AGENT_CONFIG_DIR — Directory containing .taskorchestrator/config.yaml for workflow schemas (default: working dir)
MCP_TRANSPORT — Transport mode: stdio (default) or http
MCP_HTTP_PORT — HTTP port when using http transport (default: 3001)
MCP_SESSION_IDLE_TIMEOUT_MINUTES — Minutes without a request before an HTTP MCP session is closed; the client re-initializes on its next call (default: 120)
UPGRADE_BACKUP — Verified pre-upgrade copy of a 3.x database (<db>.pre-4.0.<yyyyMMddTHHmmssZ>.bak) taken before its first 4.0 migration; a failed copy fails startup. Set false to skip (default: true)
Claude Code'un yerel komutlar için kurulum bağlantısı yok. Aşağıdaki komutu kopyalayın ve bir terminalde çalıştırın.
Server-enforced workflow discipline for AI agents.
Prompt-based frameworks hope the LLM follows instructions. This one blocks the call if it doesn't.
Server-enforced workflow discipline for AI agents. An MCP server providing persistent work items, dependency graphs, quality gates, and actor attribution. Schemas define what agents must produce — the server blocks the call if they don't. Works with any MCP-compatible client.
PlugMCP’de çalıştır
Host a copy with our keys and logs.
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
Task Orchestrator is an MCP server that gives AI coding agents a persistent work item graph with quality gates enforced by the server, not the prompt. It is built for developers running multi-agent or multi-session coding workflows: an orchestrator dispatching sub-agents, a fresh session picking up yesterday's work, or an autonomous loop draining a backlog. It ships as a Docker image, works with any MCP client, and has an optional Claude Code plugin that adds skills and hooks on top.
New here? Start with the illustrated field guide. It explains the ideas on this page in short visual pages, several of them interactive: fire triggers at a phase gate, click a work breakdown through its dependencies, and watch a schema resolve.
The Problem
Multi-agent workflows need infrastructure the model doesn't provide. When an orchestrator dispatches sub-agents across sessions, there's no built-in way to enforce what documentation must exist before work starts, track which agent made which change, or guarantee dependency ordering across a work breakdown. These are structural concerns — they belong in the server, not in prompts.
Task Orchestrator puts them in the server. If a required design note isn't filled, advance_item returns an error naming the missing note. If an upstream dependency isn't complete, the transition is blocked. Every transition and note records who made it. A new session recovers the full state in one call instead of replaying a conversation. And the rules are YAML config, not hardcoded prompts — change them without changing code.
Restart your client. The server creates its SQLite database on first run. Without a config file every tool works in schema-free mode: no gates, no required notes. Add schemas when you want enforcement.
2. Enable workflow schemas
Create .taskorchestrator/config.yaml in your project (see Workflow Enforcement for an example, or use the plugin's /task-orchestrator:manage-schemas skill) and mount that folder into the container:
Use an absolute host path. Claude Code's .mcp.json expands only environment variables (${VAR} and ${VAR:-default}), so editor-style placeholders such as ${workspaceFolder} are not substituted. Only the .taskorchestrator/ folder is exposed; the server has no access to the rest of your project.
3. Multi-project setup (HTTP + config-sync)
If you work across several repositories, run one persistent server with the REST API enabled. Each project's .taskorchestrator/config.yaml then syncs into it automatically through the plugin's config-sync hook: no per-project container, no manual mount, and config changes hot-reload without a restart. The plugin's /task-orchestrator:configure-server skill renders this setup interactively; the manual equivalent is:
Alternatively, a client.json (apiUrl only, no token) supplies the URL. Project-mode /task-orchestrator:init writes it beside the project config, only for a loopback server and only as a bare origin with no path, query or fragment (add .taskorchestrator/client.json to .gitignore, the URL is machine-specific); /task-orchestrator:init --user writes the user-level file, which works for any host. The order is the environment variable, then the project-level file, then the user-level file. /task-orchestrator:init sets up a project root for the current directory, and /task-orchestrator:init --user creates a personal root that serves every directory without its own config.
SECURITY: unauthenticated REST means anyone who can reach the port has full read/write/delete
access. This is only safe because the port is published loopback-only (-p 127.0.0.1:3001:3001).
