2d665c9a67
### Sandbox Agents This release adds **Sandbox Agents**, a beta SDK surface for running agents with a persistent, isolated workspace. Sandbox agents keep the normal `Agent` and `Runner` flow, but add workspace manifests, sandbox-native capabilities, sandbox clients, snapshots, and resume support so agents can work over real files, run commands, edit repositories, generate artifacts, and continue work across runs. Key pieces: - `SandboxAgent`: an `Agent` with sandbox defaults such as `default_manifest`, sandbox instructions, capabilities, and `run_as`. - `Manifest`: a fresh-workspace contract for files, directories, local files, local directories, Git repos, environment, users, groups, and mounts. - `SandboxRunConfig`: per-run sandbox wiring for client creation, live session injection, serialized session resume, manifest overrides, snapshots, and materialization concurrency limits. - Built-in capabilities for shell access, filesystem editing and image inspection, skills, memory, and compaction. - Workspace snapshots and serialized sandbox session state for reconnecting to existing work or seeding a fresh sandbox from saved contents. ### Sandbox clients and hosted providers Sandbox agents now support local, containerized, and hosted execution backends: - `UnixLocalSandboxClient` for fast local development. - `DockerSandboxClient` for container isolation and image parity. - Hosted sandbox clients for Blaxel, Cloudflare, Daytona, E2B, Modal, Runloop, and Vercel through optional extras. The release also adds provider-specific examples and mount strategies for common storage backends, including S3, Cloudflare R2, Google Cloud Storage, Azure Blob Storage, and S3 Files where supported by the selected backend. ### Sandbox memory Adds a sandbox memory capability that lets future sandbox-agent runs learn from prior runs. Memory stores extracted lessons in the sandbox workspace, injects a concise summary into later runs, and uses progressive disclosure so agents can search deeper rollout summaries only when useful. Memory supports: - Read-only or generate-only modes. - Live updates when the agent discovers stale memory. - Multi-turn grouping through `conversation_id`, SDK `Session`, `RunConfig.group_id`, or generated run IDs. - Separate memory layouts for isolating memory across agents or workflows. - S3-backed examples for persisted memory across runs. ### Workspace mounts, snapshots, and resume This release adds a full workspace entry and mount model for sandbox sessions: - Local files and directories. - Synthetic files and directories. - Git repository entries. - Remote storage mounts for S3, R2, GCS, Azure Blob Storage, and S3 Files. - Provider-specific mount strategies across Docker, Modal, Cloudflare, Blaxel, Daytona, E2B, and Runloop. - Portable snapshots with path normalization, symlink preservation, mount-safe snapshotting, and remote snapshot support. - Resume paths through runner-managed `RunState`, explicit `SandboxSessionState`, or saved snapshots. ### Examples and tutorials Adds a large `examples/sandbox/` suite covering: - Local Unix and Docker sandbox runners. - Docker mount smoke tests for S3, GCS, Azure Blob Storage, and S3 Files. - Sandbox coding tasks with skills. - Sandbox agents as tools and handoff patterns. - Memory examples, including multi-agent/multi-turn memory and S3-backed memory. - Tax-prep and healthcare-support workflows. - Dataroom QA and metric extraction tutorials. - Repository code review tutorial. - Vision website clone tutorial. - Provider examples for Blaxel, Cloudflare, Daytona, E2B, Modal, Runloop, Temporal, and Vercel. ### Runtime, tracing, and model plumbing The release includes the runtime plumbing needed to make sandbox agents work naturally inside the existing SDK: - Runner-managed sandbox preparation, capability binding, session lifecycle, state serialization, and resume behavior. - Sandbox-aware `RunState` serialization. - Unified sandbox tracing with SDK spans. - Token usage on tracing spans. - Runner-managed prompt cache key defaults. - OpenAI agent registration and harness ID configuration. - Safer redaction of sensitive MCP tool outputs when sensitive tracing is disabled. - Additional OpenAI client/model utilities and Chat Completions coverage. ## Documentation & Other Changes - docs: add Asqav to external tracing processors list. - docs: update translated document pages. Co-authored-by: Abdulrahman Alfozan <alfozan@openai.com> Co-authored-by: Aditya Singh <60082699+adityasingh2400@users.noreply.github.com> Co-authored-by: Andi Liu <andi@openai.com> Co-authored-by: Aron <263346377+aron-cf@users.noreply.github.com> Co-authored-by: ashwinnathan-openai <ashwinnathan@openai.com> Co-authored-by: Codex <noreply@openai.com> Co-authored-by: cploujoux <cploujoux@blaxel.ai> Co-authored-by: elainegan-openai <168589666+elainegan-openai@users.noreply.github.com> Co-authored-by: Elias Freider <freider@users.noreply.github.com> Co-authored-by: Erik Dunteman <erik@erikds-macbook-air.local> Co-authored-by: Jason Liu <jasonliu@openai.com> Co-authored-by: Jason Steving <32336750+jasonsteving99@users.noreply.github.com> Co-authored-by: Kazuhiro Sera <seratch@openai.com> Co-authored-by: Lovre Pešut <lovre.pesut@gmail.com> Co-authored-by: Lucas Wang <lucas_wang@lucas-futures.com> Co-authored-by: Matt Brockman <matt.brockman@e2b.dev> Co-authored-by: Mish Ushakov <mishushakov@users.noreply.github.com> Co-authored-by: Naresh <ghostwriternr@gmail.com> Co-authored-by: nicholasclark-openai <nicholasclark@openai.com> Co-authored-by: qiyaoq-oai <qiyaoq@openai.com> Co-authored-by: Scott Trinh <scott@scotttrinh.com> Co-authored-by: tode-rl <tony@runloop.ai> Co-authored-by: Wendy Jiao <wendyjiao@openai.com>
549 lines
17 KiB
Python
549 lines
17 KiB
Python
from __future__ import annotations
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from typing import Any, cast
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import pytest
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from openai.types.responses import (
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ResponseComputerToolCall,
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ResponseFileSearchToolCall,
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ResponseFunctionToolCall,
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ResponseFunctionWebSearch,
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)
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from openai.types.responses.response_computer_tool_call import ActionClick
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from openai.types.responses.response_function_web_search import ActionSearch
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from openai.types.responses.response_reasoning_item import ResponseReasoningItem, Summary
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from pydantic import BaseModel
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from agents import (
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Agent,
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Computer,
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ComputerTool,
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Handoff,
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HandoffInputData,
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ModelBehaviorError,
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ModelResponse,
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ReasoningItem,
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RunConfig,
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RunContextWrapper,
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RunHooks,
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RunItem,
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ToolCallItem,
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Usage,
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handoff,
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)
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from agents.run_internal import run_loop
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from agents.run_internal.run_loop import ToolRunHandoff, get_handoffs, get_output_schema
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from .test_responses import (
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get_final_output_message,
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get_function_tool,
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get_function_tool_call,
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get_handoff_tool_call,
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get_text_input_item,
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get_text_message,
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)
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def _dummy_ctx() -> RunContextWrapper[None]:
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return RunContextWrapper(context=None)
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async def process_response(
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agent: Agent[Any],
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response: ModelResponse,
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*,
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output_schema: Any = None,
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handoffs: list[Handoff[Any, Agent[Any]]] | None = None,
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) -> Any:
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"""Process a model response using the agent's tools and optional handoffs."""
