Telemem

Name: Telemem
Author: TeleAI-UAGI

作者：TeleAI-UAGI版本 1.10.1i★ –i在你的电脑上运行

注册名称io.github.TeleAI-UAGI/telemem

安装

概览

作者提供的说明（英文原文）

Long-term and multimodal memory for AI agents - character-aware, mem0-compatible, fully-local option

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

连接方式: 在你的电脑上运行
传输方式: stdio
分类: 搜索与 AI 记忆
你需要填写：OPENAI_API_KEY

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在你的电脑上运行，拥有你的用户账户的权限（文件、网络、应用）。
需要你自己填写： OPENAI_API_KEY, TELEMEM_CONFIG, TELEMEM_DEFAULT_USER_ID

标为机密的值是你账户的钥匙。千万不要分享给别人，也不要粘贴到聊天里。

你需要

PyPI已安装 Python 的 uv（用于 uvx 命令）

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config，把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "telemem": {
      "command": "uvx",
      "args": [
        "telemem"
      ],
      "env": {
        "OPENAI_API_KEY": "your-value",
        "TELEMEM_CONFIG": "your-value",
        "TELEMEM_DEFAULT_USER_ID": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add telemem --env OPENAI_API_KEY=your-value --env TELEMEM_CONFIG=your-value --env TELEMEM_DEFAULT_USER_ID=your-value -- uvx telemem

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "telemem": {
      "command": "uvx",
      "args": [
        "telemem"
      ],
      "env": {
        "OPENAI_API_KEY": "your-value",
        "TELEMEM_CONFIG": "your-value",
        "TELEMEM_DEFAULT_USER_ID": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "telemem": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "telemem"
      ],
      "env": {
        "OPENAI_API_KEY": "your-value",
        "TELEMEM_CONFIG": "your-value",
        "TELEMEM_DEFAULT_USER_ID": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

这个服务器在你的电脑上运行，而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上，无需账户。

需要你填写的值

OPENAI_API_KEY 机密 env
API key for the default OpenAI LLM/embedder (not needed with a local TELEMEM_CONFIG)
TELEMEM_CONFIG env
Path to a TeleMem YAML/JSON config, e.g. the fully-local Ollama + FAISS setup (config/config.ollama.yaml)
TELEMEM_DEFAULT_USER_ID env
Default memory scope when tool calls omit user_id/agent_id/run_id (default: telemem-mcp)

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

它在你的电脑上运行；安装后，工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日

✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 TeleAI-UAGI 验证，该账户拥有此仓库
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
– 不适用
端点有响应
它在你的电脑上运行，没有互联网端点
– 不适用
使用 OAuth 登录
在你的电脑上运行，改用本地设置
… 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行；我们只能读取互联网端点的工具列表

与“搜索与 AI 记忆”中的其他服务器比较i

星标数多于此分类中其他 7266 个服务器的 0%
发布到注册中心的时间比此分类中其他 7266 个服务器的 72% 更新
基础检查结果等于或优于此分类中其他 7266 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

Telemem 安全吗？

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

你需要填写：OPENAI_API_KEY

它在我的电脑上运行还是在云端？

它在你的电脑上运行。你的 AI 应用会在需要时启动它，数据不会经过作者的服务器（除非它自己调用在线服务）。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（1.10.1）于15天前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。

有什么可以替代它？

同一分类中有：Context7, Codebase Memory, Exa

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Context7

作者：upstash

Up-to-date docs for AI agents

电脑或云端★ 64,224MIT昨天更新

安装

Codebase Memory

作者：DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

在你的电脑上运行★ 48,148MIT21天前更新

安装

Exa

作者：exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

在云端运行★ 5,213MIT上个月更新

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

TeleMem: Building Long-Term and Multimodal Memory for Agentic AI

If you find this project helpful, please give us a ⭐️ on GitHub for the latest update.

_🤝 Contributions welcome! Feel free to open an issue or submit a pull request._

English | 简体中文

📄 Awesome-Agent-Memory →

TeleMem is an agent memory management layer that can be used as a high-performance drop-in replacement for [Mem0](https://mem0.ai/) with one line of code (import telemem as mem0) , deeply optimized for complex scenarios involving multi-turn dialogues, character modeling, long-term information storage, and semantic retrieval.

