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Mistralai CLI for Headless Automation
Use the Mistralai CLI for headless automation with headless JSON commands, schema discovery, credentials, and permission controls.
7 functions 3 read 4 write API key auth
Mistralai CLI for Headless Automation
Use KosmoKrator as a non-interactive integration runtime for local automations and wrappers.
Use headless automation when another tool needs a stable local command surface. The Mistralai CLI uses the same integration registry as the TUI, Lua runtime, and MCP gateway, but returns predictable command output for automation.
Command Shape
# Mistralai CLI for Headless Automation
kosmokrator integrations:configure mistralai --set api_key="$MISTRALAI_API_KEY" --enable --read allow --write ask --json
kosmo integrations:call mistralai.mistralai_chat '{"model":"example_model","messages":"example_messages","temperature":1,"max_tokens":1}' --jsonDiscovery Before Execution
Agents and scripts can inspect Mistralai docs and schemas before choosing a function.
kosmo integrations:docs mistralai --json
kosmo integrations:docs mistralai.mistralai_chat --json
kosmo integrations:schema mistralai.mistralai_chat --json
kosmo integrations:search "Mistralai" --json
kosmo integrations:list --jsonUseful Mistralai CLI Functions
| Function | Type | Parameters | Description |
|---|---|---|---|
mistralai.mistralai_chat | Write | model, messages, temperature, max_tokens | Generate a chat completion using a MistralAI model. Send a list of messages (with roles "system", "user", or "assistant") and receive a model-generated response. Use temperature to control creativity (0 = deterministic, 1 = creative). |
mistralai.mistralai_create_embedding | Write | model, input | Generate text embeddings using a MistralAI embedding model. Converts text into numerical vectors for semantic search, similarity comparison, or clustering. Supports single strings or arrays of strings. |
mistralai.mistralai_list_models | Read | none | List all models available in your MistralAI account. Returns model IDs, creation timestamps, and capabilities. Use this to discover which models you can use for chat completions or embeddings. |
mistralai.mistralai_finetune | Write | model, training_files, hyperparameters, suffix | Create a fine-tuning job on MistralAI. Upload training data and select a base model to create a custom fine-tuned model. The job runs asynchronously — check the returned job ID for status updates. |
mistralai.mistralai_list_agents | Read | none | List all MistralAI agents in your account. Returns agent IDs, names, models, and descriptions. Agents are AI assistants with custom instructions and tools. |
mistralai.mistralai_create_agent | Write | name, model, instructions, description | Create a new MistralAI agent. Specify a name, model, and instructions to define how the agent should behave. Agents are persistent AI assistants that can be used for conversations with custom personalities and capabilities. |
mistralai.mistralai_get_current_user | Read | none | Get information about the currently authenticated MistralAI user. Returns account details, subscription tier, and usage information. |
Automation Notes
- Use
--jsonfor machine-readable output. - Keep credentials out of argv by using environment variables or stored KosmoKrator configuration.
- Configure read/write policy before unattended runs; use
--forceonly for trusted automation. - Use the MCP gateway instead when the agent needs dynamic tool discovery inside a conversation.