gnews-agent

Persistent, semantic news intelligence layer for AI agents — built on top of GNews (106k+ monthly PyPI downloads, 141 countries, 41 languages).

The journalism layer your AI agent is missing. Fetch published news from Reuters, BBC, AP, TechCrunch, and the 141-country Google News graph; dedup it; embed it; store it persistently; and query it semantically — over a Python API, a CLI, or an MCP server that drops straight into Claude.

Table of Contents

• Installation

• Usage

  • CLI

  • MCP server

  • Claude Code skill

  • Python library

• Configuration

• Docker

• Design notes

• Status

• Pairs well with /last30days

• License

Related MCP server: Abhi-Nexus MCP Server

Installation

pip install gnews-agent

Optional extras:

pip install "gnews-agent[openai]"     # OpenAI embedding backend
pip install "gnews-agent[fulltext]"   # full-article extraction via trafilatura
pip install "gnews-agent[lance]"      # LanceDB vector backend
pip install "gnews-agent[qdrant]"     # Qdrant server backend
pip install "gnews-agent[evals]"      # DeepEval + Langfuse (v2)

Verify the install:

gnews-agent --version
gnews-agent stats              # → {"total_articles": 0, ...}

Usage

The library exposes the same six capabilities through every surface: ingest, search, timeline, brief, sentiment, stats. Pick the surface that matches how the rest of your system already works.

CLI

gnews-agent ingest "OpenAI" --method get_news
gnews-agent search "GPT-5 safety" --days 7 --limit 5
gnews-agent brief  "OpenAI this week" --days 7
gnews-agent sentiment "Tesla" --days 14 --timeline
gnews-agent timeline  "OpenAI" --days 30
gnews-agent stats
gnews-agent serve --transport stdio

Every command emits JSON to stdout. Use --no-pretty for one-line output (pipe through jq or feed straight into another agent).

MCP server

gnews-agent serve exposes five tools — search_news, get_brief, get_sentiment, get_timeline, monitor_topic — and three resources — news://latest/{topic}, news://sentiment/{topic}, news://timeline/{topic}. Works in any MCP client.

Claude Code (CLI):

claude mcp add gnews-agent -- gnews-agent serve --transport stdio

Then ask Claude:

"Use gnews-agent to ingest the latest reporting on OpenAI, then give me a cited brief on what changed this week."

Claude Desktop / Cursor / Windsurf:

Add this block to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or the platform equivalent:

{
  "mcpServers": {
    "gnews-agent": {
      "command": "gnews-agent",
      "args": ["serve", "--transport", "stdio"],
      "env": {
        "ANTHROPIC_API_KEY": "sk-ant-...",
        "GNEWS_AGENT_LLM_PROVIDER": "anthropic",
        "GNEWS_AGENT_LLM_MODEL": "claude-3-5-haiku-latest"
      }
    }
  }
}

Restart the client. If gnews-agent isn't on $PATH, use the absolute path from which gnews-agent.

HTTP transport (LangGraph, custom agents):

gnews-agent serve --transport http --port 8000

Claude Code skill

The /gnews skill wraps the MCP server with prompt scaffolding and a citation-formatted output template.

/plugin marketplace add ranahaani/gnews-agent
/gnews OpenAI this week
/gnews Pakistan economy sentiment
/gnews Tesla vs Rivian coverage last 30 days

Requires pip install gnews-agent on the same machine.

Python library

Drop into any LangGraph, CrewAI, or vanilla Python pipeline.

from gnews_agent import NewsMemory

memory = NewsMemory()                              # SQLite + Chroma, persistent
memory.ingest("OpenAI", method="get_news")         # fetch + dedup + embed + store
results = memory.search("GPT-5 safety", days=7)    # semantic re-ranked by recency
timeline = memory.timeline("OpenAI", days=30)      # SQL-only
brief = memory.brief("OpenAI this week", days=7)   # cited LLM summary
sentiment = memory.sentiment("Tesla", days=14)
print(memory.stats())

NewsMemory accepts a NewsMemoryConfig for full control over paths, embedder, vector backend, and LLM provider:

from gnews_agent import NewsMemory, NewsMemoryConfig

cfg = NewsMemoryConfig(
    db_path="~/news/news.db",
    vector_path="~/news/chroma",
    embed_model="all-MiniLM-L6-v2",
    llm_provider="anthropic",
    llm_model="claude-3-5-haiku-latest",
)
memory = NewsMemory(config=cfg)

Configuration

search, ingest, timeline, and stats are keyless — they only need GNews (free, no API key) and local storage. brief and sentiment are LLM-powered and need a provider key.

Copy .env.example to .env and fill in only the keys you need. Ollama runs locally with no key at all.

Docker

cd docker
docker compose up --build
# HTTP MCP listening on http://localhost:8000

The image pre-downloads all-MiniLM-L6-v2 at build time so the first ingest doesn't pay the ~80MB cold-start.

Design notes

• Dedup key = sha256(title_slug + "|" + publisher_norm). URL is excluded because Google News surfaces the same article under multiple URL variants (locale params, tracking suffixes, redirector vs resolved). Title-slug + publisher catches those without merging legitimately distinct outlets.

• No semantic cosine dedup at ingestion. Reuters and BBC reporting the same event are kept as separate rows. Cosine similarity is reserved for query-time re-ranking inside brief().

• Embedding model + dimension are recorded on every vector row. The store refuses cross-model queries via EmbeddingDimMismatchError. To switch from sentence-transformers to OpenAI embeddings, use a separate vector_path or wipe the existing collection.

• Story clustering is v2. brief() ranks by recency + similarity and passes the top-N to the LLM, no clustering.

• Webhook URL validation for monitor_topic. RFC1918, loopback, link-local, and cloud-metadata IPs are refused. HTTPS required in MCP-exposed mode.

• GNews ≥0.8.2 is required for the built-in 429 retry + exponential backoff. gnews-agent layers per-topic 1s spacing and topic cooldown on top of that.

Status

• v0.1.0 — Library + CLI + MCP server + Claude Code skill scaffold. End-to-end ingest → search → brief works against a real LLM key. 83 unit + 24 integration tests pass.

• v2 deferred — DeepEval FaithfulnessMetric, Langfuse tracing, CI eval gate, story clustering, get_entities MCP tool, Telegram/Slack/Email alert channels, LanceDB + Qdrant backends, multi-language summaries.

Pairs well with /last30days

/last30days searches what people say (Reddit, X, YouTube, TikTok, Hacker News). gnews-agent searches what journalism reports (Reuters, BBC, AP, TechCrunch). Different signals from different sources:

/last30days OpenAI GPT-5     → social signal
/gnews      OpenAI GPT-5     → published record

Install both and ask Claude to combine them for the full picture.

License

MIT — see LICENSE.