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This document provides an honest status of all features in MultiMind SDK. Each feature is marked with one of three status badges:
  • ✅ Stable: Production-ready, fully tested, and working
  • 🚧 Beta: Functional but may have limitations or missing features
  • 📋 Planned: Not yet implemented or placeholder only

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🧠 Core AI Model Management

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Model Integrations

  • ✅ OpenAI Integration (OpenAIModel) - Full API support with GPT-3.5, GPT-4, embeddings
    • Example: examples/api/model_wrapper.py
  • ✅ Claude Integration (ClaudeModel) - Anthropic API support
    • Example: examples/api/model_wrapper.py
  • ✅ Ollama Integration (OllamaModel) - Local model support
    • Example: examples/cli/chat_ollama_cli.py
  • ✅ Mistral Integration - MistralAIModel (direct Mistral API) and MistralModel (local via Ollama)
  • ✅ OpenRouter Integration (OpenRouterModel) - 300+ models from dozens of providers via one API key
  • ✅ Together AI Integration (TogetherModel) - Open-weight models with embeddings support
  • ✅ xAI Integration (XAIModel) - Grok models
  • ✅ Perplexity Integration (PerplexityModel) - Sonar online models
  • ✅ Fireworks AI Integration (FireworksModel) - Open-weight models with embeddings support
  • ✅ Cerebras Integration (CerebrasModel) - Fast inference on Cerebras hardware
  • ✅ 100+ Model Support - 13 first-party providers (OpenAI, Claude, Gemini, Groq, Mistral, DeepSeek, Ollama, OpenRouter, Together, xAI, Perplexity, Fireworks, Cerebras) plus 300+ models via OpenRouter aggregation

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Model Features

  • ✅ Vision/Multimodal Input - images= on generate/chat for OpenAI-compatible providers (content-parts with base64 data URLs or URLs) and Claude (base64/URL image blocks); providers without a vision-capable endpoint raise NotImplementedError
    • Example: examples/models/vision_demo.py
  • ✅ Multi-Model Wrapper - Unified interface for multiple models
    • Example: examples/api/multi_model_wrapper.py
  • ✅ Model Routing - Basic routing between models
    • Example: examples/api/ensemble_api.py
  • ✅ Model Sessions (ModelSession, multimind/client/model_session.py) - Stateful chat over any BaseLLM-compatible model (including guard-wrapped ones) with history, a rolling summary when the token budget is exceeded, and switch(model, strategy=...) to move a conversation to another model using full, summary, or adapted transfer; every switch is logged in transfers
    • Tests: tests/test_model_session.py
  • ✅ Context Transfer (multimind.context_transfer, multimind context-transfer) - Reformats a saved conversation from one provider into a prompt for another with last-N extraction, concise/detailed/structured summaries, and provider adapters; the CLI’s --list_models, --model_info, argument validation, and missing-file paths are tested. Two tests for private formatter methods that no longer exist are skipped, not failing
    • Tests: tests/test_context_transfer.py, tests/test_context_transfer_integration.py, tests/test_cli.py
  • 🚧 Model Conversion (ModelConversionManager, multimind convert) - HuggingFace/PyTorch/TensorFlow/ONNX/Ollama sources to GGUF/Safetensors/TFLite/ORT/ONNX targets; needs the [finetune] extra (torch). Only the CLI’s no-torch failure path is tested; the converters themselves have no automated coverage, so this stays Beta
    • Tests: tests/test_cli.py (test_convert_without_torch_fails_cleanly)
  • 🚧 Mixture-of-Experts (MoE) - Basic implementation. MoEFactory.create_model was passing its config dict positionally into MoEModel(config) instead of unpacking it as keyword arguments, so model creation via the factory was broken; fixed. Edge-case tests added for config validation and (torch-gated) routing/capacity behavior; stays Beta pending fuller real-model coverage
    • Example: examples/moe/
    • Tests: tests/test_moe_edge_cases.py
  • 🚧 Model Compression - Basic quantization support. Fixed a real bug where dynamic quantization’s result was discarded: torch.quantization.quantize_dynamic() returns a new module rather than mutating in place, so the quantized layer was never actually installed on the model. Edge-case tests added (torch-gated; this venv has no torch so they run in CI’s torch job); stays Beta pending broader real-model coverage
    • Tests: tests/test_model_compression_edge_cases.py
  • 📋 Federated Learning - Not implemented
  • 📋 Model Watermarking - Not implemented