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return run_loop.process_model_response(
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agent=agent,
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response=response,
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output_schema=output_schema,
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handoffs=handoffs or [],
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all_tools=await agent.get_all_tools(_dummy_ctx()),
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)
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def test_empty_response():
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agent = Agent(name="test")
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response = ModelResponse(
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output=[],
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usage=Usage(),
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response_id=None,
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)
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result = run_loop.process_model_response(
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agent=agent,
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response=response,
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output_schema=None,
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handoffs=[],
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all_tools=[],
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)
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assert not result.handoffs
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assert not result.functions
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def test_no_tool_calls():
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agent = Agent(name="test")
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response = ModelResponse(
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output=[get_text_message("Hello, world!")],
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usage=Usage(),
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response_id=None,
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)
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result = run_loop.process_model_response(
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agent=agent, response=response, output_schema=None, handoffs=[], all_tools=[]
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)
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assert not result.handoffs
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assert not result.functions
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@pytest.mark.asyncio
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async def test_single_tool_call():
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agent = Agent(name="test", tools=[get_function_tool(name="test")])
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response = ModelResponse(
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output=[
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get_text_message("Hello, world!"),
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get_function_tool_call("test", ""),
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],
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usage=Usage(),
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response_id=None,
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)
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result = await process_response(agent=agent, response=response)
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assert not result.handoffs
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assert result.functions and len(result.functions) == 1
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func = result.functions[0]
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assert func.tool_call.name == "test"
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assert func.tool_call.arguments == ""
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@pytest.mark.asyncio
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async def test_missing_tool_call_raises_error():
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agent = Agent(name="test", tools=[get_function_tool(name="test")])
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response = ModelResponse(
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output=[
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get_text_message("Hello, world!"),
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get_function_tool_call("missing", ""),
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],
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usage=Usage(),
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response_id=None,
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)
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with pytest.raises(ModelBehaviorError):
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await process_response(agent=agent, response=response)
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@pytest.mark.asyncio
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async def test_multiple_tool_calls():
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agent = Agent(
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name="test",
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tools=[
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get_function_tool(name="test_1"),
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get_function_tool(name="test_2"),
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get_function_tool(name="test_3"),
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],
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)
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response = ModelResponse(
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output=[
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get_text_message("Hello, world!"),
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get_function_tool_call("test_1", "abc"),
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get_function_tool_call("test_2", "xyz"),
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],
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usage=Usage(),
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response_id=None,
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)
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result = await process_response(agent=agent, response=response)
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assert not result.handoffs
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assert result.functions and len(result.functions) == 2
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func_1 = result.functions[0]
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assert func_1.tool_call.name == "test_1"
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assert func_1.tool_call.arguments == "abc"
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func_2 = result.functions[1]
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assert func_2.tool_call.name == "test_2"
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assert func_2.tool_call.arguments == "xyz"
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@pytest.mark.asyncio
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async def test_handoffs_parsed_correctly():
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agent_1 = Agent(name="test_1")
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agent_2 = Agent(name="test_2")
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agent_3 = Agent(name="test_3", handoffs=[agent_1, agent_2])
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response = ModelResponse(
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output=[get_text_message("Hello, world!")],
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usage=Usage(),
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response_id=None,
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)
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result = await process_response(agent=agent_3, response=response)
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assert not result.handoffs, "Shouldn't have a handoff here"
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response = ModelResponse(
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output=[get_text_message("Hello, world!"), get_handoff_tool_call(agent_1)],
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usage=Usage(),
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response_id=None,
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)
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result = await process_response(
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agent=agent_3,
