TeleAI-UAGI/telemem

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📄 Awesome-Agent-Memory →
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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📢 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 is on the latest MCP standard: migrated to the official MCP Python SDK v2 (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.**
- **[2026-07-11] 🎉 TeleMem 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!**
- **[2026-06-12] 🎉 TeleMem v1.7.1 is live on the official MCP registry — run the memory server with zero install: uvx telemem! Also new: evaluation principles and a LongMemEval harness with built-in baselines.**
- **[2026-06-12] 🎉 TeleMem is now on PyPI: pip install telemem! v1.6.0 adds Ollama/DeepSeek/Kimi configs, LangChain & LlamaIndex examples, and a documentation site.**
- **[2026-06-12] 🎉 TeleMem v1.5.0 has been released: true mem0 drop-in API, lightweight core install, and CI!**
- **[2026-06-11] 🎉 TeleMem v1.4.0 has been released with MCP support!**
- **[2026-01-28] 🎉 TeleMem v1.3.0 has been released!**
- **[2026-01-22] 🎉 TeleMem Tech Report has been updated to its 4th version!**
- **[2026-01-13] 🎉 TeleMem Tech Report has been released on arXiv!**
- **[2026-01-09] 🎉 TeleMem v1.2.0 has been released!**
- **[2025-12-31] 🎉 TeleMem v1.1.0 has been released!**
- **[2025-12-05] 🎉 TeleMem v1.0.0 has been released!**
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🔥 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