AG2

Connect AG2 multi-agent systems to TwexAPI for tweet search, profiles, timelines, and delegated research workflows.

AG2 is an open-source Python framework for multi-agent systems. TwexAPI does not ship a native AG2 search toolkit yet. Connect AG2 agents to the TwexAPI MCP server with MCPToolkit so agents can call explore and twexapi_request while keeping API credentials inside your infrastructure.

Preserve every tweet ID, user ID, and cursor the API returns.

Why use AG2 with TwexAPI?

AG2 gives you explicit control over tool exposure, delegation, and middleware. Pair that with TwexAPI MCP when you want multi-agent research without hardcoding every REST route.

This suits research, monitoring, and reporting agents. Use the Python SDK or Prefect collection for deterministic jobs that need no model decisions.

Prerequisites

• Python 3.10 or later
• A TwexAPI API key
• An LLM provider key supported by AG2

Public X reads need no X Developer credentials. Authenticate with TwexAPI.

Install AG2

python -m pip install "ag2>=1.0.0" python-dotenv

Install your model provider extra as well.

python -m pip install "ag2[anthropic]>=1.0.0"

Store secrets outside source control.

export TWEXAPI_API_KEY="YOUR_API_KEY"
export ANTHROPIC_API_KEY="YOUR_ANTHROPIC_KEY"

Connect TwexAPI MCP with MCPToolkit

Use client-side MCPToolkit when credentials must stay in your infrastructure. TwexAPI MCP exposes explore and twexapi_request.

import asyncio
import os

from ag2 import Agent
from ag2.config import AnthropicConfig
from ag2.tools import MCPToolkit, MCPServerConfig
from dotenv import load_dotenv

load_dotenv()
config = AnthropicConfig(model="claude-sonnet-4-20250514")

twexapi_mcp = MCPToolkit(
    MCPServerConfig(
        server_url="https://api.twexapi.io/mcp",
        server_label="twexapi",
        headers={"x-api-key": os.environ["TWEXAPI_API_KEY"]},
        allowed_tools=["explore", "twexapi_request"],
    )
)

agent = Agent(
    "x-researcher",
    prompt=(
        "Search X for evidence before answering. "
        "Always call explore before twexapi_request. "
        "Quote tweet text verbatim and keep every tweet ID you receive. "
        "Ask for confirmation before read_only: false actions."
    ),
    config=config,
    tools=[twexapi_mcp],
)


async def main() -> None:
    reply = await agent.ask(
        "What are developers saying about AI agents on X this week? "
        "Return tweet IDs, authors, and a 5-bullet summary."
    )
    print(reply.body)


asyncio.run(main())

headers with x-api-key is required. Unauthenticated requests to https://api.twexapi.io/mcp return 401.

Restrict tools by agent role

Give discovery-only agents access to explore. Give execution agents both tools.

catalog_agent_tools = [
    MCPToolkit(
        MCPServerConfig(
            server_url="https://api.twexapi.io/mcp",
            headers={"x-api-key": os.environ["TWEXAPI_API_KEY"]},
            allowed_tools=["explore"],
            server_label="twexapi-catalog",
        )
    )
]

execution_agent_tools = [
    MCPToolkit(
        MCPServerConfig(
            server_url="https://api.twexapi.io/mcp",
            headers={"x-api-key": os.environ["TWEXAPI_API_KEY"]},
            allowed_tools=["explore", "twexapi_request"],
            server_label="twexapi-execute",
        )
    )
]

Delegate search in a multi-agent team

Agent.as_tool() exposes an agent as a tool for another agent. The coordinator receives the delegate’s final answer, not its internal tool-call history.

searcher = Agent(
    "searcher",
    prompt=(
        "Use TwexAPI MCP to search public X posts. "
        "Call explore first. Return tweet text with IDs. Do not summarise away IDs."
    ),
    config=config,
    tools=[twexapi_mcp],
)

analyst = Agent(
    "analyst",
    prompt="Turn tweet records into a factual brief. Keep every tweet ID.",
    config=config,
)

coordinator = Agent(
    "coordinator",
    prompt="Delegate the search, then pass the tweets to the analyst.",
    config=config,
    tools=[
        searcher.as_tool(description="Search public X posts and return raw tweet records."),
        analyst.as_tool(description="Analyse tweet records. Pass them in the context parameter."),
    ],
)

reply = await coordinator.ask("Brief me on this week's discussion of AI agents on X.")
print(reply.body)

Handoff checklist

Store durable fields from MCP responses so later workflow steps do not depend on chat history.

See Agent MCP Handoff for the full checklist.

Pagination

When explore returns a paginated route, pass the documented cursor fields back through twexapi_request unchanged. Treat cursors as opaque strings. Deduplicate rows on tweet_id or user_id.

checkpoint = {
    "route_used": "/twitter/advanced_search/page",
    "query": "AI agents",
    "has_more": True,
    "next_cursor": "cursor_123",
}

Combine with Docs MCP

Add the Docs MCP server when agents should search TwexAPI documentation before choosing routes.

tools=[
    MCPToolkit(
        MCPServerConfig(
            server_url="https://docs.twexapi.io/mcp",
            server_label="twexapi-docs",
        )
    ),
    twexapi_mcp,
]

Handle failures

MCP and REST errors surface through the toolkit as HTTP failures. Branch on status before retrying.

Wrap MCPToolkit with AG2 tool middleware when you need retries, approval gates, or audit logging around every call.

Provider-side MCP (Anthropic only)

If you target Anthropic and accept forwarding credentials to the provider, use MCPServerTool instead of MCPToolkit. Prefer MCPToolkit for provider-agnostic deployments and when API keys must stay in your infrastructure.

Related guides

• AG2 MCP Servers documentation
• MCP Server
• MCP Tools
• LangChain
• CrewAI
• Python SDK