GPT Lab vs OpenAI Agents SDK
Side-by-side comparison · Updated October 2026
| Description | Streamlit is a powerful open-source app framework specifically designed for creating and sharing data science and machine learning apps. With Streamlit, developers can turn data scripts into interactive web apps in just minutes. This easy-to-use tool allows seamless UI integration, supports various data sources, and is scalable to handle large projects. Its ability to host apps directly online makes data science projects accessible and shareable. | The OpenAI Agents SDK is the official framework from OpenAI for building multi-agent systems in Python. Despite the name, it is provider-agnostic: it works with OpenAI APIs plus over 100 other LLMs, so you are not locked into a single provider. The SDK revolves around nine core concepts. Agents are LLMs configured with instructions, tools, guardrails, and handoffs. Tools can be Python functions, MCP servers, or hosted tools. Guardrails provide configurable safety checks for input and output validation. Handoffs let agents delegate tasks to other agents. Human-in-the-loop mechanisms let you involve people at any step. Sessions manage conversation history automatically across runs. Tracing tracks every step for viewing, debugging, and optimization. Realtime agents support voice workflows with gpt-realtime-1.5. The newest addition is Sandbox Agents (v0.14.0+). A sandbox agent operates in a containerized environment with its own filesystem, shell access, and workspace. You define a Manifest specifying files, directories, Git repos, environment variables, and mounts. The agent can then inspect files, run commands, apply patches, and carry workspace state across longer tasks. Sandbox providers include local Unix, Docker, and hosted options (Blaxel, Cloudflare, Daytona, E2B, Modal, Runloop, Vercel). Sandbox Memory lets future runs learn from prior runs with read-only and generate-only modes, live updates when stale memory is discovered, and multi-turn grouping. Workspace mounts support local files, S3, Cloudflare R2, GCS, and Azure Blob Storage. Installation is straightforward: `pip install openai-agents`. Optional extras include voice support and Redis sessions. The SDK requires Python 3.10+ and is MIT licensed with 25,400 GitHub stars and 278 contributors. |
| Category | Collaboration | DeveloperApplication |
| Rating | No reviews | No reviews |
| Pricing | Free | Free |
| Starting Price | Free | N/A |
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| Tags | Streamlitopen-sourceapp frameworkdata sciencemachine learning | agentsopenaiframeworkmulti-agentpython |
| Features | ||
| Open-source | ||
| Easy-to-use | ||
| Supports various data sources | ||
| Scalable | ||
| Interactive web apps | ||
| Direct online hosting | ||
| Python compatible | ||
| Supports dashboards | ||
| Extensive documentation | ||
| Active community | ||
| Provider-agnostic: supports 100+ LLMs beyond OpenAI | ||
| Agent configuration with instructions, tools, and guardrails | ||
| Handoffs for delegating tasks between agents | ||
| Human-in-the-loop mechanisms | ||
| Automatic session and conversation history management | ||
| Built-in tracing for debugging and optimization | ||
| Sandbox agents with containerized workspaces | ||
| MCP server integration for tool access | ||
| Realtime voice agents with gpt-realtime-1.5 | ||
| Workspace mounts for S3, GCS, Azure Blob, and R2 | ||
| View GPT Lab | View OpenAI Agents SDK | |
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