Through its unique context-aware enhancement mechanism, TeleMem provides conversational AI with core infrastructure offering higher accuracy, faster performance, and stronger character memory capabilities.

Building upon this foundation, TeleMem implements video understanding, multimodal reasoning, and visual question answering capabilities. Through a complete pipeline of video frame extraction, caption generation, and vector database construction, AI Agents can effortlessly store, retrieve, and reason over video content just like handling text memories.

The ultimate goal of the TeleMem project is to _use an agent's hindsight to improve its foresight_.

TeleMem, where memory lives on and intelligence grows strong.

Why TeleMem?

🎭 Character memory done right — the only open-source memory layer that automatically builds isolated, per-character memory profiles, built for role-play, companion AI, NPCs, and multi-persona assistants.
🎬 Memory for video, not just text — a full video → frames → captions → vector DB pipeline with ReAct-style multi-step video QA.
🏠 Fully local by default — runs end-to-end on your hardware (Qwen + FAISS); no cloud service, no paid tier, no data leaving your machine.
🔌 mem0-compatible API — add() / search() accept the same arguments and return the same {"results": [...]} shapes, so existing Mem0 code keeps working.

📢 Latest Updates

**[2026-08-15] 🎉 TeleMem v1.10.0 adds first-class DeepSeek Harness support: an opt-in Cordis/MCP patch starts TeleMem with uvx, registers all 8 memory tools under mcp__telemem__*, and securely forwards provider configuration. See the MCP server docs.**
[2026-08-06] 🎉 TeleMem [v1.9.0](https://github.com/TeleAI-UAGI/telemem/releases/tag/v1.9.0) is on the latest MCP standard: migrated to the official [MCP Python SDK v2](https://github.com/modelcontextprotocol/python-sdk) (spec 2026-07-28) — all 8 tools now declare titles, behavior annotations, and structured output, while staying compatible with older MCP clients. See the [MCP server docs](docs/MCP.md).
[2026-07-11] 🎉 TeleMem [v1.8.0](https://github.com/TeleAI-UAGI/telemem/releases/tag/v1.8.0) — the "claims = contracts" release: character-memory extraction fix, `infer=False`/`prompt`/`memory_type` now fully honored, offline contract test suite, telemetry disabled by default, and a [multi-NPC demo](examples/multi_npc.py)!
[2026-06-12] 🎉 TeleMem [v1.7.1](https://github.com/TeleAI-UAGI/telemem/releases/tag/v1.7.1) is live on the [official MCP registry](https://registry.modelcontextprotocol.io) — run the memory server with zero install: `uvx telemem`! Also new: [evaluation principles](https://teleai-uagi.github.io/telemem/evaluation/) and a LongMemEval harness with built-in baselines.
[2026-06-12] 🎉 TeleMem is now on PyPI: `pip install telemem`! [v1.6.0](https://github.com/TeleAI-UAGI/telemem/releases/tag/v1.6.0) adds Ollama/DeepSeek/Kimi configs, LangChain & LlamaIndex examples, and a [documentation site](https://teleai-uagi.github.io/telemem/).
[2026-06-12] 🎉 TeleMem [v1.5.0](https://github.com/TeleAI-UAGI/telemem/releases/tag/v1.5.0) has been released: true mem0 drop-in API, lightweight core install, and CI!
[2026-06-11] 🎉 TeleMem [v1.4.0](https://github.com/TeleAI-UAGI/telemem/releases/tag/v1.4.0) has been released with [MCP support](docs/MCP.md)!
[2026-01-28] 🎉 TeleMem [v1.3.0](https://github.com/TeleAI-UAGI/telemem/releases/tag/v1.3.0) has been released!
[2026-01-22] 🎉 TeleMem [Tech Report](https://arxiv.org/abs/2601.06037) has been updated to its 4th version!
[2026-01-13] 🎉 TeleMem [Tech Report](https://arxiv.org/abs/2601.06037) has been released on arXiv!
[2026-01-09] 🎉 TeleMem [v1.2.0](https://github.com/TeleAI-UAGI/telemem/releases/tag/v1.2.0) has been released!
[2025-12-31] 🎉 TeleMem [v1.1.0](https://github.com/TeleAI-UAGI/telemem/releases/tag/v1.1.0) has been released!
[2025-12-05] 🎉 TeleMem [v1.0.0](https://github.com/TeleAI-UAGI/telemem/releases/tag/v1.0.0) has been released!