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📚 Vector Databases & RAG

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Vector Store Backends

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✅ Core (Recommended, most exercised)

  • ✅ FAISS - Fully functional local vector store, now with real add/search/delete/persist/load round-trip integration tests (example: examples/vector_store/)
  • ✅ Chroma - Complete implementation with metadata support (example: examples/rag/rag_example.py)
  • ✅ Pinecone, Qdrant, Weaviate, Milvus, Zilliz
  • ✅ Elasticsearch, OpenSearch, MongoDB Atlas
  • ✅ PGVector, PGVectoRS, PGEmbedding (PostgreSQL family)
  • ✅ Annoy, sklearn, SQLiteVSS, LanceDB - fixed real bugs that made Annoy/sklearn/SQLiteVSS impossible to even instantiate (missing the required initialize() override) or return correct results (SQLiteVSS’s query syntax and an empty-table crash in its underlying native library; a missing vector field on sklearn’s and SQLiteVSS’s search results; Annoy losing index density after deletes). All four now have real add/search/delete round-trip integration tests
    • Tests: tests/integration/test_vector_stores_live.py (pytest -m integration), tests/integration/README.md

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🚧 Client-backed (Real implementations, less battle-tested)

AlibabaCloud OpenSearch, AnalyticDB, AstraDB, Hippo, Hologres, Marqo, Matching Engine, Meilisearch, Momento, MyScale, Neo4j Vector, NucliaDB, Rockset, SingleStoreDB, StarRocks, Supabase, Tair, TencentVectorDB, Tigris, TimescaleVector, Typesense, USearch, Vald, Vectara, Xata, Zep — each calls its actual client library and returns real results. Partially implemented (unsupported operations raise NotImplementedError): Cassandra (no search), Azure AI Search / Azure Cosmos DB (no vector search), LLMRails (no delete), TileDB (no delete).

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📋 Not Implemented (all methods raise NotImplementedError)

AwaDB, BagelDB, BaiduCloud, Clarifai, ClickHouse, DashVector, Databricks Vector Search, DeepLake, DingoDB, Elastic Vector Search (legacy module), Epsilla
Note: ~44 of the ~60 backends are real client-library-backed implementations. The 11 unimplemented ones raise NotImplementedError immediately — no backend silently pretends to work.

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RAG Features

  • ✅ Basic RAG Pipeline - Core RAG implementation with document processing
    • Example: examples/rag/rag_example.py
  • ✅ Document Processing - Text splitting and basic processing
    • Example: examples/rag/rag_example.py
  • ✅ Embedding Management - Basic embedding support
  • 🚧 Advanced RAG - Enhanced retrieval with metadata filtering. Edge-case tests added for Retriever/EnhancedRetriever/HybridRetriever (malformed filters, empty corpus, oversized k, fusion-weight/feedback behavior); stays Beta pending broader coverage
    • Example: examples/rag/rag_advanced_example.py
    • Tests: tests/test_advanced_rag_edge_cases.py
  • ✅ Hybrid RAG Architecture - GraphRetriever builds an entity/relation graph via LLM extraction (GraphRAG) and ranks documents by query-entity overlap plus one-hop neighbor expansion
    • Example: tests/test_graph_retrieval.py
  • 📋 Quantum-Enhanced Search - Quantum algorithms not implemented
  • 🚧 Multi-Modal Document Processing - Table extraction implemented (pdfplumber for PDFs, pandas for pre-parsed rows); image extraction does OCR via pytesseract ([documents] extra) with no object detection/captioning (no vision model configured); LLM-based structure analysis remains a placeholder

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🤖 AI Agents

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Agent Framework

  • ✅ Basic Agents - Agent class with real native function-calling: builds JSON-schema tool definitions from registered tools, executes returned tool_calls in a bounded loop (max_turns), and feeds results back to the model — used automatically when the model exposes an OpenAI-compatible client; falls back to keyword-based tool routing otherwise (duck-typed detection)
    • Example: examples/cli/basic_agent.py
  • ✅ Agent Registry - Agent registration and management
    • Example: examples/agents/agent_registry_example.py
  • ✅ ReAct Toolchain - ReAct pattern implementation
    • Example: examples/agents/react_toolchain_example.py
  • 🚧 Multi-Agent Orchestration - Basic coordination
  • ✅ Self-Evolving Agents (bounded) - SelfEvolvingAgent records task outcomes and injects the top-utility few-shot exemplars into future prompts; utility rises on success and decays on failure. Bound: no self-modification of code or prompts beyond exemplar selection
    • Example: tests/test_self_evolving.py
  • ✅ Cognitive Scratchpad - Step dependencies (add_step_dependency, cycle-checked) and revision history (revise_step/get_revision_history) added on top of the existing LLM-based reasoning-chain tracking
    • Example: tests/test_cognitive_scratchpad.py

