Team-Level Memory for AI Agents: Architecture Patterns

Multi-agent systems are everywhere in 2026 — research agents, coding agents, review agents, each with a role. But there's a gap most architectures don't address: how do agents share what they've learned?

Without shared memory, every agent starts from scratch. The scout agent does market research; the builder agent doesn't benefit. The reviewer finds a bug; next sprint, a different agent hits the same bug.

Team-level memory closes that gap.

What Team-Level Memory Means

It's not a shared chat log. It's a governed asset library where:

Architecture: The Memory Hub Pattern

TencentDB Agent Memory implements team-level memory through a central Memory Hub:

┌─────────────────────────────────────────────┐
│                Memory Hub                    │
│                                               │
│  ┌──────────┐ ┌──────┐ ┌─────┐ ┌──────────┐  │
│  │Chat Mem  │ │Skill │ │Wiki │ │CodeGraph │  │
│  └──────────┘ └──────┘ └─────┘ └──────────┘  │
│                                               │
│  Permissions · Versions · Ownership · ACL    │
└──────────┬──────────┬──────────┬─────────────┘
           │          │          │
      ┌────┴───┐ ┌────┴───┐ ┌────┴───┐
      │ Scout  │ │Builder │ │Reviewer│
      │ Agent  │ │ Agent  │ │ Agent  │
      └────────┘ └────────┘ └────────┘

Key design decisions:

1. Framework-agnostic

Agents connect via a Proxy — no plugin, hook, or MCP server. Claude Code, Codex, DeepSeek Harness, CodeBuddy all share the same memory.

2. Cold-start friendly

Import existing codebases (CodeGraph auto-indexes), documents (Wiki auto-structures), and past conversations (Skills and Chat Memory auto-extract).

3. Visibility by design

• private — only the Owner can read; even team admins cannot access
• team — all team members can read; Owner/Admin can manage
• restricted — precise access via User/Role/Agent ACL
• agent — targeted equipping of specific agents within the team

Agent Loadout: Different Roles, Different Memory

Scout Agent
   ├── User interview Chat Memory
   ├── Market research Wiki
   └── Competitive analysis Skill

Builder Agent
   ├── Product Wiki
   ├── Project CodeGraph
   └── Feature Delivery Skill

Reviewer Agent
   ├── Historical incident Chat Memory
   ├── Project CodeGraph
   └── Release Checklist Skill

Each agent gets only the memory assets it needs — less noise, better focus.

The idea: you're not opening four disconnected chat windows. You're assembling a squad with different roles that inherits the team's accumulated experience.

The Four-Layer Memory Architecture

Memory isn't flat records — it grows in layers:

Retrieval: L2/L3 provides fast context guidance; when specifics are needed, BM25 + vector search + RRF falls back to L1/L0. Results are constrained by entry count, character budget, and timeout.

Benchmark

On PersonaMem (tests whether agents correctly understand and apply user information after long interactions):

Supported Agents

DeepSeek Harness · Claude Code · Codex · CodeBuddy · WorkBuddy · Hermes · OpenClaw

One Proxy, unchanged protocol, zero-code integration.

Getting Started

MIT licensed.

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
./start-all.sh     # Launch everything with one command

Panel: http://localhost:8125

Repo: https://github.com/TencentCloud/TencentDB-Agent-Memory