Add AG2 (formerly AutoGen) multi-agent examples

ag2-agents/payment-approval/.env.example

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+OPENAI_API_KEY=your-openai-api-key-here
+
+# Optional
+LLM_MODEL=gpt-4o-mini
+OPENAI_BASE_URL=https://api.openai.com/v1

ag2-agents/payment-approval/README.md

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+# AG2 Two-Agent Payment Approval
+
+Demonstrates AG2's `human_input_mode="ALWAYS"` pattern as an approval gate
+before triggering a Skyfire payment — the first example in this repo requiring
+explicit user confirmation before a financial action.
+
+Two agents collaborate:
+- **researcher** — investigates the recipient and produces a risk assessment
+- **payment_executor** — presents the assessment, pauses for human confirmation,
+  then executes or aborts based on the response
+
+## Key AG2 Features
+
+- **`human_input_mode="ALWAYS"`** — executor pauses before every response; human
+  types "yes" to proceed or "no" to abort — no custom routing logic needed
+- **Two-agent `initiate_chat`** — researcher hands off to executor via the
+  natural conversation flow; the shared message history carries the assessment
+
+## Quick Start
+
+```bash
+cd ag2-agents/payment-approval
+pip install -r requirements.txt
+cp .env.example .env
+python main.py
+```

ag2-agents/payment-approval/main.py

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+"""
+AG2 two-agent payment approval with human-in-the-loop gate.
+
+researcher — investigates the recipient and produces a risk assessment
+executor   — presents the assessment and waits for explicit human confirmation
+             before executing the Skyfire payment
+
+human_input_mode="ALWAYS" on executor is the approval gate: the agent
+pauses before every response so the human can type "yes" to proceed or
+"no" to abort. No custom routing logic required.
+"""
+import os
+from dotenv import load_dotenv
+from autogen import ConversableAgent
+
+load_dotenv()
+
+llm_config = {
+    "config_list": [{
+        "model": os.getenv("LLM_MODEL", "gpt-4o-mini"),
+        "api_key": os.environ["OPENAI_API_KEY"],
+        "base_url": os.getenv("OPENAI_BASE_URL", "https://api.openai.com/v1"),
+    }],
+    "temperature": 0.2,
+    "cache_seed": None,
+}
+
+researcher = ConversableAgent(
+    name="researcher",
+    system_message=(
+        "You are a payment risk analyst. Investigate the payment recipient using available "
+        "tools: check their Fetch.ai address history, reputation, and any known flags. "
+        "Produce a concise risk assessment with a clear recommendation (proceed / do not proceed). "
+        "End your assessment with ASSESSMENT COMPLETE."
+    ),
+    llm_config=llm_config,
+    is_termination_msg=lambda m: "ASSESSMENT COMPLETE" in (m.get("content") or ""),
+)
+
+executor = ConversableAgent(
+    name="payment_executor",
+    system_message=(
+        "You handle payment execution. Present the researcher's risk assessment clearly, "
+        "state the exact payment details (recipient, amount, reason), then ask the human "
+        "to confirm. If the human approves, call the skyfire_send tool to execute the payment. "
+        "If the human declines, acknowledge and terminate. End with TERMINATE."
+    ),
+    llm_config=llm_config,
+    human_input_mode="ALWAYS",  # pauses before every response — the human types yes/no
+    is_termination_msg=lambda m: "TERMINATE" in (m.get("content") or ""),
+)
+
+
+def run_payment_approval(recipient: str, amount: float, reason: str) -> None:
+    researcher.initiate_chat(
+        executor,
+        message=(
+            f"Payment request: {amount} USDC to {recipient} — reason: '{reason}'. "
+            f"Investigate the recipient and produce a risk assessment."
+        ),
+        max_turns=6,
+    )
+
+
+if __name__ == "__main__":
+    run_payment_approval(
+        recipient="alice.fetch",
+        amount=50.0,
+        reason="research report delivery",
+    )

ag2-agents/payment-approval/requirements.txt

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+ag2[openai]>=0.11.0
+python-dotenv>=1.0.0

ag2-agents/payment-approval/tests/conftest.py

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+"""
+Ensure payment-approval/ is on sys.path so 'import main' works in tests.
+"""
+import sys
+import os
+
+parent = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+if parent not in sys.path:
+    sys.path.insert(0, parent)

ag2-agents/payment-approval/tests/test_payment.py

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+import os
+os.environ.setdefault("OPENAI_API_KEY", "test-key")
+
+def test_agents_instantiate():
+    """Verify agent setup without initiating a chat."""
+    import main as m
+    assert m.researcher.name == "researcher"
+    assert m.executor.name == "payment_executor"
+
+def test_executor_human_input_mode():
+    """Executor must have human_input_mode=ALWAYS — the approval gate."""
+    import main as m
+    assert m.executor.human_input_mode == "ALWAYS"
+
+def test_researcher_termination_condition():
+    import main as m
+    assert m.researcher._is_termination_msg({"content": "Risk: low. ASSESSMENT COMPLETE"}) is True
+    assert m.researcher._is_termination_msg({"content": "Still investigating..."}) is False
+
+def test_executor_termination_condition():
+    import main as m
+    assert m.executor._is_termination_msg({"content": "Payment aborted. TERMINATE"}) is True
+    assert m.executor._is_termination_msg({"content": "Please confirm."}) is False

