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TrendsAGI

PyPI Version Python Versions License: MIT

Real-time trend intelligence for your code and your AI agents: what is trending on social platforms right now, with measured volume, velocity and growth, AI insights on why each topic is moving, evidence-backed recommendations, crisis alerts and watchlists. Paid media and growth teams use it to target what is rising, time spend to the trend curve and brief creative on measured demand.

Python client TrendsAGIClient, a typed REST client
Command line trendsagi, one command per API action, tables in a terminal and JSON in a pipe
Agent tools TrendsAGITools for OpenAI, Claude, LangChain and WebMCP
Hosted MCP server https://api.trendsagi.com/mcp for Claude, ChatGPT, Grok, Cursor and Gemini CLI
Browser agents (WebMCP) Signed in to app.trendsagi.com, your browser's agent gets the same tools (webmcp/)
Local MCP server trendsagi mcp, the same tools over stdio
Plugins Claude, Grok and Cursor plugin in plugins/trendsagi, Gemini CLI extension at the repo root
Ad execution Executors that preview, then apply, Google, Meta, TikTok and LinkedIn ad changes from your own infrastructure

Install

pip install trendsagi            # client, agent tools and the trendsagi command
pip install "trendsagi[mcp]"     # adds the local MCP server (Python 3.10+)
pipx install trendsagi           # just the command line, in its own environment

Create an API key under Profile > API Keys at app.trendsagi.com and export it:

export TRENDSAGI_API_KEY="your_api_key"

Quickstart

import os
from trendsagi import TrendsAGIClient

client = TrendsAGIClient(api_key=os.environ["TRENDSAGI_API_KEY"])

# Fastest-moving Technology topics right now
trends = client.get_trends(category="Technology", sort_by="velocity", limit=5)
for trend in trends.trends:
    print(trend.id, trend.name, trend.volume)

# Why is the top one moving?
insight = client.get_ai_insights(trends.trends[0].id)
if insight:
    print(insight.sentiment_summary, insight.key_themes)

Command line

trendsagi login                  # paste an API key; saved to ~/.config/trendsagi/config.json
trendsagi trends list --category Technology --sort velocity --limit 10
trendsagi insights get 123       # audience, sentiment and marketing angles
trendsagi sql "SELECT category, count(*) FROM trends GROUP BY 1 ORDER BY 2 DESC"
trendsagi recommendations list --priority high
trendsagi watchlist add "oat milk" --threshold 50000 --email
trendsagi api /api/integrations/delivery/health -q lookback_hours=24   # any endpoint
Commands What they do
login, logout, whoami Save, forget and check the API key
trends list get analytics categories autocomplete Trending topics with measured volume, velocity and growth
insights get generate [--wait] status search AI insights per trend
recommendations list action feedback Evidence-backed recommendations and their workflow status
crisis list action Crisis events: acknowledge, archive, reopen
watchlist list add remove Tracked keywords and categories, with alerts
sql, schema One read-only PostgreSQL SELECT over your data, and the tables it can read
tools list call, find The agent tools, and whether TrendsAGI can do a task
webhooks, exports Create, change, test and run webhooks and data exports
ads providers resources preview apply report report-export Connected ad platforms: trend-based changes and reports
integrations availability status health value Integration status and delivery health
stream Live trend updates as JSON lines
usage, status, api API usage, service status, and any endpoint without its own command

Every command has --help. In a terminal, lists print as tables; piped or with --json they print JSON, so trendsagi trends list | jq works. The key comes from --api-key, then TRENDSAGI_API_KEY, then the saved login. Commands that delete something or change live ads ask first; pass --yes in scripts. Exit codes: 0 success, 1 API error, 2 invalid usage, 4 missing or rejected key.

The commands are declared once in src/trendsagi/cli_spec.json; a dependency-free Node.js build of the same CLI lives in npm/. Guide: trendsagi.com/api-docs/integrations/cli.

