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Telemem
TeleMem is a high-performance drop-in replacement for Mem0, featuring semantic deduplication, long-term dialogue memory, and multimodal video reasoning.
Install
$
uvx telememClaude Code
claude mcp add telemem -- uvx telememCursor / Claude Desktop (mcp.json)
{
"mcpServers": {
"telemem": {
"command": "uvx",
"args": [
"telemem"
]
}
}
}Install command taken from the project's README or registry entry. Always review code before running it.
README excerpt
Read full README on GitHub →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
<strong>📄 Awesome-Agent-Memory →</strong>
TeleMem is an agent memory management layer that can be used as a high-performance drop-in replacement for Mem0 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.
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