Microsoft AutoGen

Source-backed facts for citing this resource, derived directly from the registry — also available as plain text for AI assistants.

Canonical URL

https://heyclau.de/entry/tools/microsoft-autogen

Source URLs

https://microsoft.github.io/autogen/stable/, https://github.com/microsoft/autogen, https://microsoft.github.io/autogen/

Brand

Microsoft

Brand domain

microsoft.github.io

Brand asset source

brandfetch

Safety notes

AutoGen runs multi-agent workflows that can execute code and call external tools autonomously; sandbox execution and review agent actions before granting tool or system access.

Privacy notes

AutoGen agents send prompts, code, and tool outputs to the configured LLM provider(s); review what data your agents transmit and each provider's data-handling and retention terms.

Author

Microsoft

Claim status

unclaimed

Last verified

2026-04-27

• AutoGen agents send prompts, code, and tool outputs to the configured LLM provider(s); review what data your agents transmit and each provider's data-handling and retention terms.

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Microsoft AutoGen side by side with 2 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.

Field	Microsoft AutoGen Open-source framework for building multi-agent AI applications, conversations, workflows, and autonomous systems. Open dossier	LangGraph Agent orchestration framework for building stateful, controllable, multi-step LLM and agent workflows. Open dossier	AG2 Agent Framework Open-source Python AgentOS and multi-agent framework, evolved from AutoGen, for building conversable agents, group chats, swarms, human-in-the-loop workflows, tool use, RAG, code execution, and provider-backed agent systems. Open dossier
Trust	Review first	Review first	Review first
Install risk	Review first	Review first	Review first
Notes	Safety ✓ Privacy ✓	Safety · Privacy ✓	Safety ✓ Privacy ✓
Brand	Microsoft	LangGraph	AG2 Agent Framework
Category	tools	tools	tools
Source	source-backed	source-backed	source-backed
Author	Microsoft	LangChain	AG2
Added	2026-04-27	2026-04-27	2026-06-18
Platforms	CLI	CLI	CLI
Source repo	—	—	—
Safety notes	✓AutoGen runs multi-agent workflows that can execute code and call external tools autonomously; sandbox execution and review agent actions before granting tool or system access.	— missing	✓AG2 agents can converse, call tools, execute code, use retrieval systems, run browser workflows, and coordinate group chats; require explicit permissions and approval gates for high-impact actions. The upstream install docs and examples commonly involve provider credentials; keep API keys, config files, notebooks, and `.env` files out of commits and support tickets. Code execution, Docker, Jupyter, browser-use, and RAG extras can touch local files, network services, notebooks, databases, and external websites; scope them tightly before granting agent access. Multi-agent conversations can continue through nested chats, swarms, group chats, and custom reply handlers; define termination, escalation, retry, and human takeover behavior. Track the release roadmap before upgrading because deprecations and the v1.0 transition can change which APIs should be used for new work.
Privacy notes	✓AutoGen agents send prompts, code, and tool outputs to the configured LLM provider(s); review what data your agents transmit and each provider's data-handling and retention terms.	✓LangGraph sends prompts and graph state to your configured model provider (including Claude); persisted state and checkpoints can contain message and tool-call data.	✓Prompts, messages, tool arguments, tool outputs, code snippets, notebook state, retrieved documents, vector-store contents, provider responses, traces, and execution logs may contain sensitive user or workspace data. Do not expose secrets, API keys, private file paths, customer records, internal documents, database rows, or raw exceptions through agent messages, logs, notebooks, screenshots, or public examples. Provider extras and retrieval integrations can route data through OpenAI, Anthropic, Google, AWS, local model servers, databases, vector stores, browser automation, or other third-party services. If AG2 is used for code execution or browser automation, define which files, domains, credentials, downloads, screenshots, and logs can be read or retained.
Prerequisites	— none listed	— none listed	Python 3.10 or newer and a Python environment managed with pip, uv, or another package manager. Model provider credentials for the selected provider extra, such as OpenAI, Anthropic, Gemini, Bedrock, Mistral, Ollama, Groq, xAI, or another supported route. A secrets strategy for provider keys, AG2 config files, `.env` files, notebooks, and example `OAI_CONFIG_LIST`-style credentials. A reviewed execution boundary for code execution, Docker, Jupyter, browser-use, RAG, retrieval, database, and external tool extras.
Install	—	—	pip install 'ag2[openai]' Copy install
Config	—	—	—
Citations	Source repositorygithub.com 2026-07-04T21:34:14+00:00 Documentationmicrosoft.github.io Websitemicrosoft.github.io Source methodology →	Source repositorygithub.com 2026-07-04T21:34:14+00:00 Documentationlangchain-ai.github.io Source methodology →	Source repositorygithub.com 2026-07-04T21:34:14+00:00 Documentationdocs.ag2.ai Source methodology →
Claim	Unclaimed	Unclaimed	Unclaimed

Source-backed guides for putting this to work.

guides

Microsoft AI Agents for Beginners

Learn AI agents through Microsoft's course covering frameworks, design patterns, tool use, agentic RAG, production, protocols, memory, and security.

Review firstSource-backedReview firstAdded 17d ago

Safety ✓ Privacy ✓by Microsoft

Copy install

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