ag2-agents/research-synthesis-team/.env.example

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+OPENAI_API_KEY=your-openai-api-key-here
+AGENTVERSE_API_KEY=your-agentverse-api-key-here
+
+# Optional
+LLM_MODEL=gpt-4o-mini
+OPENAI_BASE_URL=https://api.openai.com/v1
+AGENT_PORT=8008
+AGENTVERSE_URL=https://agentverse.ai
+MCP_SERVER_URL=

ag2-agents/research-synthesis-team/Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /app
+
+COPY . .
+
+RUN apt-get update && apt-get install -y gcc \
+    && pip install --no-cache-dir --upgrade pip \
+    && pip install --no-cache-dir -r requirements.txt \
+    && apt-get clean && rm -rf /var/lib/apt/lists/*
+
+ENV PYTHONUNBUFFERED=1
+EXPOSE 8008
+CMD ["python", "main.py"]

ag2-agents/research-synthesis-team/README.md

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+# AG2 Research Synthesis Team
+
+A multi-agent research pipeline using [AG2](https://github.com/ag2ai/ag2) (formerly AutoGen)
+integrated with the Fetch.ai uAgents ecosystem via the A2A protocol.
+
+## Architecture
+
+Four specialists collaborate under GroupChat with LLM-driven speaker selection, wrapped as
+an A2A executor and exposed as a discoverable agent on Agentverse.
+
+```
+GroupChat (AG2)
+├── web_researcher   — searches and gathers information
+├── financial_analyst — analyses market and economic aspects
+├── tech_analyst     — evaluates technical feasibility
+└── synthesizer      — produces the final report
+         ↓
+SingleA2AAdapter (Fetch.ai uagents-adapter)
+         ↓
+Agentverse (discoverable at port 8008)
+```
+
+## Quick Start
+
+```bash
+pip install -r requirements.txt
+cp .env.example .env  # add OPENAI_API_KEY and AGENTVERSE_API_KEY
+python main.py
+```
+
+## AG2 Features Demonstrated
+
+- **`GroupChat` with `speaker_selection_method="auto"`** — LLM-driven dynamic speaker selection
+- **Native MCP client** — optional connection to Fetch.ai's MCP gateway for web search tools
+- **Pattern B (A2A Outbound)** — same integration pattern as LangChain and Google ADK examples

ag2-agents/research-synthesis-team/agent_executor.py

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+"""
+Wraps the AG2 GroupChat workflow as a LangChain-compatible AgentExecutor
+for use with SingleA2AAdapter (Pattern B — matches LangChain/ADK examples).
+"""
+import asyncio
+from autogen import LLMConfig
+from workflow import run_research
+
+
+class AG2ResearchExecutor:
+    """Drop-in AgentExecutor interface for SingleA2AAdapter."""
+
+    def __init__(self, llm_config: LLMConfig, mcp_url: str | None = None):
+        self.llm_config = llm_config
+        self.mcp_url = mcp_url
+
+    def invoke(self, inputs: dict) -> dict:
+        topic = inputs.get("input", "")
+        result = asyncio.run(run_research(topic, self.llm_config, self.mcp_url))
+        return {"output": result}

ag2-agents/research-synthesis-team/agents.py

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+"""
+AG2 (formerly AutoGen) research synthesis team.
+Four specialists collaborate under GroupChat with LLM-driven speaker selection.
+"""
+from autogen import AssistantAgent, LLMConfig
+
+def build_agents(llm_config: LLMConfig) -> list[AssistantAgent]:
+    web_researcher = AssistantAgent(
+        name="web_researcher",
+        system_message=(
+            "You are a research specialist. Search and gather comprehensive information "
+            "on the assigned topic using available tools. Cite sources clearly."
+        ),
+        llm_config=llm_config,
+    )
+    financial_analyst = AssistantAgent(
+        name="financial_analyst",
+        system_message=(
+            "You are a financial analyst. Analyse market data, trends, and economic "
+            "implications of the research topic. Be quantitative when possible."
+        ),
+        llm_config=llm_config,
+    )
+    tech_analyst = AssistantAgent(
+        name="tech_analyst",
+        system_message=(
+            "You are a technology analyst. Evaluate technical aspects, feasibility, "
+            "and innovation potential of the research topic."
+        ),
+        llm_config=llm_config,
+    )
+    synthesizer = AssistantAgent(
+        name="synthesizer",
+        system_message=(
+            "You are a synthesis expert. Once all specialists have contributed, "
+            "produce a final structured report combining all perspectives. "
+            "Format as Markdown with sections: Summary, Financial Analysis, "
+            "Technical Analysis, Conclusions. End with TERMINATE."
+        ),
+        llm_config=llm_config,
+        is_termination_msg=lambda m: "TERMINATE" in (m.get("content") or ""),
+    )
+    return [web_researcher, financial_analyst, tech_analyst, synthesizer]

