--- type: Guide title: AI agents in the MESA apps description: Use the AI coding agents and MESA MCP servers built into every MESA featured app — sign in to CyVerse, connect AI Verde or local Ollama models, and reach your Data Store. tags: - apps - vice - agents - claude-code - codex - opencode - antigravity - ai-verde - ollama generated: by: "claude-code/2.1.294" at: "2026-10-08T00:00:00Z" sources: - id: jupyterlab resource: "https://github.com/idss-mesa/jupyterlab" title: "MESA JupyterLab image (README: Sign in to CyVerse, Connect AI Verde LLMs, GPU variant)" author: "team:idss-mesa" - id: cli resource: "https://github.com/idss-mesa/cli" title: "MESA CLI image (README)" author: "team:idss-mesa" - id: ai-verde resource: "https://aiverde-docs.cyverse.ai/" title: "CyVerse AI Verde documentation" author: "team:cyverse" status: stable stale_after: "2027-04-08T00:00:00Z" --- # AI agents in the MESA apps All five [featured apps](https://idss-mesa.github.io/docs/apps/) share the same *MESA agentic stack*: AI coding-agent command-line tools, the MESA MCP servers already registered with each of them, and the CyVerse Data Store tools[^jupyterlab]. This page covers what is the same in every app; each app's page covers what is different. ## What every app includes | Category | Tools | |---|---| | **AI agent CLIs** | Claude Code (`claude`), OpenAI Codex (`codex`), OpenCode (`opencode`), Antigravity (`agy`), and Claude Code Router (`ccr`). The [MESA CLI](https://idss-mesa.github.io/docs/apps/cli/) also has Goose (`goose`). | | **MCP servers** | `irods` (CyVerse Data Store), `mesa` ([mesa-mcp](https://idss-mesa.github.io/docs/servers/mesa-mcp/) with [mesa-ducklake](https://idss-mesa.github.io/docs/servers/mesa-ducklake/)), `formation` ([formation-mcp](https://idss-mesa.github.io/docs/servers/formation-mcp/), the Discovery Environment), and `filesystem`, registered for every agent CLI | | **CyVerse data** | GoCommands (`gocmd`), an iRODS configuration, `osn-mount.sh` for Open Storage Network and other S3 buckets, the AWS CLI | | **Developer tools** | GitHub CLI (`gh`), Git Credential Manager, Go 1.25, Node.js 22 | The agents are command-line tools: open a terminal in the app (a JupyterLab or VS Code terminal, the RStudio **Terminal** tab, a terminal on the KASM desktop, or the MESA CLI itself) and type `claude`, `codex`, `opencode`, or `agy`. ## Your Data Store inside the app When the app starts from the portal or the Discovery Environment, your Data Store is mounted under `~/data-store`, and the app opens there. Save work you want to keep under `~/data-store`: everything else lives on the container's disk and is gone when the analysis ends. At start-up each app also copies `.gitconfig`, `.aws/`, and `.ssh/` from your Data Store home folder (`/iplant/home//`) into the app's home folder, so Git and AWS settings you keep there follow you into every session. ## 1. Sign in to CyVerse ```bash cyverse-login # your CyVerse username and password ``` `cyverse-login` writes the standard iRODS credentials (`~/.irods/`), so GoCommands, the `mesa` and `formation` MCP servers, and the agents act as **you**, with access to your home folder and to what is shared with you. Without it they have anonymous, read-only access to public data. Restart an agent after signing in so its MCP servers pick up the credentials. **Claude Code** also registers the hosted CyVerse Data Store MCP servers: `irods` (the anonymous public endpoint, which works at once) and `irods-auth` (the authenticated endpoint). To reach your private home folder through `irods-auth`, sign in once per session: ```bash claude mcp login irods-auth --no-browser # opens a kc.cyverse.org URL; paste the redirect back ``` OpenCode, Codex, and Antigravity use the local servers and `gocmd`, which read the `~/.irods` credentials that `cyverse-login` wrote. ## 2. Connect a language model The images contain no API keys: each person brings their own. ### CyVerse AI Verde [AI Verde](https://aiverde-docs.cyverse.ai/) is CyVerse's hosted LLM service[^ai-verde]. Get an API key from (**Course → API Key**), then in a terminal: ```bash aiverde-setup # paste your AI Verde key ``` It checks the key, lists the models your course can use, and saves the settings to `~/.config/aiverde/env` (readable only by you). Then: | Agent | How it uses AI Verde | |---|---| | **OpenCode** | Through its `aiverde` provider | | **Claude Code** | Through Claude Code Router: `ccr code` (or directly, if your course serves Anthropic models) | | **Goose** (MESA CLI) | Through its OpenAI-compatible provider: `goose` | | **Codex** | Not supported: Codex uses its own OpenAI sign-in | ### Your own accounts Each agent can also use its vendor's own sign-in, for example `claude` with an Anthropic account or `codex` with an OpenAI account. Follow the prompts the first time you start it. ### Local models on a GPU The GPU builds of every app run an [Ollama](https://ollama.com) server inside the container, so agents can use an open model on the GPU with no API key and nothing leaving the analysis: ```bash ollama-setup # pulls qwen3.5:9b (the default) and prints these commands ollama launch claude --model qwen3.5:9b # Claude Code on the local model codex --oss --local-provider ollama -m qwen3.5:9b opencode -m ollama/qwen3.5:9b ``` One 16 GB NVIDIA A16 GPU fits `qwen3.5:9b`, `gpt-oss:20b`, `gemma4:12b`, or `qwen3:4b` (`ollama-setup --help` lists them); larger models spill onto the CPU. Models are stored in `~/.ollama/models` on the container's disk and are deleted when the analysis ends. `ollama stop ` frees the GPU memory for PyTorch or other work. `mesa-gpu-check` (`mesa-gpu-check --ollama` for a short model test) checks the GPU, the driver, CUDA, PyTorch, and Ollama. ## 3. Ask With credentials in place, ask an agent for things such as: - *"List the folders in my CyVerse home and tell me which ones have AVU metadata."* - *"Find NEON soil-moisture data in the MESA community folder and load it into a pandas DataFrame."* - *"Launch a MESA JupyterLab analysis and tell me when it is running."* The agent calls the MESA MCP servers to do the work; see [Servers](https://idss-mesa.github.io/docs/servers/) for what each one can do. [^jupyterlab]: https://idss-mesa.github.io/docs/apps/MESA JupyterLab README, ; the same sections appear in the README of each MESA app repository. [^ai-verde]: https://idss-mesa.github.io/docs/apps/CyVerse AI Verde documentation, .