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Engines

Palo Memory Engines

Welcome to the detailed documentation for Mpalo's Palo AI Memory Engines. Each engine is designed to provide unique capabilities for integrating persistent, context-aware memory into your applications. Below, you'll find comprehensive information on Palo Mini, Palo Bloom, Palo DEEP, and Palo DEEP-Research, including their features, API names, technical specifications, and operational modes.

Please note: The previously announced "Palo Large" model is currently postponed as we focus on refining and enhancing our current suite of offerings.

Operating Modes

Palo AI Memory Engines offer two distinct operational modes that you can select based on your application's needs. This choice defines how Palo stores and recalls information, giving you powerful control over your AI's behavior.

1. Personalization Mode

Focus: Adaptive, "humanlike" memory.

How it Works: This mode uses vector reconstruction to recall memories. This can result in "blurry" recall, where the core patterns and context are remembered, but the exact wording might shift, similar to human memory. It allows for creative connections and emergent behavior.

Best For: Conversational chatbots, personal assistants, and creative applications where a humanlike feel is more important than perfect factual recall.

Available on: Palo Mini, Palo Bloom, Palo DEEP.

2. Research Mode

Focus: 100% accurate, factual recall with zero hallucinations.

How it Works: This mode stores the original text as metadata alongside the vector. When recalling a memory, Palo retrieves this exact, unaltered metadata, bypassing reconstruction entirely. This guarantees that what you put in is exactly what you get out.

Best For: Enterprise knowledge bases, legal or medical Q&A bots, technical documentation search—any application where factual precision is non-negotiable.

Available on: Palo DEEP, Palo DEEP-Research.

Memory Engine Comparison

Feature Palo Mini Palo Bloom Palo DEEP Palo DEEP-Research
API Name palo-lite palo palo-deep palo-deep-research
Primary Function Fast contextual memory for LLMs Balanced memory & performance Advanced, deep memory for complex applications Specialized memory for 100% accurate factual recall
Supported Context (per Input) ~4096+ tokens ~8192+ tokens Significantly Larger Significantly Larger
Key Memory Features Episodic Recall, Semantic Search Enhanced Recall & Search, Basic Relationship Linking Memory Mapping, Advanced Accurate Recall, Traversal All DEEP Features + Specialized Fine-tuning
Primary Use Cases Simple chatbots, CLI tools, basic personalization. Personal assistants, mobile apps, educational tools. Enterprise knowledge bases, complex robotics, advanced support. Legal/Medical Q&A, compliance checks, technical lookups.
Operational Mode(s) Personalization Personalization Personalization & Research Personalization & Research
Performance Exceptionally Fast Very Fast Fast, optimized for depth Fast, optimized for accuracy
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Palo Mini

API Name: palo-lite

Palo Mini is an exceptionally fast and cost-effective AI memory engine designed to augment external LLMs. It provides essential episodic memory, enabling quick contextual recall for LLM-driven applications supporting interaction context windows of approximately 4096 tokens or more. Ideal for scenarios requiring rapid, memory-enhanced responses with minimal latency and resource usage. Operates primarily in "Personalization Mode."

Key Features & Specifications:

Function: AI Memory Engine for external LLMs.
LLM Interaction Context: ~4096+ tokens.
Multilingual Memory: Stores/retrieves text in various languages (primarily English optimized) for processing by an external LLM.
Vision Memory: Stores references/metadata for images (e.g., URLs, identifiers). External LLM handles actual image processing.
Memory Management for LLM: Basic Episodic Recall (specific past interactions), simple semantic search over recent memories to provide context to the LLM.
Performance: Exceptionally Fast & Cost-Effective. Low latency.
Memory Capacity: Optimized for short-term context, recent interactions (e.g., single conversation, few dozen memory entries).
Primary Mode: Personalization.
Use Cases: Augmenting simple customer service LLM bots, FAQ retrieval with LLMs, CLI tool context for LLM interaction, basic in-app LLM user preference storage.
Code Integration: Accessible via REST API. SDKs (Python, JavaScript, Java) planned. See SDKs page.

Palo Bloom

API Name: palo

Palo is a versatile and exceptionally fast, cost-effective AI memory engine that enhances external LLMs. It offers a balance of deeper memory capabilities and high performance, optimized for LLM applications on mobile/edge devices or those requiring robust memory for interaction context windows of approximately 8192 tokens or more. Operates primarily in "Personalization Mode."

