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Adedeji Olamide
Adedeji Olamide

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I Built an Agent That Does VC Due Diligence in Minutes

#hermesagentchallenge #devchallenge #agents

Hermes Agent Challenge Submission: Build With Hermes Agent

This is a submission for the Hermes Agent Challenge: Build With Hermes Agent

What I Built

A few weeks ago I was looking at a list of hackathon submissions and noticed something. Every single project was for developers. Observability agents. Code review bots. Database query assistants. Debugging tools. All useful, all for people who already spend their days in a terminal.

Nobody had built anything for the people who don't.

That's when I thought about VC due diligence. The process that takes 2-4 weeks per deal is, at its core, mechanical. You need to know the market size. You need to map the competition. You look up the founders' previous companies. You read what people say on Reddit and Hacker News. You check how much funding they've raised and who from. None of this is strategic thinking. It's information retrieval, repeated across dozens of sources, by analysts who are overqualified for it.

So I built Axiom, an autonomous startup due diligence agent powered by Hermes Agent.

You type one command:

/axiom-diligence Perplexity AI perplexity.ai
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Axiom spawns 7 parallel research sub-agents using Hermes's delegate_task tool:

Agent What It Researches
Market TAM, growth rate, timing signal
Competition Direct/indirect competitors, moat assessment
Founders Prior companies, exits, reputation signals
Technology GitHub activity, stack, build vs. buy assessment
Customer Sentiment Reddit, HackerNews, review sites
Regulatory Risk Relevant laws, litigation signals, compliance
Financial Signals Crunchbase funding, investor quality, revenue signals

The orchestrator synthesizes all 7 findings into a structured investment memo with a verdict (INVEST / MONITOR / PASS), confidence score, bull case, bear case, open diligence questions, and all sources cited. Output saves as JSON and Markdown, and renders in a Streamlit dashboard.

Demo

The Streamlit dashboard renders the full VC-style memo with color-coded verdict, five signal metrics, and section-by-section research breakdowns.

GitHub: github.com/cybort360/axiom

Code

github.com/cybort360/axiom

The project structure:

axiom/
├── skills/
│   └── axiom-diligence/
│       └── SKILL.md          ← Hermes skill: the full research methodology
├── mcp/
│   └── axiom_mcp.py          ← Custom MCP server: HN, Reddit, Crunchbase, GitHub
├── web/
│   └── dashboard.py          ← Streamlit: VC-style memo viewer
├── setup.sh                  ← One-command install
└── demo.sh                   ← Run demo on any company
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One-command setup:

git clone https://github.com/cybort360/axiom
cd axiom
bash setup.sh
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My Tech Stack

  • Hermes Agent: the agent runtime, skills system, and delegate_task for parallel sub-agents
  • FastMCP / mcp Python package: custom MCP server for structured data access
  • httpx: async HTTP client for HackerNews, Reddit, Crunchbase, GitHub APIs
  • Streamlit: investment memo dashboard
  • Python venv: dependency isolation

Data sources used (all free, no paid APIs):

Source What We Pull How
HackerNews Stories, comments, founder mentions Algolia HN API (free, no auth)
Reddit User discussions, complaints, praise Reddit public JSON API
Crunchbase Funding rounds, investors, headcount Public profile scrape
GitHub Repos, stars, contributor signals GitHub public API
Greenhouse / Lever Open roles by department Public ATS APIs
Web search Market size, news, competitors Hermes built-in web_search

How I Used Hermes Agent

Hermes Agent's delegate_task tool is the reason Axiom works. Without it, a single-agent loop running 7 research threads sequentially would take 10x longer and would hit context limits trying to hold everything in memory.

With delegate_task, each sub-agent runs concurrently, stays focused on its own research slice, and hands back a clean JSON object. The orchestrator gets structured inputs from specialists rather than trying to do everything itself.

Here's what the market research delegation looks like in the SKILL.md:

delegate_task: |
  Research the total addressable market for [company].
  Use web_search to find:
  1. Market size estimates from analyst reports
  2. Year-over-year growth rate
  3. Timing signal: too early, right moment, or saturated?
  4. Key market drivers

  Return JSON with keys:
  tam_estimate, growth_rate_yoy, timing_assessment,
  market_drivers, comparables, sources
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Every sub-agent returns a strict JSON schema. The orchestrator never parses free text. It calculates.

The MCP server gives Hermes structured access to six data tools (HackerNews, Reddit, Crunchbase, GitHub, hiring signals, report saving) without any paid API keys. Adding new data sources means adding one Python function.

The skills system makes the research methodology portable. The 363-line SKILL.md defines how a trained analyst would approach each dimension, what questions to ask, what signals to weight, and what the output schema must look like. Install the skill once and use it from CLI, Telegram, or Discord.

Two things surprised me during the build. First, the quality of free data: HackerNews comment threads on a startup's launch post contain more honest signal than most analyst reports. Second, how much the output schema matters. Early versions returned free-form text from sub-agents. Once I forced strict JSON schemas, synthesis became deterministic.

Axiom turned a use case that normally takes weeks and a team of analysts into a command you type once.

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