-docs] “Multi-agent workflows through a directed graph of agents.”
- [claimed-docs] “GraphFlow: Multi-agent workflows through a directed graph of agents.”
- [claimed-docs] “SelectorGroupChat: A team that selects the next speaker using a ChatCompletion model after each message.”
- [claimed-docs] “Swarm: A team that uses HandoffMessage to signal transitions between agents.”
- [claimed-docs] “RoundRobinGroupChat is a simple yet effective team configuration where all agents share the same context and take turns responding in a roun…”
Privacy posture — data-handling and privacy storiesPrivacy posture
Data-handling and privacy stories
ai-native userControl data retention and deletion
weight 2 · round drawnOpenAI Agents SDKnone0/10The evidence pack covers agents, tools, tracing, sessions, handoffs, etc., but contains no documentation of data retention policies, data deletion controls, or privacy configuration options for the SDK. This is an applicable axis (an AI SDK could plausibly document retention/deletion controls or link to API data-usage policies) but no such evidence is present.
AutoGennone0/10AutoGen is an open-source, self-hosted framework, so data retention/deletion would be determined by the user's own infrastructure, but no evidence pack item discusses any built-in retention policy, data deletion controls, or configuration for purging stored conversation/memory data.
ai-native userOpt out of telemetry and usage tracking
weight 2 · round drawnOpenAI Agents SDKnone0/10The SDK ships built-in tracing that records LLM generations, tool calls, handoffs, and guardrails to a Traces dashboard, making telemetry opt-out a fair and relevant question, but no evidence pack item documents any environment variable, config flag, or API to disable tracing/telemetry.
- [claimed-docs] “The Agents SDK includes built-in tracing, collecting a comprehensive record of events during an agent run: LLM generations, tool calls, hand…”
- [claimed-docs] “Using the Traces dashboard, you can debug, visualize, and monitor your workflows during development and in production.”
- [claimed-docs] “The Agents SDK includes built-in tracing, collecting a comprehensive record of events during an agent run: LLM generations, tool calls, hand…”
State durability — stories about state durability in this arenaState durability
Stories about state durability in this arena
Durable state
developerCheckpoint agent state so a run can resume exactly where it left off after a crash or restart
weight 3 · round to OpenAI Agents SDKThe SDK provides built-in session memory to persist conversation history across turns and a RunState/interruptions mechanism that lets a paused (e.g., approval-pending) run resume from where it left off, which covers part of the checkpoint/resume story. However, there is no documented mechanism for durably persisting full agent run state to survive a process crash or restart—community evidence explicitly notes that true durable/crash-resilient execution requires wrapping the SDK with an external engine like Temporal, implying it isn't a native capability. Missing for 10: first-party docs on serializing/restoring full run state after an unexpected crash, and any built-in persistence layer beyond conversation history/session memory.
- [claimed-docs] “The Agents SDK provides built-in session memory to automatically maintain conversation history across multiple agent runs, eliminating the n…”
- [claimed-docs] “When a tool call requires approval, the SDK pauses the run, returns interruptions, and lets you resume later from the same RunState.”
- [claimed-docs] “When a tool call requires approval, the SDK pauses the run, returns `interruptions`, and lets you resume later from the same `RunState`.”
- [community] “Comparing the Temporal-based durable version to the original OpenAI Agents SDK repo's examples, it's really not all that different - some me…”
Docs mention a 'Serialize Components' feature for serializing/deserializing components and a separate logging/tracing feature, which are the building blocks for state persistence, but there is no explicit documentation of a checkpoint/resume workflow after a crash or restart. missing for 10: explicit checkpoint/resume API or tutorial, crash-recovery guarantees, independent confirmation that resumed runs continue exactly where they left off.
- [claimed-docs] “Serialize Components: Serialize and deserialize components”
- [claimed-docs] “Logging: Log traces and internal messages”
engineering-leadRun long-lived agents durably across process restarts and deploys, natively or via durable-execution integrations
weight 2 · round to OpenAI Agents SDKThe SDK has built-in session memory across turns and human-in-the-loop pause/resume via RunState, plus a community reference to a Temporal-based durable-execution integration confirming feasibility, but there's no native, documented mechanism for surviving process restarts/deploys out of the box — durability requires an external integration like Temporal. missing for 10: first-party durable-execution/persistence docs, official Temporal (or similar) integration guide, evidence of native crash/restart recovery for long-lived agent runs.
- [claimed-docs] “The Agents SDK provides built-in session memory to automatically maintain conversation history across multiple agent runs, eliminating the n…”
- [claimed-docs] “When a tool call requires approval, the SDK pauses the run, returns interruptions, and lets you resume later from the same RunState.”
- [claimed-docs] “When a tool call requires approval, the SDK pauses the run, returns `interruptions`, and lets you resume later from the same `RunState`.”