The client calls any endpoint too: client.request("GET", "/api/integrations/delivery/health", params={"lookback_hours": 24}).

Use TrendsAGI in your AI assistant (no install)

Add the hosted MCP server, sign in to TrendsAGI and approve the connection:

Assistant Setup
Claude Open TrendsAGI in the Claude directory, or Settings > Connectors > Add custom connector with the URL below
Claude Code claude mcp add --transport http trendsagi https://api.trendsagi.com/mcp, or /plugin marketplace add TrendsAGI/TrendsAGI then /plugin install trendsagi@trendsagi
ChatGPT Add a custom connector with the URL below
Grok Install the TrendsAGI plugin, or add the URL as a remote MCP server
Cursor { "mcpServers": { "trendsagi": { "url": "https://api.trendsagi.com/mcp" } } }
Gemini CLI gemini extensions install https://github.com/TrendsAGI/TrendsAGI
https://api.trendsagi.com/mcp

Then ask "What is accelerating fastest in Technology this week, and who should I target with it?" The hosted tools are read-only, and each call counts as one API request. Each connection gets its own key, listed as "AI connector: " under Profile > API Keys; delete it to disconnect. Clients without OAuth can send an API key as Authorization: Bearer <key>. Guide: trendsagi.com/mcp.

Use TrendsAGI from your browser's agent (WebMCP)

WebMCP lets a web page hand tools to the AI agent built into the browser (Chrome and Edge in origin trials, ChatGPT desktop, Brave Leo). While you are signed in to app.trendsagi.com, the app registers TrendsAGI's read-only tools with document.modelContext, so the agent uses them with your session: no connector or API key, and calls count as app use rather than API usage. trendsagi.com offers public tools (docs search, trend reports, plans and the free calculators) to every visitor.

To put TrendsAGI tools on your own site, register them in your page and run them on your server, which keeps the API key:

tools = TrendsAGITools(TrendsAGIClient(api_key=os.environ["TRENDSAGI_API_KEY"]))

tools.webmcp()                     # serve to your page: name, description, inputSchema, annotations
tools.call("list_trends", args)    # run the call your page forwards

webmcp/ has the browser module and a FastAPI server. Guide: trendsagi.com/webmcp.

Agent tools

One registry, every format. The tools are read-only by default; add_to_watchlist and update_recommendation appear only with allow_writes=True (or trendsagi mcp --allow-writes).

Tool What the agent gets
list_trends Trending topics by category or phrase, sorted by velocity, growth or volume
list_trend_categories Valid category names
search_trend_insights Trends and insights matching a theme
get_trend / get_trend_analytics One trend and its volume time series
get_trend_ai_insights Summary, key themes, sentiment, audience, marketing angles
list_recommendations Evidence-backed actions with a decision brief
list_crisis_events Reputation and crisis events for monitored topics
list_watchlist / get_api_usage Watched topics and remaining daily quota
read_data_schema / execute_sql Read-only SQL for questions that need filters, joins or aggregation
find_capability Whether TrendsAGI can do a task, and which tool or app page does it

OpenAI

import json, os
from openai import OpenAI
from trendsagi import TrendsAGIClient, TrendsAGITools

tools = TrendsAGITools(TrendsAGIClient(api_key=os.environ["TRENDSAGI_API_KEY"]))
openai = OpenAI()
messages = [{"role": "user", "content": "Find a fast-rising Technology trend and suggest a campaign angle."}]

while True:
    reply = openai.chat.completions.create(model="gpt-5-mini", messages=messages, tools=tools.openai())
    message = reply.choices[0].message
    messages.append(message)
    if not message.tool_calls:
        print(message.content)
        break
    for call in message.tool_calls:
        result = tools.call(call.function.name, call.function.arguments)
        messages.append({"role": "tool", "tool_call_id": call.id, "content": json.dumps(result)})

Claude, LangChain and others

tools.anthropic()         # Anthropic Messages API tools=
tools.openai_responses()  # OpenAI Responses API tools=
tools.langchain()         # LangChain StructuredTools (pip install "trendsagi[langchain]")
tools.webmcp()            # WebMCP descriptors for document.modelContext.registerTool

With Claude, pass tools=tools.anthropic() and answer each tool_use block with {"type": "tool_result", "tool_use_id": block.id, "content": json.dumps(tools.call(block.name, block.input))}. tools.call never raises: errors come back as {"error": ...} so the model can read a rate limit or a bad argument and recover.

