# TencentDB Agent Memory: A Team-Level Memory Hub, Not Just Per-Agent Recall

> Source: <https://dibi8.com/resources/llm-frameworks/tencentdb-agent-memory-team-memory-hub-2026/>
> Published: 2026-08-02 13:45:00+00:00

# TencentDB Agent Memory: A Team-Level Memory Hub, Not Just Per-Agent Recall

TencentDB Agent Memory is Tencent's MIT-licensed memory hub for AI agent teams, turning conversations, docs, and code into four governed, shareable memory assets — Chat Memory, Skill, Wiki, and CodeGraph — with a layered L0-L3 distillation pipeline and a reported +59% PersonaMem score.

- ⭐ 10663
- Node.js
- MIT
- Updated 2026-08-02

[AI Agent Memory: Letta vs Mem0 vs A-Mem](https://dibi8.com/resources/llm-frameworks/ai-agent-memory-persistence-letta-mem0-a-mem-2026/) •
[Hermes Agent: Self-Improving AI Agent](https://dibi8.com/resources/llm-frameworks/hermes-agent-self-improving-ai-agent/)

*Project banner — from github.com/TencentCloud/TencentDB-Agent-Memory*

## What Is TencentDB Agent Memory? [#](#what-is-tencentdb-agent-memory)

**TencentDB Agent Memory** starts from a specific question the maintainers state directly: *“How do you reduce repetitive work when using Agents?”* If project context has already been explained once, a new session shouldn’t need it re-explained. If a document’s already been read, the next agent shouldn’t start from page one. TencentDB Agent Memory’s answer is a **Memory Hub** that extracts, governs, and routes four types of reusable memory assets across a *team* of agents — not just a single agent’s own conversation history.

🔗 **GitHub**: [https://github.com/TencentCloud/TencentDB-Agent-Memory](https://github.com/TencentCloud/TencentDB-Agent-Memory)

MIT licensed, at **10,600+ GitHub stars**, with a commit from **July 29, 2026**, it’s a Tencent Cloud project still explicitly labeled “Team Memory Beta” — evolving quickly rather than a finished, stable product.

## Four Memory Assets, Not One Chat Log [#](#four-memory-assets-not-one-chat-log)

| Chat History | Standard RAG | TencentDB Agent Memory | |
|---|---|---|---|
| Cross-session user understanding | Partial | Partial | Chat Memory |
| Distilled executable experience | No | No | Skill |
| Document structure & relationships | No | Chunk retrieval only | Wiki + Link Graph |
| Code call graphs & impact scope | No | Text match only | CodeGraph |
| Ownership / Version / Status | No | No | Yes |
| Team sharing & Agent loadout | No | No | Yes |
| Private / Team / ACL | No | Partial | Yes |

The framing the maintainers draw: RAG answers “what can be found?” — TencentDB Agent Memory also answers **“who can use it, which version is valid, and which Agent should receive it.”**

### The four asset types [#](#the-four-asset-types)

**🧠 Chat Memory**— preferences, facts, decisions, and interaction history. Each agent gets its own automatically on creation. Distilled layer by layer: L0 Conversation → L1 Atom → L2 Scenario → L3 Persona.**⚡ Skill**— a reusable procedure extracted from completed work, with versions, resource files, trigger boundaries, execution steps, and validation rules — private by default, shareable with the team after review.**📖 Wiki**— documents, specs, and runbooks turned into structured, link-graphed pages, explicitly inspired by[Andrej Karpathy’s “LLM Wiki” concept](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f).**🕸️ CodeGraph**— indexes code symbols, files, call relationships, and impact paths, so an agent can check callers/callees and impact scope before modifying code.

## Installation [#](#installation)

```
git clone https://github.com/TencentCloud/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     # Launches memory-core + memory-hub + proxy in one command
```

`start-all.sh`

starts all three services (`memory-core`

, `memory-hub`

, `proxy`

) together and prints a one-liner you can paste directly into Claude when it finishes. The panel is then reachable at `http://localhost:8125`

.

