Memory and Context

Manages agent skills and long-term memory as a layered data structure for persistent context.

Cross-vendor shared memory layer exposed as a remote MCP server at `memory.agentage.io/mcp` (Streamable HTTP, OAuth 2.1 + PKCE + DCR) that Claude, Cursor, and ChatGPT read and write as plain markdown you own.

Lightweight, embeddable vector store for building memory-augmented AI agents with fast semantic retrieval.

Knowledge engine for AI agent memory, set up in 6 lines of code with graph-based knowledge extraction.

Full-stack solution for agent memory covering extraction, vector search, and optimization.

Stores agent memory as a typed knowledge graph with hybrid search and a tamper-evident journal, embedded in SQLite.

Build real-time knowledge graphs for AI agents with automatic entity extraction and linking.

Git-native memory for coding agents that stores decisions and fixes as repo files and verifies them against the codebase, withholding stale knowledge.

Serverless vector search database embedded directly in the agent process with no infrastructure needed.

Helps agents learn and adapt from their interactions over time with persistent memory.

Gives any AI agent local-first memory with corrections, point-in-time recall, and inspectable history.

Memory layer for AI applications with long-term, short-term, and semantic memory extraction.

Local-first AI memory desktop app that parses files into searchable vector indexes.

Git-like versioned semantic memory for AI agents with branching and commits.

Replace complex RAG pipelines with a serverless, single-file memory layer for instant retrieval.

Scales vector search to billions of embeddings for large-scale agent knowledge bases.

Sovereign shared memory layer for AI coding agents with zero-instrumentation capture via lifecycle hooks, a dream pipeline that distills sessions into curated governed memories, and support for Claude Code, Cursor, Codex, and Antigravity.

Manages conversation context windows for agents with automatic background summarization.

Builds typed knowledge graphs with hybrid search and read/write MCP tools for domain-specific agents.

Live data RAG engine with real-time streaming for agents that need up-to-the-second knowledge.

Managed vector database with agent namespaces for multi-tenant isolation, hybrid search (vector + keyword), serverless auto-scaling, and $11B valuation.

High-performance vector similarity search engine with rich payload filtering for agent memory.

Open-source RAG engine with agent capabilities and deep document understanding for knowledge bases.

Efficient lifelong memory for LLM agents supporting both text and multimodal inputs.

Gives coding agents persistent memory of what worked across sessions, tracked against real-world outcomes.

Extremely fast and scalable memory engine and API designed for the AI era.

Manages local agent memory with recall, forgetting, audit trails, and session consolidation.

Provides local-first memory for coding agents with FSRS-6 retention, active forgetting, and correction tools.

Stores and searches vector embeddings with hybrid keyword and semantic retrieval for agent knowledge.

Enriches agent long-term memory with automatic summarization, entity extraction, and search.