# telemem [Health: Active]

**Category:** 🧠 Knowledge & Memory  
**Repository:** https://github.com/TeleAI-UAGI/telemem  
**GitHub Stars:** 493  
**Views:** 3  
**Installs:** 0  
**Upvotes:** 0  
**Directory Page:** https://allmcps.com/mcp/telemem

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

## Tools
Capabilities this server exposes over MCP:

- **add_memory** — Store a fact, preference, or conversation in TeleMem long-term memory. Provide `text` for a single statement or `messages` for conversation turns. Scoped by user_id/agent_id/run_id; defaults to the server's default user.
- **search_memories** — Semantic search over TeleMem memories. Returns the matching memories as one consolidated text passage (TeleMem fuses related memories). Shared event memories (pseudo-user "events") are searched automatically.
- **get_memories** — List stored memories for a user/agent/run with their memory_ids. Use user_id "events" to list shared conversation-event memories.
- **get_memory** — Fetch a single memory by its memory_id.
- **update_memory** — Overwrite the text of an existing memory by memory_id.
- **delete_memory** — Delete a single memory by memory_id.
- **delete_all_memories** — Delete every memory in the given scope. Destructive: requires an explicit user_id, agent_id, or run_id — the default user is never assumed.
- **memory_history** — Show the change history (ADD/UPDATE/DELETE events) of a memory.

## Claude Desktop Quick Installation
Install path detected from listing signals. Uses `uvx` (confidence: high):

```json
"mcpServers": {
  "telemem": {
    "command": "uvx",
    "args": ["telemem"]
  }
}
```

## Documentation

## What telemem does

TeleMem provides persistent memory for agents operating across multiple conversations or runs. The MCP interface accepts either a single statement or a sequence of conversation messages, then stores the resulting memory in a selected scope. Scopes can identify a user, agent, or run; when no user is supplied for ordinary operations, the server uses its configured default user.

Semantic retrieval is handled by `search_memories`. Results are returned as one consolidated passage rather than as unrelated individual matches, because TeleMem fuses related memories. Shared conversation-event memories use the reserved pseudo-user `events` and are included automatically in searches. The underlying project also targets character-aware memory, multimodal reasoning, and video processing, alongside its text-memory workflow.

## How it works

TeleMem's memory pipeline extracts important information from dialogue, groups semantically similar content, removes or merges duplicates, and stores searchable data in a local FAISS index with JSON metadata. Retrieval combines vector search with reranking. A write buffer supports batched persistence, while an LLM-based clustering and fusion stage helps consolidate related memories.

The MCP layer exposes the memory lifecycle directly. Agents can add or search content, enumerate memories and their identifiers, fetch one memory, replace its text, remove one memory, clear a specifically named scope, or inspect change events. History records ADD, UPDATE, and DELETE operations for an individual memory.

## Setup and configuration

The package is available through PyPI and can be installed with `pip install telemem`. The MCP registry also supports starting it without a separate installation by running `uvx telemem`. TeleMem is designed to run locally by default, using Qwen and FAISS rather than sending memory data to a cloud service.

The project provides a Mem0-compatible API, including matching `add()` and `search()` argument and result shapes, so applications using that interface can use TeleMem as a replacement outside the MCP tool layer. The repository also includes configuration options for Ollama, DeepSeek, and Kimi, as well as examples for LangChain and LlamaIndex.

## Tools and capabilities

- `add_memory` stores a fact, preference, or conversation using `text` or `messages`.
- `search_memories` performs semantic search and returns a consolidated text passage.
- `get_memories` lists memories and their IDs for a user, agent, or run; `events` lists shared event memories.
- `get_memory` retrieves one memory by ID.
- `update_memory` replaces the text associated with an existing ID.
- `delete_memory` removes one memory.
- `delete_all_memories` clears a scope only when an explicit user, agent, or run identifier is supplied.
- `memory_history` reports a memory's add, update, and delete events.

## Limitations and notes

Deleting all memories is intentionally stricter than ordinary operations: the tool requires an explicit scope and never assumes the default user. The local deployment model keeps data on the host, but the memory pipeline can use language models for extraction and semantic clustering according to the selected configuration. Video and multimodal processing are project capabilities, while the listed MCP interface centers on memory records and conversation content.

_Full upstream README: https://allmcps.com/mcp/telemem/readme_