operator-ai 0.2.2

Local AI agent connecting chat platforms to LLMs via LiteLLM

Operator

Operator is a local agent runtime that connects chat messages to LLMs via LiteLLM.

It is intentionally small and file-driven:

• Markdown files for agent prompts, jobs, and skills.
• SQLite for durable runtime state (memory, messages, runs, jobs, etc).
• A single execution path for both inbound chat and scheduled jobs.

Core Features

• Multiple agents (~/.operator/agents/*/AGENT.md)
• Multiple transports (currently Slack)
• Skills discovery from ~/.operator/skills/*/SKILL.md
• Scheduled jobs with prerun gating and postrun hooks
• Durable conversation history and run tracking in SQLite
• Slack thread continuity via persistent platform message index
• Turn-safe context truncation against model token budgets
• Vector memory with automatic harvesting and semantic search (sqlite-vec)

Quickstart

pip install operator-ai
operator init

This creates ~/.operator/ with a starter config, system prompt, and a default agent. Next:

1. Edit ~/.operator/operator.yaml — set your model, transport, and API key source.
2. Set API keys — export ANTHROPIC_API_KEY (or whichever provider you chose), plus transport tokens (e.g. SLACK_BOT_TOKEN, SLACK_APP_TOKEN).
3. Run it:

operator

The init command is idempotent — running it again won't overwrite existing files.

Install

pip install operator-ai

Or for development:

pip install -e .

Configuration

Runtime config lives at ~/.operator/operator.yaml. The starter config from operator init looks like:

defaults:
  models:
    - "anthropic/claude-sonnet-4-6"
  max_iterations: 25
  context_ratio: 0.5
  # env_file: "~/.env"        # Load API keys from a dotenv file

agents:
  default:
    transport:
      type: slack
      bot_token_env: SLACK_BOT_TOKEN
      app_token_env: SLACK_APP_TOKEN

A more advanced example with multiple agents and model fallbacks:

defaults:
  models:
    - "anthropic/claude-opus-4-6"
    - "openai/gpt-5.3-codex"
  max_iterations: 25
  context_ratio: 0.5
  max_output_tokens: null    # null = use each model's max; set to cap output length
  env_file: "~/.env"

agents:
  hermy:
    models:
      - "anthropic/claude-sonnet-4-6"
    transport:
      type: slack
      bot_token_env: SLACK_BOT_TOKEN_HERMY
      app_token_env: SLACK_APP_TOKEN_HERMY

models is a fallback chain — if the first model errors (overloaded, rate limited, down), the next is tried automatically. Always use list format, even for a single model.

max_output_tokens controls the maximum response length per LLM call. When null (default), each model's full output capacity is used. Set an integer to cap all models uniformly. Can be overridden per-agent.

Agents without a transport block are available for jobs and sub-agent spawning but have no chat interface.

Memory

memory:
  embed_model: "openai/text-embedding-3-small"   # required when any service is enabled
  embed_dimensions: 1536
  max_memories: 10000                             # per scope, soft cap
  inject_top_k: 5                                 # memories injected per message
  inject_min_relevance: 0.1                       # cosine similarity threshold
  harvester:
    enabled: true
    schedule: "*/30 * * * *"                      # required when enabled — cron
    model: "openai/gpt-4.1-mini"                  # required when enabled
  cleaner:
    enabled: true
    schedule: "0 3 * * *"                         # required when enabled — cron
    model: "anthropic/claude-haiiku-4-5"          # required when enabled

The harvester and cleaner each have their own enabled flag. When enabled, schedule and model are required — startup will error if missing. When disabled, the fields can be left empty. embed_model is required when either service is enabled.

• Harvester — extracts facts from conversations using an LLM and stores them as vector embeddings in operator.db (via sqlite-vec).
• Cleaner — deduplicates, merges, and tidies stored memories by sending them through an LLM for normalization.

On each incoming message, relevant memories are retrieved by semantic similarity and injected into the user message as context.