- [community] “Comparing the Temporal-based durable version to the original OpenAI Agents SDK repo's examples, it's really not all that different - some me…”
AutoGennone0/10Evidence shows serialization of components and logging/tracing, but there is no mention of durable execution, process-restart recovery, checkpointing/resume across deploys, or integrations with durable-execution engines (e.g., Temporal). missing for 10: durable-execution runtime or integration, state checkpoint/resume across restarts, deploy-survival guarantees, any documentation or example of long-lived agent persistence.
- [claimed-docs] “Serialize Components: Serialize and deserialize components”
- [claimed-docs] “Logging: Log traces and internal messages”
Streaming output — stories about streaming output in this arenaStreaming output
Stories about streaming output in this arena
Streaming
developerStream tokens and intermediate agent events (tool calls, steps) to my UI in real time
weight 3 · round to OpenAI Agents SDKDocs explicitly describe Runner.run_streamed() returning a RunResultStreaming, and a dedicated Streaming guide describing subscribing to updates of the agent run including partial responses and progress, plus built-in tracing capturing tool calls/handoffs/steps that can be surfaced. This directly supports streaming tokens and intermediate events to a UI. Missing for 10: independent hands-on developer confirmation of real-time UI integration beyond official docs.
- [claimed-docs] “Streaming lets you subscribe to updates of the agent run as it proceeds. This can be useful for showing the end-user progress updates and pa…”
- [claimed-docs] “You can run agents via the Runner class. You have 3 options: Runner.run(), Runner.run_sync(), Runner.run_streamed()”
- [claimed-docs] “Runner.run_streamed(), which runs async and returns a RunResultStreaming”
- [claimed-docs] “The Agents SDK includes built-in tracing, collecting a comprehensive record of events during an agent run: LLM generations, tool calls, hand…”
AutoGennone0/10The evidence pack mentions logging of traces/internal messages and an AutoGen Studio interactive environment, but nowhere describes token-level streaming or real-time emission of intermediate agent/tool events to a UI. Missing for 10: any mention of a streaming API (e.g., token/event streaming methods), UI integration examples, or independent confirmation of real-time event delivery.
- [claimed-docs] “Logging: Log traces and internal messages”
- [claimed-docs] “Interactive environment for testing and running agent teams”
Structured output
developerGet schema-validated structured output from an agent, with automatic retries when validation fails
weight 3 · round to OpenAI Agents SDKDocs confirm Pydantic-powered schema validation for function tools/outputs and guardrails for validating agent output, implying structured-output support, but there is no explicit documentation of an 'output_type' structured output feature with automatic retry-on-validation-failure behavior tied to streaming. Missing for 10: explicit documentation of structured output schema enforcement on agent final output, explicit automatic retry-on-validation-failure mechanism, and independent/hands-on confirmation that retries occur when validation fails.
- [claimed-docs] “Function tools: Turn any Python function into a tool with automatic schema generation and Pydantic-powered validation.”
- [claimed-docs] “Guardrails enable you to do checks and validations of user input and agent output.”
- [claimed-docs] “An agent is a large language model (LLM) configured with instructions, tools, and optional runtime behavior such as handoffs, guardrails, an…”
AutoGennone0/10No evidence in the pack mentions schema-validated structured output or automatic retry-on-validation-failure behavior for AutoGen agents; docs cover agents, teams, tools, and orchestration but not structured output validation. Missing for 10: any mention of structured output schemas (e.g., Pydantic models), validation error handling, or retry logic tied to output parsing.
Not comparable on these axes
ai-native userConnect an agent via an official MCP server
weight 3 · not comparableOpenAI Agents SDKn/aOpenAI Agents SDK is an agent-building framework (the client role) — its MCP evidence describes connecting to/consuming existing MCP servers (docs-10, docs-35, docs-25 hosted MCP tool), not exposing itself as an MCP server for other agents to connect to. Per the agent-role exception this axis is out of scope; no evidence of an 'mcp serve' mode or hosted MCP endpoint from the SDK itself.
- [claimed-docs] “The Agents Python SDK understands multiple MCP transports. This lets you reuse existing MCP servers or build your own to expose filesystem, …”
- [claimed-docs] “The Agents Python SDK understands multiple MCP transports. This lets you reuse existing MCP servers or build your own”
- [claimed-docs] “Use OpenAI-managed tools (web search, file search, code interpreter, hosted MCP, image generation)”
AutoGenn/aAutoGen is an agent framework/client, and the story concerns serving tools via an official MCP server (agent-as-server role). The only relevant evidence (autogen-gh-2) shows AutoGen agents connecting to an external Playwright MCP server, which is client-side usage and does not make the server axis applicable per the agent-role exception.
- [github] “Create a web browsing assistant agent that uses the Playwright MCP server.”
ai-native userGet AI-generated insights and suggestions from my data inside the product
weight 2 · not comparableOpenAI Agents SDKn/aThe OpenAI Agents SDK is a developer framework for building AI agents, not an end-user product with a data corpus that surfaces insights to users; the story presumes a product with 'my data' and in-product insight generation, which is a category mismatch for a backend SDK/toolkit.
