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 workspaceread_file— read contents of a text filewrite_file— write content to a text fileedit_file— search-and-replace editing within a filedelete_file— delete a filecreate_link— create a symbolic link in the workspaceread_link— read the target of a symbolic linksearch_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 timeschedule_every— schedule a recurring nudge on an intervalcancel_task— cancel a scheduled tasklist_scheduled— list all scheduled and running tasks
Chat
list_rooms— list available chat roomsroom_messages— read recent messages from a roomsend_message— send a message to a roomreact— react to a message with an emojiwrap_up_silently— end a turn without replying
Vault
list_secrets— list stored secret namesstore_secret— store a secret by namegenerate_secret— generate and store a random secretdelete_secret— delete a stored secretcapture_secret— capture a secret from command output
Executor
run— run a command in the sandboxshell— 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:
- docketeer-1password — 1Password secret vault
- docketeer-agentskills — Agent Skills support
- docketeer-anthropic — Anthropic inference backend
- docketeer-autonomy — autonomous inner life (reverie, consolidation, journaling, profiles)
- docketeer-bubblewrap — sandboxed command execution via bubblewrap
- docketeer-deepinfra — DeepInfra inference backend
- docketeer-git — automatic git-backed workspace backups
- docketeer-mcp — MCP server support
- docketeer-monty — sandboxed Python execution
- docketeer-rocketchat — Rocket.Chat backend
- docketeer-search — semantic workspace search via fastembed
- docketeer-subprocess — unsandboxed command execution for containers and non-Linux hosts
- docketeer-tui — terminal chat backend
- docketeer-web — web search, HTTP requests, file downloads
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