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Discover · GitHub · TencentDB-Agent-Memory↑ +318 today

Layered memory for AI agents

TencentDB Agent Memory offers a completely local long-term memory solution for artificial intelligence agents with a four-stage process. It performs data storage and recall operations without the need for external application programming interfaces (APIs).

Updates

  • September 7, 2026: Stars 24,804 → 26,048, latest release v2.0.1 (August 25, 2026).
  • August 27, 2026: Stars 23,144 → 24,804, latest release v2.0.1 (August 25, 2026).
  • August 19, 2026: Stars 21,959 → 23,144, latest release v2.0.0 (August 3, 2026).
  • August 15, 2026: Stars 20,021 → 21,959, latest release v2.0.0 (August 3, 2026).

What you get

  • Reduces token usage by up to 61%
  • Increases success rate in complex tasks
  • Stores data in a symbolic and layered structure

Installation

Package installation
mkdir -p ~/.memory-tencentdb
TEMP_DIR=$(mktemp -d)
cd "$TEMP_DIR"
npm init -y --silent
npm install @tencentdb-agent-memory/memory-tencentdb@latest --omit=dev
cp -r node_modules/@tencentdb-agent-memory/memory-tencentdb \
      ~/.memory-tencentdb/tdai-memory-openclaw-plugin
rm -rf "$TEMP_DIR"
Installing dependencies
cd ~/.memory-tencentdb/tdai-memory-openclaw-plugin
npm install --omit=dev
npm install tsx

Running it

Starting the server
cd ~/.memory-tencentdb/tdai-memory-openclaw-plugin
  npx tsx src/gateway/server.ts
Verify the connection
curl http://127.0.0.1:8420/health

If you don't write code

🤖 Paste this into your AI agent (Claude Code · Codex · Antigravity)

Configure the long-term memory of my AI agent using TencentDB Agent Memory. Instead of a flat vector stack of data, use symbolic Mermaid graphs for short-term tasks and a layered memory pyramid L0-L3 for long-term experiences. Enable the agent to store past conversations, atomic facts, and user preferences in this hierarchical structure and recall them whenever needed with full traceability via node_id.

Related dictionary terms

MermaidLong-term MemoryMemoryTokenAgentAPI
Who it is forIt is for developers who do not want their AI agents to forget the context and aim to get more consistent results by reducing token costs.

Links

TreScout did not build this tool · we found it in GitHub trends and wrote it up. This page describes the repository as of 2026-07-09: The star count and our text belong to that day, the repository may have changed since. Check the repository link for the current state. This page was machine-translated from the Turkish original · the Turkish version prevails.