Multimind SDK

<p align="center"> <img src="https://raw.githubusercontent.com/multimindlab/multimind-sdk/develop/assets/Logo-with-name-final2.png" alt="MultiMind SDK" /> </p> <p align="center"> <strong>The compliance-first AI agent framework.</strong><br> Multi-model AI with built-in GDPR, HIPAA & NIS2 support. Works with any model. Runs anywhere. </p> <p align="center"> <a href="https://pypi.org/project/multimind-sdk/"><img src="https://img.shields.io/pypi/v/multimind-sdk.svg" alt="PyPI"></a> <a href="https://github.com/multimindlab/multimind-sdk/actions/workflows/ci.yml"><img src="https://github.com/multimindlab/multimind-sdk/actions/workflows/ci.yml/badge.svg?branch=develop" alt="CI"></a> <a href="https://pypi.org/project/multimind-sdk/"><img src="https://img.shields.io/pypi/pyversions/multimind-sdk.svg" alt="Python versions"></a> <a href="https://github.com/multimindlab/multimind-sdk/blob/develop/LICENSE"><img src="https://img.shields.io/badge/license-Apache%202.0-blue.svg" alt="License"></a> <a href="https://discord.gg/K64U65je7h"><img src="https://img.shields.io/badge/chat-Discord-5865F2?logo=discord&logoColor=white" alt="Discord"></a> </p>

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New to AI? Start with the plain-language getting started guide — no coding required.

Why MultiMind?

Most AI frameworks assume you'll handle compliance yourself. MultiMind doesn't.

• One API for all models — OpenAI, Anthropic Claude, Google Gemini, Mistral, Groq, DeepSeek, xAI, Together, Perplexity, Fireworks, Cerebras, 300+ models via OpenRouter, and local models via Ollama through a single async interface with streaming
• Built-in compliance — a drop-in PII guard that detects, redacts, and audits sensitive data on every model call, plus GDPR & HIPAA policy modeling and compliance dashboards as a first-class module, not an afterthought
• Govern the stack you already have — don't migrate off LangChain, LlamaIndex, or CrewAI; wrap them. Run multimind serve and point any OpenAI-compatible app at it to add PII redaction, budgets, and audit with one line changed
• Governed by default — cost budgets that stop overspending before the call, hallucination detection with sentence-level evidence, and audit trails — each a one-line wrapper around any model
• Switch models without losing knowledge — move a live conversation from GPT to Claude to a local model; the context travels with you
• Adaptive routing — route requests across providers by cost, latency, or fallback strategy
• RAG that works — FAISS and Chroma with document processing out of the box; 40+ client-backed vector stores including Pinecone, Qdrant, Weaviate, Milvus, pgvector, and LanceDB
• Beyond transformers — run Mamba and RWKV models through the same interface
• Runs anywhere — Cloud, on-prem, air-gapped with local models

Quick Start

pip install multimind-sdk

import asyncio
from multimind import OpenAIModel

async def main():
    model = OpenAIModel(model_name="gpt-4o-mini")
    response = await model.generate("Explain quantum computing simply")
    print(response)

asyncio.run(main())

Requires OPENAI_API_KEY in your environment. The same pattern works for ClaudeModel (Anthropic, ANTHROPIC_API_KEY), GeminiModel (Google, GEMINI_API_KEY), MistralAIModel (MISTRAL_API_KEY), GroqModel (GROQ_API_KEY), DeepSeekModel (DEEPSEEK_API_KEY), and OllamaModel (local models — Mistral, Llama, and anything else Ollama serves). Also: OpenRouterModel (OPENROUTER_API_KEY — 300+ models via one key), TogetherModel (TOGETHER_API_KEY), XAIModel (XAI_API_KEY), PerplexityModel (PERPLEXITY_API_KEY), FireworksModel (FIREWORKS_API_KEY), and CerebrasModel (CEREBRAS_API_KEY).

Install what you need

pip install multimind-sdk                # Core (incl. Ollama via HTTP, no extra needed)
pip install multimind-sdk[rag]           # + RAG & vector stores (FAISS, Chroma)
pip install multimind-sdk[agents]        # + Agent framework with memory
pip install multimind-sdk[compliance]    # + GDPR/HIPAA/NIS2 compliance + dashboards
pip install multimind-sdk[finetune]      # + LoRA/QLoRA fine-tuning (CPU)
pip install multimind-sdk[finetune-gpu]  # + 8-bit quantization (Linux/CUDA only)
pip install multimind-sdk[gateway]       # + FastAPI gateway server
pip install multimind-sdk[all]           # Everything

Ollama users: no extra needed — multimind.models.ollama talks to a running Ollama instance over HTTP. Just pip install multimind-sdk and point at http://localhost:11434.