🔥 Research Highlights

Significantly improved memory accuracy: Achieved 86.33% accuracy on the ZH-4O Chinese multi-character long-dialogue benchmark, 19% higher than Mem0.
Doubled speed performance: Millisecond-level semantic retrieval enabled by efficient buffering and batch writing.
Greatly reduced token cost: Optimized token usage delivers the same performance with significantly lower LLM overhead.
Precise character memory preservation: Automatically builds independent memory profiles for each character, eliminating confusion.
Automated Video Processing Pipeline: From raw video → frame extraction → caption generation → vector database, fully automated
ReAct-Style Video QA: Multi-step reasoning + tool calling for precise video content understanding

📌 Table of Contents

Project Introduction
TeleMem vs Mem0: Core Advantages
Experimental Results
Quick Start
Project Structure
Core Functions
Multimodal Extensions
MCP Server
Framework Integrations
Data Storage Explanation
Telemetry & Privacy
Development and Contribution
Acknowledgements
Citation

Project Introduction

TeleMem enables conversational AI to maintain stable, natural, and continuous worldviews and character settings during long-term interactions through a deeply optimized pipeline of character-aware summarization → semantic clustering deduplication → efficient storage → precise retrieval.

flowchart LR

A["Dialogue messages"] --> B["Character-aware summarization (global + per-character)"] B --> C["Embedding + similar-memory retrieval"] C --> D["Write buffer (batch flush)"] D --> E["LLM semantic clustering & fusion"] E --> F[("FAISS index + JSON metadata")] Q["Query"] --> S["Vector search

rerank"] F --> S S --> R["results"]

Features

Automatic memory extraction: Extracts and structures key facts from dialogues.
Semantic clustering & deduplication: Uses LLMs to semantically merge similar memories, reducing conflicts and improving consistency.
Character-profiled memory management: Builds independent memory archives for each character in a dialogue, ensuring precise isolation and personalized management.
Efficient asynchronous writing: Employs a buffer + batch-flush mechanism for high-performance, stable persistence.
Precise semantic retrieval: Combines FAISS + JSON dual storage for fast recall and human-readable auditability.

Applicable Scenarios

Multi-character virtual agent systems
Long-memory AI assistants (e.g., customer service, companionship, creative co-pilots)
Complex narrative/world-building in virtual environments
Dialogue scenarios with strong contextual dependencies
Video content QA and reasoning
Multimodal agent memory management
Long video understanding and information retrieval

TeleMem vs Mem0: Core Advantages

TeleMem deeply refactors Mem0 to address characterization, long-term memory, and high performance. Key differences:

Capability Dimension	Mem0	TeleMem
Multi-character separation	❌ Not supported	✅ Automatically creates independent memory profiles per character
Summary quality	Basic summarization	✅ Context-aware + character-focused prompts covering key entities, actions, and timestamps
Deduplication mechanism	Vector similarity filtering	✅ LLM-based semantic clustering: merges similar memories via LLM
Write performance	Streaming, single writes	✅ Batch flush + concurrency: 2–3× faster writes
Storage format	SQLite / vector DB	✅ FAISS + JSON metadata dual-write: fast retrieval + human-readable
Multimodal Capability	Single image to text only	✅ Video Multimodal Memory: Full video processing pipeline + ReAct multi-step reasoning QA

Experimental Results

Dataset

We evaluate the ZH-4O Chinese long-character dialogue dataset constructed in the paper MOOM: Maintenance, Organization and Optimization of Memory in Ultra-Long Role-Playing Dialogues:

Average dialogue length: 600 turns per conversation
Scenarios: daily interactions, plot progression, evolving character relationships

Memory capability was assessed via QA benchmarks, e.g.:

"question": "What is Zhao Qi's nickname for Bai Yulan? A Xiaobai B Xiaoyu C Lanlan D Yuyu", "answer": "A" }, { "question": "What is the relationship between Zhao Qi and Bai Yulan? A Classmates B Teacher and student C Enemies D Neighbors", "answer": "B" }

Experimental Configuration

LLM: Qwen3-8B (thinking mode disabled)
Embedding model: [Qwen3-Embedding-8B](https://huggingface.co/Qwen/Qwen3-E

只显示了开头部分；其余内容请在仓库中阅读