> ## Documentation Index > Fetch the complete documentation index at: https://docs.mka1.com/llms.txt > Use this file to discover all available pages before exploring further. # Commands > Tour the mka1 CLI command tree with worked examples for Responses, Conversations, Files, Vector Stores, Extract, Speech, Agents, and more. Every command follows the same shape: ```text theme={null} mka1 [flags] ``` For example, `mka1 llm responses create` or `mka1 llm vector-stores search`. Drill into any level with `--help` to see the flags that apply: ```bash theme={null} mka1 --help mka1 llm --help mka1 llm responses --help mka1 llm responses create --help ``` Or launch the interactive TUI to browse every command: ```bash theme={null} mka1 explore ``` The rest of this page walks through the most common workflows. All examples assume you have authenticated the CLI — see [authenticate the CLI](/docs/cli/authentication). ## Responses Generate text, continue a thread, or run an agent with tools. ```bash theme={null} mka1 llm responses create \ --model auto \ --input '"Write a one-sentence summary of the MKA1 API."' ``` Add `instructions` to steer the reply: ```bash theme={null} mka1 llm responses create \ --model auto \ --instructions 'Reply in plain English. Keep answers under 80 words.' \ --input '"Explain what embeddings are used for."' ``` Continue an earlier response without resending the history: ```bash theme={null} mka1 llm responses create \ --model auto \ --previous-response-id resp_123 \ --input '"Now turn that into an email subject line."' ``` Stream tokens as they are produced: ```bash theme={null} mka1 llm responses create \ --model auto \ --input '"Write three release-notes bullets for the docs update."' \ --stream \ --output-format json ``` Offload long-running work to the background and poll later with `mka1 llm responses get`: ```bash theme={null} mka1 llm responses create \ --model auto \ --input '"Produce a 1,000-word brief."' \ --background ``` See the [Responses guide](/docs/generate-a-response) for the full resource model. ## Conversations Wrap multi-turn exchanges in a reusable container: ```bash theme={null} # Create a conversation mka1 llm conversations create --metadata '{"session_id":"web-42"}' # Use it in a response mka1 llm responses create \ --model auto \ --conversation conv_123 \ --input '"What should I ask next to refine this draft?"' # Inspect the stored items mka1 llm conversations list-items --conversation-id conv_123 ``` See the [conversations guide](/docs/conversations) for the full lifecycle. ## Files Upload once, then reference the file from vector stores, fine-tuning jobs, or the Extract API: ```bash theme={null} mka1 llm files upload --file ./support-manual.pdf --purpose assistants mka1 llm files list mka1 llm files get --file-id file_123 mka1 llm files content --file-id file_123 --output-file ./downloaded.pdf ``` ## Vector stores Index files for semantic search and retrieval: ```bash theme={null} # Create a store mka1 llm vector-stores create --name support-knowledge # Attach a file mka1 llm vector-stores create-file \ --vector-store-id vs_123 \ --file-id file_123 # Search it mka1 llm vector-stores search \ --vector-store-id vs_123 \ --query 'How do I reset an account password?' ``` See the [files and vector stores guide](/docs/files-and-vector-stores) for the full pattern. ## Extract structured data Run inline extraction with a JSON Schema: ```bash theme={null} mka1 llm extract extract \ --model auto \ --file ./invoice.pdf \ --prompt 'Extract invoice number, vendor, total, and date.' \ --schema '{ "type": "object", "properties": { "invoice_number": { "type": "string" }, "vendor_name": { "type": "string" }, "total_amount": { "type": "number" }, "date": { "type": "string", "format": "date" } }, "required": ["invoice_number", "total_amount"] }' ``` Or save a schema once and reuse it: ```bash theme={null} mka1 llm extract create-schema --name invoice --schema @./invoice.schema.json mka1 llm extract extract-with-schema --schema-id sch_123 --file ./invoice.pdf ``` See the [extract structured data guide](/docs/extract-structured-data) for schema design tips. ## Speech Transcribe audio or generate speech from text: ```bash theme={null} # Speech to text mka1 llm speech transcribe --file ./call.wav # Text to speech — save to a .wav file mka1 llm speech speak \ --text 'Hello, welcome to our service.' \ --language en \ --output-file ./welcome.wav ``` See the [speech guide](/docs/speech) for language and voice options. ## Agents Create a reusable agent definition, then run it later: ```bash theme={null} mka1 agents create \ --name release-research-agent \ --model auto \ --instructions 'Use web search when the question depends on current external information.' \ --tools '[{"type":"web_search","search_context_size":"medium"}]' mka1 agent-runs create \ --agent-id agt_123 \ --input '"What is the current stable version of Bun?"' mka1 agent-runs list --agent-id agt_123 ``` See the [managing agents guide](/docs/managing-agents) for the full resource model. ## Prompts Save prompts centrally and version them: ```bash theme={null} mka1 llm prompts create --name welcome-email --template @./welcome.tpl mka1 llm prompts create-version --prompt-id prm_123 --template @./welcome.v2.tpl mka1 llm prompts list-versions --prompt-id prm_123 mka1 llm prompts rollback --prompt-id prm_123 --version 1 ``` See the [prompt repository guide](/docs/prompt-repository). ## Models and usage List the models available to your account and check usage: ```bash theme={null} mka1 llm models list mka1 llm models get --model-id meetkai:functionary-pt mka1 llm usage-stats responses --start-time 2026-04-01 --end-time 2026-04-22 mka1 llm usage-stats embeddings --start-time 2026-04-01 --end-time 2026-04-22 ``` ## Permissions Grant, revoke, and check fine-grained permissions on individual resources: ```bash theme={null} mka1 permissions llm grant \ --resource-type completion \ --resource-id my-completion-123 \ --user-id user-abc456 \ --role writer mka1 permissions llm check \ --resource-type completion \ --resource-id my-completion-123 \ --user-id user-abc456 ``` See the [authorization guide](/docs/authorization) for the permission model. ## Guardrails Inspect and test the content moderation settings applied to your traffic: ```bash theme={null} mka1 guardrails get mka1 guardrails test --content 'Evaluate this input.' ``` ## Discover everything else The command tree is large and most subcommands follow the same `list` / `get` / `create` / `update` / `delete` pattern. Lean on `--help` and `mka1 explore` to find the one you need.