Features

| Feature | Status | Install Extra | | ---------------------------------------------------- | -------- | ------------------ | | Multi-model chat (OpenAI, Claude, Gemini, Groq, ...) | Stable | core | | Streaming responses | Stable | core | | Runtime PII guard (detect, redact, block, audit) | Stable | core | | Cost tracking & budgets | Stable | core | | Hallucination detection (grounding checks) | Stable | core | | Mid-conversation model switching (ModelSession) | Stable | core | | Compliance proxy (multimind serve, OpenAI-compat) | Stable | [gateway] | | Governance dashboard UI (multimind dashboard) | Stable | [gateway] | | Compliance evidence reports (md/HTML) | Stable | core | | Framework adapters (LangChain, LlamaIndex, CrewAI) | Stable | [langchain] etc. | | AI usage audit & cost chargeback (multimind audit) | Stable | core | | Anthropic MCP compliance server | Stable | [mcp] | | RAG pipeline (FAISS, Chroma) | Stable | [rag] | | Context transfer between models | Stable | core | | CLI interface | Stable | core | | REST gateway with Swagger UI (/docs) | Stable | [gateway] | | Docker deployment (lean ~300 MB image) | Stable | — | | Vision/multimodal input (images=) | Stable | core | | AI Agents with native function-calling & memory | Stable | [agents] | | Self-evolving agents (bounded exemplar learning) | Stable | [agents] | | GDPR & HIPAA compliance (runtime enforcement) | Stable | [compliance] | | Vector stores, core set (FAISS, Chroma, Pinecone, Qdrant, Weaviate, Milvus, ...) | Stable | [rag]/[vector-stores] | | Self-orchestrating agents (bounded spawning) | Beta | [agents] | | Non-transformer models (Mamba, RWKV) | Beta | [finetune] | | Vector stores, extended set (26 client-backed, less battle-tested) | Beta | [vector-stores] | | Fine-tuning (LoRA, real QLoRA) | Beta | [finetune] |

Note: multimind.mcp is MultiMind's internal Model Composition Protocol — a workflow executor for chaining models. Anthropic's Model Context Protocol is supported separately via multimind.mcp_server (see docs/mcp-server.md).

Full status: FEATURES.md · Roadmap: ROADMAP.md

Examples

Snippets using await assume an async context — wrap them in asyncio.run(main()) as shown in the Quick Start. Full runnable versions live in examples/ and the cookbook.

Govern an app you already have — change one line

Start the compliance proxy, then point any OpenAI-compatible client (LangChain, LlamaIndex, the raw openai SDK, anything) at it:

multimind serve --port 8400 --upstream openai --block-on ssn,credit_card --budget 25.00 --audit-log audit.jsonl

from openai import OpenAI

# The ONLY change: base_url. Every call now gets PII redaction, budgets, and an audit trail.
client = OpenAI(base_url="http://localhost:8400/v1", api_key="unused-upstream-key-is-server-side")
client.chat.completions.create(model="gpt-4o-mini", messages=[{"role": "user", "content": "..."}])

Prefer to stay in code? Wrap your existing framework objects instead — see docs/integrations.md for LangChain, LlamaIndex, CrewAI, and OpenAI-SDK adapters.

Guard any model against PII leaks

from multimind import OpenAIModel
from multimind.compliance import guard

model = guard(
    OpenAIModel(model_name="gpt-4o-mini"),
    strategy="mask",                    # emails become [EMAIL], SSNs [SSN], ...
    block_on=("credit_card", "ssn"),    # refuse these outright
    audit_log="compliance_audit.jsonl", # types & counts only — never raw PII
)

response = await model.generate("Email jane.doe@corp.com about the invoice")
# The provider only ever saw: "Email [EMAIL] about the invoice"

Detects emails, phone numbers, SSNs, credit cards (Luhn-validated), IPs, IBANs, API keys (entropy-checked), and more — including across streaming chunk boundaries. Works with any model object that has generate/chat, so you can wrap non-MultiMind clients too. No extra dependencies. Runnable demo: examples/compliance/guarded_model.py

Multi-model chat

from multimind import OpenAIModel, ClaudeModel, GeminiModel, GroqModel

gpt = OpenAIModel(model_name="gpt-4o-mini")
claude = ClaudeModel(model_name="claude-3-5-sonnet-20241022")
gemini = GeminiModel(model_name="gemini-2.0-flash")
groq = GroqModel(model_name="llama-3.3-70b-versatile")

# Same interface, different providers
response = await gpt.generate("Hello!")
response = await claude.generate("Hello!")
response = await gemini.generate("Hello!")

MistralAIModel and `Deep