maif 0.1.2
Multimodal Artifact File Format - Production-ready AI-native container with trustworthy AI capabilities
License: MIT
Requires Python: >=3.8
Keywords: ai, multimodal, security, provenance, forensics, trustworthy-ai, file-format, compression, streaming, cross-modal, attention, semantic, cryptography, aws, cloud, production, enterprise

<p align="center">
  <img src="docs/assets/maif-logo.svg" alt="MAIF Logo" width="200"/>
</p>

<h1 align="center">MAIF</h1>
<h3 align="center">Multimodal Artifact File Format for Trustworthy AI Agents</h3>

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<p align="center">
  Cryptographically-secure, auditable file format for AI agent memory with provenance tracking
</p>

---

## Overview

MAIF is a file format and SDK designed for AI agents that need **trustworthy memory**. Every piece of data is cryptographically linked, creating tamper-evident audit trails that prove exactly what happened, when, and by which agent.

**Key Capabilities:**

- **Cryptographic Provenance** - Hash-chained blocks for tamper-evident audit trails
- **Multi-Agent Coordination** - Shared artifacts with agent-specific logging
- **Multimodal Storage** - Text, embeddings, images, video, knowledge graphs
- **Privacy-by-Design** - Encryption, anonymization, access control built-in
- **High Performance** - Memory-mapped I/O, streaming, semantic compression

## Use Cases

- **Multi-Agent Systems** - Shared memory with full provenance (see LangGraph example)
- **RAG Pipelines** - Document storage with embeddings, search, and citation tracking
- **Compliance & Audit** - Immutable audit trails for regulated industries
- **Research** - Reproducible experiments with complete data lineage
- **Enterprise AI** - Secure, auditable AI workflows with access control

## Framework Integrations

MAIF provides drop-in integrations for popular AI agent frameworks:

| Framework | Status | Description |
|-----------|--------|-------------|
| LangGraph | Available | State checkpointer with provenance |
| CrewAI | Available | Crew/Agent callbacks, Memory |
| LangChain | Coming Soon | Callbacks, VectorStore, Memory |
| AWS Strands | Coming Soon | Agent callbacks |

```bash
pip install maif[integrations]
```

### LangGraph

```python
from langgraph.graph import StateGraph
from maif.integrations.langgraph import MAIFCheckpointer

checkpointer = MAIFCheckpointer("state.maif")
app = graph.compile(checkpointer=checkpointer)
result = app.invoke(state, config)
checkpointer.finalize()
```

### CrewAI

```python
from crewai import Crew
from maif.integrations.crewai import MAIFCrewCallback

callback = MAIFCrewCallback("crew.maif")
crew = Crew(
    agents=[...],
    tasks=[...],
    task_callback=callback.on_task_complete,
    step_callback=callback.on_step,
)
result = crew.kickoff()
callback.finalize()
```

See the [integrations documentation](docs/guide/integrations/) for full details.

---

## Quick Start

**Prerequisites:** Python 3.9+

### Installation

```bash
# Clone the repository
git clone https://github.com/vineethsai/maif.git
cd maif

# Install MAIF
pip install -e .

# With ML features (embeddings, semantic search)
pip install -e ".[ml]"
```

### Your First MAIF Artifact

```python
from maif import MAIFEncoder, MAIFDecoder, verify_maif

# Create an agent memory artifact (Ed25519 signed automatically)
encoder = MAIFEncoder("agent_memory.maif", agent_id="my-agent")

# Add content with automatic provenance tracking
encoder.add_text_block("User asked about weather in NYC", metadata={"type": "query"})
encoder.add_text_block("Temperature is 72°F, sunny", metadata={"type": "response"})

# Finalize (signs and seals the file)
encoder.finalize()

# Later: Load and verify integrity
decoder = MAIFDecoder("agent_memory.maif")
decoder.load()

is_valid, errors = decoder.verify_integrity()
print(f"Valid: {is_valid}, Blocks: {len(decoder.blocks)}")

# Read content
for i, block in enumerate(decoder.blocks):
    text = decoder.get_text_content(i)
    print(f"Block {i}: {text}")
```

**Secure MAIF Format:**
- **Self-contained** - No separate manifest files, everything in one `.maif` file
- **Ed25519 signatures** - Fast, compact 64-byte signatures on every block
- **Immutable blocks** - Each block is signed immediately on write
- **Tamper detection** - Cryptographic verification catches any modification
- **Embedded provenance** - Full audit trail built into the file

---

## Featured Example: Multi-Agent RAG System

A production-ready multi-agent system with **LangGraph orchestration** and **MAIF provenance tracking**.

```bash
cd examples/langgraph

# Configure API key
echo "GEMINI_API_KEY=your_key" > .env

# Install dependencies
pip install -r requirements_enhanced.txt

# Create knowledge base with embeddings
python3 create_kb_enhanced.py

# Run the interactive demo
python3 demo_enhanced.py
```

**What's Included:**
- 5 specialized agents (Retriever, Synthesizer, Fact-Checker, Citation, Web Search)
- ChromaDB vector store with semantic search
- Gemini API integration for LLM reasoning
- Complete audit trail of every agent action
- Multi-turn conversation support

See [`examples/langgraph/README.md`](examples/langgraph/README.md) for full documentation.

---

## NEW: Enterprise AI Governance Demo

Interactive demonstration of MAIF's enterprise-grade governance features:

```bash
cd examples/integrations/langgraph_governance_demo
python main.py
```

**Features demonstrated:**
- Cryptographic provenance (Ed25519 signatures, hash chains)
- Tamper detection and data integrity verification
- Role-based access control with audit logging
- Multi-agent coordination with clear handoffs
- Compliance report generation (Markdown, JSON, CSV)

See [`examples/integrations/langgraph_governance_demo/README.md`](examples/integrations/langgraph_governance_demo/README.md) for details.