Memories are scoped: user (personal facts), agent (agent-specific context), and global (shared knowledge). Memories can be pinned — pinned memories are always injected into the system prompt regardless of similarity.

API keys for the models are resolved from the environment (e.g. OPENAI_API_KEY), loaded via env_file in the config.

Key-Value Store

Agents have a scoped key-value store in SQLite for operational state — tracking processed items, cursors, watermarks, etc. Keys are scoped by agent name and grouped by namespace.

• kv_set(key, value, namespace?, ttl_hours?) — store a value, optionally with auto-expiry.
• kv_get(key, namespace?) — retrieve a value.
• kv_delete(key, namespace?) — remove a key.
• kv_list(namespace?, prefix?) — list keys and values.

Use namespaces to group related keys (typically by job name). Use TTL to prevent unbounded growth for tracking sets (e.g. seen email IDs).

Agent Workspace

Each agent has a workspace directory at ~/.operator/agents/<name>/workspace/. This is the working directory for all tool calls — shell commands, file reads/writes, and list_files all resolve relative paths against it. Files written here persist across conversations and job runs.

Filesystem Layout

Everything lives under ~/.operator/:

~/.operator/
├── operator.yaml
├── SYSTEM.md               # system preamble (auto-created from template)
├── logs/
├── state/
│   └── operator.db
├── agents/
│   └── <agent>/
│       ├── AGENT.md
│       └── workspace/
├── jobs/
│   └── <job>/
│       ├── JOB.md
│       └── scripts/
└── skills/
    └── <skill>/
        ├── SKILL.md
        └── scripts|references|assets/

Running

operator

Logs are written to ~/.operator/logs/operator.log.

CLI

The operator command doubles as a CLI for inspecting and managing runtime state. Subcommands run standalone (no running service required).

Init

operator init                  # scaffold ~/.operator with starter config and agent

Service

operator service install       # generate and load a service definition (launchd/systemd)
operator service uninstall     # unload and remove the service definition
operator service start         # start the background service
operator service stop          # stop the background service
operator service restart       # restart the background service
operator service status        # show whether the service is running

Logs

operator logs [-f/--follow] [-n/--lines N]

Tails ~/.operator/logs/operator.log. Defaults to the last 50 lines.

Jobs

operator job list              # all jobs with status, schedule, counters
operator job info <job-name>   # job config and runtime state
operator job run <job-name>    # trigger a job immediately (outside cron)
operator job enable <job-name> # enable a job
operator job disable <job-name># disable a job

KV Store

operator kv get <key> [--agent/-a NAME] [--ns/-n NAMESPACE]
operator kv set <key> <value> [--agent/-a NAME] [--ns/-n NAMESPACE] [--ttl HOURS]
operator kv delete <key> [--agent/-a NAME] [--ns/-n NAMESPACE]
operator kv list [--agent/-a NAME] [--ns/-n NAMESPACE] [--prefix/-p PREFIX]

kv get prints the raw value and exits 0, or exits 1 if not found. kv list outputs JSON.

Memories

operator memories [--scope/-s SCOPE] [--scope-id/-i ID] [--pinned] [--limit/-n N]
operator memories stats        # memory counts per scope

Inspection

operator config                # print resolved configuration as JSON
operator agents                # list configured agents with transport and model info
operator skills                # list discovered skills with env status

Agent Resolution

CLI commands that need an agent name resolve it in order: --agent flag, then OPERATOR_AGENT env var, then the default agent from config. In job hook scripts, OPERATOR_AGENT is set automatically so --agent can be omitted.

Hook Environment

Job hook scripts (prerun, postrun) receive these environment variables:

Variable	Description
JOB_NAME	Name of the job being executed
OPERATOR_AGENT	Agent running the job
OPERATOR_HOME	Path to ~/.operator
OPERATOR_DB	Path to the SQLite database

Job Format

Each job is ~/.operator/jobs/<name>/JOB.md with YAML frontmatter and markdown body.