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🧠 Memory Systems

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✅ Stable Memory Types

  • ✅ Buffer Memory - Working conversation buffer
  • ✅ Summary Memory - Working summarization
  • ✅ Summary Buffer Memory - Hybrid summary + buffer
  • ✅ Agent Memory - Agent state management
    • Example: examples/memory/basic_usage.py
  • ✅ Vector Store Memory - Real add/get/search/clear/save-load round-trips against a live FAISS backend, with correct similarity ordering. Fixed real bugs: the default config crashed on construction (passed kwargs the real VectorStoreConfig doesn’t accept), several call sites read config values as attributes that don’t exist on VectorStoreConfig (now uses .get(...)), the embedding call used a nonexistent BaseLLM method name, and VectorStore.load() discarded the backend instance its own load() classmethod returned. Known limitation: the FAISS backend ignores filter_criteria, so get(memory_id) isn’t reliable once more than one vector is stored
    • Tests: tests/test_memory_beta.py
  • ✅ Episodic Memory - Real add/retrieve round-trips through the spatial/temporal/emotional indices, episode chaining, and save/load persistence. Fixed real bugs: a len() called on an int in get_episode_suggestions, chain-list mutation of a predecessor’s own stored list, sets not JSON-serializable on save (and not restored as sets on load), and self-similarity inflating episode chaining
    • Tests: tests/test_memory_beta.py
  • ✅ Semantic Memory - Real concept-extraction, relationship-inference, and save/load round-trips. Fixed real bugs: a missing await on embeddings() during load() (silently storing coroutine objects instead of vectors), sets not JSON-serializable on save, and self-similarity inflating relationship inference
    • Tests: tests/test_memory_beta.py
  • ✅ Procedural Memory - Real procedure-extraction, record_execution/adaptation, and save/load round-trips. Fixed real bugs: two methods named get_procedure_stats/get_procedure_suggestions in the same class silently shadowed each other, so super().get_procedure_stats() in the subclass resolved to a nonexistent parent method instead of the intended base implementation (renamed the base versions); a missing await on embeddings() during load(); sets not JSON-serializable on save
    • Tests: tests/test_memory_beta.py
  • ✅ Hybrid Memory - Real routing across two live child memories (both full- and partial-routing cases), get_hybrid_stats, and save/load round-trips. Fixed a real bug: load() restored the hybrid bookkeeping but never reloaded each child memory’s own persisted content, so reloaded child memories came back empty
    • Example: examples/memory/advanced_memory_manager.py
    • Tests: tests/test_memory_beta.py

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📋 Planned/Experimental Memory Types

  • 📋 Quantum Memory - Simulation code only (not real quantum computing)
    • Note: This is a classical simulation, not actual quantum hardware
    • Example: examples/memory/quantum_memory.py (for educational purposes)
  • 🚧 Consensus Memory - Local in-process replication with majority-vote reads; networked RAFT (elections, RPCs) not implemented and raises NotImplementedError
  • ✅ Planning Memory - Plan-step recording plus success-weighted next-step suggestion from historical state/action outcomes (same style as ReinforcementMemory); the old fake multi-step “rollout” simulation (fabricated outcomes, no real environment) was removed rather than kept as invented output
    • Example: tests/test_planning_memory.py
  • 🚧 Declarative Memory - Fact storage with timestamp-based temporal relations, lexical-cue causal heuristics, and knowledge-graph updates (delegates to a configured graph memory, else a plain dict graph); verification/consistency/reasoning analyses depend on a JSON-capable LLM
  • 🚧 Implicit Memory - Skill tracking with prerequisite graph and practice-based proficiency
  • 🚧 Reinforcement Memory - Pure-python tabular Q-learning for budget management (keep/evict/compress)
  • 🚧 Generative Memory - Regeneration bookkeeping, drift tracking, and due-memory regeneration via a caller-supplied generator
  • 🚧 Active Learning Memory - Explicit feedback loop: per-memory utility scores, utility-weighted recency retrieval, low-utility eviction

