# openInvest

openInvest gives AI agents an investment committee, portfolio ledger, and traceable decision records. Use it for research, backtests, and review.

- MCP server · `type=mcp` · Claude Code, Codex, Cursor, GitHub Copilot, Windsurf (Devin Desktop), Cline, Zed · ★ 133
- Author: longsizhuo
- License: MIT · policy=link-only · description only; the file stays with the author
- Source: https://github.com/longsizhuo/openInvest
- Updated: 2026-10-23
- HTML: https://affpapa.org/en/nevibecode/mcp/openinvest

## What it does

openInvest is a self-hosted decision engine for AI agents. It runs a multi-agent investment committee with separated analysis contexts and rebuttal rounds. A coordinator assigns the work, while the recorded trail keeps the reasoning behind each decision visible.

The server maintains a portfolio ledger, supports long-horizon backtesting, and compares results with control benchmarks. Once self-hosted, its chart reads your own `memory/portfolio_history.jsonl` and appears after the ledger covers 2 trading days. The project lists Python 3.13+ for the runtime. Its own audit positions the system as a way to enforce discipline and transparency, not as a source of excess returns or a directional prediction engine.

## Requirements
- Needs: Python and uv (commands run via uvx)
- Environment variables: `INVEST_HOME` — keys and tokens: ask the human for the values, never commit them or put them in shared files.

## Install

### Claude Code (native platform)

**Platform CLI**
```bash
claude mcp add --env INVEST_HOME=<INVEST_HOME> --transport stdio openinvest -- uvx openinvest
```
Replace <…> with your own values. uvx runs the command named after the package; if the README names another one, use uvx --from openinvest <command>

### Codex

**Platform CLI**
```bash
codex mcp add openinvest --env INVEST_HOME=<INVEST_HOME> -- uvx openinvest
```
Replace <…> with your own values. uvx runs the command named after the package; if the README names another one, use uvx --from openinvest <command>

### Cursor

**Config file**
File `.cursor/mcp.json`:
```json
{
    "mcpServers": {
        "openinvest": {
            "command": "uvx",
            "args": [
                "openinvest"
            ],
            "env": {
                "INVEST_HOME": "<INVEST_HOME>"
            }
        }
    }
}
```
Replace <…> with your own values. uvx runs the command named after the package; if the README names another one, use uvx --from openinvest <command>

### GitHub Copilot

**Config file**
File `.vscode/mcp.json`:
```json
{
    "servers": {
        "openinvest": {
            "type": "stdio",
            "command": "uvx",
            "args": [
                "openinvest"
            ],
            "env": {
                "INVEST_HOME": "<INVEST_HOME>"
            }
        }
    }
}
```
Replace <…> with your own values. uvx runs the command named after the package; if the README names another one, use uvx --from openinvest <command>

### Gemini CLI

**Platform CLI**
```bash
gemini mcp add -e INVEST_HOME=<INVEST_HOME> openinvest uvx openinvest
```
Replace <…> with your own values. uvx runs the command named after the package; if the README names another one, use uvx --from openinvest <command>

## When to use it
- run an investment committee with separate analyst roles
- keep quant and risk contexts apart
- record why a portfolio decision was made
- compare a portfolio with control benchmarks
- test a trading idea with long-horizon backtesting

## Example prompts
- Ask the investment committee to assess an asset, challenge the first view, and record each role’s reasoning.
- Run a long-horizon backtest for this strategy and compare the result with the control benchmarks.
- Review the portfolio decision trail and explain the reasoning behind the latest action.

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