What it actually does
This is the OpenAI Agents SDK for Python, a framework for building LLM agents. It provides an Agent abstraction (instructions, tools, handoffs, guardrails, MCP servers), a Runner loop that calls models and executes tools, and sessions, tracing, realtime/voice, and sandbox subsystems with several sandbox backends. The excerpt shows only part of the large codebase, but what is visible matches the README's description. The static analysis reported no findings.
- Agent dataclass configuring instructions, tools, handoffs, guardrails, output type, model settings, and tool-use behavior; agents can be wrapped as tools via as_tool()src/agents/agent.py:318-420, 606
- Runner exposes run, run_sync and run_streamed. They delegate to an AgentRunner that loops over model calls, tool execution, handoffs, guardrails, and max_turnssrc/agents/run.py:279-565, 568-610
- MCP server integration over stdio, SSE and streamable HTTP, with approval policies, tool filtering and guardrails, and credential-safe error handlingsrc/agents/mcp/server.py:562-610, 306-328
- Realtime voice agents that use a single shared model per session, with handoffs and output guardrailssrc/agents/realtime/agent.py:27-100
- Computer-use abstractions (sync and async) and an apply_patch editor protocol; the host implements the actual actionssrc/agents/computer.py:8-134, src/agents/editor.py:34-48
- Run-error handlers for max_turns, model_refusal and invalid_final_outputsrc/agents/run_error_handlers.py:44-56
- Demo REPL that loops on stdin input and runs the agent with streaming or non-streaming outputsrc/agents/repl.py:15-77
- Dynamic prompt resolution for OpenAI stored prompts (id, version, variables)src/agents/prompts.py:23-82
- Sandbox agents with local, Docker and hosted-provider backends, plus mount security and memory subsystems (visible only in the file tree and test/config files)src/agents/sandbox/*, pyproject.toml optional extras (docker, e2b, modal, etc.)
- Sensitive-data log suppression, on by default, for model and tool datasrc/agents/_debug.py:12-28
README claims vs the code
| Lightweight, powerful framework for multi-agent workflows with handoffs and agents-as-tools | Backed by code | agent.py has handoffs and as_tool; run.py documents the handoff loop (run.py:299-305) |
| Provider-agnostic: supports the OpenAI Responses and Chat Completions APIs and 100+ other LLMs | Partly | Responses and Chat Completions model classes exist, and LiteLLM and any-llm extras are declared in pyproject.toml:46-50. The '100+ LLMs' count depends on those third-party packages. |
| Built-in guardrails for input and output validation | Backed by code | Agent.input_guardrails and output_guardrails (agent.py:373-381); Runner docs describe tripwire exceptions (run.py:307-311) |
| Sessions provide automatic conversation history management | Backed by code | Runner accepts a session argument and calls prepare_input_with_session (run.py:341, 720-743); several session backends are listed in pyproject.toml extras |
| Human-in-the-loop mechanisms | Backed by code | needs_approval parameter in Agent.as_tool (agent.py:623); MCP require_approval policy (mcp/server.py:583-587); RunState resume support in run.py |
| Built-in tracing of agent runs | Backed by code | Tracing is wired into run.py (imports at lines 174-185, TraceCtxManager use at 594); the src/agents/tracing package is present |
| Realtime and voice agents | Backed by code | RealtimeAgent (realtime/agent.py) and the voice pipeline modules (voice/pipeline.py) are in the tree |
| Sandbox agents that work over long time horizons in containers | Partly | The sandbox package and multiple backends exist in the tree. The excerpt does not show the implementation, and long-horizon behavior is not verifiable from it. |
Code findings
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Who made it and how it got popular
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