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🔄 Fine-Tuning & Model Training

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Fine-Tuning Features

  • 🚧 Basic LoRA - Basic LoRA support
    • Example: examples/fine_tuning/
  • 🚧 Adapter Training - Basic adapter support
  • 🚧 Non-Transformer Models - Mamba and RWKV run real inference (HuggingFace-backed); the other listed architectures (Hyena, S4 variants, RetNet, H3, MLP-only, diffusion-text, etc.) are extension scaffolds only and raise NotImplementedError
    • Example: examples/non_transformer/
  • 🚧 QLoRA - Real 4-bit NF4 quantization + LoRA adapters via transformers/peft/bitsandbytes ([finetune-gpu] extra); raises NotImplementedError when those deps are absent. Compacter remains a scaffold (peft ships no Compacter config)
  • 📋 HyperLoRA - Complex hypernetwork not implemented
  • ✅ RAG Fine-tuning - SyntheticQAGenerator generates Q/A pairs from document chunks via strict-parsed LLM JSON, dedups by question hash, quality-filters by grounding score, and exports Alpaca-style instruction-tuning JSONL; pure Python, no torch required
    • Example: tests/test_synthetic_data.py
  • 📋 Advanced Optimization - Many techniques not implemented

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🔐 Compliance & Security

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✅ Stable Compliance Features

  • ✅ Basic Compliance Framework - Core compliance infrastructure
    • Example: examples/compliance/healthcare_compliance_example.py
  • ✅ Healthcare Compliance - PHI tracking with audit-logged access, breach modeling, and state-based HIPAA/HITECH rule checks; requirements needing external systems report unverifiable instead of a fabricated pass
    • Example: examples/compliance/healthcare/
  • ✅ GDPR Runtime Enforcement - GDPRPolicy.check_processing(purpose, data_categories, consent, retention_days) evaluates a concrete processing request against a configured lawful-basis/purpose-limitation/retention policy and returns an allowed/denied/not_configured decision with reasons; unknown purposes get an explicit not_configured denial rather than a guess. gdpr_enforce(policy) derives real ComplianceGuard settings from the policy (blocks special-category PII when no purpose permits sensitive data; hash vs mask redaction strategy from the pseudonymization setting), wiring the policy into the actual runtime guard
    • Tests: tests/compliance/test_gdpr_enforcement.py
  • ✅ Framework Governance Adapters (multimind.integrations.frameworks) - Drop-in PII guard/budget/cost/audit wrappers for LangChain (guard_runnable, guard_tool/guard_tools for agent tool inputs, MultiMindCallbackHandler with pii_detected audit events since LangChain callbacks can’t mutate prompts), LlamaIndex (guard_llm, MultiMindLlamaIndexHandler), CrewAI (guard_crew_llm, verified against installed crewai 1.15.1), AutoGen (guard_autogen_client, targets autogen-core’s ChatCompletionClient ABC), Haystack (guard_haystack_generator, duck-typed — never imports haystack), and raw OpenAI clients (guard_openai, covers chat.completions.create, responses.create, and embeddings.create). All lazy-imported; a missing framework raises a pip install ... hint only when its adapter is used
    • Docs: docs/integrations.md
    • Tests: tests/test_framework_adapters.py, tests/test_framework_adapter_autogen.py, tests/test_framework_adapter_haystack.py
  • ✅ Guard Proxy (multimind serve, multimind/gateway/guard_proxy.py) - OpenAI-compatible /v1/chat/completions and /v1/embeddings proxy in front of a named or custom upstream, with PII redaction (mask/hash/remove), --block-on rejection, budget enforcement, JSONL audit and cost logs, output scanning, and guardrails JSON hot-reload via POST /v1/guardrails/reload. Requires the [gateway] extra
    • Docs: docs/guard-proxy.md
    • Tests: tests/test_guard_proxy.py
  • ✅ Hallucination Detection (multimind/evaluation/hallucination.py) - HallucinationDetector scores each response sentence against supplied sources (containment-based grounding) and, with a judge model, extracts and verifies claims; GuardedModel wraps any model and on a low grounding score annotates, raises HallucinationError, or retries; detect_hallucinations() is the one-call form. Malformed judge output raises ValueError rather than being guessed at
    • Tests: tests/test_hallucination.py
  • ✅ Evidence Reports (build_evidence_report, multimind compliance report-evidence) - Markdown or self-contained HTML report built from the guard audit log, the cost log, and an inventory scan, mapped to EU AI Act / SOC 2 / HIPAA control themes. Absent inputs are stated as absent, never fabricated; the framework mapping shrinks to the sources present; rendering is deterministic
    • Tests: tests/test_compliance_reporting.py
  • ✅ Governance Dashboard (multimind dashboard, multimind/dashboard/server.py) - Local read-only web UI and JSON API (/api/summary, /api/audit, /api/costs, /api/inventory, /api/scan-text, /api/evidence-report, /api/guardrails) over the audit trail, cost log, and inventory scan, plus a no-code guardrails editor whose file feeds multimind serve --config. Requires the [gateway] extra
    • Docs: docs/dashboard.md
    • Tests: tests/test_dashboard.py
  • ✅ MCP Server (python -m multimind.mcp_server, [mcp] extra) - Anthropic Model Context Protocol server exposing scan_text, redact_text, check_grounding, check_policy, and audit_log tools to Claude Desktop, Claude Code, and other MCP clients; findings carry spans, never raw PII values. Importable without the MCP SDK and fails with an install hint only when started
    • Docs: docs/mcp-server.md
    • Tests: tests/test_mcp_server.py

