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>
88 lines
3.4 KiB
Python
88 lines
3.4 KiB
Python
import asyncio
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from typing import Annotated, Any
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from openai.types.responses import ResponseFunctionCallArgumentsDeltaEvent
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from agents import Agent, Runner, function_tool
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@function_tool
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def write_file(filename: Annotated[str, "Name of the file"], content: str) -> str:
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"""Write content to a file."""
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return f"File {filename} written successfully"
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@function_tool
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def create_config(
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project_name: Annotated[str, "Project name"],
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version: Annotated[str, "Project version"],
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dependencies: Annotated[list[str] | None, "Dependencies (list of packages)"],
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) -> str:
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"""Generate a project configuration file."""
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return f"Config for {project_name} v{version} created"
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async def main():
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"""
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Demonstrates real-time streaming of function call arguments.
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Function arguments are streamed incrementally as they are generated,
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providing immediate feedback during parameter generation.
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"""
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agent = Agent(
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name="CodeGenerator",
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instructions="You are a helpful coding assistant. Use the provided tools to create files and configurations.",
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tools=[write_file, create_config],
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)
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print("🚀 Function Call Arguments Streaming Demo")
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result = Runner.run_streamed(
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agent,
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input="Create a Python web project called 'my-app' with FastAPI. Version 1.0.0, dependencies: fastapi, uvicorn",
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)
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# Track function calls for detailed output
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function_calls: dict[Any, dict[str, Any]] = {} # call_id -> {name, arguments}
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current_active_call_id = None
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async for event in result.stream_events():
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if event.type == "raw_response_event":
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# Function call started
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if event.data.type == "response.output_item.added":
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if getattr(event.data.item, "type", None) == "function_call":
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function_name = getattr(event.data.item, "name", "unknown")
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call_id = getattr(event.data.item, "call_id", "unknown")
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function_calls[call_id] = {"name": function_name, "arguments": ""}
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current_active_call_id = call_id
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print(f"\n📞 Function call streaming started: {function_name}()")
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print("📝 Arguments building...")
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# Real-time argument streaming
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elif isinstance(event.data, ResponseFunctionCallArgumentsDeltaEvent):
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if current_active_call_id and current_active_call_id in function_calls:
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function_calls[current_active_call_id]["arguments"] += event.data.delta
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print(event.data.delta, end="", flush=True)
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# Function call completed
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elif event.data.type == "response.output_item.done":
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if hasattr(event.data.item, "call_id"):
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call_id = getattr(event.data.item, "call_id", "unknown")
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if call_id in function_calls:
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function_info = function_calls[call_id]
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print(f"\n✅ Function call streaming completed: {function_info['name']}")
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print()
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if current_active_call_id == call_id:
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current_active_call_id = None
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print("Summary of all function calls:")
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for call_id, info in function_calls.items():
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print(f" - #{call_id}: {info['name']}({info['arguments']})")
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print(f"\nResult: {result.final_output}")
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if __name__ == "__main__":
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asyncio.run(main())
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