ag2-quickstart

name	ag2-quickstart
description	Build a minimal AG2 `Agent` end to end — pick a model provider, set a prompt, call `agent.ask()`, then continue the conversation with `reply.ask()` (multi-turn). Use when the user is starting a new AG2 project, has no working `Agent` yet, or needs the multi-turn chaining pattern. Covers `OpenAIConfig`, `AnthropicConfig`, `GeminiConfig`, `OllamaConfig` etc., and env-var fallback for API keys.
license	Apache-2.0

Quickstart: build your first AG2 Agent

When to use

• The user is starting from a blank file and wants a working AG2 agent.
• The user is unsure which provider config to use.
• The user wants to chain follow-up turns without losing conversation context.
• A larger task needs the basic Agent setup as its skeleton — start here, then layer the relevant feature skill on top.

Prerequisites

Required step: install dependencies before finishing. After writing AG2 code, install the matching provider extra (plus any feature extra the task needs, e.g. ag-ui, tracing). Run the install:

pip install "ag2[openai]"   # swap in the provider/extra you used

If you cannot run commands, state the exact pip install command. This is part of finishing the task, not an optional note.

For a multi-file project (more than a throwaway script), also drop a requirements.txt pinning ag2 with the extras you used (e.g. ag2[openai]>=0.14.0) so the environment is reproducible.

Install the right provider extra and have a key for it. Each *Config requires its provider SDK — without the matching extra you'll see ImportError: ... requires optional dependencies. Install with pip install "ag2[<provider>]".

Provider	Install	Env var	Config class
OpenAI	pip install "ag2[openai]"	OPENAI_API_KEY	OpenAIConfig, OpenAIResponsesConfig
Anthropic	pip install "ag2[anthropic]"	ANTHROPIC_API_KEY	AnthropicConfig
Gemini (API key)	pip install "ag2[gemini]"	GEMINI_API_KEY (or GOOGLE_API_KEY)	GeminiConfig
Vertex AI (Gemini)	pip install "ag2[gemini]"	service-account / ADC	VertexAIConfig
Ollama (local)	pip install "ag2[ollama]"	—	OllamaConfig
DashScope (Qwen)	pip install "ag2[dashscope]"	DASHSCOPE_API_KEY	DashScopeConfig

Load env vars from a project-root .env with python-dotenv so scripts pick up keys without exporting them in your shell:

from dotenv import load_dotenv
load_dotenv()  # reads .env at project root

Quick sanity-check before debugging weird import errors — make sure you're running against the ag2 you think:

python -c "import sys, ag2; from importlib.metadata import version; print(sys.executable); print('ag2', version('ag2'))"

60-second recipe

import asyncio
from ag2 import Agent
from ag2.config import OpenAIConfig

async def main() -> None:
    agent = Agent(
        "assistant",
        prompt="You are a helpful assistant. Reply in one sentence.",
        config=OpenAIConfig(model="gpt-4o-mini"),
    )

    # First turn
    reply = await agent.ask("What is the capital of France?")
    print(reply.body)

    # Continue the same conversation — context is preserved
    reply = await reply.ask("And of Germany?")
    print(reply.body)

asyncio.run(main())

Agent.ask(...) starts a new turn and returns an AgentReply. AgentReply.ask(...) continues the same conversation, preserving its context and history. The reply text is in reply.body; for typed output see the ag2-structured-output skill (reply.content()).

Picking a provider

Each provider has its own config class in ag2.config. All accept model=, optional api_key=, and (where supported) streaming=True. Streaming is recommended — AG2 is async- and streaming-first.

from ag2.config import OpenAIConfig          # gpt-4o, gpt-5-*, o-series, etc.
from ag2.config import OpenAIResponsesConfig # OpenAI Responses API (image gen, file_id support)
from ag2.config import AnthropicConfig       # claude-sonnet-4-6, claude-opus-4-7, etc.
from ag2.config import GeminiConfig          # Gemini Developer API (api_key)
from ag2.config import VertexAIConfig        # Gemini on Google Vertex AI (project + location)
from ag2.config import OllamaConfig          # local Ollama
from ag2.config import DashScopeConfig       # Alibaba Qwen

config = AnthropicConfig(model="claude-sonnet-4-6", streaming=True)

If api_key= is omitted, the config reads the standard env var — OPENAI_API_KEY, ANTHROPIC_API_KEY, GEMINI_API_KEY (or GOOGLE_API_KEY), etc.

For OpenAI-compatible endpoints (vLLM, LM Studio, Together, NVIDIA NIM, etc.) use OpenAIConfig with base_url= set:

config = OpenAIConfig(
    model="qwen-3",
    base_url="http://localhost:8000/v1",
    api_key="NotRequired",  # pragma: allowlist secret
)

Multi-turn — chain reply.ask()

agent = Agent("planner", prompt="...", config=config)
reply = await agent.ask("Plan a 5-day Japan trip in late April.")
reply = await reply.ask("Budget is $2500 per person, two travellers.")
reply = await reply.ask("Prefer trains. Day-by-day itinerary.")
print(reply.body)

reply.ask() keeps the prior turns in scope so the LLM remembers the constraints. Calling agent.ask(...) again instead would start a fresh conversation. See assets/multi_turn.py for the full travel-planner example.

Reusing model configs

Configs are immutable. Use .copy(...) to fork one with overrides:

base = OpenAIConfig(model="gpt-5")
hot = base.copy(temperature=0.8)
cheap = base.copy(model="gpt-5-mini")

You can also override the model per ask — useful when the user brings their own API key per request:

agent = Agent("assistant", prompt="Help.")
reply = await agent.ask("Hello!", config=OpenAIConfig(model="gpt-5", api_key="sk-..."))  # pragma: allowlist secret

The per-ask config completely replaces the agent's config for that turn.

Going deeper

• Working starter (single-turn): assets/hello_agent.py (mirrors code_examples/01).
• Multi-turn starter: assets/multi_turn.py (mirrors code_examples/03).
• Full provider reference, including VertexAIConfig auth, extra_body, custom httpx client, env-var fallback table: website/docs/user-guide/model_configuration.mdx.
• Agent communication API surface (events, observing, HITL): website/docs/user-guide/agents.mdx.
• Static, dynamic, per-turn prompts: website/docs/user-guide/system_prompts.mdx.

Common pitfalls

• Forgetting to await — every method on Agent / AgentReply is async. Wrap in asyncio.run(main()) for scripts.
• Calling agent.ask() twice expecting context to carry — it doesn't; use reply.ask() instead.
• Hardcoding API keys — prefer env-var fallback (OPENAI_API_KEY, etc.) so configs commit cleanly.
• Skipping streaming=True — AG2 is streaming-first; you'll get a worse user experience without it on supported providers.
• Per-ask config= is total override, not a partial merge — be deliberate about which knobs you set.