agentmemory is an open-source persistent memory layer purpose-built for AI coding agents like Claude Code, Cursor, Cline, Gemini CLI, Hermes, and OpenClaw. Released by rohitg00 and trending in May 2026 with the v0.9.9 release, the project addresses a problem every developer using AI coding tools has hit: the agent forgets your project the moment a session ends, and you spend the first ten minutes of every new session re-explaining the codebase, the conventions, the gotchas. agentmemory captures that context once and feeds the relevant slice back automatically. ## Numbers Worth Quoting The README reports **95.2% retrieval R@5** and **92% fewer tokens** compared to pasting full context into a new session. These are benchmark-driven numbers rather than marketing claims and are backed by a public retrieval evaluation in the repo. The token reduction is the more interesting figure in practice, because for any team paying for AI coding tools, replacing a 30k-token context paste with a few hundred tokens of relevant retrieved memory is a direct cost reduction. ## Automatic Capture via 12 Hooks Most memory tools in this space require manual tagging or explicit save commands, which means users forget to use them. agentmemory ships **12 hooks** that capture observations automatically as the agent works: file edits, command outputs, test results, conversation turns, and tool calls all flow into memory without manual configuration. The default behavior is zero-configuration capture, with privacy filtering and deduplication applied before storage. ## Multi-Stream Retrieval Retrieval combines three streams: **BM25 keyword matching** for exact-name queries ("that function in payments.ts"), **vector embeddings** for semantic similarity, and a **knowledge graph** that captures relationships between code entities, tasks, and decisions. The combination handles both lookup-style queries and broader "what did we decide about X" questions that pure vector search tends to miss. ## 4-Tier Memory Consolidation The system implements a four-tier consolidation pipeline inspired by cognitive memory research: **working memory** holds the active session, **episodic memory** stores specific events, **semantic memory** distills repeated patterns into general facts, and **procedural memory** captures how-to knowledge such as build commands and deployment steps. Older memories get compressed or forgotten according to use frequency, which keeps the store from bloating over months of work. ## 51 MCP Tools and 107 REST Endpoints Integration is broad: 51 Model Context Protocol tools make the memory store accessible to any MCP-aware agent, and a 107-endpoint REST API covers non-MCP integrations. A real-time viewer on port 3113 lets developers watch memories form and inspect what is being retrieved for any given query, which is useful both for debugging and for trust. ## Built on the iii Engine Unusually, agentmemory does not lean on the traditional Express + Postgres + Redis stack. It is built on the **iii engine**, a primitives-based runtime that replaces conventional infrastructure with composable workers extended via `iii worker add`. Embeddings can run locally with no API key, and LLM-powered compression is optional. This makes the project notably easier to self-host than memory tools that assume a cloud database. ## Where It Fits agentmemory is a strong fit for any developer or team using a coding agent daily. It is not a replacement for the agent itself, but a sidecar that makes the agent dramatically better at remembering the project. For 2026, where most teams have settled on one or two AI coding tools, a vendor-neutral memory layer that works across all of them is exactly the kind of infrastructure the space has been missing.