| --- |
| license: mit |
| language: |
| - en |
| tags: |
| - agents |
| - llm |
| - venture-capital |
| - decision-support |
| - behavioral-bias |
| - typescript |
| - react |
| - trpc |
| - drizzle |
| --- |
| |
| # VC Performance Agent |
|
|
| VC Performance Agent is a full-stack web application for venture capital decision support. It combines a deal-flow CRM with an LLM-backed "AI Copilot". The Copilot analyzes deals for behavioral biases and red flags, suggests due-diligence questions, and scores founders, alongside a research-insights library, a Monte Carlo portfolio simulator and fund performance metrics (IRR, MOIC, batting average). The target users are VC fund managers, investment teams and fund operations staff. |
|
|
| This is an application codebase, not a trained model. LLM calls go to an external OpenAI-compatible chat-completions endpoint. |
|
|
| **Author:** AgenThink, Kuwait City |
|
|
| > The application lives in the nested folder **[`vc-performance-agent/`](https://huggingface.co/agenthinkmesh/vc-performance-agent/tree/main/vc-performance-agent)**. Its own README is here: [vc-performance-agent/README.md](https://huggingface.co/agenthinkmesh/vc-performance-agent/blob/main/vc-performance-agent/README.md). |
|
|
| --- |
|
|
| ## Key features |
|
|
| Features as implemented in `server/routers.ts` and `client/src/pages/`: |
|
|
| | Area | What it does | |
| |---|---| |
| | **Deal flow CRM** | Deals move through a pipeline with statuses Sourced → Meeting → Due Diligence → Term Sheet → Closed / Passed (as coded in the schema and UI). Also covers companies, contacts, an activity timeline and pipeline statistics. | |
| | **AI Copilot** | `copilot.analyzeDeal` flags 7 bias types: gambling mentality, ego-driven decisions, network homogeneity, confirmation bias, anchoring, herd mentality, and over-reliance on gut feel. It also flags red flags. `copilot.suggestQuestions` generates due-diligence questions and `copilot.detectPatterns` analyzes patterns across the portfolio. | |
| | **Deal evaluation** | `deals.analyzeWithAI` scores deals from 0 to 100 using weighted criteria from the research notes: Team Quality 95%, Business Model 74%, Market Size 68%. | |
| | **Founder assessment** | Founder profiles with an LLM-generated 0–100 assessment (`founders.assessWithAI`) covering leadership, domain expertise, track record, team building, execution ability, and vision. | |
| | **Research library** | Curated research insights (power law, J-curve, founder assessment, behavioral biases), filterable by category. | |
| | **Portfolio simulator** | A server-side Monte Carlo simulation of portfolio outcomes from success rate and return-multiple parameters. | |
| | **Performance dashboard** | Fund metrics (IRR, MOIC, batting average) with history. | |
| | **Due diligence** | Per-deal checklists. | |
|
|
| --- |
|
|
| ## Architecture |
|
|
| - **Frontend:** React 19, Tailwind CSS 4, shadcn/ui, wouter routing, tRPC client |
| - **Backend:** Express with tRPC. The routers are `auth`, `companies`, `deals`, `founders`, `contacts`, `activities`, `portfolio`, `dueDiligence`, `research`, `simulations`, `performance` and `copilot`. |
| - **Database:** MySQL/TiDB via Drizzle ORM. Tables: `users`, `companies`, `deals`, `founders`, `contacts`, `activities`, `portfolio_investments`, `due_diligence_checklists`, `research_insights`, `portfolio_simulations`, `fund_metrics`. |
| - **LLM:** `server/_core/llm.ts` calls an OpenAI-compatible `/v1/chat/completions` endpoint with model `gemini-2.5-flash`. |
| - **Auth:** OAuth via the Manus platform (session cookie with JWT) |
| - **Tests:** Vitest (`server/*.test.ts`) |
|
|
| ### Repository structure |
|
|
| | Path | Description | |
| |---|---| |
| | `VC_PERFORMANCE_AGENT_EXECUTIVE_SUMMARY.md` | Stakeholder overview: features, target users, methodology, roadmap | |
| | `vc_research_findings.md` | Research notes on VC returns (power law, the math of VC returns, VC decision-making surveys) | |
| | `vc_success_factors.md` | An analysis of the key drivers of venture capital success | |
| | `vc-performance-agent/client/` | React frontend. `src/pages/` has Home, Deals, DealDetail, Companies, CompanyDetail, Founders, Portfolio, Performance, Research and Simulator. | |
| | `vc-performance-agent/server/` | Express + tRPC backend: `routers.ts`, `db.ts`, `storage.ts`, `_core/` (LLM, OAuth, env, Vite integration) | |
| | `vc-performance-agent/drizzle/` | Schema, relations and SQL migrations | |
| | `vc-performance-agent/shared/` | Types and constants shared by client and server | |
| | `vc-performance-agent/todo.md` | Feature checklist (73 items done, 32 open) | |
|
|
| --- |
|
|
| ## Getting started |
|
|
| Requirements: Node.js 22+, pnpm, and a MySQL-compatible database. |
|
|
| ```bash |
| cd vc-performance-agent |
| pnpm install |
| # create .env with the variables below (the upstream README refers to a .env.example that is not included) |
| pnpm db:push # drizzle-kit generate && drizzle-kit migrate |
| pnpm dev # development server |
| ``` |
|
|
| Other scripts: `pnpm build`, `pnpm start` (production), `pnpm test` (Vitest), `pnpm check` (type-check), `pnpm format`. |
|
|
| Environment variables read by `server/_core/env.ts` (names only): |
| `DATABASE_URL`, `JWT_SECRET`, `VITE_APP_ID`, `OAUTH_SERVER_URL`, `OWNER_OPEN_ID`, `BUILT_IN_FORGE_API_URL`, `BUILT_IN_FORGE_API_KEY`. |
|
|
| --- |
|
|
| ## Results / metrics |
|
|
| The files report no evaluation results for the AI Copilot or the scoring model. The criterion weights (95% / 74% / 68%) come from the research notes. The "80% of VC investments fail" framing is the project's own motivation statement, not a measured outcome of this tool. |
|
|
| --- |
|
|
| ## Limitations |
|
|
| - **Tied to the Manus platform.** Authentication and the default LLM endpoint depend on Manus OAuth and the Manus "Forge" API, so running elsewhere requires replacing `server/_core/oauth.ts` and `llm.ts`, or pointing them at compatible services. |
| - **Referenced files are missing.** The upstream README mentions a `.env.example`, a `seed-research.mjs` seed script, a Dockerfile, and several specification documents (technical spec, dev guide, process flows, flowcharts). None of these are in this repository. |
| - **Simplified simulator.** The Monte Carlo simulation is a basic random-draw model and is not calibrated to real fund data. |
| - **Unvalidated scores.** Bias detection and scoring are LLM-generated judgments, not validated predictors. Treat them as prompts for discussion, not investment advice. |
| - **Unfinished items.** Several items in `todo.md` remain open, among them network analysis, reference-check tracking and industry benchmarking. |
|
|
| --- |
|
|
| ## License |
|
|
| MIT (see [`vc-performance-agent/LICENSE`](https://huggingface.co/agenthinkmesh/vc-performance-agent/blob/main/vc-performance-agent/LICENSE)). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @software{agenthink2026vcperformance, |
| title = {VC Performance Agent: AI-Assisted Venture Capital Decision Support}, |
| author = {{AgenThink}}, |
| year = {2026}, |
| address = {Kuwait City, Kuwait}, |
| url = {https://huggingface.co/agenthinkmesh/vc-performance-agent} |
| } |
| ``` |
|
|
