The compliance-first
AI agent framework
Multi-model AI with built-in GDPR, HIPAA & EU AI Act evidence reporting. Works with any model. Runs anywhere.
Most AI frameworks assume you’ll handle compliance yourself.
MultiMind doesn’t.
One API, every model
OpenAI, Claude, Gemini, Mistral, Grok, DeepSeek, xAI, Together, Perplexity, Fireworks, Cerebras, 300+ models via OpenRouter, and local models via Ollama — one async interface, with streaming.
Built-in compliance
A drop-in PII guard detects, redacts, and audits sensitive data on every model call. GDPR & HIPAA policy modeling and compliance dashboards are a first-class module, not an afterthought.
Govern the stack you already have
Don’t migrate off LangChain, LlamaIndex, CrewAI, AutoGen, or Haystack — 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, keep context
Move a live conversation from GPT to Claude to a local model — the context travels with you via ModelSession.
RAG that works
FAISS and Chroma with document processing out of the box, plus 40+ client-backed vector stores including Pinecone, Qdrant, Weaviate, Milvus, pgvector, and LanceDB.
Persistent memory — agents that remember
Agents ship with persistent memory: context, decisions, and preferences survive across sessions instead of resetting with every conversation. Your agent on Monday knows what it learned on Friday.
A second brain from your documents
Pair agent memory with the RAG pipeline and 40+ vector stores to build a durable second brain from your own documents — what your team knows becomes something your agents can retrieve.
Your memory, your infrastructure
The second brain lives where you run it — with local models via Ollama it works entirely on-premises, so accumulated knowledge never leaves your environment.
Features
Every status below is honest — Beta means beta. Full detail in FEATURES.md.
Multi-model chat
OpenAI, Claude, Gemini, Grok, and more via one async interface.
Streaming responses
Token-by-token streaming across providers.
Runtime PII guard
Detect, redact, block, and audit sensitive data on every call.
Cost tracking & budgets
Stop overspending before the call goes out.
Hallucination detection
Sentence-level grounding checks against your sources.
Mid-conversation model switching
ModelSession carries context from GPT to Claude to local.
Compliance proxy (multimind serve)
OpenAI-compatible proxy adding guardrails with one line.
Governance dashboard UI
multimind dashboard — visualize guardrails and usage.
Compliance evidence reports
Markdown/HTML reports mapped to real control themes.
Framework adapters
LangChain, LlamaIndex, CrewAI, AutoGen, Haystack — wrap, don't migrate.
AI usage audit & chargeback
multimind audit — cost and usage per team or project.
Anthropic MCP server
Compliance tooling exposed over Model Context Protocol.
RAG pipeline
FAISS and Chroma with document processing out of the box.
Vector stores (core set)
Pinecone, Qdrant, Weaviate, Milvus, pgvector, LanceDB.
AI Agents with tools & memory
Native function-calling agents with persistent memory.
GDPR & HIPAA compliance
Runtime enforcement, not just policy documents.
Vision / multimodal input
Pass images= to generate() or chat() directly.
Docker deployment
Lean ~300MB image, ready to ship.
Self-orchestrating agents
Bounded agent spawning for multi-step tasks.
Non-transformer models
Mamba and RWKV through the same interface.
Fine-tuning (LoRA / QLoRA)
Parameter-efficient fine-tuning, CPU and GPU paths.
Vector stores (extended set)
26 additional client-backed stores, less battle-tested.
Sovereign-ready AI for Europe
Digital sovereignty isn’t a feature you buy — it’s an architecture where your data, your models, and your evidence stay under your control. MultiMind is built for exactly that.
Runs entirely on your infrastructure
Local models via Ollama, on-premises or on EU-hosted infrastructure — prompts, documents, and audit logs never have to leave your environment. The PII scanner even works offline, with no API key.
Maintaining full digital sovereignty with lock-in-free, EU-based solutions
The same one-line swap that moves a workload from GPT to Claude moves it from a US cloud to an EU-hosted or local model — via multimind serve, without touching application code.
