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Tencent AI (@TencentAI_News) released Team Memory v2.0.0 beta (announcement…

Brief

Tencent AI (@TencentAI_News) announced Team Memory, an open‑source agent memory system with a 2.0.0 beta (repo trending #1 on GitHub TypeScript). The system centralizes memory for solo builders and teams into Chat Memory, Skill, LLM‑Wiki, and Code‑Graph, and the team reports concrete gains—61% token savings, better 30+ step workflow stability, and persona coherence rising from 48% to 76%—with an AMA and repo links available.

Why it matters

Tencent AI (@TencentAI_News) released Team Memory v2.0.0 beta (announcement posted 2026-08-06) and said the repo hit #1 on GitHub's TypeScript trending list that week.

Key details

  • Team Memory provides a shared memory hub for teams and solo builders, turning conversations, docs and code into four reusable assets—Chat Memory, Skill, LLM‑Wiki, and Code‑Graph—governed and shared across agents and frameworks.
  • After six months of development, their benchmarks showed: compressing stale context mid‑session cut token usage by 61%; a mermaid‑based structured task map markedly reduced failures in 30+ step workflows; persona coherence improved from 48% to 76% with dedicated persona memory.
Source evidence

🤯Introducing Team Memory, same idea as Agent Memory, except your teammates' agents can read it too

2.0.0 beta out today, and the repo hit #1 on github's typescript trending this week

Highlights:
> Solo builders: one place to manage memory across all your agents and AI tools, chat, code, tasks. Built for the one-person company

> Teams: a shared memory hub that turns conversations, docs and code into four reusable assets, Chat Memory, Skill, LLM-Wiki, Code-Graph, governed and shared across agents and frameworks

changelog and repo → github.com/TencentCloud/Tenc…

Tencent AI (@TencentAI_News)

We spent 6 months on one problem: agents losing context in long sessions.

Ended up building and open-sourcing an agent memory system. A few things we learned:

🪄compressing stale context mid-session cut token usage by 61%
🪄giving agents a structured task map (mermaid-based) made them way less likely to lose track in 30+ step workflows
🪄persona coherence jumped from 48% to 76% once we added dedicated persona memory

repo 👉 github.com/Tencent/TencentDB…

Agent memory is genuinely hard and we don't have all the answers. Happy to dig into architecture, benchmarks, tradeoffs, whatever. AMA👇 @TencentDBAbxo2 team is here to talk about it.

— https://nitter.net/TencentAI_News/status/2054822609863496178#m