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🚧 Beta Compliance Features

  • 🚧 Data Governance - Basic governance framework

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📋 Planned Compliance Features

  • 📋 Zero-Knowledge Proofs - cryptography.zkp import fails
  • 📋 Federated Shards - FederatedShard class missing
  • 📋 Homomorphic Encryption - Basic implementation only
  • 📋 Self-Healing Compliance - Patch generation not implemented
  • 📋 Adaptive Privacy - Feedback mechanisms not functional
  • ✅ Regulatory Change Detection - RegulatoryWatcher (multimind/compliance/regulatory_watch.py) fetches source pages via httpx, hashes content, and reports ChangeEvents with a raw unified diff on change — no fabricated legal interpretation; offline check_offline() mode for dry runs. Note: distinct from the pre-existing RegulatoryChangeDetector stub in advanced.py, which remains unimplemented
    • Example: examples/compliance/regulatory_watch_demo.py
  • 📋 Quantum-Safe Encryption - Not implemented
  • ✅ Differential Privacy - Laplace mechanism (DPMechanism) with epsilon-budget accounting (PrivacyBudget, BudgetExhaustedError) in multimind/compliance/advanced.py; composition is simple sequential (sum of epsilons per call) — (epsilon, delta)-advanced composition is not implemented

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🔄 Workflow & Orchestration

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Workflow Features

  • ✅ Prompt Chains - Basic chaining
    • Example: examples/cli/prompt_chain.py
  • ✅ Task Runner - Simple task execution
    • Example: examples/cli/task_runner.py
  • 🚧 MCP (Model Composition Protocol) - Basic executor and parser for MultiMind’s internal workflow format. Note: this is not Anthropic’s Model Context Protocol — that is supported via the multimind-compliance MCP server (python -m multimind.mcp_server, [mcp] extra, see docs/mcp-server.md).
    • Example: examples/mcp_workflows/
  • 🚧 Pipeline Builder - Basic pipeline construction
    • Example: examples/pipeline/pipeline_example.py
  • 📋 Visual Workflow Builder - Drag-and-drop not implemented
  • 📋 Event-Driven Architecture - Reactive workflows not fully implemented
  • 📋 Advanced Workflow Automation - Complex features missing