EU AI Act & GDPR evidence, from your own logs
Art. 12 record-keeping, Art. 50 transparency, lawful-basis and purpose-limitation enforcement — evidenced from audit and cost logs your app already produces, generated on your own machines.
MultiMind supports European digital-sovereignty goals through architecture — local execution, provider portability, and self-generated evidence. That is a design property, not a certification or a legal determination.
Built for the EU AI Act and every framework you’re already accountable to
MultiMind generates compliance evidence reports from your own runtime artifacts — audit logs, cost logs, PII events — and maps them to real control themes:
- EU AI Act — Art. 12 record-keeping, Art. 50 transparency
- SOC 2 — monitoring, logical access
- HIPAA — 164.312(b) audit controls
- GDPR — lawful-basis & purpose-limitation runtime enforcement
Every mapping is phrased as “supports,” never “satisfies.” A report is technical evidence generated from runtime artifacts — not legal advice, a certification, or a compliance determination. An absent artifact means absent evidence, not absent risk.
multimind compliance report-evidence \
--audit-log audit.jsonl \
--costs-log costs.jsonl \
--project . \
--period 2026-07 \
--format html -o evidence.htmlComing soon: train and manage models from the dashboard
The governance dashboard below (multimind dashboard) is real and shipped today — Overview, Audit Trail, Costs, Inventory, and Guardrails, reading only local artifacts your own SDK produces. Next up: bringing model lifecycle work into the same screen.
Planned — not built yet
- No-code fine-tuning UI — a dashboard front-end for the existing LoRA/QLoRA fine-tuning engine, with hyperparameter optimization and multi-task fine-tuning.
- RAG fine-tuning with synthetic data — generate and train on synthetic examples from your own documents.
- Advanced analytics dashboard — deeper cost and performance trends beyond today’s spend and PII charts.
- No-code agent builder — a visual, drag-and-drop workflow builder for composing agents and multi-model pipelines. Agents are code-first today (
Agent+ tools in Python); no visual interface exists yet. - Multi-agent orchestration upgrades — agent-to-agent communication and hierarchical agent systems, building on today’s bounded agent orchestration.
Sourced from ROADMAP.md — kept honest, not a release commitment.
Case studies
Two ways teams actually use MultiMind — one for developers, one for everyone else.
For developers: govern an app you already have — change one line
No migration. Start the compliance proxy, then point any OpenAI-compatible client (LangChain, LlamaIndex, the raw openai SDK, anything) at it.
1. Start the proxy
multimind serve --port 8400 --upstream openai \
--block-on ssn,credit_card \
--budget 25.00 \
--audit-log audit.jsonl2. Change the base_url — that’s it
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": "..."}],
)Result: every call through the proxy is PII-redacted, budget-checked, and logged to audit.jsonl — with zero changes to LangChain, LlamaIndex, or CrewAI application code.
For everyone else: catch sensitive data without writing code
MultiMind is seatbelts for AI. Compliance officers, analysts, and PMs can check whether a document contains sensitive data straight from a terminal — no Python knowledge, no API key, no internet connection needed. See the full non-technical guide.
1. Scan a sentence for sensitive data
multimind compliance scan-text \
"Contact Jane at jane.doe@corp.com or 555-123-4567"2. Read the result — plain and simple
PII Findings (2)
┏━━━━━━━┳━━━━━━━┳━━━━━┳━━━━━━━━━━━━━━━━━━━┓
┃ Type ┃ Start ┃ End ┃ Match ┃
┡━━━━━━━╇━━━━━━━╇━━━━━╇━━━━━━━━━━━━━━━━━━━┩
│ email │ 16 │ 34 │ jane.doe@corp.com │
│ phone │ 38 │ 50 │ 555-123-4567 │
└───────┴───────┴─────┴───────────────────┘Result: sensitive data is caught before it ever reaches an AI vendor — and the same guard produces a tamper-evident audit trail that records what was found and when, without ever storing the sensitive values themselves. Handed to an auditor as-is.