--- name: ag2-326d086ca6f9 description: >- AG2 (formerly AutoGen) is an open-source Python framework for building AI agents and multi-agent systems that use tools, human approval and structured conversations. Use it when you need agents that call functions, collaborate in a group chat, execute code, or when you are migrating AutoGen or pyautogen code to AG2 v1 or ag2-classic. license: Apache-2.0 compatibility: Python 3.10+, an API key for the chosen model provider metadata: author: terminal-skills version: 1.1.0 category: data-ai repository: https://github.com/ag2ai/ag2 tags: "multi-agent, autogen, orchestration, python, agents" --- # AG2 (AutoGen) — Multi-Agent Conversation Framework ## Overview AG2 is the community continuation of Microsoft's AutoGen. Since **v1.0** (current: 1.1.1, September 2026) it is a new, async-first framework and is **not backward compatible** with classic AutoGen: | You see in code | You have | Install | |---|---|---| | `from ag2 import Agent`, `from ag2.config import OpenAIConfig` | AG2 v1 | `pip install "ag2[openai]"` | | `from autogen import ConversableAgent, GroupChat, UserProxyAgent` | classic AutoGen / AG2 0.x | `pip install ag2-classic` (its README also shows `autogen[openai]`) | Classic is in maintenance mode (security fixes only). Do not mix the two APIs in one file. Provider extras for v1: `ag2[openai]`, `ag2[anthropic]`, `ag2[gemini]`, `ag2[ollama]` and others. Docs: https://docs.ag2.ai (v1) and https://classic.docs.ag2.ai (classic). ## Instructions ### AG2 v1: an agent with a tool ```python import asyncio from ag2 import Agent, tool from ag2.config import OpenAIConfig # also AnthropicConfig, GeminiConfig, OllamaConfig, ... @tool async def count_lines(path: str) -> str: """Count the lines in a source file inside the current project.""" with open(path, encoding="utf-8") as f: return f"{path}: {sum(1 for _ in f)} lines" reviewer = Agent( "reviewer", prompt="You review Python code. Use tools to inspect files before judging them.", config=OpenAIConfig("gpt-4o-mini"), # key is read from OPENAI_API_KEY tools=[count_lines], ) async def main() -> None: reply = await reviewer.ask("How big is app/main.py?") print(reply.body) follow_up = await reply.ask("Is that too large for one module?") # keeps the history print(follow_up.body) asyncio.run(main()) ``` - `agent.ask(...)` returns a reply; `reply.body` is the text and `reply.ask(...)` continues the same conversation. - A tool is a typed function with a docstring decorated with `@tool`; the signature and docstring become the schema the model sees. - Stream or observe a turn with `async with agent.run("...") as run: run.start(); async for event in run.stream...` and `await run.result()`. - Human approval: pass `hitl_hook=` to `Agent`; it receives a `HumanInputRequest` (from `ag2.events`) and returns a `HumanMessage(content=...)`. - Test without an LLM: `from ag2.testing import TestConfig` and pass `TestConfig("scripted reply")` (or `ToolCallEvent`s) as `config`. ### AG2 v1: several agents v1 replaces `GroupChat`, swarm and nested chats with a **Network**: a `Hub` registers agents, and they talk over typed channels (`conversation` for free two-party chat, `consulting` for one question and one answer, `discussion` for round-robin among N agents, `workflow` for a `TransitionGraph` of conditional handoffs, the closest analogue to GroupChat). Read the group-chat migration guide on docs.ag2.ai before writing this part; the classes live in `ag2.network` (`Hub`, `Channel`, `TransitionGraph`, `Handoff`). ### Classic AutoGen (`ag2-classic`): two agents and group chat Only for existing code that imports `autogen`. ```python from autogen import ConversableAgent, LLMConfig, UserProxyAgent, register_function from autogen.agentchat import run_group_chat from autogen.agentchat.group.patterns import AutoPattern llm_config = LLMConfig(api_type="openai", model="gpt-4o-mini") # key from OPENAI_API_KEY engineer = ConversableAgent( name="engineer", system_message="You write clean, tested Python. Explain design decisions briefly.", llm_config=llm_config, ) runner = UserProxyAgent( name="runner", human_input_mode="NEVER", # NEVER / ALWAYS / TERMINATE max_consecutive_auto_reply=10, is_termination_msg=lambda m: "TERMINATE" in (m.get("content") or ""), code_execution_config={"work_dir": "workspace", "use_docker": True}, ) result = runner.initiate_chat(engineer, message="Write a FastAPI /health endpoint with a pytest test.", max_turns=6) reviewer = ConversableAgent(name="reviewer", system_message="You review code for bugs and security issues.", llm_config=llm_config) pattern = AutoPattern( agents=[engineer, reviewer], initial_agent=engineer, group_manager_args={"name": "manager", "llm_config": llm_config}, ) response = run_group_chat(pattern=pattern, messages="Build a rate limiter for our API.", max_rounds=12) ``` Tools in classic: `register_function(fn, caller=engineer, executor=runner, description="...")`. The caller proposes the call, the executor runs it. Classic also has `LLMConfig.from_json(path="OAI_CONFIG_LIST")` for a config file. Only the register and `run_group_chat` calls above were checked against the classic README; check other classic details at classic.docs.ag2.ai. ## Examples ### Example 1: Single agent that inspects a repository **User request:** "Make me an AG2 agent that can count lines in files and answer questions about them." Install with `python -m venv .venv && .venv/bin/pip install "ag2[openai]"`, save the v1 code above as `reviewer.py`, set `OPENAI_API_KEY` in the environment and run `.venv/bin/python reviewer.py`. The agent calls `count_lines` for `app/main.py`, then prints a sentence such as "app/main.py has 212 lines". The follow-up question reuses the same history. ### Example 2: Unit-test an agent without calling an LLM **User request:** "I want to test my AG2 tool wiring in CI without an API key." ```python from ag2 import Agent from ag2.events import ToolCallEvent from ag2.testing import TestConfig config = TestConfig( ToolCallEvent(name="count_lines", arguments='{"path": "app/main.py"}'), "app/main.py has 212 lines", ) agent = Agent("reviewer", prompt="Use tools.", config=config, tools=[count_lines]) reply = await agent.ask("How big is app/main.py?") assert "212" in reply.body ``` The scripted model requests the tool, AG2 runs the real `count_lines`, and the scripted second turn is the answer. Each `ask` consumes scripted turns, so supply enough for follow-ups. ## Guidelines - Check which API a file uses (`ag2` vs `autogen` imports) before editing; v1 and classic are different libraries. - Keep keys in environment variables, never in code or a committed `OAI_CONFIG_LIST`. - Give each agent a narrow prompt and only the tools it needs; vague prompts produce unfocused loops. - Always bound loops: `max_turns` / `max_rounds` and `max_consecutive_auto_reply` in classic, and watch token usage in group chats. - Classic code execution runs model-written code: keep `use_docker: True`, and never point `work_dir` at a real project. - Require human approval (`hitl_hook` in v1, `human_input_mode="ALWAYS"` or `"TERMINATE"` in classic) before irreversible actions. - Do not migrate a working classic system to v1 unprompted; v1 is a rewrite of orchestration, not a version bump. --- Source attribution: terminal-skills Original project: https://github.com/TerminalSkills/skills/blob/8a9852d489f274538be9bf1c9121aeb403750b1c/skills/ag2/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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See the License for the specific language governing permissions and limitations under the License. # Mega AI Hub resource metadata Creator / publisher: terminal-skills Original project: https://github.com/TerminalSkills/skills/blob/8a9852d489f274538be9bf1c9121aeb403750b1c/skills/ag2/SKILL.md Attribution basis: Source repository record Fields of work: Development & Engineering, Data & Analytics, Product Management, Project & Program Management Roles: ML / AI Engineer, Backend Developer, Data Scientist, Product Manager, Project Manager Specialties: API, Backend Tools: Autogen Country applicability: Country not specified Classification basis: Source category, role and specialization labels Instruction SHA-256: 22fd18a34d0a0b4a0e3f33db5f3576840c2f74249511289d20fe250e26139fcd