Ag2 Quickstart

Quickstart: build your first AG2 beta Agent

When to use

• The user is starting from a blank file and wants a working AG2 beta 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:

>

```bash

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]>=0.13.4) 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>]".

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, autogen; print(sys.executable); print('ag2', autogen.__version__)"

60-second recipe

import asyncio
from autogen.beta import Agent
from autogen.beta.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 autogen.beta.config. All accept model=, optional api_key=, and (where supported) streaming=True. Streaming is recommended — AG2 beta is async- and streaming-first.

from autogen.beta.config import OpenAIConfig          # gpt-4o, gpt-5-*, o-series, etc.
from autogen.beta.config import OpenAIResponsesConfig # OpenAI Responses API (image gen, file_id support)
from autogen.beta.config import AnthropicConfig       # claude-sonnet-4-6, claude-opus-4-7, etc.
from autogen.beta.config import GeminiConfig          # Gemini Developer API (api_key)
from autogen.beta.config import VertexAIConfig        # Gemini on Google Vertex AI (project + location)
from autogen.beta.config import OllamaConfig          # local Ollama
from autogen.beta.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/beta/model_configuration.mdx.
• Agent communication API surface (events, observing, HITL): website/docs/beta/agents.mdx.
• Static, dynamic, per-turn prompts: website/docs/beta/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 beta 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.