agentmemory

Persistent memory for coding agents on the iii engine: MCP server with 53 tools, 12 auto-capture hooks, hybrid search + knowledge graph, zero external DBs. Claims 95% R@5 and 92% token cuts.

25,926 2,157 TypeScript Apache-2.0updated yesterday

memory coding

View on GitHubDispute this mapping →

Curator's take

The benchmark-forward entry in a crowded field — '#1 on real-world benchmarks' is self-run, so weigh it accordingly; what's independently real: 1,428 tests, the viral design gist it implements (Karpathy's LLM-wiki pattern plus confidence scoring and lifecycle), and the smoothest onboarding in the category — hand your agent one URL and it installs itself. The iii engine is your infrastructure bet: a server on :3111, not a library. A 53-tool MCP surface is the opposite of the plain-files philosophy — richer, but agents need guidance to use it well (they ship 15 skills for exactly that reason). Same prolific author as pro-workflow and tailclaude — expect fast movement, budget for churn.

Mapped by ShipWithAI editors · links verified

README.md

Your coding agent remembers everything. No more re-explaining. Built on iii engine
Persistent memory for Claude Code, GitHub Copilot CLI, Cursor, Gemini CLI, Codex CLI, Hermes, OpenClaw, pi, OpenCode, and any MCP client.

The gist extends Karpathy's LLM Wiki pattern with confidence scoring, lifecycle, knowledge graphs, and hybrid search: agentmemory is the implementation.

95.2% retrieval R@5 92% fewer tokens 53 MCP tools 12 auto hooks 0 external DBs 1,428+ tests passing

agentmemory demo

Install • Quick Start • Benchmarks

Continue your stack

What teams reach for next — and why each earns a place beside agentmemory. Ranked by curator confidence.

Alternative to See all + compare →

memsearch

Zilliz's unified memory for coding agents: one Markdown + Milvus store shared across Claude Code, Codex, OpenCode and OpenClaw — hybrid search, plus repeated workflows distilled into skills.

Why it fitsSame job — one persistent memory shared across your coding agents — different substrates: memsearch bets on human-readable Markdown + Milvus embeddings; agentmemory bets on the iii engine with hybrid search, a knowledge graph and memory lifecycle, no external DB.

memanto

Companion memory agent for 20+ coding agents, built on Moorcheh — its own information-theoretic engine, no third-party vector DB to manage. Runs local (Docker + Ollama, keyless) or on their cloud.

Why it fitsBoth give many coding agents persistent memory without a third-party DB: Memanto is a companion memory agent — three primitives (remember/recall/answer) on its own Moorcheh engine; agentmemory is a 53-tool MCP toolbelt with auto-capture hooks and a knowledge graph.