---

## Features

### Cryptographic Provenance

Every block is cryptographically signed and linked - any tampering is detectable.

```python
from maif import MAIFEncoder, MAIFDecoder

# Each block is signed with Ed25519 on creation
encoder = MAIFEncoder("memory.maif", agent_id="agent-1")
encoder.add_text_block("First message")   # Signed immediately
encoder.add_text_block("Second message")  # Linked to previous via hash
encoder.add_text_block("Third message")   # Chain continues
encoder.finalize()

# Verify the entire chain + all signatures
decoder = MAIFDecoder("memory.maif")
decoder.load()
is_valid, errors = decoder.verify_integrity()

# Check provenance chain
for entry in decoder.provenance:
    print(f"{entry.action} by {entry.agent_id} at {entry.timestamp}")
```

### Privacy & Security

Built-in encryption, anonymization, and access control.

```python
from maif import PrivacyLevel, EncryptionMode

# Add encrypted content
maif.add_text(
    "Sensitive data",
    encrypt=True,
    anonymize=True,  # Auto-redact PII
    privacy_level=PrivacyLevel.CONFIDENTIAL
)

# Access control
maif.add_access_rule(AccessRule(
    role="analyst",
    permissions=[Permission.READ],
    resources=["reports"]
))
```

### Multimodal Support

Store and search across text, images, video, embeddings, and knowledge graphs.

```python
# Text with metadata
maif.add_text("Analysis results", title="Report", language="en")

# Images with feature extraction
maif.add_image("chart.png", title="Sales Chart")

# Semantic embeddings
maif.add_embeddings([[0.1, 0.2, ...]], model_name="all-MiniLM-L6-v2")

# Multimodal content
maif.add_multimodal({
    "text": "Product description",
    "image_path": "product.jpg",
    "metadata": {"category": "electronics"}
})
```

### Novel Algorithms

Advanced semantic processing capabilities:

- **ACAM** - Adaptive Cross-Modal Attention for multimodal fusion
- **HSC** - Hierarchical Semantic Compression (up to 64× compression)
- **CSB** - Cryptographic Semantic Binding for embedding authenticity

---

## Performance

| Metric | Performance |
|--------|-------------|
| Semantic Search | ~30ms for 1M+ vectors |
| Compression Ratio | Up to 64× (HSC) |
| Integrity Verification | ~0.1ms per file |
| Read Performance | 11× faster than legacy format |
| Tamper Detection | 100% detection in <0.1ms |
| Signature Overhead | Only 64 bytes per block (Ed25519) |

---

## Project Structure

```
maif/
├── maif/                  # Core library
│   ├── core.py           # MAIFEncoder, MAIFDecoder
│   ├── security.py       # Signing, verification
│   ├── privacy.py        # Encryption, anonymization
│   ├── integrations/     # Framework integrations (LangGraph, etc.)
│   └── semantic*.py      # Embeddings, compression
├── maif_api.py           # High-level API
├── examples/
│   ├── langgraph/        # Multi-agent RAG system
│   ├── integrations/     # Framework integration demos
│   ├── basic/            # Getting started
│   ├── security/         # Privacy & encryption
│   └── advanced/         # Agent framework, lifecycle
├── tests/                # 450+ tests
├── docs/                 # VitePress documentation
└── benchmarks/           # Performance tests
```

---

## Documentation

| Resource | Description |
|----------|-------------|
| [Online Docs](https://vineethsai.github.io/maif/) | Full documentation site |
| [API Reference](docs/api/) | Detailed API documentation |
| [User Guides](docs/guide/) | Step-by-step tutorials |
| [Examples](examples/) | Working code examples |

---

## Examples

### Basic Usage
```bash
python examples/basic/simple_api_demo.py
python examples/basic/basic_usage.py
```

### Privacy & Security
```bash
python examples/security/privacy_demo.py
python examples/security/classified_api_simple_demo.py
```

### Advanced Features
```bash
python examples/advanced/maif_agent_demo.py          # Agent framework
python examples/advanced/lifecycle_management_demo.py # Lifecycle management
python examples/advanced/video_demo.py               # Video processing
```

---

## Contributing

We welcome contributions! Please ensure:

1. All tests pass (`pytest tests/`)
2. Code follows PEP 8 style
3. New features include tests and documentation
4. Security-sensitive changes include impact analysis

See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed guidelines.

---

## References

- [FIPS 140-2 Standards](https://csrc.nist.gov/publications/detail/fips/140/2/final) - Cryptographic module requirements
- [NIST 800-53](https://csrc.nist.gov/publications/detail/sp/800-53/rev-5/final) - Security and privacy controls
- [ISO BMFF](https://www.iso.org/standard/68960.html) - Binary format inspiration

---

## License

MIT License - See [LICENSE](LICENSE) for details.

---

## Community & Support

- **[GitHub Discussions](https://github.com/vineethsai/maif/discussions)** - Ask questions, share ideas
- **[Issue Tracker](https://github.com/vineethsai/maif/issues)** - Report bugs or request features  
- **[Documentation](https://vineethsai.github.io/maif/)** - Complete guides and API reference
- **[Security](SECURITY.md)** - Report security vulnerabilities
- **[Changelog](CHANGELOG.md)** - See what's new
- **[Specification](SPECIFICATION.md)** - MAIF file format specification

---

<p align="center">
  <b>Build trustworthy AI agents with cryptographic provenance</b>
</p>