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A reasonably sized autonomous AI construction kit

Project description

Docketeer

The core agent engine for building autonomous AI assistants with Docket.

Docketeer is a small, opinionated toolkit for running an AI agent that can manage its own memory, schedule its own future work, and extend itself through plugins. The inference backend is pluggable — bring your own LLM provider. The core package provides the agent loop, a persistent workspace for the agent's files, and a plugin system based on standard Python entry points.

Tools

Workspace

  • list_files — list files and directories in the workspace
  • read_file — read contents of a text file
  • write_file — write content to a text file
  • edit_file — search-and-replace editing within a file
  • delete_file — delete a file
  • create_link — create a symbolic link in the workspace
  • read_link — read the target of a symbolic link
  • search_files — semantic search across workspace files (falls back to keyword grep without docketeer-search)

Scheduling

  • schedule — schedule a future nudge to prompt the agent at a given time
  • schedule_every — schedule a recurring nudge on an interval
  • cancel_task — cancel a scheduled task
  • list_scheduled — list all scheduled and running tasks

Chat

  • list_rooms — list available chat rooms
  • room_messages — read recent messages from a room
  • send_message — send a message to a room
  • react — react to a message with an emoji
  • wrap_up_silently — end a turn without replying

Vault

  • list_secrets — list stored secret names
  • store_secret — store a secret by name
  • generate_secret — generate and store a random secret
  • delete_secret — delete a stored secret
  • capture_secret — capture a secret from command output

Executor

  • run — run a command in the sandbox
  • shell — run a shell command in the sandbox

Configuration

Variable Default Description
DOCKETEER_DATA_DIR ~/.docketeer Where the agent stores memory and audit logs
DOCKETEER_DOCKET_URL redis://localhost:6379/0 Redis connection for task scheduling
DOCKETEER_DOCKET_NAME docketeer Name of the Docket instance
DOCKETEER_CHAT_MODEL balanced Model tier for chat conversations
DOCKETEER_LOG_LEVEL INFO Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
DOCKETEER_CHAT (auto) Entry point name to select when multiple chat plugins are installed
DOCKETEER_INFERENCE (auto) Entry point name to select when multiple inference plugins are installed
DOCKETEER_EXECUTOR (auto) Entry point name to select when multiple executor plugins are installed
DOCKETEER_VAULT (auto) Entry point name to select when multiple vault plugins are installed
DOCKETEER_SEARCH (auto) Entry point name to select when multiple search plugins are installed

Plugins

Docketeer discovers plugins through these entry point groups:

  • docketeer.inference — inference backends (which LLM provider powers the agent)
  • docketeer.chat — chat backends (how the agent talks to people)
  • docketeer.executor — command executors (sandboxed process execution)
  • docketeer.vault — secret vaults (store and resolve secrets)
  • docketeer.search — search catalogs (semantic search over workspace and tools)
  • docketeer.tools — tool plugins (what the agent can do)
  • docketeer.prompt — system prompt providers (contribute blocks to the system prompt)
  • docketeer.tasks — background task plugins (periodic or scheduled work)

Available plugins:

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