Mistral integration¶

fortranspire is built Mistral-first but Mistral-only is not required. The agent talks to any OpenAI-compatible endpoint, so the same code paths work against:

Mistral La Plateforme (https://api.mistral.ai/v1) — sovereign EU hosting, the default.
Self-hosted vLLM / TGI / Ollama — full sovereignty, on an OpenStack tenant, an HPC site (GENCI Jean Zay, EuroHPC LUMI / MareNostrum), or any private datacenter.
EU OpenAI-compatible gateways — any third-party gateway exposing the /chat/completions schema and respecting EU data residency.

Azure is not part of the integration. A previous version of the project proxied LLM calls through Azure OpenAI; this dependency was removed in commit ccfe221. There is no AZURE_* environment variable to set, no Azure-specific code path, and no Azure resource in the install instructions.

This page walks through the four integration paths.

Path 1 — Agent as a Mistral La Plateforme consumer¶

The most common setup. The agent uses your Mistral API key to call any Mistral chat model.

One-time setup¶

Create an API key at https://console.mistral.ai/api-keys.
Copy it into .env at the repo root:

MISTRAL_ENDPOINT="https://api.mistral.ai/v1"
MISTRAL_API_KEY="<paste-your-key>"

Verify the key works:

curl https://api.mistral.ai/v1/models \
  -H "Authorization: Bearer $MISTRAL_API_KEY" | jq '.data[].id' | head

You should see mistral-large-latest, codestral-latest, mistral-nemo, etc.

Run a transformation¶

fortranspire gpu path/to/kernel.f90

The pipeline issues up to four LLM calls (~0.06 USD at Mistral-Large tariffs, ~2 min wall-clock).

Path 2 — Per-stage model selection (Codestral + Mistral-Large)¶

The pipeline has two kinds of LLM work:

Reasoning stages (extractor, openacc) — refactoring a monolithic PROGRAM into a MODULE, placing !$acc data copyin/copy clauses around the time loop. Wrong placement = silent GPU corruption. Needs a large reasoning model.
Code-gen stages (cython_wrapper) — emitting a .pyx wrapper and an iso_c_binding header. Mostly boilerplate. Codestral is faster and cheaper for this kind of work.

The agent picks the right model per stage automatically. Override with two environment variables:

MISTRAL_MODEL_REASONING="mistral-large-latest"   # extractor, openacc
MISTRAL_MODEL_CODE="codestral-latest"            # cython_wrapper

The legacy MISTRAL_MODEL variable still works as a single-model override for both roles, so older .env files keep working unchanged.

Cost comparison¶

For a typical kernel (4 LLM calls), against the November 2024 Mistral tariffs:

Setup	Wall-clock	Cost
All-Mistral-Large (legacy)	~2 min	~0.06 USD
Mistral-Large reasoning + Codestral code-gen (default)	~90 s	~0.03 USD
All-Codestral (cheapest)	~75 s	~0.02 USD

Codestral reasoning is good enough on simple kernels but degrades visibly on monolithic codes with deep COMMON / SAVE patterns — keep Mistral-Large on the reasoning stages unless you know your kernels are clean.

Path 3 — Agent as an MCP provider for Le Chat / Mistral Agents API¶

The agent ships an MCP server (fortranspire mcp) that publishes the transformation tools (translate_kernel_gpu, translate_kernel, profile_kernels, ask_agent) over HTTP/SSE on port 8000. Anything that speaks MCP can drive it — Claude Desktop, Cursor, VS Code agents, Mistral Le Chat, the Mistral Agents API.

Start the server¶

uv sync --extra mcp     # FastMCP + the [gpu] transformation stack
fortranspire mcp          # listens on http://0.0.0.0:8000/sse

Set API_KEY to require a bearer token on every request:

API_KEY=$(openssl rand -hex 32) fortranspire mcp

Use from Le Chat (connector — beta)¶

Le Chat’s connector directory is in beta. The mechanics:

Public endpoint — Le Chat needs to reach your MCP server. Locally, tunnel with cloudflared / ngrok; in production, deploy on an OpenStack tenant or on-prem cluster — see the deployment roadmap.
Manifest — submit the connector descriptor. A draft is shipped in integration/le-chat-connector.json, ready to send when the directory opens to general submissions.
Auth — Le Chat passes a bearer token; the agent’s MCP server checks it against API_KEY.

Use from the Mistral Agents API (now)¶

The Agents API supports MCP servers natively. Minimal Python client:

from mistralai import Mistral
client = Mistral(api_key=...)
agent = client.beta.agents.create(
    name="fortran-gpu-port",
    model="mistral-large-latest",
    instructions="You port Fortran kernels to GPU. Always call translate_kernel_gpu.",
    tools=[{
        "type": "mcp",
        "server": {
            "url": "https://<your-public-mcp-host>/sse",
            "auth": {"type": "bearer", "token": "<API_KEY>"},
        },
    }],
)

A runnable smoke-test that exercises this pattern lives at examples/mistral_agents_api_smoke_test.py. Run it with your own Mistral API key — it consumes tokens.

Path 4 — Submission to the Le Chat connector directory¶

Goal: get the agent listed as a public connector in Le Chat so any Mistral Enterprise customer can plug it in with one click.

Status: the directory is in beta; an HPC / scientific vertical does not yet exist, so fortranspire is a credible first-mover. The submission package is prepared in integration/le-chat-connector.json and documented in Le Chat connector.

Prerequisites before submission:

A stable public MCP endpoint (HTTPS, valid certificate, monitored). Today this means deploying on an OpenStack tenant or an on-prem cluster — see the deployment roadmap.
A privacy notice (the connector receives user-provided Fortran source).
A demo video / screenshots (a typical kernel transformation run).

When those are in place, submit through the directory portal (currently invite-only; Mistral partner managers handle the onboarding).

Cheat sheet¶

Goal	Command
Validate your API key	`curl -H "Authorization: Bearer $MISTRAL_API_KEY" https://api.mistral.ai/v1/models`
Port a kernel (default models)	`fortranspire gpu kernel.f90`
Use Codestral everywhere (cheapest)	`MISTRAL_MODEL=codestral-latest fortranspire gpu kernel.f90`
Run the MCP server with auth	`API_KEY=… fortranspire mcp`
Quick API smoke-test	`uv run python examples/mistral_agents_api_smoke_test.py --key $MISTRAL_API_KEY`
Build the Apptainer analyze image (no LLM cost)	`apptainer build analyze.sif Apptainer.analyze`