> ## Documentation Index > Fetch the complete documentation index at: https://docs.multimind.dev/llms.txt > Use this file to discover all available pages before exploring further. # MultiMind CLI Interface The MultiMind CLI provides a comprehensive command-line interface for using the MultiMind SDK. This interface allows you to perform various AI tasks using multiple providers, ensemble methods, and specialized tools. ## Installation Make sure you have the MultiMind package installed: ```bash theme={null} pip install multimind-sdk ``` ## The `multimind` Command Installing the package adds a `multimind` entry point with the following command groups: ```bash theme={null} multimind --help ``` Commands: `audit`, `backend`, `chat`, `compliance`, `config`, `dashboard`, `models`, `serve`. Two extra commands are dispatched before the Click group and therefore do not appear in `multimind --help`: `multimind convert` and `multimind context-transfer`. Both are documented under Model Conversion and Context Transfer below. ### Model Management ```bash theme={null} # List registered model providers (or local fine-tuned models with --output-dir) multimind models list multimind models list --output-dir ./finetuned # Compare responses from multiple models multimind models compare "Explain quantum computing" -m openai -m claude # Check health of models (all, or one with -m) multimind models health multimind models health -m openai # Show metrics and health status for models multimind models metrics -m openai # Download a pretrained or fine-tuned model multimind models download -m bert-base-uncased # Export a model to ONNX or TorchScript format multimind models export -m ./finetuned/my-model -f onnx -o my-model.onnx # Delete a local fine-tuned model multimind models delete -m ./finetuned/my-model ``` `models metrics` reads the gateway's `ModelMonitor` (`multimind.core.monitoring`), which is an in-memory, per-process object fed by `/v1/chat` and `/v1/generate` requests. A fresh CLI process has its own empty monitor, so the command prints empty tables unless it runs inside the same process as the server; to see live gateway numbers query `GET /v1/metrics` on the running server instead. ### Chat Sessions ```bash theme={null} # Start an interactive chat session with a model (-m is required) multimind chat start -m openai # Send a single prompt instead of an interactive session multimind chat start -m openai -p "Hello, world!" # Manage sessions multimind chat list-sessions multimind chat save SESSION_ID multimind chat load SESSION_ID multimind chat delete SESSION_ID ``` ### Configuration ```bash theme={null} # Show environment and configuration info multimind config info # View or set global CLI configuration (stored in ~/.multimind_cli_config) multimind config manage multimind config manage --set default_model openai multimind config manage --get default_model # Generate shell completion script (bash, zsh, or fish) multimind config completion zsh ``` ### Compliance #### PII Text Scanning Scan text or a file for PII using the ComplianceGuard detector. Exits with code 1 if any PII is found, which makes it usable in CI pipelines. ```bash theme={null} multimind compliance scan-text "Contact john@example.com or 555-123-4567" ``` Options: * `--file`, `-f`: Scan a file instead of inline text * `--redact`, `-r`: Also print the text redacted with this strategy (`mask`, `hash`, or `remove`) Example: ```bash theme={null} multimind compliance scan-text -f support_ticket.txt -r mask ``` #### Monitoring, Reports, and Alerts ```bash theme={null} # Run compliance monitoring from a configuration file multimind compliance run-compliance -c compliance_config.json -o results.json # Generate a compliance report multimind compliance generate-report -c compliance_config.json -o report.json # Show the compliance dashboard for an organization multimind compliance dashboard -o my-org -t 7d # Show compliance alerts (filter by status/severity) multimind compliance alerts -o my-org -s active -v high # Configure compliance alert rules multimind compliance configure-alerts -o my-org -c alert_rules.json # Run a bundled compliance example (healthcare or general) multimind compliance run-example -t healthcare -u diagnosis # Evidence report (md or html) from the guard audit log, cost log, and an inventory scan multimind compliance report-evidence --audit-log audit.jsonl --costs-log costs.jsonl \ --project . --period 2026-07 --organization "Acme" --format md -o evidence.md ``` `compliance dashboard` and `compliance alerts` read the same in-process store as the `/v1/compliance/dashboard` and `/v1/compliance/alerts` routes: one `ComplianceShard` per organization, held for the life of the backend process and filled by `POST /v1/compliance/monitor` calls (`multimind/gateway/compliance_api.py`). Nothing is persisted to disk, so a CLI invocation in its own process reports zero checks and no alerts; use the routes on the running server (`multimind backend`) to see what that process recorded. ### Compliance Proxy Server Start the OpenAI-compatible compliance proxy. Point any existing OpenAI client's `base_url` at this proxy to get PII redaction, budget enforcement, and an audit trail with zero code changes. ```bash theme={null} multimind serve --upstream openai --port 8400 \ --strategy mask \ --block-on ssn,credit_card \ --budget 10.0 \ --audit-log audit.jsonl ``` Options: * `--host`: Bind host (default 127.0.0.1) * `--port`, `-p`: Bind port (default 8400) * `--upstream`: Named upstream provider: `openai`, `groq`, `mistral`, `gemini`, `deepseek`, `ollama` * `--upstream-base-url`: Custom OpenAI-compatible upstream URL (overrides `--upstream`) * `--strategy`: PII redaction strategy: `mask`, `hash`, or `remove` (default `mask`) * `--block-on`: Comma-separated PII types that reject the request, e.g. `ssn,credit_card` * `--budget`: Max session spend in USD * `--audit-log`: Path for the JSONL audit trail * `--costs-log`: Path for the JSONL cost log (default `costs.jsonl`) * `--scan-output` / `--no-scan-output`: Also redact PII in model output (default on) * `--config`: Guardrails JSON file authored via `multimind dashboard` The proxy can also be configured through `MULTIMIND_PROXY_*` environment variables; see the [Configuration Guide](/configuration). ### Governance Dashboard Serve a read-only local web UI over the guard audit trail, cost log, shadow-AI inventory, and a no-code guardrails editor whose output feeds `multimind serve --config`: ```bash theme={null} multimind dashboard --port 8501 --audit-log audit.jsonl --costs-log costs.jsonl \ --project . --guardrails guardrails.json ``` See the [dashboard guide](/dashboard). ### Backend API Server Serve every supported REST route (gateway, governance, guard proxy, RAG when installed) from one process with one Swagger UI at `/docs`: ```bash theme={null} multimind backend --host 127.0.0.1 --port 8080 ``` ### AI Usage Audit #### Shadow-AI Inventory Scan Scan a project directory for AI usage. Exits with code 1 if hardcoded AI API keys are found. ```bash theme={null} multimind audit scan ./my-project --include-env --json ``` Options: * `--include-env`: Also report which known AI env keys are set (names only, never values) * `--json`: Machine-readable JSON output #### Cost Chargeback Report Per-tag chargeback report from the session tracker or a JSONL log. ```bash theme={null} multimind audit costs --log costs.jsonl --period 2026-07 ``` Options: * `--log`: Rebuild the tracker from a persisted JSONL cost log (e.g. `costs.jsonl`) * `--period`: Filter by ISO timestamp prefix, e.g. `2026-07` ### Model Conversion `multimind convert` wraps `ModelConversionManager` (`multimind/model_conversion`). It is dispatched before the Click group, so it is absent from `multimind --help`, and it imports torch, so it needs the `finetune` extra: ```bash theme={null} pip install "multimind-sdk[finetune]" ``` Without torch the command exits with code 1 and a message pointing at that extra. ```bash theme={null} # HuggingFace model to GGUF multimind convert --source huggingface --target gguf --model-path Qwen/Qwen1.5-7B --output-dir ./models # PyTorch to Safetensors with compression multimind convert --source pytorch --target safetensors --model-path ./model.pt --output-dir ./out --compression lz4 # ONNX to ONNX Runtime multimind convert --source onnx --target ort --model-path ./model.onnx --output-dir ./out --optimization-level all ``` Required options: * `--source`: Source format: `huggingface`, `pytorch`, `tensorflow`, `onnx`, or `ollama` * `--target`: Target format: `gguf`, `safetensors`, `tflite`, `ort`, or `onnx` * `--model-path`: Path to the source model or a HuggingFace model ID * `--output-dir`: Directory for the converted model (created if missing) Optional: * `--quantization`: `q4_k_m`, `q4_0`, `q5_k_m`, `q8_0`, `int8`, or `fp16` * `--compression`: `lz4` or `zstd` (Safetensors); `--compression-level` 1-9 (default 9) * `--optimizations`: One or more optimization names, e.g. `DEFAULT OPTIMIZE_FOR_LATENCY` (TFLite) * `--optimization-level`: `basic`, `all`, or `extreme` (ONNX Runtime) * `--device`: `cpu` (default) or `cuda` * `--context-length`: Context length for GGUF output * `--metadata`: One or more `key=value` pairs attached to the output * `--validate`: Validate the model before and after conversion (a failed validation exits 1) * `--test`: Run the converted model; implemented for `gguf` only, other targets print "not implemented" * `--verbose`: Print source and converted model