MultiMind SDK: Features & Functions

For the authoritative, per-feature status (Stable / Beta / Planned), see feature-status.md. This page gives a structural overview; items marked “Planned” below are not implemented yet.

Project Structure

The main packages under multimind/ (see the repository for the full tree):

multimind-sdk/
├── multimind/
│   ├── __init__.py                # Public API; heavy features are lazy-imported per extra
│   ├── _lazy.py                   # Lazy attribute loader used by the package
│   ├── main_config.py             # Central config loader
│   ├── config/                    # Config helpers
│   ├── core/                      # Gateway config, chat sessions, monitoring, exceptions
│   ├── models/                    # Model wrappers: openai.py, claude.py, ollama.py, huggingface.py,
│   │                              #   groq.py, gemini.py, mistral.py, deepseek.py, openrouter.py,
│   │                              #   together.py, xai.py, perplexity.py, fireworks.py, cerebras.py,
│   │                              #   multi_model.py, factory.py, moe/
│   ├── llm/                       # Non-transformer LLMs (Mamba, RWKV, scaffolds), model registry
│   ├── router/                    # router.py, strategy.py, fallback.py, adaptive.py, multi_modal_router.py
│   ├── rag/                       # rag.py (RAG, RAGConfig), base.py, fluent.py, graph_retrieval.py, postprocessing.py
│   ├── retrieval/                 # Retriever, EnhancedRetriever, HybridRetriever
│   ├── embeddings/                # EmbeddingConfig, EmbeddingModel
│   ├── vector_store/              # VectorStore + ~60 backends (FAISS, Chroma, Qdrant, ...)
│   ├── document_loader/           # File/data ingestion
│   ├── document_processing/       # Chunkers and processors
│   ├── fine_tuning/               # lora_trainer.py, qlora_trainer.py, PEFT and meta-learning modules
│   ├── agents/                    # agent.py, memory.py, agent_loader.py, agent_registry.py, tools/ (calculator.py)
│   ├── memory/                    # ~50 memory classes (see memory.md)
│   ├── orchestration/             # prompt_chain.py, task_runner.py
│   ├── mcp/                       # Model Composition Protocol: parser.py, executor.py, schema.json
│   ├── mcp_server/                # Anthropic Model Context Protocol server
│   ├── ensemble/                  # AdvancedEnsemble and ensemble methods
│   ├── compliance/                # PII guard, privacy, audit, regulation checkers
│   ├── integrations/              # github.py, slack.py, discord.py, jira.py, model_adapters.py, frameworks/
│   ├── multimind_logging/         # trace_logger.py, usage_tracker.py
│   ├── observability/, metrics/   # Tracing and cost metrics
│   ├── gateway/                   # FastAPI apps: api.py, rag_api.py, compliance_api.py, guard_proxy.py
│   ├── api/                       # multi_model_api.py, unified_api.py
│   ├── server/                    # MultiMindServer base app and platform.py (combined "MultiMind API")
│   ├── dashboard/                 # Governance dashboard
│   ├── client/                    # RAGClient, ModelClient family, FederatedRouter, ModelSession
│   └── cli/                       # click CLI: chat, models, config, compliance, audit, serve, dashboard, backend
├── examples/                      # Example scripts (see examples/README.md)
├── tests/                         # Test suite
├── docs/                          # Documentation
├── README.md
├── LICENSE
├── pyproject.toml
└── setup.py

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Core Features

Model Support

• Model Wrappers (available today):
  • OpenAI, Anthropic Claude, Ollama (local models, including Mistral and Llama), HuggingFace (Inference API or local transformers)
  • Groq, Google Gemini, Mistral AI, DeepSeek, OpenRouter, Together, xAI, Perplexity, Fireworks, Cerebras
  • MultiModelWrapper for primary + fallback models
• Unified Interface: All wrappers derive from BaseLLM and support generate, chat and streaming (generate_stream, chat_stream); embeddings where the provider offers it
• Non-Transformer Inference: Mamba and RWKV run via HuggingFace-backed wrappers; other listed architectures are extension scaffolds that raise NotImplementedError

Agent System

• Agent Framework (available today):
  • Agent(model, memory, tools, system_prompt, max_turns) with native function calling on OpenAI-compatible models and keyword-based tool routing elsewhere
  • AgentMemory for task/response history
  • Agent configuration loading from MCP workflow files (agent_loader)
  • One built-in tool (CalculatorTool); extensible via BaseTool subclasses
  • AgentOrchestrator, SelfEvolvingAgent, ReasoningChain (ReAct-style steps)
• Planned: hierarchical multi-agent systems, inter-agent communication, AutoML-based agent configuration, additional built-in tools (web search, file operations)

Orchestration

• Prompt Chains: multi-step workflows with variable substitution and conditional execution
• Task Runner: task dependencies, retry logic, context management, parallel execution

Model Composition Protocol (MCP)

Note: multimind.mcp is MultiMind’s own Model Composition Protocol, a JSON/YAML workflow executor for chaining models. Anthropic’s Model Context Protocol is supported separately by multimind.mcp_server (pip install "multimind-sdk[mcp]", python -m multimind.mcp_server; see mcp-server.md).

