Agent Web Search gives an agent two ways to reach the same provider-neutral search core: a native MCP tool, or a CLI taught through a standard Agent Skill.

This is not a Google/Bing/Baidu metasearch wrapper. Traditional search aggregation fans a keyword query out to conventional engines and merges their result pages. Agent Web Search instead aggregates search capabilities built for agents: model-native web grounding, agent-oriented search APIs, and context-ready sources that accept natural-language questions and return answers, citations, or structured evidence in forms an agent can use directly. DDGS is the only conventional search backend in the current provider set.

Natural-language question
             │
             ▼
        SearchEngine
       ┌─────┼──────────────┐
       ▼     ▼              ▼
     DDGS  Model providers  Agent search providers
           ARK · Gemini     Exa · Parallel · Brave
           Grok · DeepSeek  Perplexity · Tavily · You.com
           Codex Alpha      Zhipu Web Search
           Zhipu Chat

Why Agent Web Search

Traditional search aggregation (Google/Bing/Baidu wrappers, scraped SERPs) sends a keyword query to conventional engines and merges result pages. Agent Web Search instead aggregates search capabilities built for agents: one tool call returns structured, citation-ready evidence — or, through model-native grounding providers, a synthesized answer with explicit citations. A measured benchmark shows the practical difference: on a natural-language Chinese query asking for official sources, conventional SERP backends returned no government-domain results in the top 5, while the grounding provider returned the 海关总署 figures with a working citation.

• Agent-native by design. The primary interface is a complete natural-language question, not a thin keyword fan-out to Google, Bing, or Baidu.
• Model-native search backends. ARK, Gemini, Grok, DeepSeek, Zhipu Chat Search, and Codex Alpha can combine web retrieval with model-generated synthesis and explicit citations.
• Agent search providers. Exa, Parallel, Brave, Perplexity, Tavily, You.com, and Zhipu Web Search expose search APIs intended to provide structured, citation-friendly, or context-ready evidence to downstream agents.
• One provider-neutral contract. Every backend is available through the same MCP tool, CLI, Python API, and normalized results; model-backed providers may also return an answer.
• Independent providers. Selected providers run concurrently, and one provider's failure never discards another provider's successful result.
• DDGS remains a simple fallback. DDGS is the only conventional search backend; it requires no API key and keeps the project usable without paid provider credentials. Exa and Parallel are also keyless by default.
• No telemetry, no shared secrets. Provider keys stay in runtime environment variables; there is no shared API-key service.

Providers

The provider list is intentionally split by the kind of search capability it provides. Only DDGS is a conventional search backend; the other two groups are built around model-native grounding or agent-facing search services.

Free, keyless defaults: DDGS, Exa, and Parallel all work without an API key. Exa and Parallel automatically use their free MCP transports until a paid API key is provided.

Traditional search backend

Model providers

These providers use a model-native search or grounding surface. Their responses can include a model-generated answer together with citations or other explicit search evidence.

Agent search providers

These providers expose search services for agent consumption: natural-language queries, structured source rows, high-signal excerpts, or citation-friendly metadata rather than a conventional search-page experience.

The provider architecture is intentionally open: another search-capable backend can be added without changing the MCP, Hermes, CLI, or Python-facing interfaces.

Use with an agent

Requirements: Python 3.10+. The default providers — DDGS, Exa, and Parallel — need no API key. Choose one integration shape for your agent; both use the same package and search engine. The PyPI package installs both agent-web-search-mcp and agent-web-search commands.

Option 1: MCP

Choose MCP when the agent supports tool servers and you want typed discovery, protocol-level errors, or remote access. The same agent-web-search-mcp command supports local stdio and stateless Streamable HTTP.

Local stdio MCP

Install the package once:

# Recommended isolated installation
pipx install agent-web-search-mcp

# Or install into the active Python environment
python -m pip install agent-web-search-mcp

Then configure the MCP client to launch agent-web-search-mcp:

{
  "mcpServers": {
    "agent-web-search": {
      "command": "agent-web-search-mcp",
      "args": []
    }
  }
}

If uvx is already available, a client can run the package without a persistent install by using command uvx with args ["agent-web-search-mcp"].

# Codex CLI
codex mcp add agent-web-search -- agent-web-search-mcp

# Claude Code
claude mcp add agent-web-search -- agent-web-search-mcp