metadata, and a traceback on error ### Context Transfer `multimind context-transfer` reformats a saved conversation from one provider into a prompt for another (`multimind.context_transfer`). It is dispatched before the Click group, needs no extras, and uses argparse-style underscore options. ```bash theme={null} # List supported models with context length and capability flags multimind context-transfer --list_models # Capabilities of one model multimind context-transfer --model_info deepseek # Basic transfer multimind context-transfer --from_model chatgpt --to_model deepseek \ --input_file conversation.json --output_file deepseek_prompt.txt # Detailed summary, smart extraction, JSON output multimind context-transfer --from_model claude --to_model gemini \ --input_file chat.json --output_file gemini_prompt.txt \ --last_n 10 --summary_type detailed --smart_extraction \ --output_format json --include_code_context ``` Transfer options (`--from_model`, `--to_model`, `--input_file`, and `--output_file` are all required for a transfer; the command exits 2 if one is missing): * `--from_model`, `--to_model`: Source and target model names, e.g. `chatgpt`, `claude`, `deepseek`, `gemini`, `mistral`; see `--list_models` * `--input_file`: Conversation history file (the output directory is created if needed) * `--output_file`: Where the formatted prompt is written * `--last_n`: Number of recent turns to extract (default 5) * `--no_summary`: Skip summarization and use only the last message * `--summary_type`: `concise` (default), `detailed`, or `structured` * `--smart_extraction`: Importance-based context extraction * `--output_format`: `txt` (default), `json`, or `markdown` Formatting flags, each adding instructions to the generated prompt: `--include_code_context`, `--include_reasoning`, `--include_safety`, `--include_creativity`, `--include_examples`, `--include_step_by_step`, `--include_multimodal`, `--include_web_search`. Information and general options: * `--list_models`: List supported models and exit * `--model_info MODEL`: Show one model's capabilities and exit * `--verbose`, `-v`: Debug logging and a traceback on unexpected errors Exit codes: 0 on success, 1 for a missing input file, unsupported model, or any other error. ## Example CLI Scripts The repository also ships standalone example scripts under `examples/cli/` (run from a checkout of the repository, not the installed package). ### Ensemble System #### Text Generation Generate text using an ensemble of models: ```bash theme={null} python -m examples.cli.ensemble_cli generate "Your prompt here" [OPTIONS] ``` Options: * `--providers`, `-p`: Provider to use; repeat the flag for multiple (default: openai, anthropic, ollama) * `--method`, `-m`: Ensemble method to use (default: weighted\_voting) * `--output`, `-o`: Output file path (optional) Example: ```bash theme={null} python -m examples.cli.ensemble_cli generate "Explain quantum computing" \ -p openai -p anthropic -p ollama \ --method weighted_voting \ --output result.json ``` #### Code Review Review code using an ensemble of models: ```bash theme={null} python -m examples.cli.ensemble_cli review path/to/your/code.py [OPTIONS] ``` Options: * `--providers`, `-p`: Provider to use; repeat the flag for multiple (default: openai, anthropic, ollama) * `--output`, `-o`: Output file path (optional) Example: ```bash theme={null} python -m examples.cli.ensemble_cli review my_code.py \ -p openai -p anthropic -p ollama \ --output review.json ``` #### Image Analysis Analyze images using an ensemble of models: ```bash theme={null} python -m examples.cli.ensemble_cli analyze-image path/to/your/image.jpg [OPTIONS] ``` Options: * `--providers`, `-p`: Provider to use; repeat the flag for multiple (default: openai, anthropic, ollama) * `--prompt`, `-q`: Instruction sent to vision models (optional) * `--output`, `-o`: Output file path (optional) Example: ```bash theme={null} python -m examples.cli.ensemble_cli analyze-image photo.jpg \ -p openai -p anthropic \ --output analysis.json ``` #### Embedding Generation Generate embeddings using an ensemble of models: ```bash theme={null} python -m examples.cli.ensemble_cli embed "Your text here" [OPTIONS] ``` Options: * `--providers`, `-p`: Provider to use; repeat the flag for multiple (default: openai, ollama, huggingface) * `--model`, `-m`: Model to use (provider-specific; optional) * `--output`, `-o`: Output file path (optional) Example: ```bash theme={null} python -m examples.cli.ensemble_cli embed "This is a sample text" \ -p openai -p huggingface \ --output embeddings.json ``` ### Model Wrapper Query various LLM models directly: ```bash theme={null} python -m examples.cli.multi_model_wrapper_cli --model MODEL_NAME --prompt "Your prompt" [OPTIONS] ``` Options: * `--list-models`: List all available models and exit * `--model`: Model to use (choices: dynamically loaded from available models) * `--prompt`: Your input prompt * `--temperature`: Sampling temperature (default: 0.7) * `--max-tokens`: Maximum tokens to generate Example: ```bash theme={null} python -m examples.cli.multi_model_wrapper_cli --model openai --prompt "Hello, world!" ``` ### Ollama Chat Interface Interactive chat with Ollama models: ```bash theme={null} python -m examples.cli.chat_ollama_cli [OPTIONS] ``` Options: * `--model`: Ollama model to use (default: mistral) * `--history`: Path to save chat history (optional) * `--no-stream`: Disable streaming responses * `--debug`: Enable debug logging Special commands in chat: * `exit`: Exit the chat * `history`: Show chat history * `models`: List available models * `clear`: Clear chat history * `pull MODEL_NAME`: Pull a new model Example: ```bash theme={null} python -m examples.cli.chat_ollama_cli --model llama2 --history chat.log ``` ### Compliance and Governance Manage compliance and governance features: ```bash theme={null} python -m examples.cli.compliance_cli [COMMAND] [OPTIONS] ``` Available commands include `ingest`, `validate-output`, `monitor-anomalies`, `export-logs`, `dsar`, `model-approve`, `policy-publish`, `audit-verify`, `plugin-register`, `test-run`, `drift-check`, and `risk-override`. #### DSAR (Data Subject Access Request) ```bash theme={null} # Export user data python -m examples.cli.compliance_cli dsar export --user-id USER_ID --request-id REQUEST_ID [--format json|csv] # Erase user data python -m examples.cli.compliance_cli dsar erase --user-id USER_ID --request-id REQUEST_ID [--verify|--no-verify] ``` #### Model Approval ```bash theme={null} python -m examples.cli.compliance_cli model-approve --model-id MODEL_ID --approver APPROVER_EMAIL [--metadata JSON_STRING] ``` #### Policy Management ```bash theme={null} python -m examples.cli.compliance_cli policy-publish --policy-file POLICY_FILE --version VERSION [--metadata JSON_STRING] ``` #### Audit Verification ```bash theme={null} python -m examples.cli.compliance_cli audit-verify --chain-id CHAIN_ID [--start-time ISO_TIME] [--end-time ISO_TIME] ``` ### Basic Agent Run a basic agent with different models: ```bash theme={null} python -m examples.cli.basic_agent ``` This will run example tasks using different models (OpenAI, Claude, Mistral) with a calculator tool. ### Task Runner Run predefined task workflows: ```bash theme={null} python -m examples.cli.task_runner ``` This will execute a research workflow with multiple tasks. ### Prompt Chain Run a code review prompt chain: ```bash theme={null} python -m examples.cli.prompt_chain ``` This will execute a chain of prompts for code review. ## Output Format The ensemble CLI commands output JSON data with the following structure: ```json theme={null} { "result": "Generated text or analysis", "confidence": 0.95, "explanation": "Explanation of the ensemble decision", "provider_votes": { "provider1": "result1", "provider2": "result2", ... } } ``` ## Available Ensemble Methods * `weighted_voting`: Combines results based on provider weights * `confidence_cascade`: Uses results based on confidence thresholds * `parallel_voting`: Combines results from all providers in parallel * `majority_voting`: Uses the most common result among providers * `rank_based`: Selects results based on provider ranking ## Environment Variables The CLI uses the following environment variables (should be set in your `.env` file): * `OPENAI_API_KEY`: Your OpenAI API key * `ANTHROPIC_API_KEY`: Your Anthropic API key (`CLAUDE_API_KEY` is also accepted) * `MISTRAL_API_KEY`: Your Mistral API key (if using Mistral AI) * `GROQ_API_KEY`: Your Groq API key (if using Groq) * `GEMINI_API_KEY`: Your Gemini API key (`GOOGLE_API_KEY` is also accepted) * `DEEPSEEK_API_KEY`: Your DeepSeek API key * `HUGGINGFACE_API_KEY`: Hugging Face token for gated models (optional) * `OLLAMA_HOST`: Ollama server URL (default: `http://localhost:11434`) See the [Configuration Guide](/configuration) for the full list, including the `MULTIMIND_PROXY_*` variables read by `multimind serve`. ## Error Handling The CLI provides clear error messages for common issues: * Missing API keys * Invalid file paths * Unsupported providers * Invalid ensemble methods * Model availability issues * Compliance and governance errors ## Examples See the following example files for comprehensive usage: * `examples/ensemble/usage_examples.py`: Ensemble system examples * `examples/cli/basic_agent.py`: Basic agent examples * `examples/cli/task_runner.py`: Task workflow examples * `examples/cli/prompt_chain.py`: Prompt chain examples This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.