ag2-agents/research-synthesis-team/main.py

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+"""
+Fetch.ai uAgent exposing the AG2 research team via A2A protocol (Pattern B).
+Discoverable on Agentverse; callable from ASI:One or other uAgents.
+"""
+import os
+from dotenv import load_dotenv
+from uagents_adapter import SingleA2AAdapter
+from autogen import LLMConfig
+
+from agent_executor import AG2ResearchExecutor
+
+load_dotenv()
+
+llm_config = LLMConfig(
+    config_list=[{
+        "model": os.getenv("LLM_MODEL", "gpt-4o-mini"),
+        "api_key": os.getenv("OPENAI_API_KEY", ""),
+        "base_url": os.getenv("OPENAI_BASE_URL", "https://api.openai.com/v1"),
+    }],
+    temperature=0.3,
+    cache_seed=None,
+)
+
+executor = AG2ResearchExecutor(
+    llm_config=llm_config,
+    mcp_url=os.getenv("MCP_SERVER_URL"),  # optional: Fetch.ai MCP gateway
+)
+
+adapter = SingleA2AAdapter(
+    agent_executor=executor,
+    name="AG2 Research Synthesis Team",
+    description=(
+        "Multi-agent research team using AG2 (formerly AutoGen). "
+        "Four specialists (web researcher, financial analyst, tech analyst, synthesizer) "
+        "collaborate to produce comprehensive research reports on any topic."
+    ),
+    port=int(os.getenv("AGENT_PORT", "8008")),
+    agentverse_url=os.getenv("AGENTVERSE_URL", "https://agentverse.ai"),
+    mailbox_api_key=os.getenv("AGENTVERSE_API_KEY", ""),
+)
+
+if __name__ == "__main__":
+    adapter.run()

ag2-agents/research-synthesis-team/requirements.txt

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+ag2[openai,mcp]>=0.11.0
+mcp>=1.0.0
+uagents>=0.20.0
+uagents-adapter>=0.4.0
+python-dotenv>=1.0.0

ag2-agents/research-synthesis-team/tests/conftest.py

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+"""
+Ensure the project root (research-synthesis-team/) is on sys.path so that
+local modules (agents.py, agent_executor.py, workflow.py) are importable.
+Also evict the installed 'agents' package (OpenAI Agents SDK) that would
+otherwise shadow the local agents.py.
+"""
+import sys
+import os
+
+parent = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+if parent not in sys.path:
+    sys.path.insert(0, parent)
+
+# Evict the installed 'agents' package from sys.modules cache so
+# 'from agents import build_agents' finds our local agents.py instead.
+for key in list(sys.modules.keys()):
+    if key == "agents" or key.startswith("agents."):
+        mod = sys.modules[key]
+        if hasattr(mod, "__file__") and mod.__file__ and parent not in (mod.__file__ or ""):
+            del sys.modules[key]

ag2-agents/research-synthesis-team/tests/test_agent_executor.py

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+from unittest.mock import patch, MagicMock
+from autogen import LLMConfig
+
+TEST_LLM = LLMConfig(
+    {"model": "gpt-4o-mini", "api_key": "test", "base_url": "https://api.openai.com/v1"}
+)
+
+def test_executor_invoke_calls_run_research():
+    from agent_executor import AG2ResearchExecutor
+    executor = AG2ResearchExecutor(llm_config=TEST_LLM)
+    with patch("agent_executor.asyncio.run", return_value="Mock research report") as mock_run:
+        result = executor.invoke({"input": "quantum computing"})
+    mock_run.assert_called_once()
+    assert result == {"output": "Mock research report"}

ag2-agents/research-synthesis-team/tests/test_agents.py

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+"""Unit tests — no LLM calls, no network, no uAgents runtime."""
+import pytest
+import os
+os.environ.setdefault("OPENAI_API_KEY", "test-key")
+from autogen import LLMConfig
+
+TEST_LLM_CONFIG = LLMConfig(
+    {"model": "gpt-4o-mini", "api_key": "test", "base_url": "https://api.openai.com/v1"}
+)
+
+def test_agents_instantiate():
+    from agents import build_agents
+    agents = build_agents(TEST_LLM_CONFIG)
+    assert len(agents) == 4
+    names = [a.name for a in agents]
+    assert "web_researcher" in names
+    assert "financial_analyst" in names
+    assert "tech_analyst" in names
+    assert "synthesizer" in names
+
+def test_synthesizer_termination():
+    from agents import build_agents
+    agents = build_agents(TEST_LLM_CONFIG)
+    synthesizer = next(a for a in agents if a.name == "synthesizer")
+    assert synthesizer._is_termination_msg({"content": "Report done. TERMINATE"}) is True
+    assert synthesizer._is_termination_msg({"content": "Still analysing..."}) is False
+
+def test_executor_instantiates():
+    from autogen import UserProxyAgent
+    executor = UserProxyAgent(
+        name="executor", human_input_mode="NEVER",
+        code_execution_config=False,
+        is_termination_msg=lambda m: "TERMINATE" in (m.get("content") or ""),
+    )
+    assert executor.name == "executor"