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response=response,
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handoffs=await get_handoffs(agent_3, _dummy_ctx()),
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)
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assert len(result.handoffs) == 1, "Should have a handoff here"
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handoff = result.handoffs[0]
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assert handoff.handoff.tool_name == Handoff.default_tool_name(agent_1)
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assert handoff.handoff.tool_description == Handoff.default_tool_description(agent_1)
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assert handoff.handoff.agent_name == agent_1.name
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handoff_agent = await handoff.handoff.on_invoke_handoff(
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RunContextWrapper(None), handoff.tool_call.arguments
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)
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assert handoff_agent == agent_1
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@pytest.mark.asyncio
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async def test_handoff_can_disable_run_level_history_nesting(monkeypatch: pytest.MonkeyPatch):
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source_agent = Agent(name="source")
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target_agent = Agent(name="target")
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override_handoff = handoff(target_agent, nest_handoff_history=False)
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tool_call = cast(ResponseFunctionToolCall, get_handoff_tool_call(target_agent))
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run_handoffs = [ToolRunHandoff(handoff=override_handoff, tool_call=tool_call)]
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run_config = RunConfig(nest_handoff_history=True)
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context_wrapper = RunContextWrapper(context=None)
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hooks = RunHooks()
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original_input = [get_text_input_item("hello")]
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pre_step_items: list[RunItem] = []
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new_step_items: list[RunItem] = []
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new_response = ModelResponse(output=[tool_call], usage=Usage(), response_id=None)
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calls: list[HandoffInputData] = []
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def fake_nest(
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handoff_input_data: HandoffInputData,
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*,
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history_mapper: Any,
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) -> HandoffInputData:
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_ = history_mapper
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calls.append(handoff_input_data)
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return handoff_input_data
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monkeypatch.setattr("agents.run_internal.turn_resolution.nest_handoff_history", fake_nest)
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result = await run_loop.execute_handoffs(
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public_agent=source_agent,
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original_input=list(original_input),
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pre_step_items=pre_step_items,
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new_step_items=new_step_items,
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new_response=new_response,
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run_handoffs=run_handoffs,
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hooks=hooks,
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context_wrapper=context_wrapper,
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run_config=run_config,
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)
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assert calls == []
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assert result.original_input == original_input
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@pytest.mark.asyncio
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async def test_handoff_can_enable_history_nesting(monkeypatch: pytest.MonkeyPatch):
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source_agent = Agent(name="source")
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target_agent = Agent(name="target")
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override_handoff = handoff(target_agent, nest_handoff_history=True)
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tool_call = cast(ResponseFunctionToolCall, get_handoff_tool_call(target_agent))
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run_handoffs = [ToolRunHandoff(handoff=override_handoff, tool_call=tool_call)]
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run_config = RunConfig(nest_handoff_history=False)
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context_wrapper = RunContextWrapper(context=None)
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hooks = RunHooks()
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original_input = [get_text_input_item("hello")]
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pre_step_items: list[RunItem] = []
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new_step_items: list[RunItem] = []
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new_response = ModelResponse(output=[tool_call], usage=Usage(), response_id=None)
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def fake_nest(
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handoff_input_data: HandoffInputData,
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*,
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history_mapper: Any,
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) -> HandoffInputData:
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_ = history_mapper
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return handoff_input_data.clone(
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input_history=(
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{
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"role": "assistant",
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"content": "nested",
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},
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)
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)
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monkeypatch.setattr("agents.run_internal.turn_resolution.nest_handoff_history", fake_nest)
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result = await run_loop.execute_handoffs(
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public_agent=source_agent,
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original_input=list(original_input),
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pre_step_items=pre_step_items,
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new_step_items=new_step_items,
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new_response=new_response,
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run_handoffs=run_handoffs,
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hooks=hooks,
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context_wrapper=context_wrapper,
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run_config=run_config,
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)
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assert result.original_input == [
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{
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"role": "assistant",
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"content": "nested",
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}
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]
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@pytest.mark.asyncio
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async def test_missing_handoff_fails():
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agent_1 = Agent(name="test_1")
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agent_2 = Agent(name="test_2")
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agent_3 = Agent(name="test_3", handoffs=[agent_1])
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response = ModelResponse(
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output=[get_text_message("Hello, world!"), get_handoff_tool_call(agent_2)],
