Agents remember. Humans innovate.

Installation · Supported Agents · What is it? · Team Play · Technical Implementation · Benchmark · Roadmap

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Latest: Team Memory Beta is evolving quickly — install it and start exploring in minutes.

Installation

Start all three services in one go (memory-core + memory-hub + proxy):

git clone https://github.com/Tencent/TencentDB-Agent-Memory.git
cd TencentDB-Agent-Memory/deploy/global-images
cp .env.example .env
$EDITOR .env       # Fill in two sets of LLM parameters (memory group + proxy group)
./start-all.sh     # Launch everything with one command; when finished, it prints a one-liner you can paste directly into Claude

Open the panel: http://localhost:8125.

Complete installation documentation (standalone Memory Hub deployment, Proxy + Claude Code / CodeBuddy usage, stop and cleanup, port reference, etc.) is available in INSTALL.md (中文: INSTALL_CN.md).

Migrating data from an older version

If you're already on an older release (v1.x / v0.x) and want to bring your existing data over to v2.0.0+, we provide a migration tool:

See Data Migration Tool (v2 → v3) for full usage and flags. New installations can skip this.

All Agents Share the Same Memory Server

One Proxy, unchanged protocol, zero-code integration — point the Agent's base URL to the Proxy and it's done. No plugin, hook, or MCP server is required.

See INSTALL.md for the exact configuration steps of each client.

Don't see your favorite Agent? You can try adapting it yourself with the Generic integration guide — and we'd love a PR adding native support for it. See CONTRIBUTING.md to get started.

What is TencentDB Agent Memory?

We started from a practical question: How do you reduce repetitive work when using Agents?

If project context has already been explained, it shouldn't need to be repeated in a new session. If documents have already been read, every Agent shouldn't have to start again from page one. A workflow that already works shouldn't have to be rediscovered next time.

Memory here means more than just "remembering conversations." Any information that helps the next Agent avoid reinventing the wheel should be saved, organized, and reused.

Existing information → Reusable memory assets → Fewer turns → Less rework → More stable results and higher efficiency

Let experience accumulate, flow, and pass on to the next Agent

Memory Hub for Agent teams closes the loop across the entire experience lifecycle: work produces assets, assets circulate through the team, and new members can load the team's save file on day one.

• Automatic asset extraction: Extract Chat Memory and Skills from conversations and tasks; convert documents and code into Wiki and CodeGraph; then manage, review, and route them consistently.

• Portable & multi-Agent compatible: Memory assets are decoupled from Agent frameworks — they can move across frameworks and be shared and maintained by multiple Agents and team members.

• Cold-start friendly: Import existing documents, codebases, and Agent conversation sessions. New Agent teams can start from existing experience instead of learning from scratch.

🧠 A brain that remembers people and context

• Chat Memory retains preferences, facts, decisions, and interaction history.

• Each Agent automatically gets its own memory when created — no need to re-introduce yourself next time.

• L0 Conversation → L1 Atom → L2 Scenario → L3 Persona — raw conversations are distilled layer by layer.

"Don't refactor the old auth module — mobile is still using it." — Context this costly shouldn't depend on humans repeating it every time.

⚡ A Skill library that accumulates expertise

• After completing complex work, Agents can extract and manage reusable Skills from conversations and tool calls, and import them into the context of a designated Agent when needed.

• A Skill isn't just a prompt snippet; it has versions, resource files, trigger boundaries, execution steps, and validation rules.

• Personal Skills are private by default; after review, they can be shared with the team and assigned to other Agents.

Troubleshooting, code review, release checklists — learn it once, and the whole team can use it.

📖 A knowledge map that reads both docs and code

• Wiki turns product docs, design specs, and ops runbooks into structured pages with a link graph. (Inspired by Karpathy's LLM knowledge base.)

CodeGraph indexes code symbols, files, call relationships, and impact paths.

Agents can search, read, inspect callers/callees, and perform impact analysis before modifying code.

Wiki keeps Agents from reading every file list before getting to work. CodeGraph doesn't just tell them "the code is here" — it tells them "changing this might affect those."

🛡️ A team memory panel controlled by humans

• Create teams and Agents in Memory Hub; review, share, and equip memory assets.

• Manage ownership, versions, status, visibility, usage counts, and Agent bindings in one place.

• private belongs strictly to the Owner; team is visible to all team members; restricted grants precise access via User / Role / Agent ACLs.

• Two role layers: global System Admin manages users and teams (creating teams, adding members) and can also use Wiki, CodeGraph, Skill, and other asset management features; Team-level roles include Admin (team manager) and Member (regular member), responsible for asset collaboration and access control within a team. Asset ownership is tracked via Owner — the Owner automatically has management permissions for their assets.

Cold Start: Load the Save File, Then Get to Work

Most Agents' first task is re-learning your project. TencentDB Agent Memory turns the learning cost you've already paid into a save file:

Specifically, these existing assets can be imported directly and processed automatically in the panel:

• Codebases: Import existing repositories — CodeGraph automatically indexes symbols, files, call relationships, and impact paths.

• Documents & files: Import relevant docs and files — Wiki automatically generates structured pages wit