Migrating from an older v1.x/v0.x install has a dedicated tool (v2 → v3); new installs can skip it.

## Cold Start: Import What You Already Have [#](#cold-start-import-what-you-already-have)

Rather than starting a new agent team from zero, existing assets can be imported directly:

*Cold-start import flow — from github.com/TencentCloud/TencentDB-Agent-Memory*

**Codebases**→ CodeGraph automatically indexes symbols, files, call relationships, and impact paths** Documents & files**→ Wiki automatically generates structured, link-graphed pages** Conversation sessions**→ Skills and Chat Memory are automatically extracted as reusable assets

## Team Play: Building an Agent Team, Not Four Chat Windows [#](#team-play-building-an-agent-team-not-four-chat-windows)

The README’s own worked example is a “one-person company” with role-specific agents:

```
Tiny but Serious Inc.
├── You      · Set goals / Make decisions
├── Scout    · Research / Find opportunities
├── Builder  · Write code / Build products
├── Reviewer · Test / Find issues
└── Agent Memory · Preserve the team's experience
```

Each role gets a different **loadout** of memory assets — not everything, just what that role needs:

```
Scout:     User-interview Chat Memory, Market-research Wiki, Competitive-analysis Skill
Builder:   Product Wiki, Project CodeGraph, Feature-Delivery Skill
Reviewer:  Historical-incident Chat Memory, Project CodeGraph, Release-Checklist Skill
```

The pitch: you’re not opening four disconnected chat windows, you’re assembling a squad that inherits the team’s accumulated experience — and a small team’s experience can keep compounding rather than resetting with every new session.

## Governance: Private by Default, Sharing Is Explicit [#](#governance-private-by-default-sharing-is-explicit)

| Visibility | Semantics |
|---|---|
`private` | Only the Owner can read — not even team admins |
`team` | Team members can read; Owner/Admin can manage |
`restricted` | Precise access via User / Role / Agent ACL |
`agent` | Targeted equipping of specific agents on the same team |

New Chat Memory and Skills are private by default — sharing is an explicit action, not a default leak. This matters once a “memory hub” is holding real decisions and preferences: you can assign a Release Skill only to the Release Agent, an Architecture Wiki to all development agents, and CodeGraph specifically to Coder and Reviewer agents.

## Technical Implementation [#](#technical-implementation)

*Technical architecture overview — from github.com/TencentCloud/TencentDB-Agent-Memory*

The stated design goal isn’t “store everything” — it’s **what’s worth keeping, who can use it, and how to retrieve less while retrieving the right thing.**

### Layered memory, not flat records [#](#layered-memory-not-flat-records)

| Layer | What it stores | Primary use |
|---|---|---|
| L0 Conversation | Raw conversations, full context | Verify exact wording, timestamps, sources |
| L1 Atom | Extracted facts, preferences, constraints, events | Precise recall of actionable information |
| L2 Scenario | Knowledge blocks organized by project/scenario | Quickly restore a working context |
| L3 Core / Persona | Long-term profiles, stable patterns | Rapid entry into a user’s/team’s context |

Retrieval is layered too: L2/L3 provide a fast context bootstrap by default; when specific facts are needed, it falls back to BM25 + vector retrieval + RRF (Reciprocal Rank Fusion) across L1/L0, with results capped by item count, character budget, and timeout to keep memory from overwhelming the context window.

### Memory as loadout, not global prompt injection [#](#memory-as-loadout-not-global-prompt-injection)

Chat Memory, Skill, Wiki, and CodeGraph are all registered uniformly as **Memory Assets**, and access is resolved via **Fixed Binding + ACL** — narrowing by Team, User, Agent, and visibility first, then retrieving based on the current query. Switching an agent or framework means re-equipping assets, not retraining.