---
name: daily-summary
description: Summarize today's activity
schedule: "0 9 * * *"
agent: hermy
model: "anthropic/claude-sonnet-4-6"
hooks:
  prerun: scripts/check.sh
  postrun: scripts/notify.sh
enabled: true
---

Summarize the key events from the last 24 hours.
Post a one-line teaser to #general, then reply in a thread with the full summary.

Notes:

• model overrides the agent's model for this job (litellm format). When omitted, the agent's configured model chain is used.
• prerun is a gate: non-zero exit skips LLM execution.
• postrun receives model output on stdin.
• The agent uses send_message to post results to Slack channels. The prompt body should include posting instructions (which channels, whether to thread, etc.).
• If you have nothing to post, simply don't call send_message.

Job Counters

Each job tracks four counters in SQLite:

Counter	Incremented when
run_count	LLM actually executed (success or error)
error_count	LLM ran but threw an exception
gate_count	prerun hook returned non-zero (job skipped)
skip_count	Cron fired but the previous run was still in progress

Built-in Tools

• run_shell
• read_file
• write_file
• list_files
• web_fetch
• send_message
• spawn_agent
• manage_job
• save_memory
• search_memories
• forget_memory
• list_memories
• kv_get
• kv_set
• kv_delete
• kv_list

Commands

Messages starting with ! bypass the LLM.

• !stop cancels the active request in the current conversation.

System Prompt Assembly

Ordered from most stable (cache-friendly) to least stable:

1. SYSTEM.md — system preamble (auto-created at ~/.operator/SYSTEM.md)
2. AGENT.md — agent prompt body (verbatim)
3. # Context block (platform/channel/user/workspace) or # Job block
4. Pinned memories (from SQLite, always injected) — chat only
5. Available skills from scanned skills/*/SKILL.md
6. Transport extras (transport.get_prompt_extra()) — e.g. Slack channel list, messaging instructions

Conversation and Routing Model

Slack conversations use canonical IDs:

• slack:{agent_name}:{channel_id}:{root_ts}

Where root_ts is:

• thread_ts for threaded replies
• ts for top-level posts

The runtime stores platform_message_id -> conversation_id mappings so replies to proactive job messages continue in the correct history.

Architecture

src/operator_ai/
├── cli.py               # typer CLI (kv, job subcommands) + service entry point
├── main.py              # service lifecycle, dispatch, commands
├── config.py            # yaml + env config
├── agent.py             # litellm loop and tool execution
├── truncation.py        # token-budget, exchange-safe history shaping
├── store.py             # sqlite persistence, vector search, KV, caches
├── memory.py            # MemoryStore (embed/save/search) + MemoryHarvester + MemoryCleaner
├── jobs.py              # job scan/schedule/hooks/delivery
├── skills.py            # skill scan/frontmatter/prompt block
├── prompts/
│   ├── __init__.py      # load_system_prompt, load_agent_prompt
│   ├── system.md        # default SYSTEM.md template
│   ├── harvester.md     # memory harvester prompt
│   └── cleaner.md       # memory cleaner prompt
├── transport/
│   ├── base.py
│   └── slack.py
└── tools/
    ├── registry.py
    ├── workspace.py
    ├── shell.py
    ├── files.py
    ├── web.py
    ├── messaging.py
    ├── subagent.py
    ├── memory.py
    ├── kv.py
    └── jobs.py

Development

All work happens on the dev branch. Feature branches are optional — branch off dev for larger changes, or commit directly to dev for small fixes.

main ← dev ← feat/whatever

• dev — integration branch. Always runnable. Push freely.
• main — release branch. Only updated by merging dev at release time.
• feat/* — short-lived feature branches off dev (optional).

Releasing

1. Merge dev into main: git checkout main && git merge dev
2. Update version in pyproject.toml.
3. Add an entry to CHANGELOG.md under a new ## [x.y.z] - YYYY-MM-DD heading.
4. Commit: git commit -am "release: vx.y.z"
5. Tag: git tag vx.y.z
6. Push: git push && git push --tags

Pushing a v* tag triggers the GitHub Actions workflow that builds and publishes to PyPI.