AG2 Twitter API Search Guide for Python Agents

AG2 is an open-source Python framework for multi-agent systems. AG2 1.0.0 and later ship XquikSearchToolkit in ag2.extensions.tools.search, so Xquik tweet search reaches AG2 agents without a community adapter. AG2 documents the toolkit in its Xquik Tweet Search reference. Preserve every tweet ID and cursor the API returns.

Why Use AG2 With the Xquik Twitter API?

AG2 binds search parameters when a tool is constructed, not when the model calls it. The agent chooses the query. You choose the window, the ordering, and the result limit.

Boundary	AG2 control	Benefit
Tool construction	`XquikSearchToolkit(...)`	Fix `query_type`, `limit`, and time windows outside model control
Model surface	Single `query` argument	The model cannot widen the search window or result limit
Runtime scope	`Variable`	Resolve per-user or per-tenant values at execution time
Delegation	`Agent.as_tool()`	Keep the searcher’s tool-call history out of the coordinator’s context
Transport	`base_url`, `timeout`	Point at a proxy or tighten deadlines per deployment

This suits research, monitoring, and reporting agents. Use direct REST for deterministic jobs that need no model decisions.

AG2 Twitter API Prerequisites

Python 3.10 or later
An Xquik API key beginning with xq_
An LLM provider key supported by AG2

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

Install AG2

python -m pip install "ag2>=1.0.0"

XquikSearchToolkit calls the Xquik REST API over httpx, which AG2 already depends on. No extra package is required. Install your model provider extra as well.

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

Store secrets outside source control.

.env

XQUIK_API_KEY=xq_YOUR_KEY_HERE
ANTHROPIC_API_KEY=YOUR_ANTHROPIC_KEY

.gitignore

.env

Register the Xquik Tweet Search Tool

Passing the toolkit to an agent registers one tool, xquik_tweet_search. It takes a single query argument holding an X search string.

import asyncio
import os

from ag2 import Agent
from ag2.config import AnthropicConfig
from ag2.extensions.tools.search import XquikSearchToolkit
from dotenv import load_dotenv

load_dotenv()
config = AnthropicConfig(model="claude-sonnet-4-6")

agent = Agent(
    "x-researcher",
    prompt=(
        "Search X for evidence before answering. "
        "Quote tweet text verbatim and keep every tweet ID you receive."
    ),
    config=config,
    tools=[XquikSearchToolkit(api_key=os.environ["XQUIK_API_KEY"])],
)


async def main() -> None:
    reply = await agent.ask("What are developers saying about the Xquik API this week?")
    print(reply.body)


asyncio.run(main())

api_key is required. The toolkit raises ValueError when it is empty, so a missing key fails at construction rather than on the first search.

Bind the Search Window Outside Model Control

Search defaults belong on the constructor. The model then cannot widen the window or raise the result limit.

toolkit = XquikSearchToolkit(
    api_key=os.environ["XQUIK_API_KEY"],
    query_type="Latest",              # "Latest" or "Top"
    limit=50,
    since_time="2026-01-01T00:00:00Z",
    until_time="2026-02-01T00:00:00Z",
)

Parameter	Type	Purpose
`query_type`	`"Latest"` \| `"Top"`	Result ordering
`limit`	`int`	Upper bound on returned results
`since_time`	`str`	Start of the time window
`until_time`	`str`	End of the time window
`cursor`	`str`	Resume from a prior page
`base_url`	`str`	Defaults to `https://xquik.com`
`timeout`	`float`	Request deadline in seconds, defaults to `60.0`

The same parameters are available on the search() factory method when you want several differently scoped tools from one toolkit.

toolkit = XquikSearchToolkit(api_key=os.environ["XQUIK_API_KEY"])

latest = toolkit.search(
    query_type="Latest",
    limit=50,
    name="search_latest_posts",
    description="Search the newest public X posts.",
)

top = toolkit.search(
    query_type="Top",
    limit=20,
    name="search_top_posts",
    description="Search the highest-engagement public X posts.",
)

agent = Agent("analyst", config=config, tools=[latest, top])

Read the Structured Result

xquik_tweet_search returns a typed response rather than a raw payload.

Field	Type	Meaning
`query`	`str`	The query that was searched
`tweets`	`list[dict]`	Tweet records exactly as returned by the API
`has_next_page`	`bool`	Whether another page exists
`next_cursor`	`str`	Cursor for the next page, empty when absent

Tweet records pass through unmodified, so every tweet ID, profile ID, and timestamp survives the hop into the agent. Feed next_cursor back through the cursor parameter to continue pagination.

Resolve Values at Runtime With Variables

Every runtime parameter accepts an AG2 Variable. AG2 resolves it from the run context when the tool executes, so one tool instance serves many users or tenants.

from ag2.annotations import Variable

toolkit = XquikSearchToolkit(
    api_key=os.environ["XQUIK_API_KEY"],
    since_time=Variable("window_start"),
    until_time=Variable("window_end"),
    limit=Variable("page_size"),
)

Delegate Search in a Multi-Agent Team

Agent.as_tool() exposes an agent as a tool for another agent. Each delegated task runs on its own stream with its own history. The coordinator receives the delegate’s final answer, not its internal tool-call history.

searcher = Agent(
    "searcher",
    prompt="Search X and return tweet text with IDs. Do not summarise.",
    config=config,
    tools=[XquikSearchToolkit(api_key=os.environ["XQUIK_API_KEY"], query_type="Latest", limit=50)],
)

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 X API pricing.")
print(reply.body)

Handle Failures

The tool raises on non-success HTTP status codes through httpx. Read Error Handling for status semantics and Rate Limits for retry guidance. Wrap the toolkit with AG2 tool middleware when you need retries, approval gates, or audit logging around every search.

AG2 Xquik Tweet Search reference

Getting Started

SDKs

Guides

Webhooks

MCP Server

Agent Plugins

Framework Guides

OAuth

MPP

Resources

AG2 Twitter API Search Guide for Python Agents

Why Use AG2 With the Xquik Twitter API?

AG2 Twitter API Prerequisites

Install AG2

Register the Xquik Tweet Search Tool

Bind the Search Window Outside Model Control

Read the Structured Result

Resolve Values at Runtime With Variables

Delegate Search in a Multi-Agent Team

Handle Failures

​Why Use AG2 With the Xquik Twitter API?

​AG2 Twitter API Prerequisites

​Install AG2

​Register the Xquik Tweet Search Tool

​Bind the Search Window Outside Model Control

​Read the Structured Result

​Resolve Values at Runtime With Variables

​Delegate Search in a Multi-Agent Team

​Handle Failures

​Related Guides

Why Use AG2 With the Xquik Twitter API?

AG2 Twitter API Prerequisites

Install AG2

Register the Xquik Tweet Search Tool

Bind the Search Window Outside Model Control

Read the Structured Result

Resolve Values at Runtime With Variables

Delegate Search in a Multi-Agent Team

Handle Failures

Related Guides