Never publish it on 0.0.0.0 or a wider interface. For shared or networked deployments use bearer
tokens or JWKS auth — see Fleet Deployment.
Prefer to run without Docker? CONTRIBUTING.md covers building the fat JAR from source.
Core Capabilities
The Phase Model
Every work item has a role: queue (not started), work (in progress), review (optional, opt-in per schema), blocked (an upstream dependency is unmet), and terminal (done or cancelled). Each role maps to one or more configurable statuses. Agents move items with advance_item(trigger=...) rather than editing status directly, and that call is where every gate is checked. Schemas attach note requirements to roles: a note declared with role: queue must exist before the item can leave the queue phase.
Illustrated: Phase Gates draws the roles and triggers as a track and includes a gate simulator.
Workflow Enforcement
Schemas define what agents must produce at each phase, and the server blocks progression until it's done. They also set a planning floor: when an agent enters plan mode, the schema tells it what documentation must exist before implementation can start, shaping the plan itself.
# .taskorchestrator/config.yaml
work_item_schemas:
feature-task:
notes:
- key: requirements
role: queue
required: true
description: "Acceptance criteria before starting"
guidance: "Cover: problem statement, acceptance criteria, alternatives considered, test strategy."
skill: "spec-quality"
- key: implementation-notes
role: work
required: true
description: "What was built and why"
With this schema, advance_item(trigger="start") from queue requires requirements to be filled. The server returns an error listing exactly which notes are missing.
The guidance field provides authoring instructions surfaced at the right moment: when an agent is about to fill that note, the response carries the guidance as a guidancePointer. The skill field goes further, naming a skill the agent should invoke before filling the note so the evaluation follows a defined framework rather than freeform prose.
Composable Traits
Traits add cross-cutting note requirements to any schema without duplicating definitions. Define a trait once, apply it to any item type:
traits:
needs-security-review:
notes:
- key: security-assessment
role: review
required: true
description: "Security review of auth, data handling, and access control"
skill: "security-review"
work_item_schemas:
feature-task:
default_traits:
- needs-security-review
notes:
# ... base notes
Every feature-task item automatically inherits the security-assessment requirement. Traits can also be applied per item via the traits parameter on manage_items, so a task touching authentication gets needs-security-review while a CSS cleanup doesn't.
Illustrated: Schemas and Traits shows how a type, its default traits and per-item traits resolve into one gate. Seats covers splitting a phase between several agents.
Persistent Work Item Graph
Everything is a WorkItem in a hierarchical graph. Items nest to any depth and are connected by typed dependency edges. Create an entire work breakdown atomically:
When schema reaches terminal, api is automatically unblocked. When all children complete, the parent cascades to terminal. Dependency ordering is enforced by the server, structurally, not by convention.
Illustrated: Work Graph has a clickable version of this tree that shows blocking, unblocking and parent cascades.
Actor Attribution
Every advance_item transition and manage_notes upsert accepts an optional actor claim, and the server records it on the transition or note:
Query responses include the full delegation chain: which orchestrator dispatched which sub-agent, who wrote which note, who made which transition. Post-mortem debugging becomes a data query rather than conversation archaeology.
To make the claim mandatory, enable actor authentication in config:
actor_authentication:
enabled: true # read by the plugin hook (workspace/user config), not the server
With the Claude Code plugin installed, a hook then rejects any write that lacks an actor before the call leaves the client. Other MCP clients need to supply the claim by convention. Optional JWKS verification checks that the claim is genuine; see Fleet Deployment.
Session Continuity
No context rebuilding. One call recovers the full picture:
Returns active items, recent transitions with actor attribution, blocked items, stalled items with their missing notes, and full ancestor chains. A new session has complete state in a single response.
Illustrated: Finding Work maps each question a session has to the read call that answers it, and shows how the next item is ranked.
Notes as Structured Context
Notes are phase-specific documentation attached to work items. An implementation agent reads a concise requirements note scoped to its task instead of scanning broader project context.
Metadata-only queries (includeBody=false) let agents check what exists without paying the token cost of every note body.
