InternalAI Agent

A documentation and pattern library that gives LLM agents the context they need to build data analysis workflows against Monadical's internal systems — ContactDB (people directory) and DataIndex (unified data from email, calendar, Zulip, meetings, documents).

The goal is to use opencode (or any LLM-powered coding tool) to iteratively create marimo notebook workflows that query and analyze company data.

Getting Started

Prerequisites

opencode installed
Access to the InternalAI platform (ContactDB + DataIndex running locally, accessible via http://localhost:42000)

Configuring opencode with LiteLLM

To use models through LiteLLM, add the following to ~/.config/opencode/config.json:

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "litellm": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "Litellm",
      "options": {
        "baseURL": "https://litellm.app.monadical.io",
        "apiKey": "xxxxx"
      },
      "models": {
        "Kimi-K2.5-dev": {
          "name": "Kimi-K2.5-dev"
        }
      }
    }
  }
}

Replace xxxxx with your actual LiteLLM API key.

Running opencode

From the project root:

opencode

opencode will pick up AGENTS.md automatically and use it as the entry point to understand the project, the available APIs, and how to write workflows.

How AGENTS.md Works

AGENTS.md is the routing guide for LLM agents. It is structured as follows:

Purpose statement — Explains that the agent's job is to build marimo notebooks that analyze company data.

Documentation routing table — Directs the agent to the right file depending on the topic:

Topic	File
Company context, tools, connectors overview	`docs/company-context.md`
People, contacts, relationships	`docs/contactdb-api.md`
Querying emails, meetings, chats, docs	`docs/dataindex-api.md`
Connector-to-entity-type mappings	`docs/connectors-and-sources.md`
Notebook creation patterns and templates	`docs/notebook-patterns.md`

API base URLs — ContactDB and DataIndex endpoints (both via Caddy proxy and direct).
Common query translation table — Maps natural-language questions (e.g. "Who am I?", "Recent meetings") to the corresponding API calls.
Workflow rules — When to create a notebook vs. answer inline, naming conventions, and the requirement to propose a plan before implementing.

Workflow

How it works

Ask a question in opencode — Describe what you want to analyze (e.g. "Show me all meetings about Greyhaven in January").
Agent reads AGENTS.md — opencode picks up the routing guide and navigates to the relevant docs to understand the APIs.
Agent proposes a plan — Before writing code, the agent outlines: Goal, Data Sources, Algorithm, and Output Format.
Agent creates a marimo notebook — A .py file is written to workflows/ following the naming convention <NNN>_<topic>_<scope>.py.
Iterate — Run the notebook with marimo edit workflows/<name>.py, review the output, and ask the agent to refine.

Workflow output format

Workflows are marimo notebooks — plain Python files with @app.cell decorators. They typically follow this structure:

params cell — User-editable parameters (search terms, date ranges, contact names)
config cell — API base URLs
setup cell — Shared imports (httpx, polars, marimo)
data cells — Fetch and transform data from ContactDB / DataIndex
output cells — Tables, charts, or markdown summaries

Naming convention

workflows/<NNN>_<topic>_<scope>.py

Examples:

001_greyhaven_meetings_january.py
002_email_activity_q1.py

Project Structure

internalai-agent/
├── AGENTS.md                        # LLM agent routing guide (entry point)
├── README.md
├── docs/
│   ├── company-context.md           # Monadical org, tools, key concepts
│   ├── contactdb-api.md             # ContactDB REST API reference
│   ├── dataindex-api.md             # DataIndex REST API reference
│   ├── connectors-and-sources.md    # Connector → entity type mappings
│   └── notebook-patterns.md         # Marimo notebook templates and patterns
└── workflows/                       # Generated analysis notebooks go here

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