AutoGen provides building blocks (AssistantAgent with tool use, memory, multi-agent teams, AutoGen Studio for building/testing agents) that a developer could use to construct data-analysis or insight-generating agents, but there is no evidence of a built-in, turnkey feature that ingests a user's data and surfaces AI-generated insights inside the product itself — it remains a framework requiring custom agent construction. Missing for 10: a documented out-of-the-box 'analyze my data / dashboard insights' feature, evidence of automatic data ingestion, and independent hands-on confirmation of insight quality on real datasets.
- [claimed-docs] “AssistantAgent is a built-in agent that uses a language model and has the ability to use tools.”
- [claimed-docs] “Add memory capabilities to your agents”
- [claimed-docs] “Interactive environment for testing and running agent teams”
- [claimed-docs] “A visual interface for creating agent teams through declarative specification (JSON) or drag-and-drop”
- [github] “You can use `AgentTool` to create a basic multi-agent orchestration setup.”
ai-native userDo everything through the API that I can do in the UI
weight 2 · not comparableOpenAI Agents SDKn/aThe Agents SDK is a code-first, API/SDK product with no separate consumer-facing UI whose feature set the API must match (the Traces dashboard is a monitoring add-on, not a primary interaction surface). The 'API vs UI parity' axis is a category mismatch for a product that is itself an SDK.
AutoGen's core framework is code/API-first, and AutoGenStudio (the visual UI) explicitly supports exporting team configurations to Python code and setting up API endpoints from a team config, indicating parity between UI-built and API-driven workflows (autogen-docs-5, autogen-docs-17). However there's no evidence confirming every UI feature (e.g. community component gallery/hub, drag-and-drop specifics) has a documented equivalent API path, and no independent corroboration of full parity. Missing for 10: explicit 1:1 mapping of all AutoGenStudio UI features (gallery/hub, docker run options) to API calls, and independent/hands-on confirmation of parity.
- [claimed-docs] “A visual interface for creating agent teams through declarative specification (JSON) or drag-and-drop”
- [claimed-docs] “Export and run teams in python code”
- [claimed-docs] “Export and run teams in python code * Setup and test endpoints based on a team configuration * Run teams in a docker container”
- [claimed-docs] “Central hub for discovering and importing community-created components”
ai-native userExport all of my data in open formats and leave
weight 3 · not comparableOpenAI Agents SDKn/aThe Agents SDK is an open-source developer framework/library that users run themselves; it does not act as a hosted data-storage service from which a user would need to 'export and leave.' Session memory, traces, and other state are managed within the user's own code/infrastructure (except for optional OpenAI-hosted tracing, for which no export/lock-in evidence exists either way), so the 'export data and leave' axis is a category mismatch for this kind of product.
AutoGen Studio supports exporting team configurations as JSON or Python code and the framework has component serialization/deserialization features, which are open, portable formats. However, there is no evidence of exporting broader user data (conversation history, memory stores, logs) in a comprehensive open-format package, and no documentation of a full account/data 'leave' export process. Missing for 10: evidence of exporting full conversation/memory history, a documented data-portability/export-all workflow, and independent confirmation of format openness beyond configs.
- [claimed-docs] “A visual interface for creating agent teams through declarative specification (JSON) or drag-and-drop”
- [claimed-docs] “Export and run teams in python code”
- [claimed-docs] “Export and run teams in python code * Setup and test endpoints based on a team configuration * Run teams in a docker container”
- [claimed-docs] “Serialize Components: Serialize and deserialize components”
ai-native userChoose where my data is stored (region/residency)
weight 2 · not comparableOpenAI Agents SDKnone0/10No evidence in the pack mentions data residency, regional storage, or data location controls for the Agents SDK; the SDK is a developer framework that relies on underlying model providers/APIs for storage, and no such configuration is documented here.
AutoGenn/aAutoGen is a self-hosted, open-source multi-agent framework/library, not a hosted SaaS that stores user data — deployment location and data residency are entirely determined by the user's own infrastructure, not a product feature to select. No evidence pack content addresses region/residency selection, and the axis is a category error for a framework with no first-party data storage.
ai-native userPrevent my data from being used to train AI models
weight 3 · not comparableOpenAI Agents SDKnone0/10The evidence pack covers SDK features (tools, handoffs, tracing, sessions, MCP, voice, sandboxing) but contains no mention of data-training opt-out, privacy controls, or API data usage policies relevant to model training. This is a developer SDK that wraps OpenAI API calls, so training-data opt-out would be governed by OpenAI's platform-level API terms, not documented anywhere in this evidence pack.
AutoGenn/aAutoGen is an open-source multi-agent framework you self-host, not a hosted AI service with a data-training policy for user data; there's no vendor relationship where 'my data used for training' applies (users bring their own LLM API keys/providers). This axis is a category error for a framework rather than a hosted product with its own training policy.