Full-Text Search
Search work items and notes by keyword. Results are relevance-ranked, so an agent looking for related work or picking up after a long gap gets the best matches first.
Search can be scoped to a subtree and filtered by role, tag, or priority, or run across the whole workspace.
REST API
An optional HTTP layer (API_ENABLED=true) exposes items, notes, dependencies, transitions, config, and real-time SSE events to dashboards, CI systems, and operators. It supports static bearer tokens, JWKS JWT auth, and the unauthenticated loopback mode shown in the Quick Start. It also powers config-sync, so one server can serve many projects with per-project schemas. See the REST API Reference.
Illustrated: Runtime Map covers the server's layers, both deployment shapes, the REST API, claims and actor identity.
Design Philosophy
Task Orchestrator enforces workflow structure without imposing methodology. The server owns the guardrails: role transitions, dependency ordering, gate enforcement, and attribution. Agents own everything else. There are no mandatory planning ceremonies and no opinion on how agents approach implementation. Schemas, traits, and actor authentication are opt-in layers configured in .taskorchestrator/config.yaml. As models gain capabilities, the harness stays out of the way.
Claude Code Plugin
The plugin adds workflow automation on top of the MCP server: skills, hooks, and an orchestration context.
Slash commands for common workflows, including /task-orchestrator:quick-start, /task-orchestrator:init, /task-orchestrator:configure-server, /task-orchestrator:manage-schemas, and /task-orchestrator:create-item
Hooks
Context injection at session start, plan-mode integration, sub-agent context handoff, per-project config sync, and actor-attribution enforcement
Orchestration context
A SessionStart hook delivers the orchestration core (with the orchestrate skill for tier and delegation detail): Claude plans, delegates to sub-agents, and tracks progress without writing code directly
The MCP server works without the plugin. The plugin makes it seamless with Claude Code.
Optional mod (early access):task-orchestrator-mod is a separate, opt-in plugin of in-process function hooks: a live work-graph pane, an in-flight band and status line, and in-process retrospective, phase-guard and call-shape hooks. It relies on an early-access Claude Code API and is best paired with a current task-orchestrator plugin.
Illustrated: Orchestrating a Run follows one feature from request to finished items. Improvement Loop shows how friction noticed during work becomes a reviewed config change.
Trigger-based transitions with gate enforcement, dependency validation, and atomic find-and-claim for multi-agent fleets
Project config
manage_project_config, manage_plan_documents
Push per-project schema config and stash planning documents ahead of work item creation
Item IDs accept short hex prefixes: itemId="a3f2" works in place of a full UUID once the prefix is unambiguous. See the API Reference for every tool's parameters and response shapes.
SQLite + Exposed ORM with FTS5 full-text search, zero configuration
Flyway versioned schema migrations
MCP Kotlin SDK with STDIO and Streamable HTTP transports
Ktor for the REST API and SSE
Docker for one-command deployment
A layered architecture (Domain → Application → Infrastructure → Interfaces, enforced by a Konsist test against a ratcheting baseline of pre-existing violations) with an extensive JUnit 5 test suite.
License
MIT License — Free for personal and commercial use.
T
Task Orchestrator
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Server-enforced workflow discipline for AI agents. An MCP server providing persistent work items, dependency graphs, quality gates, and actor attribution. Schemas define what agents must produce — the
Server-enforced workflow discipline for AI agents. An MCP server providing persistent work items, dependency graphs, quality gates, and actor attribution. Schemas define what agents must produce — the server blocks the call if they don't. Works with any MCP-compatible client.
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Task Orchestrator nasıl kurulur?
Bu sayfadan Cursor, Claude Desktop, Claude Code veya VS Code için stdio yapılandırmasını kopyalayın. PlugMCP proxy'sini değil, yayıncının kurulumunu kullanır.
Task Orchestrator yerel olarak stdio üzerinden çalışır.
Task Orchestrator en son ne zaman kontrol edildi?
Son kontrol: 2026-11-12. Server-enforced workflow discipline for AI agents. An MCP server providing persistent work items, dependency graphs, quality gates, and actor attribution. Schemas define what agents must produce — the server blocks the call if they don't. Works with any MCP-compatible client.
