The control plane for AI coding tools.
One daemon. All your agents. No more context window roulette.
Gobby is a local-first daemon that unifies your AI coding assistantsβClaude Code, Gemini CLI, and Codexβunder one persistent, extensible platform. It handles the stuff these tools forget: sessions that survive restarts, context that carries across compactions, workflows that keep agents from going off the rails, and an MCP proxy that doesn't eat half your context window just loading tool definitions.
Gobby is built with Gobby. Most of this codebase was written by AI agents running through Gobby's own task system and workflows. Dogfooding isn't a buzzword hereβit's the development process.
Note: Gobby is currently in alpha. Expect rough edges and breaking changes until the first stable release.
If you've tried Beads or TaskMaster, you know the pain: databases that corrupt, agents that can't figure out the schema, worktrees that fall out of sync. Gobby's task system was designed by someone who got fed up with all of them.
- Dependency graphs that agents actually understand
- TDD expansion β describe a feature, get red/green/blue subtasks with test-first ordering
- Validation gates β tasks can't close without passing criteria (with git diff context)
- Git-native sync β
.gobby/tasks.jsonllives in your repo, works with worktrees - Commit linking β
[task-id] feat: thingauto-links commits to tasks
# Create a task
gobby tasks create "Add user authentication" --type feature
# Let the AI break it down with TDD ordering
gobby tasks expand <task-id>
# See what's ready to work on
gobby tasks list --readyConnect 5 MCP servers and watch 50K+ tokens vanish before you write a single line of code. Gobby's proxy uses progressive disclosureβtools stay as lightweight metadata until you actually need them:
list_tools() β Just names and descriptions (~200 tokens)
get_tool_schema(name) β Full inputSchema when you need it
call_tool(name, args) β Execute
Add servers dynamically. Import from GitHub repos. Search semantically. Your context window stays yours.
When you /compact in Claude Code, Gobby captures what matters: the goal, what you changed, git status, recent tool calls. Next session, it injects that context automatically. No more "wait, what were we doing?"
Works across CLIs too. Start in Claude Code, pick up in Gemini. Gobby remembers.
YAML-defined workflows with state machines, tool restrictions, and exit conditions:
# auto-task workflow: autonomous execution until task tree is complete
name: auto-task
steps:
- name: work
description: "Work on assigned task until complete"
allowed_tools: all
transitions:
- to: complete
when: "task_tree_complete(variables.session_task)"
- name: complete
description: "Task work finished - terminal step"
exit_condition: "task_tree_complete(variables.session_task)"
on_premature_stop:
action: guide_continuation
message: "Task has incomplete subtasks. Use suggest_next_task() and continue."Built-in workflows: auto-task, plan-execute, test-driven. Or write your own.
Spawn agents in isolated git worktrees. Run tasks in parallel without stepping on each other. Gobby tracks which agent is where and what they're doing.
call_tool("gobby-worktrees", "spawn_agent_in_worktree", {
"prompt": "Implement OAuth flow",
"branch_name": "feature/oauth",
"task_id": "task-123"
})Gobby transparently intercepts Claude Code's built-in task system (TaskCreate, TaskUpdate, etc.) and syncs operations to Gobby's persistent task store. Benefits:
- Tasks persist across sessions (unlike CC's session-scoped tasks)
- Commit linking β tasks auto-link to git commits
- Validation gates β define criteria for task completion
- LLM expansion β break complex tasks into subtasks
No configuration needed β just use Claude Code's native task tools and Gobby handles the rest.
Reusable instructions that teach agents how to perform specific tasks. Compatible with the Agent Skills specification and SkillPort.
- Core skills bundled with Gobby for tasks, sessions, memory, workflows
- Project skills in
.gobby/skills/for team-specific patterns - Install from anywhere β GitHub repos, local paths, ZIP archives
- Search and discovery β TF-IDF and semantic search across your skill library
# Install a skill from GitHub
gobby skills install github:user/repo/skills/my-skill
# Search for relevant skills
gobby skills search "testing coverage"uvx gobby --help# With uv (recommended)
uv tool install gobby
# With pipx
pipx install gobby
# With pip
pip install gobbyRequirements: Python 3.13+
# Start the daemon
gobby start
# In your project directory
gobby init
gobby install # Installs hooks for detected CLIsRequirements: At least one AI CLI (Claude Code, Gemini CLI, or Codex CLI)
Works with your Claude, Gemini, or Codex subscriptionsβor bring your own API keys. Local model support coming soon.