Local MCP server

pip install "trendsagi[mcp]"
claude mcp add trendsagi --env TRENDSAGI_API_KEY=your_api_key -- trendsagi mcp

Or in any MCP client's JSON config (Claude Desktop, Cursor, VS Code):

{ "mcpServers": { "trendsagi": { "command": "trendsagi-mcp", "env": { "TRENDSAGI_API_KEY": "your_api_key" } } } }

Query with SQL

For answers no single endpoint returns, such as growth by category or the trends that match a watchlist:

schema = client.get_data_schema()   # tables, columns, rules, examples
result = client.execute_sql(
    """
    SELECT category, count(*) AS trends, round(avg(growth_pct)::numeric, 1) AS avg_growth_pct
    FROM trends
    WHERE last_seen_at > now() - interval '7 days' AND growth_pct IS NOT NULL
    GROUP BY category ORDER BY avg_growth_pct DESC
    """,
    max_rows=20,
)
print(result["columns"], result["rows"])

Tables: trends, trend_snapshots (90 days of readings), trend_categories, trend_insights, watchlist, recommendations and crisis_events; the last three only contain your own account's rows. Queries are one validated SELECT, run read-only for at most 5 seconds, and count as one API request. A refused query raises exceptions.QueryError with a code (such as unknown_table or timeout), plus hint and position when known.

Recommendations with decision briefs

recommendations = client.get_recommendations(match_user_interests=True, priority="high", sort="priority", limit=10)
for rec in recommendations.recommendations:
    brief = rec.decision_brief
    if brief and brief.actionable:
        print(rec.title, brief.why_now, brief.next_steps[:1])

Each brief explains why an action is timely, the measured signals behind it and concrete next steps. Its confidence score measures evidence completeness, not the chance of success; use actionable and data_quality to hold back when signals are stale or incomplete.

Ad execution in your own infrastructure

GoogleAdsExecutor, MetaAdsExecutor, TikTokAdsExecutor and LinkedInAdsExecutor take credentials at runtime, use deterministic idempotency keys, and support dry-run previews and strict validation:

from trendsagi.integrations import GoogleAdsExecutor

insight = client.get_ai_insights(trend_id)  # the trend you decided to act on
google = GoogleAdsExecutor({
    "access_token": os.environ["GOOGLE_ADS_ACCESS_TOKEN"],
    "developer_token": os.environ["GOOGLE_ADS_DEVELOPER_TOKEN"],
})
preview = google.apply_targeting(insight, customer_id="1234567890", campaign_id="9876543210", dry_run=True, strict_mode=True)
print(preview.response)  # inspect before any real change

See INTEGRATIONS_BYOC.md for every executor.

Errors

from trendsagi import exceptions

try:
    client.get_trends(limit=5)
except exceptions.RateLimitError:
    ...  # back off and retry
except exceptions.AuthenticationError:
    ...  # invalid or revoked key

Other commands

trendsagi mcp [--allow-writes]                              # MCP server over stdio
trendsagi scaffold --type docker --output-dir ./runner      # executor runner template
trendsagi scaffold --type terraform --output-dir ./infra    # infrastructure template

Plans and limits

The free Developer plan includes one API key and 100 API calls a day; Advantage, Scale and Enterprise raise the daily limit and add keys and team features. Calls above the daily allowance are billed as overage. See pricing.

License

MIT. See LICENSE.

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