Rewind.tg - Local-AI Telegram Agent Search

A multi-agent system that lets a local LLM running on Ollama search, explore, and reason over your Telegram: your own chats, groups, channels, the public channel directory, and arbitrary links inside messages. No cloud, no vendor, no content filtering.

Designed around an agent harness (ReAct + JSON tool-calling) plus a multi-agent orchestrator that can spawn focused sub-agents (dialog_search, channel_explorer, link_crawler, generic). An optional MCP server exposes every tool to Cursor / VS Code Copilot.

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Features

• Uses MTProto (Telethon): the agent has the same reach as your own Telegram app: DMs, groups, joined channels, public channel directory, joining new channels, and resolving / following t.me/... links.
• Multi-agent: orchestrator plans, dispatches spawn_subagent calls to specialists, then synthesises the result.
• Hybrid local index: SQLite FTS5 BM25 + Ollama embeddings (qwen3-embedding:0.6b by default) with cosine similarity, fused with min-max normalised scores.
• Web tool: http_fetch for non-Telegram links found in messages, routed through your local proxy.
• Optional vision: qwen3.5 already has built-in vision, so by default VISION_MODEL points at the same qwen3.5 model and the agent can call tg_describe_media on photos/videos it finds.
• MCP server: expose the whole tool belt to Cursor / Copilot Chat.

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Requirements

• Windows / Linux / macOS
• Python >= 3.10 (conda / venv / pixi all work)
• Ollama running locally at 127.0.0.1:11434
• A Telegram account + API id/hash from https://my.telegram.org
• Local proxy at 127.0.0.1:7890 (Clash-style HTTP proxy)

Recommended Models

Qwen3.5 (released April 2026) has tool-calling, vision, and thinking mode in one model, which is exactly what this system needs. The default setup uses the Qwen3.5 family throughout and does not require a separate vision model.

Role	Pick	~VRAM (Q4)
Default / low VRAM	qwen3.5:0.8b	~1 GB
Balanced / 8GB GPU	qwen3.5:4b	~3 GB
Best for 3060 12GB	qwen3.5:9b	~5-6 GB
Embeddings	qwen3-embedding:0.6b or bge-m3	tiny
Heavy-duty (24GB+)	qwen3.6:35b or qwen3:30b	~15-20 GB

Pulls:

ollama pull qwen3.5:0.8b
ollama pull qwen3-embedding:0.6b

If you want image understanding, point VISION_MODEL=qwen3.5:0.8b in .env. That reuses the same weights with zero extra VRAM.

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Install

conda activate <your-env>   # or activate your venv
cd /path/to/Rewind.tg
pip install -r requirements.txt
copy .env.example .env
notepad .env   # fill TG_API_ID / TG_API_HASH / TG_PHONE, tweak models

First login (interactive; saves data/tg.session):

python -m tg_search.telegram_client

Enter the SMS / app code, and your 2FA password if enabled. The session is reused after that.

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Usage

One-Shot

python cli.py "find every message mentioning 'stable diffusion LoRA' across all my chats, summarise the top 10 with links"

REPL

python cli.py --repl

Force A Specific Specialist Role

python cli.py --role channel_explorer "discover high-quality public channels about local LLM fine-tuning and index their last 500 msgs"

Bulk-Download History To The Local Index

# all my dialogs, 1000 msgs each
python -m scripts.download_history --all --limit 1000

# just one channel, 5000 msgs
python -m scripts.download_history --dialog @somechannel --limit 5000

Once indexed, the agent will naturally reach for idx_semantic_search for fuzzy queries such as Chinese -> English matching and paraphrases.

MCP Server (Cursor / VS Code Copilot)

Add to your MCP client config, for example ~/.cursor/mcp.json:

{
  "mcpServers": {
    "tg-agent": {
      "command": "python",
      "args": ["-m", "mcp_server.server"],
      "cwd": "/absolute/path/to/Rewind.tg",
      "env": { "PYTHONUNBUFFERED": "1" }
    }
  }
}

For VS Code Copilot Chat, add the same under the github.copilot.chat.mcpServers setting. Cursor / Copilot will then have tg_search_global, tg_join_channel, idx_semantic_search, tg_describe_media, spawn_subagent, and more.

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Architecture

user query
  -> Orchestrator agent (ReAct loop, tool-calls)
  -> spawn_subagent(role=...)
     -> dialog_search
     -> channel_explorer
     -> link_crawler
  -> shared tool belt
     -> Telethon (MTProto, proxy)
     -> Ollama (local LLM / vision / embeddings)
     -> SQLite hybrid index (BM25 + cosine)
  -> final answer

Tool Belt (Agents + MCP)

Tool	Purpose
tg_list_dialogs	list user's chats / groups / channels / bots
tg_search_global	global search across all user's chats
tg_search_in_dialog	search inside one dialog
tg_message_context	expand around a hit
tg_search_public_channels	public directory search
tg_join_channel	join public / invite-only channels
tg_resolve_link	inspect a t.me/... link without joining
tg_index_channel	download channel history into local index
idx_semantic_search	BM25 + embedding hybrid search
http_fetch	fetch web URLs via proxy, cleaned HTML
tg_describe_media	download + describe media with the vision model
spawn_subagent	recursive delegation to a focused sub-agent

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Safety / Data Policy

All data stays on your machine (SQLite file + Telethon session). The agent performs no content filtering: your Telegram, your rules. Inbound prompt injection from message content is possible in theory, so do not run this against an account whose tool belt you would not trust to run locally.

Your api_hash is a secret, so keep it in .env (git-ignored). If it ever leaks, rotate it at https://my.telegram.org. Never commit .env or data/ (session file + local index + downloaded media).