Key Features & Specifications:

Function: AI Memory Engine for external LLMs.
LLM Interaction Context: ~8192+ tokens.
Multilingual Memory: Stores/retrieves text in a broader range of languages for processing by an external LLM. Good support for English & major European languages.
Vision Memory: Stores references/metadata for images (e.g., for images up to 512px as reference). External LLM handles actual image processing.
Memory Management for LLM: Enhanced Episodic Recall, improved Semantic Search over a larger memory span, basic relationship linking within stored memories to provide rich context to the LLM.
Performance: Exceptionally Fast & Cost-Effective. Low to medium latency.
Memory Capacity: Suitable for medium-term context, user preferences over multiple sessions (few hundred memory entries).
Primary Mode: Personalization.
Use Cases: Augmenting LLM-powered personal assistants, mobile LLM applications, smarter IoT device interactions with LLMs, context-aware educational tools using LLMs.
Code Integration: Accessible via REST API. SDKs (Python, JavaScript, Java) planned. See SDKs page.

Palo DEEP

API Name: palo-deep

Palo DEEP is an advanced AI memory engine for external LLMs. Engineered for complex applications, it provides highly reliable and profound memory recall. It integrates sophisticated features like Memory Mapping and Memory Traversal for comprehensive semantic network building, ensuring nuanced and dependable context for the external LLM.

Key Features & Specifications:

Function: Advanced AI Memory Engine for external LLMs.
LLM Interaction Context: Significantly larger than Palo.
Memory Management for LLM: Memory Mapping (interconnected knowledge graphs), Memory Traversal (navigating complex event chains), and options for either adaptive or 100% accurate recall.
Primary Modes: Personalization & Research Mode (User Selectable).
Use Cases: Augmenting enterprise knowledge management LLMs, advanced research assistants, sophisticated robotics requiring long-term learning, complex customer support with deep history.
Multilingual Memory: Extensive multilingual text storage/retrieval for external LLM processing, with strong cross-lingual memory linking capabilities.
Vision Memory: Stores references/metadata for images (e.g., for images >=1024px as reference) and supports semantic links between visual and textual memories. External LLM handles actual image processing.
Performance: Exceptionally Fast & Cost-Effective, with latency optimized for depth and accuracy of recall.
Memory Capacity: Designed for extensive knowledge bases, long-term user/system memory (thousands to millions of entries).
Code Integration: Accessible via REST API. SDKs (Python, JavaScript, Java) planned. Supports complex data structures. See SDKs page.

Palo DEEP-Research

API Name: palo-deep-research

Palo DEEP-Research is our premier memory engine, specialized for applications where 100% factual accuracy and verifiable recall are non-negotiable. It leverages the full power of the DEEP engine and is fine-tuned for understanding and retrieving information from dense, specialized documents. It is the definitive choice for building mission-critical AI systems.

Key Features & Specifications:

Function: Specialized AI Memory Engine for high-fidelity, accurate recall with external LLMs.
Memory Management for LLM: All features of Palo DEEP, with a strong emphasis on providing verifiable, citation-ready context to the LLM, especially when used in Research Mode.
Primary Modes: Personalization & Research Mode (User Selectable).
Use Cases: Legal document analysis and e-discovery, medical record summarization and research, financial compliance checks, academic paper review, and building enterprise-grade "expert" assistants.
LLM Interaction Context: Same as Palo DEEP.
Multilingual Memory: Same as Palo DEEP; for evaluating multilingual memory retrieval.
Vision Memory: Same as Palo DEEP; for evaluating memory system with visual references. External LLM handles image processing.
Performance: Optimized for evaluation throughput and detailed logging rather than interactive latency. Exceptionally fast & cost-effective for its purpose.
Memory Capacity: Suitable for very large evaluation datasets.
Code Integration: Accessible via REST API, often used with data analysis and LLM evaluation tools (Python predominant).

Keep in Mind

All engines come with either a Personalization Mode, which offers humanlike blurry memory and forgetting, or a Research Mode that aims to enhance accuracy, knowledge breadth, and depth while ensuring that important details are not forgotten.

Our Mission, in short

At Mpalo, we stand against profit-over-people capitalism. The majority of profit is reinvested into research to ensure our technology remains consumer-friendly and transparent. We deliver modular, humanlike memory solutions that safeguard user data, prevent bias, and foster long-term, reliable storage of experiences.

Our commitment is to create technology that serves businesses, developers, and consumers alike—building trust, enhancing engagement, and igniting nostalgia through memory-driven AI that truly resonates.

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