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mono-agent

Personal Telegram Assistant with BuJo Memory

This playbook wires a private Telegram bot to mono-agent’s BuJo tiered memory so it captures every conversation turn, consolidates the store automatically, and recalls past notes semantically. It is a complete config-first recipe: pull the local models, init, fill in the Telegram and memory sections, validate, and start.

Who this is for

Section titled “Who this is for”

Individual power users who want a private assistant that remembers — a personal Telegram bot scoped to your own chat that builds up durable memory across days rather than starting fresh every conversation.

Goal

Section titled “Goal”

A Telegram bot that answers via long-polling, captures every turn into BuJo memory with scheduled consolidation, and recalls past notes semantically.

Features used

Section titled “Features used”

Configuration

Section titled “Configuration”

The bujo tier requires both an embeddings provider (for semantic recall) and an app-level memory.llm (for capture and effective tier selection for scheduled consolidation). Guided init lets you choose Ollama or LM Studio for embeddings and keeps the capture LLM explicit: generated configs use agent-host, while this older fully-local recipe deliberately keeps an explicit Ollama memory.llm. Selecting LM Studio embeddings would not move capture to LM Studio or create a cross-provider fallback.

Put MONO_AGENT_TELEGRAM_BOT_TOKEN=... in .env; the source config omits the credential.

{
  "runtime": {
    "model": "anthropic:claude-sonnet-4-6"
  },
  "telegram": {
    "enabled": true,
    "allowedChatIds": ["123456789"]
  },
  "memory": {
    "mode": "bujo",
    "path": "./.mono-agent/memory",
    "writeMode": "capture",
    "embeddings": {
      "provider": "ollama",
      "model": "nomic-embed-text:v1.5",
      "endpoint": "http://localhost:11434",
      "dim": 768
    },
    "llm": {
      "provider": "ollama",
      "model": "qwen3.6:latest",
      "endpoint": "http://localhost:11434"
    },
    "consolidation": { "enabled": true, "cron": "0 */2 * * *" }
  }
}

Keep botToken out of the file by setting MONO_AGENT_TELEGRAM_BOT_TOKEN in .env instead. The memory LLM provider/model/endpoint can also come from MONO_AGENT_MEMORY_LLM_PROVIDER, MONO_AGENT_MEMORY_LLM_MODEL, and MONO_AGENT_MEMORY_LLM_ENDPOINT.

Steps

Section titled “Steps”
  1. Pull the local models the memory tier needs: ollama pull nomic-embed-text:v1.5 && ollama pull qwen3.6:latest.
  2. Scaffold the agent: mono-agent init --model anthropic:claude-sonnet-4-6 --memory bujo.
  3. In guided init choose Ollama for this recipe’s embeddings, put MONO_AGENT_TELEGRAM_BOT_TOKEN in .env, then edit mono-agent.config.json: add the telegram section with allowedChatIds, set memory.writeMode to capture, and keep the explicit Ollama memory.llm block shown above. To use LM Studio embeddings instead, choose it in the wizard and leave the capture LLM independently explicit.
  4. Run mono-agent validate and confirm memory liveness — embeddings and chat model pulled, and the consolidation cadence shown.
  5. Run mono-agent start and confirm telegram reports running.
  6. Send a fact from an allowed chat (e.g. “My dog is named Pixel”), then in a later turn ask a paraphrased question and confirm recall.

Smoke test

Section titled “Smoke test”
Section titled “Related”