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usage=Usage(),
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response_id=None,
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)
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with pytest.raises(ModelBehaviorError):
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await process_response(
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agent=agent_3,
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response=response,
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handoffs=await get_handoffs(agent_3, _dummy_ctx()),
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)
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@pytest.mark.asyncio
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async def test_multiple_handoffs_doesnt_error():
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agent_1 = Agent(name="test_1")
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agent_2 = Agent(name="test_2")
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agent_3 = Agent(name="test_3", handoffs=[agent_1, agent_2])
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response = ModelResponse(
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output=[
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get_text_message("Hello, world!"),
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get_handoff_tool_call(agent_1),
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get_handoff_tool_call(agent_2),
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],
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usage=Usage(),
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response_id=None,
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)
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result = await process_response(
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agent=agent_3,
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response=response,
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handoffs=await get_handoffs(agent_3, _dummy_ctx()),
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)
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assert len(result.handoffs) == 2, "Should have multiple handoffs here"
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class Foo(BaseModel):
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bar: str
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@pytest.mark.asyncio
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async def test_final_output_parsed_correctly():
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agent = Agent(name="test", output_type=Foo)
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response = ModelResponse(
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output=[
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get_text_message("Hello, world!"),
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get_final_output_message(Foo(bar="123").model_dump_json()),
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],
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usage=Usage(),
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response_id=None,
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)
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await process_response(
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agent=agent,
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response=response,
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output_schema=get_output_schema(agent),
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)
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@pytest.mark.asyncio
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async def test_file_search_tool_call_parsed_correctly():
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# Ensure that a ResponseFileSearchToolCall output is parsed into a ToolCallItem and that no tool
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# runs are scheduled.
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agent = Agent(name="test")
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file_search_call = ResponseFileSearchToolCall(
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id="fs1",
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queries=["query"],
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status="completed",
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type="file_search_call",
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)
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response = ModelResponse(
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output=[get_text_message("hello"), file_search_call],
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usage=Usage(),
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response_id=None,
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)
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result = await process_response(agent=agent, response=response)
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# The final item should be a ToolCallItem for the file search call
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assert any(
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isinstance(item, ToolCallItem) and item.raw_item is file_search_call
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for item in result.new_items
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)
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assert not result.functions
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assert not result.handoffs
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@pytest.mark.asyncio
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async def test_function_web_search_tool_call_parsed_correctly():
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agent = Agent(name="test")
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web_search_call = ResponseFunctionWebSearch(
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id="w1",
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action=ActionSearch(type="search", query="query"),
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status="completed",
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type="web_search_call",
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)
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response = ModelResponse(
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output=[get_text_message("hello"), web_search_call],
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usage=Usage(),
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response_id=None,
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)
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result = await process_response(agent=agent, response=response)
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assert any(
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isinstance(item, ToolCallItem) and item.raw_item is web_search_call
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for item in result.new_items
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)
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assert not result.functions
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assert not result.handoffs
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@pytest.mark.asyncio
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async def test_reasoning_item_parsed_correctly():
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# Verify that a Reasoning output item is converted into a ReasoningItem.
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reasoning = ResponseReasoningItem(
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id="r1", type="reasoning", summary=[Summary(text="why", type="summary_text")]
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)
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response = ModelResponse(
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output=[reasoning],
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usage=Usage(),
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response_id=None,
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)
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agent = Agent(name="test")
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result = await process_response(agent=agent, response=response)
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assert any(
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isinstance(item, ReasoningItem) and item.raw_item is reasoning for item in result.new_items
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)
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|
|
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class DummyComputer(Computer):
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"""Minimal computer implementation for testing."""