Add Gobby as an MCP server. Choose the command and args that match your installation:
- pip/pipx install:
"command": "gobby","args": ["mcp-server"] - uv tool install:
"command": "uv","args": ["run", "gobby", "mcp-server"]
Claude Code (.mcp.json or ~/.claude.json):
{
"mcpServers": {
"gobby": {
"command": "gobby",
"args": ["mcp-server"]
}
}
}Or with uv:
{
"mcpServers": {
"gobby": {
"command": "uv",
"args": ["run", "gobby", "mcp-server"]
}
}
}Gemini CLI (.gemini/settings.json):
{
"mcpServers": {
"gobby": {
"command": "gobby",
"args": ["mcp-server"]
}
}
}Codex CLI (~/.codex/config.toml):
[mcp_servers.gobby]
command = "gobby"
args = ["mcp-server"]Gemini Antigravity (~/.gemini/antigravity/mcp_config.json):
{
"mcpServers": {
"gobby": {
"command": "/path/to/uv",
"args": ["run", "--directory", "/path/to/gobby", "gobby", "mcp-server"],
"disabled": false
}
}
}| CLI | Hooks | Status |
|---|---|---|
| Claude Code | β All 14 types | Full support |
| Gemini CLI | β³ Ready | Waiting on upstream PR (see #9070) |
| Codex CLI | πΈ Basic | after_agent only |
| Gobby | TaskMaster | Beads | mcp-agent | |
|---|---|---|---|---|
| Task dependencies | β | β | β | β |
| TDD expansion | β | β | β | β |
| Validation gates | β | β | β | β |
| Progressive MCP discovery | β | Partial | β | β |
| Multi-CLI orchestration | β | β | β | β |
| Session handoffs | β | β | β | β |
| YAML workflows | β | β | β | β |
| Worktree orchestration | β | β | β | β |
| Zero external deps | β | β | β | β |
| Local-first | β | β | β | β |
AI CLI (Claude/Gemini/Codex)
β hooks fire
βΌ
Hook Dispatcher
β HTTP POST
βΌ
Gobby Daemon (:8765)
β
ββββββ΄βββββ
βΌ βΌ
FastAPI FastMCP
β β
βΌ βΌ
βββββββββββββββββββββββ
β HookManager β
β SessionManager β
β WorkflowEngine β
β MCPClientProxy β
β TaskStore β
β MemoryStore β
βββββββββββββββββββββββ
β
βΌ
SQLite
(~/.gobby/gobby-hub.db)
Everything runs locally. No cloud. No API keys required (beyond what your AI CLIs need). Works offline.
Gobby exposes tools via MCP that your AI coding assistant can use:
Task Management (gobby-tasks)
create_task, expand_task, validate_task, close_task, list_ready_tasks, suggest_next_task, add_dependency, get_dependency_tree, and more.
Session Management (gobby-sessions)
pickup (restore context), get_handoff_context, list_sessions
Memory (gobby-memory)
remember, recall, forget β persistent facts across sessions
Workflows (gobby-workflows)
activate, advance, set_variable, get_status
Worktrees (gobby-worktrees)
create_worktree, spawn_agent_in_worktree, list_worktrees
MCP Proxy
list_mcp_servers, add_mcp_server, import_mcp_server, list_tools, get_tool_schema, call_tool, recommend_tools
Skills (gobby-skills)
list_skills, get_skill, search_skills, install_skill, remove_skill, update_skill
If you're an AI agent reading this: Check out docs/AGENT_INSTRUCTIONS.md for your operating procedures, and CLAUDE.md for Claude Code-specific guidance.
See ROADMAP.md for the full plan, but highlights:
Shipped: Task system v2 (commit linking, validation gates), TDD expansion v2 (red/green/blue generation), workflow engine, MCP proxy with progressive discovery, session tracking and handoffs, memory, hooks integration, worktree primitives
In Progress: Memory v3 (backend abstraction layer for external memory solutions: MemU, Mem0, OpenMemory)
Next: Autonomous orchestration, TUI, Web UI (for remote access to your agents), Observability (tool call tracing, OpenTelemetry), Security posture for MCP (allow/deny lists, audit logging)
Vision: Always local first, but Pro cloud features to keep the lights on: Fleet management (manage sessions across multiple machines), Plugin ecosystem, Team workflows, Enterprise hardening
uv sync # Install deps
uv run gobby start -v # Run daemon (verbose)
uv run pytest # Tests
uv run ruff check src/ # Lint
uv run mypy src/ # Type checkCoverage threshold: 80%. We're serious about it.
We'd love your help. Gobby is built by developers who got frustrated with the state of AI coding tool orchestration. If that's you too, jump in:
- Found a bug? Open an issue
- Have a feature idea? Open a discussion first
- Want to contribute code? PRs welcome β check the roadmap for what's in flight
- UI/UX skills? We really need you. The maintainer is colorblind and Photoshop makes him itch.
See CONTRIBUTING.md for details.
Apache 2.0 β Use it, fork it, build on it.
Built with π€ by humans and AI, working together.