### Tool-based access, not wholesale injection [#](#tool-based-access-not-wholesale-injection)

Agents discover capabilities via `/v3/tools/list`

, then call `/v3/tools/call`

to read specific Wiki pages, source code, or impact paths — documents and code are part of memory, but they stay as on-demand tools rather than being dumped into context wholesale.

## Benchmark [#](#benchmark)

| Benchmark | Without | With | Relative improvement |
|---|---|---|---|
PersonaMem | 48% | 76% | +59% |

PersonaMem tests whether an agent correctly understands and applies user information after extended interactions. **This is a single benchmark reported by the maintainers**, not an independently reproduced result — a useful signal, not a guarantee it generalizes to your own workload.

## Limitations (From the Project’s Own Notes) [#](#limitations-from-the-projects-own-notes)

**Async processing delay**— Wiki and CodeGraph build asynchronously; allow time before they reach`ready`

status**CodeGraph is public-repo-first**— private repositories and SSH credentials are “still being refined,” not fully supported yet** Manual asset binding**— the Hub supports manual binding today; fully automated memory routing is still under iteration** Limited framework support today**— OpenClaw, Hermes Agent, and SDK integration are supported now; broader cross-framework migration is on the roadmap, not shipped

## Use Cases [#](#use-cases)

### 1. Onboarding a New Agent (or Teammate) Without Re-Explaining Everything [#](#1-onboarding-a-new-agent-or-teammate-without-re-explaining-everything)

Import existing docs, codebase, and past agent conversation sessions once — new team members and new agents both start from the “save file” instead of relearning the project from scratch.

### 2. Role-Scoped Agent Teams [#](#2-role-scoped-agent-teams)

Give a Reviewer agent CodeGraph and historical-incident Chat Memory, but not the Scout’s market-research Wiki — the loadout model keeps each agent’s context relevant instead of dumping everything into every agent.

### 3. Governed Knowledge Sharing Across a Team [#](#3-governed-knowledge-sharing-across-a-team)

`private`

/`team`

/`restricted`

/`agent`

visibility lets an individual’s working notes stay private by default while explicitly promoting genuinely reusable Skills and Wiki pages to the team.

### 4. Pre-Change Impact Analysis [#](#4-pre-change-impact-analysis)

CodeGraph’s call-relationship and impact-path indexing lets an agent check what else might break before modifying shared code — closer to what a careful human reviewer would do than a plain text-match RAG lookup.

## Related Repositories [#](#related-repositories)

| Repository | Purpose |
|---|---|
|

[CodeGraph (colbymchenry)](https://github.com/colbymchenry/codegraph)## Related Articles [#](#related-articles)

[AI Agent Memory: Letta vs Mem0 vs A-Mem](https://dibi8.com/resources/llm-frameworks/ai-agent-memory-persistence-letta-mem0-a-mem-2026/)— for comparing single-agent memory frameworks against this team-level hub approach[Hermes Agent: Self-Improving AI Agent](https://dibi8.com/resources/llm-frameworks/hermes-agent-self-improving-ai-agent/)— one of the frameworks TencentDB Agent Memory integrates with directly

## Conclusion [#](#conclusion)

**TencentDB Agent Memory** targets a problem most agent-memory tools don’t: not “how does one agent remember one user,” but “how does a *team* of agents share governed, versioned experience without leaking everything to everyone.” The L0-L3 layered distillation, the four distinct asset types (especially Skill and CodeGraph, which go beyond what chat-log RAG models), and the explicit-sharing-by-default governance make it a more structured answer than most single-agent memory libraries — at the cost of currently narrower framework support (OpenClaw, Hermes, SDK) and features still labeled beta or roadmap.

**Best for**: Teams running multiple agents (or agent + human teams) who need governed, shareable memory — not solo users who just want one agent to remember one conversation history.

**GitHub**: [https://github.com/TencentCloud/TencentDB-Agent-Memory](https://github.com/TencentCloud/TencentDB-Agent-Memory)

*Last updated: 2026-08-02*