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|
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@property
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def environment(self):
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return "mac" # pragma: no cover
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|
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|
@property
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|
def dimensions(self):
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return (0, 0) # pragma: no cover
|
|
|
|
def screenshot(self) -> str:
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return "" # pragma: no cover
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|
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def click(self, x: int, y: int, button: str) -> None:
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return None # pragma: no cover
|
|
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|
def double_click(self, x: int, y: int) -> None:
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|
return None # pragma: no cover
|
|
|
|
def scroll(self, x: int, y: int, scroll_x: int, scroll_y: int) -> None:
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|
return None # pragma: no cover
|
|
|
|
def type(self, text: str) -> None:
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|
return None # pragma: no cover
|
|
|
|
def wait(self) -> None:
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|
return None # pragma: no cover
|
|
|
|
def move(self, x: int, y: int) -> None:
|
|
return None # pragma: no cover
|
|
|
|
def keypress(self, keys: list[str]) -> None:
|
|
return None # pragma: no cover
|
|
|
|
def drag(self, path: list[tuple[int, int]]) -> None:
|
|
return None # pragma: no cover
|
|
|
|
|
|
@pytest.mark.asyncio
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|
async def test_computer_tool_call_without_computer_tool_raises_error():
|
|
# If the agent has no ComputerTool in its tools, process_model_response should raise a
|
|
# ModelBehaviorError when encountering a ResponseComputerToolCall.
|
|
computer_call = ResponseComputerToolCall(
|
|
id="c1",
|
|
type="computer_call",
|
|
action=ActionClick(type="click", x=1, y=2, button="left"),
|
|
call_id="c1",
|
|
pending_safety_checks=[],
|
|
status="completed",
|
|
)
|
|
response = ModelResponse(
|
|
output=[computer_call],
|
|
usage=Usage(),
|
|
response_id=None,
|
|
)
|
|
with pytest.raises(ModelBehaviorError):
|
|
await process_response(agent=Agent(name="test"), response=response)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_computer_tool_call_with_computer_tool_parsed_correctly():
|
|
# If the agent contains a ComputerTool, ensure that a ResponseComputerToolCall is parsed into a
|
|
# ToolCallItem and scheduled to run in computer_actions.
|
|
dummy_computer = DummyComputer()
|
|
agent = Agent(name="test", tools=[ComputerTool(computer=dummy_computer)])
|
|
computer_call = ResponseComputerToolCall(
|
|
id="c1",
|
|
type="computer_call",
|
|
action=ActionClick(type="click", x=1, y=2, button="left"),
|
|
call_id="c1",
|
|
pending_safety_checks=[],
|
|
status="completed",
|
|
)
|
|
response = ModelResponse(
|
|
output=[computer_call],
|
|
usage=Usage(),
|
|
response_id=None,
|
|
)
|
|
result = await process_response(agent=agent, response=response)
|
|
assert any(
|
|
isinstance(item, ToolCallItem) and item.raw_item is computer_call
|
|
for item in result.new_items
|
|
)
|
|
assert result.computer_actions and result.computer_actions[0].tool_call == computer_call
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_tool_and_handoff_parsed_correctly():
|
|
agent_1 = Agent(name="test_1")
|
|
agent_2 = Agent(name="test_2")
|
|
agent_3 = Agent(
|
|
name="test_3", tools=[get_function_tool(name="test")], handoffs=[agent_1, agent_2]
|
|
)
|
|
response = ModelResponse(
|
|
output=[
|
|
get_text_message("Hello, world!"),
|
|
get_function_tool_call("test", "abc"),
|
|
get_handoff_tool_call(agent_1),
|
|
],
|
|
usage=Usage(),
|
|
response_id=None,
|
|
)
|
|
|
|
result = await process_response(
|
|
agent=agent_3,
|
|
response=response,
|
|
handoffs=await get_handoffs(agent_3, _dummy_ctx()),
|
|
)
|
|
assert result.functions and len(result.functions) == 1
|
|
assert len(result.handoffs) == 1, "Should have a handoff here"
|
|
handoff = result.handoffs[0]
|
|
assert handoff.handoff.tool_name == Handoff.default_tool_name(agent_1)
|
|
assert handoff.handoff.tool_description == Handoff.default_tool_description(agent_1)
|
|
assert handoff.handoff.agent_name == agent_1.name
|