Photomem is an AI memory infrastructure that uses knowledge graphs and entity tracking to give your AI agents photographic precision. Unlike traditional vector databases that store isolated text chunks, Photomem automatically identifies and connects people, places, events, and concepts across conversations. It achieves 85.6% accuracy on the LoCoMo benchmark, compared to Mem0's 66.5% and Zep's <60%, while being 3x faster in retrieval speed.

How does Photomem compare to Mem0 and Zep?

Photomem outperforms Mem0 and Zep in both accuracy and speed. On the LoCoMo benchmark, Photomem achieves 85.6% accuracy compared to Mem0's 66.5% and Zep's <60%. Photomem also delivers 3x faster retrieval speeds (~50ms vs ~150ms). The key difference is Photomem's knowledge graph architecture that understands entity relationships, while Mem0 and Zep use traditional vector storage with isolated memory chunks.

What is entity tracking in AI memory?

Entity tracking automatically identifies and links people, places, events, and concepts across all conversations. For example, when you say 'Sarah's birthday is March 15th' in one conversation and 'Sarah loves chocolate cake' in another, Photomem automatically connects both facts to the same 'Sarah' entity. Then when someone asks 'What should I get Sarah?', the AI instantly recalls both her birthday date and chocolate preference, providing contextually rich responses without manual tagging.

What is AI memory and why do LLMs need it?

AI memory refers to systems that allow AI agents to retain and recall information across conversations and sessions. LLMs need external memory because they have limited context windows (typically 8K-128K tokens) and no persistent state between sessions. External memory systems solve this by storing unlimited conversation history, retrieving only relevant context for each query, maintaining user preferences and relationship data, and providing temporal awareness of past interactions.

Is Photomem suitable for enterprise applications?

Yes, Photomem is built for enterprise scale with SOC 2 Type II compliance, end-to-end AES-256 encryption, real-time analytics, and 99.9% uptime SLA. We support high-volume production workloads with millisecond-level response times, handling millions of requests per day. Enterprise features include dedicated infrastructure, custom data retention policies, on-premise deployment options, SSO/SAML authentication, advanced role-based access control, and 24/7 priority support.

How do I integrate Photomem into my AI application?

Photomem provides a RESTful API that integrates in minutes. The process is: (1) Sign up and get your API key, (2) Add memories using POST /api/v1/memories with user context, (3) Search memories using POST /api/v1/memories/search with semantic queries, (4) Retrieve relevant context automatically. We offer SDKs for Python, JavaScript, and other popular languages. Most developers complete integration in under 30 minutes.

What is the LoCoMo benchmark?

The LoCoMo (Long Context Memory) benchmark is an independent evaluation framework that tests AI memory systems on accuracy, context retention, and relationship understanding across extended conversations. It measures how well memory systems recall specific details, maintain temporal context, and connect related information. Photomem's 85.6% score means we correctly retrieve and contextualize information 85.6% of the time—significantly higher than Mem0 (66.5%) and Zep (<60%).

What is the difference between knowledge graphs and vector databases?

Vector databases store memories as isolated embeddings, making similarity search fast but losing relationship context. Knowledge graphs connect entities—when storing 'Sarah's birthday is March 15th', the graph links Sarah (person entity) to March 15th (date entity). This enables complex queries like 'Who has a birthday in March?' that pure vector search cannot answer. Photomem's knowledge graph architecture provides both fast retrieval and rich relationship understanding.

Can Photomem work with any LLM?

Yes, Photomem is LLM-agnostic and works with any AI framework including OpenAI GPT-4, Anthropic Claude, Google Gemini, open-source models like Llama and Mistral, and custom fine-tuned models. We provide context in a standardized format that any LLM can consume. Integration works with popular frameworks like LangChain, LlamaIndex, AutoGen, and custom agent architectures.

How does Photomem handle data privacy and security?

Photomem implements enterprise-grade security: (1) End-to-end AES-256 encryption for data at rest and in transit, (2) SOC 2 Type II compliance with annual audits, (3) Zero-knowledge architecture where we cannot access your unencrypted data, (4) Granular access controls with API key rotation, (5) Automatic PII detection and optional redaction, (6) GDPR and CCPA compliance. All data is isolated by tenant with no cross-contamination, and we never train models on customer data.

Performance How it Works Security Use Cases Blog

photomem Blog

Insights on AI memory, agent architecture, and building smarter AI systems.

AI Fundamentals

Dec 2, 20258 min read

Why LLMs Need Memory: The Missing Piece in AI Conversations

Large Language Models are powerful, but without memory, they forget everything between conversations. Learn why memory is the critical component for building truly intelligent AI agents.

By photomem TeamRead more

Technical Deep Dive

Dec 1, 202512 min read

Building Memory Systems for AI Agents: A Developer's Guide

From simple key-value stores to sophisticated semantic search, explore the landscape of memory architectures and learn which approach fits your AI agent's needs.

By photomem TeamRead more

Performance

Nov 30, 20256 min read

Beyond Context Windows: Why Token Limits Are Killing Your AI Agent

Context windows are expensive and limited. Discover how intelligent memory systems can help your AI agents remember more while using fewer tokens.

By photomem TeamRead more