Hindsight
Hindsight is an agent memory system built to create smarter AI agents that learn over time instead of starting every conversation from zero. Most agent memory systems focus on recalling conversation history, but Hindsight is focused on making agents learn, not just remember. It gives AI agents persistent long-term memory using biomimetic data structures, helping them retain facts, recall relevant context, and reflect on experience as part of reasoning. Hindsight is designed for agents that need to understand who a user is, what has been discussed, what preferences have emerged, what decisions were made, and how behavior should adapt across sessions. It provides three core operations: retain, recall, and reflect. Retain stores new information, recall retrieves the right memories when needed, and reflect helps agents synthesize observations, form mental models, and learn from prior interactions.
Learn more
Maximem
Maximem is an AI context management and memory platform designed to give generative AI systems a persistent, secure memory layer that retains and organizes information across conversations, applications, and models. Large language models typically operate with limited session memory, meaning they lose context between interactions and require users to repeatedly provide the same background information. Maximem addresses this limitation by creating a private memory vault that stores relevant context, preferences, historical data, and workflow information so AI systems can reference it in future interactions. It operates between AI models and applications, ensuring that conversations, knowledge, and user data are consistently available across different tools and sessions. This persistent memory allows AI assistants to deliver responses that are more personalized, accurate, and context-aware because the system can retrieve previously stored information.
Learn more
Backboard
Backboard is an AI infrastructure platform that provides a unified API layer giving applications persistent, stateful memory and seamless orchestration across thousands of large language models, built-in retrieval-augmented generation, and long-term context storage so intelligent systems can remember, reason, and act consistently over extended interactions rather than behave like one-off demos. It captures context, interactions, and long-term knowledge, storing and retrieving the right information at the right time while supporting stateful thread management with automatic model switching, hybrid retrieval, and flexible stack configuration so developers can build reliable AI systems without stitching together fragile workarounds. Backboard’s memory system consistently ranks high on industry benchmarks for accuracy, and its API lets teams combine memory, routing, retrieval, and tool orchestration into one stack that reduces architectural complexity.
Learn more
Engram
Engram is a fully managed memory and context service purpose-built to help AI agents remember, learn, and improve over time. Instead of treating memory as an ever-growing pile of raw conversations and events, it turns noisy interaction data into structured, durable, and evolving memories. Applications can send raw text, complete conversations, or pre-extracted facts through a REST API or Python SDK without preprocessing. Engram then runs asynchronous pipelines that extract relevant information, transform it by deduplicating and reconciling it with existing knowledge, and commit a clean memory state without blocking the application’s main workflow. It resolves inconsistencies, adapts to changing preferences and time-evolving facts, and keeps context relevant and efficient. Agents can retrieve ranked memories in real time through vector similarity, BM25 keyword search, or hybrid retrieval, reducing the need to resend entire conversation histories.
Learn more