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Ambitus AI Models

by · github.com/Ambitus-Intelligence/ambitus-ai-models

a TUI‑first Python package and multi‑agent market research engine that orchestrates validated data collection, analysis, and report synthesis into citation‑rich PDF reports.

Unclaimed last commit 10 months ago fastapigenaicliagentsdevtoolsaimarket-analysisai-toolsagentic-ai
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About Ambitus AI Models

Ambitus AI Models is an MCP server published by Ambitus-Intelligence in the Developer Tools category: a TUI‑first Python package and multi‑agent market research engine that orchestrates validated data collection, analysis, and report synthesis into citation‑rich PDF reports. It has been installed 0 times through Conduid.

The repository has 12 stars and 1 forks, with the last commit 10 months ago. Six months or more without a commit doesn't mean the server is broken, but check the open issues (0) before depending on it in production.

Install

Install
npx ambitus-ai-models

This server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.

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Releases

v0.1.0Release v0.1.0 · 5 Nov 2025Changes Published to PyPI Version: 0.1.0 Installation pip install ambitus-cli==0.1.0

README

ambitus-intelligence 🔍

Ambitus Intelligence is a TUI‑first Python package and multi‑agent market research engine that orchestrates validated data collection, analysis, and report synthesis into citation‑rich PDF reports.

Technical diagrams : Flowcharts
UML of Ambitus : Ambitus-AI

https://github.com/user-attachments/assets/0b6cd0a2-a02f-4ea1-8fdb-6f51012f2fad

This repository contains AI/ML models, experiments, and tools powering ambitus Intelligence's market research automation platform.
All exploratory work, prototypes, and notebooks are organized under /notebooks.

🚀 Overview

ambitus-ai-models is the core engine behind Ambitus Intelligence’s automated market research platform. It provides:

  • Orchestrated Multi‑Agent Workflows
    A centralized Orchestrator sequences specialized AI agents, handles error‑flows, and manages user hand‑offs.

  • FastMCP Tool Server
    ambitus-tools-mcp—a MCP server, backed by FastMCP—hosts all external utilities (scrapers, API clients, validators) and the CitationAgent, allowing agents to discover and invoke tools at runtime.

  • Structured Agent Outputs
    Each agent emits well‑defined JSON payloads, which are persisted to a database and exposed via REST for downstream consumption.

🔑 Key Agents

Agent Name Responsibility
CompanyResearchAgent Scrape and ingest public & proprietary sources (Crunchbase, Wikipedia, web) to produce a company profile.
IndustryAnalysisAgent Analyze the company profile via LLM prompts to rank and rationalize potential expansion domains.
MarketDataAgent Retrieve quantitative metrics (market size, CAGR, trends) from external APIs (Google Trends, Statista).
CompetitiveLandscapeAgent Compile and summarize key competitors, their products, market share, and strategic positioning.
GapAnalysisAgent Use LLM reasoning to detect unmet needs and strategic gaps by comparing capabilities vs. competitors.
OpportunityAgent Brainstorm, validate, and rank growth opportunities grounded in data from upstream agents.
ReportSynthesisAgent Aggregate all agent outputs into a citation‑rich final report (Markdown, HTML, PDF).
CitationAgent (Tool) On‑demand retrieval of citations or data snippets, serving all agents via the MCP tool server.

📖 Documentation

Legacy Notion (for archival reference only):

📁 Repository Structure

ambitus-ai-models/
├── docs/                                       # Architecture & agent specs (Markdown)
│   ├── README.md                               # Index of spec docs
│   ├── system_overview.md
│   ├── agent_specs.md
│   ├── workflow_examples.md                    # TODO
│   └── mcp_server.md                           # TODO
├── notebooks/                                  # Experimental Jupyter/Colab prototypes
│   ├── Experiment ##- <experiment_name>.ipynb   
│   └── ...                                     # Additional experiments in ##-*.ipynb format
├── src/                          # Source code
│   ├── agents/                   # Individual agent implementations
│   │   ├── __init__.py
│   │   ├── company_research_agent.py
│   │   ├── industry_analysis_agent.py
│   │   ├── market_data_agent.py
│   │   ├── competitive_landscape_agent.py
│   │   ├── gap_analysis_agent.py
│   │   ├── opportunity_agent.py
│   │   ├── report_synthesis_agent.py
│   │   └── citation_agent.py
│   │
│   ├── mcp/                      # MCP server configuration and tools
│   │   ├── __init__.py
│   │   ├── server.py             # FastMCP server implementation
│   │   ├── tools/                # Tool implementations
│   │   │   ├── __init__.py
│   │   │   └── ...               # Individual tool modules
│   │   └── data_sources/         # Data source connectors
│   │       ├── __init__.py
│   │       └── ...               # Individual data source modules
│   │
│   ├── api/                      # Backend API for web application
│   │   ├── __init__.py
│   │   └── routes.py             # API endpoints
│   │
│   └── utils/                    # Shared utilities
│       ├── __init__.py
│       └── ...
│
├── .env.example                  # Example environment variables
├── pyproject.toml                # Project configuration and dependencies
├── README.md                     # Project overview
└── .gitignore                    # Git ignore file

📧 Contacts

For questions or collaborations, contact:

Lead Developers:

Part of the Next-Gen Market Intelligence Suite

ambitus Intelligence | Documentation | Main Repository

README mirrored from the source repository just now. The original is authoritative.

Questions

About Ambitus AI Models

How do I install Ambitus AI Models?

Run npx ambitus-ai-models, then add the server to your MCP client's configuration. Conduid has recorded 0 installs, so the command is known to work with current clients.

Is Ambitus AI Models safe to use with an AI agent?

Its trust score is 51 out of 100 (fair). It passes 0 of 1 static security checks; the failures are listed above. It has no ConduID identity yet, so agent calls to it are not receipted.

Is Ambitus AI Models still maintained?

The last commit was 10 months ago, with 0 open issues. That's long enough that you should check whether the maintainer is responding to issues before depending on it.