AI Ops Agent

An always-on AI chief-of-staff for one person or a small team. It runs the day-to-day from chat: tasks, calendar, notes, habits and activity, voice-note capture, semantic search over a private markdown vault, scheduled briefings (morning, evening, weekly), and multimodal tools (vision, OCR, image generation). A live mission-control dashboard shows the agent's uptime, scheduled jobs, token usage, and cost.

This is the reusable engine, built to be pointed at your own setup. Configure your vault, your models, and your schedule, and the same pipeline runs for personal ops, a founder's second brain, or a small team's assistant. It runs locally, or on a VPS with a process manager for always-on operation.

Architecture

Three layers you change independently: the tool server (this repo), the agent runtime that drives it (any MCP-capable brain), and the schedule that wakes it. Full detail in ARCHITECTURE.md.

flowchart TD
    Cron["Scheduler: morning brief, evening digest, weekly review, sweeps"] --> Runtime["Agent runtime (any MCP brain)"]
    Chat["You (Telegram / chat)"] --> Runtime
    Runtime --> MCP["FastMCP tool server (this repo): 24 tools"]
    MCP --> Vault[("Markdown vault")]
    MCP --> DB[("SQLite: tasks, habits, activity, notes")]
    MCP --> Search["Semantic vault search (embeddings)"]
    MCP --> Multi["Vision / OCR / image gen / calendar"]
    Dash["Ops dashboard (FastAPI)"] --> DB
    Dash --> Metrics["System + gateway uptime, jobs, token cost"]

Related MCP server: Flint Note

What it does

• Vault as memory: read, append, and tree a private markdown vault; a background indexer chunks it and stores embeddings for semantic recall.

• Tasks and routines: task lifecycle in SQLite, mirrored to an Obsidian-compatible tasks.md; habit streaks; activity and daily state.

• Scheduled operating loop: morning brief, evening digest (writes a journal entry), weekly review, plus sweeps that keep the vault index and task mirror in sync.

• Voice-note routing: archive audio + transcript, then route by shape into tasks, notes, or longer entries.

• Multimodal tools: image and video analysis, OCR, and text-to-image.

• Ops dashboard: a FastAPI mission-control panel for agent status, uptime, scheduled jobs, logs, token usage, and cost.

Tool surface

A FastMCP stdio server exposing 24 tools: vault_read, vault_append, vault_tree, semantic_search, task_add, task_close, task_list, tasks_render_md, mood_log, note_quick, activity_log, activity_update, workout_archive, voicenote_archive, calendar_list, calendar_add, calendar_delete, vision_analyze, video_analyze, ocr_extract, image_generate, query_db, web_search, web_fetch. The tool layer is model-swappable and runs behind any MCP-capable agent runtime.

Quick start

python3 -m venv .venv
. .venv/bin/activate
make install
make test
cp .env.example .env
make db-init

Run the MCP server, or the dashboard, locally:

AGENT_VAULT_DIR="$PWD/.vault" python3 scripts/agent_mcp.py       # MCP tool server (stdio)
AGENT_VAULT_DIR="$PWD/.vault" python3 scripts/dashboard_main.py  # dashboard on http://localhost:7474

Real provider calls require the keys in .env. Tests require no live secrets.

Configure it for your setup

Copy config.example.json to config.json and set your vault path, the models each tier uses (scripts/models.py), and the schedule. Point the paths at your own vault and runtime with the AGENT_* variables in .env.example. Wire the tool server to an MCP runtime and a scheduler, and run it locally or on a VPS. Step by step in RUNBOOK.md.

Secrets

Credentials never live in the repo. Keep them in .env locally, or in a server environment file (referenced by AGENT_ENV_FILE) outside git. No live hostnames, IPs, vault contents, or personal data are committed.

Contact

Built and operated by Mira Solutions, an AI engineering and automation studio.

mira.solutions06@gmail.com