Scheduled Monitor¶

A persistent operative agent that runs on a cron schedule, maintains state across runs, and uses memory to track changes over time. Ideal for daily inbox monitoring, recurring status checks, and long-running research projects.

Quickstart (5 minutes)¶

1. Install and initialize¶

git clone https://github.com/open-jarvis/OpenJarvis.git
cd OpenJarvis
uv sync --extra dev
jarvis init --preset scheduled-monitor

This writes a pre-configured ~/.openjarvis/config.toml for the operative agent with scheduling support.

2. Start a local LLM via Ollama¶

# Install Ollama: https://ollama.com
ollama pull qwen3.5:9b

3. Index your data¶

jarvis memory index ~/Documents/

The operative agent uses memory to track state across runs, so indexing your data gives it context for the first run.

4. Create a scheduled task¶

jarvis scheduler start

jarvis scheduler create \
  --prompt "Check for new emails about Project X and update your notes" \
  --schedule "0 9 * * 1-5" \
  --agent operative \
  --tools "knowledge_search,knowledge_sql,memory_store,think"

This creates a task that runs at 9 AM every weekday. The operative agent will search your indexed data, process new information, and store notes in memory for the next run.

How Scheduling Works¶

The scheduler uses cron expressions to trigger agent runs at specified intervals. Each run is an independent agent session, but the operative agent persists state between sessions.

Cron expression reference¶

 .------------ minute (0-59)
 | .---------- hour (0-23)
 | | .-------- day of month (1-31)
 | | | .------ month (1-12)
 | | | | .---- day of week (0-6, 0=Sunday)
 | | | | |
 * * * * *

Common examples:

Expression	Meaning
`0 9 * * 1-5`	9 AM, Monday through Friday
`0 6 * * *`	6 AM every day
`/30 * * *`	Every 30 minutes
`0 9,17 * * *`	9 AM and 5 PM daily
`0 8 1 * *`	8 AM on the 1^st of every month

CLI commands¶

# Start the scheduler daemon
jarvis scheduler start

# Create a new scheduled task
jarvis scheduler create \
  --prompt "Summarize any new research papers in my library" \
  --schedule "0 8 * * *" \
  --agent operative

# List all scheduled tasks
jarvis scheduler list

# View task details and run history
jarvis scheduler status <task-id>

# Pause / resume / delete a task
jarvis scheduler pause <task-id>
jarvis scheduler resume <task-id>
jarvis scheduler delete <task-id>

# Run a task immediately (outside its schedule)
jarvis scheduler run <task-id>

# Stop the scheduler daemon
jarvis scheduler stop

Configuration Reference¶

The preset writes this to ~/.openjarvis/config.toml:

[engine]
default = "ollama"

[intelligence]
default_model = "qwen3.5:9b"
temperature = 0.3

[agent]
default_agent = "operative"
max_turns = 20
context_from_memory = true          # Inject relevant memory into context

[tools]
enabled = ["knowledge_search", "knowledge_sql", "scan_chunks", "memory_store", "memory_search", "think", "web_search"]

[tools.storage]
default_backend = "sqlite"

Key settings¶

Setting	Default	Description
`intelligence.default_model`	`qwen3.5:9b`	The model used for reasoning.
`intelligence.temperature`	`0.3`	Low temperature for consistent, factual outputs across runs.
`agent.default_agent`	`operative`	Persistent agent that maintains state between sessions.
`agent.max_turns`	`20`	High turn limit for thorough processing of accumulated data.
`agent.context_from_memory`	`true`	Automatically injects relevant memory chunks into the agent's context.
`tools.enabled`	7 tools	Search, store, scan, and reason tools for reading and writing to the knowledge base.

Tools explained¶

Tool	What it does
`knowledge_search`	Semantic search across indexed documents.
`knowledge_sql`	Structured queries against the document store.
`scan_chunks`	Browse through document chunks sequentially.
`memory_store`	Write new facts and notes to the knowledge base.
`memory_search`	Search previously stored agent notes.
`think`	Internal reasoning scratchpad for planning.
`web_search`	Search the web for supplementary information.

Example Use Cases¶

Daily inbox monitor¶

jarvis scheduler create \
  --prompt "Review my recent emails. Flag anything urgent and summarize the rest. Store a daily summary." \
  --schedule "0 9 * * 1-5" \
  --agent operative \
  --tools "knowledge_search,memory_store,think"

Research tracker¶

jarvis scheduler create \
  --prompt "Search for new papers related to 'efficient transformers'. Compare with papers I've already indexed and note what's new." \
  --schedule "0 8 * * 1" \
  --agent operative \
  --tools "knowledge_search,web_search,memory_store,think"

Status reporter¶

jarvis scheduler create \
  --prompt "Check the project status documents and generate a weekly progress summary. Note any blockers." \
  --schedule "0 17 * * 5" \
  --agent operative \
  --tools "knowledge_search,knowledge_sql,memory_store,think"

How State Persistence Works¶

The operative agent differs from other agents in that it maintains state across runs:

Memory storage: The agent uses the memory_store tool to save notes, summaries, and observations. These persist in the local SQLite database and are available in future runs.
Context injection: With context_from_memory = true, the agent automatically receives relevant context from previous runs when it starts a new session.
Accumulated knowledge: Over time, the agent builds a progressively richer understanding of your data. A Monday run can reference notes from the previous Friday.
All data stays local: State is stored in ~/.openjarvis/ using the configured memory backend. Nothing leaves your machine.

Troubleshooting¶

"Scheduler not running" -- Start the scheduler daemon with jarvis scheduler start. It must be running for scheduled tasks to execute.

Task doesn't run on time -- Check that Ollama is running (ollama serve). The scheduler triggers the agent, but the agent needs an inference engine. Verify the schedule with jarvis scheduler status <task-id>.

Agent produces inconsistent results -- Keep temperature at 0.3 or lower for scheduled tasks. Higher temperatures introduce randomness that compounds across runs.

Memory grows too large -- Periodically review with jarvis memory stats. Clear old entries with jarvis memory clear --before 2026-01-01 if needed.

Agent runs too long -- Reduce max_turns or simplify the prompt. The operative agent is thorough and may use all available turns.