• Workflow Definition: JSON/YAML workflow specification validated against schema.json; model composition and chaining; conditional execution; data transformation
• Execution Engine: workflow validation, model registration, step execution, error handling (executor.py, advanced_executor.py)

Routing & Strategy

• Model Router: strategy-based routing (cost-aware, latency-aware, hybrid), fallback chain, adaptive and multi-modal routers
• Federated routing: multimind.client.FederatedRouter chooses local vs. cloud models by input size, latency and privacy

Fine-Tuning & PEFT

• Available today (basic): LoRA and adapter training; QLoRA (real 4-bit NF4 + LoRA when the [finetune-gpu] deps are installed); synthetic Q/A generation for RAG fine-tuning (SyntheticQAGenerator, no torch needed)
• Experimental / partially implemented: IA³, BitFit, MAM adapters, UniPELT, meta-learning (MAML, Reptile), adaptive PEFT
• Planned: HyperLoRA, Compacter, AutoML-based parameter tuning, advanced optimization

RAG (Retrieval-Augmented Generation)

• Vector stores: FAISS and Chroma are the most exercised; Pinecone, Qdrant, Weaviate, Milvus, Zilliz, Elasticsearch, OpenSearch, MongoDB Atlas, the PostgreSQL family and ~25 other client-backed stores are implemented; eleven backends are NotImplementedError stubs (full list in feature-status.md)
• Retrieval: RAG.retrieve, EnhancedRetriever, HybridRetriever, GraphRetriever (entity-graph expansion), post-processing, fluent RAGPipeline
• Known limitation: RAG.retrieve(filter_criteria=...) currently raises; filter on result metadata instead (see rag.md)
• Planned: self-tuning indexer, quantum-enhanced search

Enterprise Integration

• Compliance Suite (available today): PII detection and redaction, GDPR/HIPAA policy modeling, hash-chained audit logging, cost chargeback, OpenAI-compatible guard proxy (multimind serve), compliance dashboards (with the [compliance] extra)
• Service Integrations (available today): GitHub, Slack, Discord, Jira adapters in multimind.integrations; framework adapters for LangChain, LlamaIndex, CrewAI, AutoGen and Haystack in multimind.integrations.frameworks (each has its own extra)
• Planned: edge deployment toolkit, plugin system (Notion, Salesforce), database connectors, real-time data integration (Kafka, MQTT)

Development Tools

• Available today: governance dashboard (multimind dashboard), shadow-AI inventory scan (multimind audit scan), combined Swagger UI (multimind backend)
• Planned: visual workflow builder, drag-and-drop components, no-code interface

Logging & Monitoring

• Usage Tracking: token usage, cost tracking, operation logging (multimind_logging.usage_tracker)
• Trace Logging: request/response tracing, performance metrics, error tracking (multimind_logging.trace_logger)
• Gateway metrics: per-model request counts, latency, error counts, health checks and rate limits (multimind.core.monitoring), exposed at GET /v1/metrics
• Export & Reporting: JSON/CSV export, usage summaries, cost chargeback (multimind audit costs)

Configuration

• YAML/JSON config files, .env and environment variable support (python-dotenv is loaded automatically)
• CLI defaults in ~/.multimind_cli_config (multimind config manage)

CLI & Integration

• CLI (multimind): chat, models (list/compare/health/metrics/download/export/delete), config, compliance, audit, serve, dashboard, backend, convert — see cli.md
• Service Integrations: GitHub, Slack, Discord, Jira; model adapter classes for custom model types

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Feature Table

Category	Feature/Functionality	Status
Model Wrappers	OpenAI, Claude, Ollama, HuggingFace + 10 more providers	Implemented
Non-Transformers	Mamba, RWKV inference; other architectures are scaffolds	Beta
Agent System	Framework, memory, calculator tool, native tool calling	Beta
Agent AutoML	Self-configuration, optimization	Planned
Orchestration	Prompt chains, task runner	Implemented
MCP (Composition)	Workflow definition, execution, model composition	Beta
MCP server	Anthropic Model Context Protocol server	Beta
Routing	Strategy, fallback, adaptive, federated	Implemented
Compliance	PII guard, proxy, audit trails, GDPR/HIPAA modeling	Beta
Enterprise	Edge deployment, integration hub	Planned
Development Tools	Visual builder, no-code interface	Planned
Fine-Tuning	Basic LoRA/QLoRA/adapters; advanced PEFT and meta-learning	Beta
RAG	FAISS/Chroma stable; hybrid/graph retrieval	Beta
Monitoring	Usage tracking, gateway metrics, dashboard	Beta
Integrations	GitHub, Slack, Discord, Jira; framework adapters	Beta

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Examples

The SDK includes examples demonstrating the major features:

• Basic Agent Usage - Agent creation and usage
• Prompt Chaining - Multi-step reasoning workflows
• Task Running - Workflow orchestration
• MCP Workflows - Model composition examples
• Usage Tracking - Monitoring and logging

See the examples README for detailed usage instructions.

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Architecture Overview

For more details, see the Architecture Overview and CONTRIBUTING.md.