TeleAI-UAGI/telemem
TeleMem is a high-performance drop-in replacement for Mem0, featuring semantic deduplication, long-term dialogue memory, and multimodal video reasoning.
478 ★35 forksPythonUpdated 5d ago
What you need to know
TeleMem — an agent memory management layer and high-performance drop-in replacement for Mem0 (one line: import telemem as mem0), with per-character memory profiles, long-term semantic retrieval, and a video/multimodal memory pipeline.
Install
pip install telemem (core text memory) pip install "telemem[mcp]" (adds MCP server) pip install "telemem[video]" (adds video/multimodal pipeline) pip install "telemem[all]" (everything) uvx telemem (zero-install MCP server run over stdio)
Usage
- •memory = mem0.Memory(); memory.add(messages=messages, user_id="Jordan"); memory.search("...", user_id="Jordan") — same result shape as mem0
- •MCP server: telemem-mcp (stdio default) or telemem-mcp --transport streamable-http (port 8421); configure via TELEMEM_CONFIG and OPENAI_API_KEY
- •Multimodal: add_mm(video_path, output_dir) then search_mm(question, output_dir) with ReAct-style video QA
- •Works with any OpenAI-compatible endpoint; ready configs for Ollama (fully local), DeepSeek, Kimi (Moonshot), and MiniMax
Key features
- ✓mem0-compatible API — add()/search() accept the same arguments and return {"results": [...]} shapes
- ✓Automatically builds isolated per-character memory profiles (role-play, companion AI, NPCs, multi-persona assistants)
- ✓LLM-based semantic clustering and deduplication merging similar memories; context-aware, character-focused summarization
- ✓FAISS + JSON dual storage for fast retrieval and human-readable auditability; buffer + batch-flush async writes (2–3x faster than streaming writes)
- ✓Video memory pipeline — frame extraction → caption generation → vector DB, with ReAct-style multi-step video QA (global_browse_tool / clip_search_tool / frame_inspect_tool)
- ✓MCP server (official SDK v2, spec 2026-07-28) exposing 8 tools: add_memory, search_memories, get_memories, get_memory, update_memory, delete_memory, delete_all_memories, memory_history
Best for
Building long-term, per-character memory for role-play/companion AI, NPCs, and multimodal agents that need video understanding — fully local if desired.
Caveats
- ⚠Requires an LLM and embedding provider (OpenAI API key, or local Qwen + FAISS via Ollama config); video pipeline needs a configured VLM (e.g. Qwen3-Omni) and embedding service
- ⚠Underlying mem0ai library ships PostHog telemetry, which TeleMem disables by default (MEM0_TELEMETRY=False)
- ⚠Destructive bulk deletion (delete_all_memories) always requires an explicit scope
- ⚠Backed by an arXiv tech report; research-oriented project
Clients: Claude Desktop · Claude Code · Cursor
Documentation ↗Reviewed 2026-08-07
Topics
agentagent-memoryai-agentscharacter-memoryconversational-aifaissllmlong-term-memorymcpmem0memorymodel-context-protocolmultimodalragsemantic-searchvideo-understanding
- Stars
- 478★
- Forks
- 35
- Language
- Python
- License
- Apache-2.0
- Created
- 2025-12-05
- Last push
- 2026-08-01