基于运行手册的研究代理。它遵循结构化的研究运行手册（SaaS 竞争对手拆解、广告与创意调研、订阅应用审核、市场 规模调整、股权研究、SEO、人工智能可见度等），称之为 Prowl MCP 工具库（440+ 数据工具：SEO、广告库， 评审、抓取、应用智能、大型语言模型交叉检查），存储提取的声明 证据账本，并生成带有来源引用和核实披露的报告。

它是面向人类的CLI，并且有两个MCP表面用于代理：

• Fabric服务（prowl-research-service，HTTP MCP at http://127.0.0.1:18764/mcp，连接fabric-agent.json声明）—— 7个工具：research.run、research.tools.call、research.tutorial， research.list_runbooks， research.artifact.get， fabric.job.get， fabric.job.cancel（参见织物服务）;
• 遗留本地 stdio MCP 服务器（prowl-research mcp）——4 个工具： research.run， research.list_runbooks， research.get_status， research.get_report。

新队友快速起步

安装

配置

两个键，都通过环境（仅键名——值绝不在仓库中）：

Prowl Research Agent

Runbook-driven research agent. It follows structured research runbooks (SaaS competitor teardowns, ads & creative research, subscription-app audits, market sizing, equity research, SEO, AI visibility and more), calls the Prowl MCP tool bank (440+ data tools: SEO, ads libraries, reviews, scraping, app intelligence, LLM cross-checks), stores extracted claims in an evidence ledger, and produces reports with source citations and verification disclosures.

It is a CLI for humans and has two MCP surfaces for agents:

• the Fabric service (prowl-research-service, HTTP MCP at http://127.0.0.1:18764/mcp, the connection fabric-agent.json declares) — 7 tools: research.run, research.tools.call, research.tutorial, research.list_runbooks, research.artifact.get, fabric.job.get, fabric.job.cancel (see Fabric service);
• the legacy local stdio MCP server (prowl-research mcp) — 4 tools: research.run, research.list_runbooks, research.get_status, research.get_report.

Quick start for a new teammate

Install

git clone https://github.com/PROWL-AI/research-agent.git
cd research-agent
python3 -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"

Configure

Two keys, both via environment (key names only — values never in the repo):

• PROWL_API_KEY — your Prowl API key (prowl_…), billed per tool call. Get one at https://prowl.chat.
• OPENROUTER_API_KEY — the agent’s own LLM (planning, synthesis, writing). Any OpenAI-compatible endpoint works: set RESEARCH_LLM_BASE_URL / RESEARCH_LLM_API_KEY / RESEARCH_LLM_MODEL to override.

Other environment knobs (all optional):

• PROWL_MCP_URL — Prowl MCP endpoint (default https://prowl.chat/mcp).
• RESEARCH_LLM_MODEL_STRONG — the strong-tier model (planning, writing, repair; default xiaomi/mimo-v2.6-pro); RESEARCH_LLM_MODEL covers the cheap tier (claim extraction, transforms, citation checks; default deepseek/deepseek-v4.1-flash); RESEARCH_JUDGE_MODEL the eval judge (default anthropic/claude-haiku-5.5 — another vendor than the default writer, so a report is not graded by the family that wrote it; keep that true if you change the writer). Defaults apply to new installs and the environment: an installed service keeps the models saved in its settings. Judge scores before and after the judge default changed (2026-10-17, from the strong model) are not comparable. Prices for the cost estimate are in research_agent/pricing.py (OpenRouter catalog, read 2026-10-17; scripts/check_prices.py re-reads it).
• RESEARCH_MAX_LLM_USD — the run’s LLM spend ceiling (see “How it works”). Claim extraction and citation checks send reasoning: {enabled: false} to OpenRouter: on the cheap tier reasoning could spend the whole 2000-token output budget before any JSON, and an answer cut that