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📊 Monitoring & Analytics

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Observability Features

  • ✅ Cost Tracking and Budgets (CostTracker, Budget, track_costs, multimind/observability/cost_tracker.py) - Per-call cost records with real token usage when the provider exposes it (else a flagged estimate), pricing from the model’s cost_per_token or MODEL_PRICING (unpriced models record $0 and are flagged, never a made-up price), per-model/provider/tag summaries, optional JSONL persistence, and Budget that warns at a ratio and raises BudgetExceededError at the cap
    • Tests: tests/test_cost_tracker.py
  • ✅ Shadow-AI Inventory and Chargeback (scan_project, CostTracker.chargeback, multimind audit) - Static scan of dependencies, API URLs, env-var reads, and hardcoded AI keys (reported by location, never by value) plus per-tag chargeback reports from the session tracker or a JSONL cost log
    • Docs: docs/ai-inventory.md
    • Tests: tests/test_ai_inventory.py, tests/test_cost_tracker.py
  • ✅ Run Tracing (RunTracer, trace_model, multimind/observability/tracing.py) - Runs and nested spans for llm/chain/tool calls with latency, tokens, and cost, written to a local SQLite sink and/or exported to a self-hosted platform (POST /api/v1/runs, buffered retry on failure). Stores only character counts and SHA-256 hashes of prompts and responses unless store_content=True is passed
    • Docs: docs/tracing.md
    • Tests: tests/test_tracing.py
  • 🚧 Basic Logging - TraceLogger and basic metrics
  • 🚧 Usage Tracking - Basic usage tracking
    • Example: examples/cli/usage_tracking.py
  • 📋 Real-time Performance Tracking - Microsecond-level monitoring not implemented
  • 📋 AI-Powered Anomaly Detection - Not implemented
  • 📋 Cost Optimization Engine - Intelligent allocation not implemented
  • 📋 Predictive Maintenance - Not implemented

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🛠️ CLI & API

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✅ Stable

  • ✅ CLI Framework - Click-based CLI with multiple commands
    • Docs: docs/cli.md; tests: tests/test_cli.py
  • ✅ Basic API - REST API gateway
    • Example: examples/api/gateway_examples.py

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🚧 Beta

  • 🚧 Advanced API Features - Some features may be limited

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📈 Test Coverage

Test counts are not kept on this page because they go stale; the CI workflow publishes the current passed/skipped/failed numbers per Python version on every run, and the test-full job enforces a 95% pass-rate gate. Tests that need optional heavy dependencies or live services skip in the default suite; the integration suite runs with pytest -m integration.

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🎯 Summary

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What Works Today (Production Ready)

  • Multi-model AI chat across 13 first-party providers (OpenAI, Claude, Gemini, Groq, Mistral, DeepSeek, Ollama, OpenRouter, Together, xAI, Perplexity, Fireworks, Cerebras) plus 300+ models via OpenRouter aggregation, with vision/multimodal input
  • Runtime PII guard, cost budgets, hallucination detection, and audit trails — one-line wrappers around any model
  • Compliance proxy (multimind serve), governance dashboard (multimind dashboard), AI-usage audit/chargeback (multimind audit), and evidence reports mapped to EU AI Act / SOC 2 / HIPAA control themes
  • Framework governance adapters for LangChain, LlamaIndex, CrewAI, AutoGen, Haystack, and raw OpenAI clients
  • Anthropic MCP server exposing compliance tools, plus an OSS tracing client for a self-hosted observability platform
  • Bounded agents with real native function-calling, tool-input scanning, and bounded self-evolving exemplar learning
  • GDPR runtime enforcement (policy → guard wiring) and state-based HIPAA validation (never a fabricated pass)
  • RAG (Retrieval-Augmented Generation) with hybrid knowledge-graph retrieval and synthetic Q/A generation for fine-tuning
  • 5 Stable memory types (Episodic, Semantic, Procedural, Hybrid, Vector Store) plus Buffer/Summary/Agent memory
  • ~44 real vector-database integrations (FAISS, Chroma, Pinecone, Qdrant, Weaviate, Milvus, and more)
  • CLI interface (7 command groups) and REST gateway with Swagger UI
  • Basic model conversion, fine-tuning (LoRA, real QLoRA), and context transfer between models

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What’s In Development (Beta)

  • Advanced vector database integrations (26 client-backed backends, less battle-tested than the Core set)
  • MoE, model compression, and advanced RAG (edge-case tested this round; real bugs fixed, broader real-model coverage still pending)
  • Consensus/Declarative/Implicit/Reinforcement/Generative/Active-Learning memory (all real, none fabricated — see Memory Systems for specifics)
  • MCP workflows (Model Composition Protocol, MultiMind’s internal workflow format — distinct from the Anthropic MCP server, which is Stable)

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What’s Planned (Future)

  • Quantum memory (real quantum hardware)
  • 50+ additional vector database backends
  • Zero-knowledge proofs, homomorphic encryption, model/federated-learning watermarking, self-healing compliance patch generation
  • Visual workflow builder
  • Advanced monitoring and analytics (real-time performance tracking, anomaly detection, cost optimization engine)
Honesty Note: MultiMind SDK is actively being developed. While we have a solid foundation, many features described in marketing materials are still in development or planned. We’re committed to transparency about what works today versus what’s coming in the future. See ROADMAP.md for our development priorities.