Telemem

Name: Telemem
Author: TeleAI-UAGI

by TeleAI-UAGIversion 1.10.1i★ –iRuns on your computer

Registry nameio.github.TeleAI-UAGI/telemem

Install

Overview

Description from the maker (original English)

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

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory
You need to fill in: OPENAI_API_KEY

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: OPENAI_API_KEY, TELEMEM_CONFIG, TELEMEM_DEFAULT_USER_ID

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into 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"
      }
    }
  }
}

Run this in your terminal

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

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.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"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

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

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

OPENAI_API_KEY secret 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)

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account TeleAI-UAGI, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 7264 servers in this category
Published to the registry more recently than 72% of the other 7264 servers in this category
Basic checks equal to or better than 100% of the other 7264 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Telemem safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: OPENAI_API_KEY

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.10.1) reached the registry 15 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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

Only the beginning is shown; read the rest in the repo

Telemem

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

Why TeleMem?

📢 Latest Updates

🔥 Research Highlights

📌 Table of Contents

Project Introduction

Features

Applicable Scenarios

TeleMem vs Mem0: Core Advantages

Experimental Results

Dataset

Experimental Configuration