aqm 0.1.1

AI agent orchestration framework — build, share, and run YAML pipelines

aqm

An orchestration framework where multiple AI agents pass tasks through explicit queues in sequence.

Build pipelines in YAML. Share them with anyone. Run them locally.

                 reject                          critical
          ┌──────────────────────┐       ┌──────────────────┐
          ▼                      │       ▼                  │
      [planner] ──► [reviewer] ──┴─approve──► [developer] ──► [qa]

Powered by Claude Code

aqm uses Claude Code (Anthropic's CLI) as the underlying LLM runtime. Both api and claude_code runtimes invoke the claude CLI as a subprocess — no API key configuration or SDK setup required.

• api runtime — Calls claude -p <prompt> --print for pure text generation (planning, reviewing, summarizing)
• claude_code runtime — Runs Claude Code CLI with full tool access (file read/write, code execution, MCP tools)
• LLM Gate — Also uses the Claude CLI for automatic approve/reject evaluation

Prerequisite: Install Claude Code CLI and authenticate before using aqm.

npm install -g @anthropic-ai/claude-code
claude login

Why aqm?

Multi-agent frameworks have come in two flavors:

• Code-based (LangGraph, AutoGen) — hard to share pipelines
• Cloud platforms (CrewAI Studio, Vertex AI) — costly, vendor lock-in

aqm takes a different path:

Declare pipelines in a single YAML file. Anyone can create them, anyone can use them — an open ecosystem.

# Use a community-built pipeline instantly
aqm pull software-dev-pipeline

# Share your pipeline with the ecosystem (creates a PR)
aqm publish --name my-pipeline

Existing approach	aqm
Single agent does everything	Role separation per agent, handoff via queues
Direct calls between agents	Queue is the contract — loose coupling
Pipelines defined in code	Declared in YAML, shareable
Context lives only in memory	File-based context (context.md) — human-inspectable
Results always accepted	approve/reject gate is a first-class feature
External infrastructure required	SQLite default, runs after pip install

Install

pip install aqm

Requires Python 3.11+.

Quick Start

# Initialize in an existing project
cd my-project
aqm init

# Choose your setup method:
#   [1] Create default template — basic planner→executor pipeline
#   [2] Pull from registry — install a community pipeline
#   [3] AI-generate — describe what you want, Claude builds the YAML

# Run a pipeline
aqm run "Add JWT authentication to login"

# Open the dashboard
aqm serve
# → http://localhost:8000

No Redis. No Docker. No cloud account. Just SQLite.

How It Works

1. Declare agents and connections in YAML

# .aqm/agents.yaml

agents:
  - id: planner
    name: Planning Agent
    model: claude-opus-4-6
    runtime: api
    mcp:
      - server: github
      - server: filesystem
    system_prompt: |
      You are a software planner.
      Analyze the requirements and write a detailed specification.
    handoffs:
      - to: reviewer
        task: review_spec
        condition: always

  - id: reviewer
    name: Review Agent
    model: claude-opus-4-6
    runtime: api
    system_prompt: |
      Review the specification. Decide approve or reject.
      If rejecting, always include the reason.
    handoffs:
      - to: developer
        task: implement
        condition: on_approve
      - to: planner
        task: revise_spec
        condition: on_reject
        payload: "{{ output }}\nREJECT_REASON: {{ reject_reason }}"

  - id: developer
    name: Development Agent
    runtime: claude_code
    mcp:
      - server: github
      - server: postgres
    handoffs:
      - to: qa
        task: test_implementation
        condition: always

  - id: qa
    name: QA Agent
    runtime: claude_code
    mcp:
      - server: browsertools
      - server: sentry
    handoffs:
      - to: planner
        task: rethink_spec
        condition: "severity == critical"
      - to: developer
        task: fix_bugs
        condition: "severity in [major, minor]"

2. Tasks move between agents through queues

Every task gets a unique ID (T-A3F2B1). Each agent picks up its assigned task from the queue, processes it, and passes the result to the next agent.

T-A3F2B1 created
  → [planner] writes specification
  → [reviewer] REJECTED — "missing security requirements"
  → [planner] revises spec (with reject reason)
  → [reviewer] APPROVED
  → [developer] implements code
  → [qa] tests pass → done

Handoffs support three routing strategies:

• Static — fixed target (to: reviewer)
• Fan-out — multiple targets in parallel (to: qa, docs, deploy)
• Agent-decided — agent chooses at runtime (condition: auto + HANDOFF: agent_id in output)

3. Context accumulates in files

Each time a task passes through an agent, the result is appended to context.md. The next agent reads this file to understand the full history.

.aqm/tasks/T-A3F2B1/
├── context.md           ← Full history (human-readable)
├── stage_01_planner.md
├── stage_02_reviewer.md
└── current_payload.md

4. MCP connects agents to the outside world

Attach MCP servers to any agent so it can act, not just generate text.

Agent	MCP Connection	Capabilities
Developer	github, filesystem	Read/write files, create PRs
Designer	figma, slack	Read designs, send notifications
QA	browsertools, sentry	E2E browser tests, error lookup
Analyst	postgres, bigquery	Direct DB queries

CLI Reference

aqm init

Initialize .aqm/ in the current project directory with an interactive setup wizard.

aqm init
aqm init --path /path/to/project

The wizard offers three setup methods:

Method	Description
[1] Default template	Start with a basic planner→executor pipeline. Edit agents.yaml to customize.
[2] Pull from registry	Browse and install pipelines from bundled examples or local registry.
[3] AI-generate	Describe your desired pipeline in plain language and Claude generates the YAML — always referencing the full YAML spec for correctness. If run inside an existing project, Claude first analyzes the tech stack (languages, frameworks, test tools, etc.) and tailors the pipeline accordingly.

AI-generate example:

$ cd my-react-app
$ aqm init

How would you like to set up your pipeline?

  [1] Create default template
  [2] Pull from registry
  [3] AI-generate from description

  Choice: 3

  Analyzing project at /Users/you/my-react-app...

  Project analysis:
  - Language: TypeScript
  - Framework: React 18 + Next.js 14
  - Package manager: pnpm
  - Testing: Vitest + Playwright
  - CI/CD: GitHub Actions
  - Styling: Tailwind CSS

  Pipeline description: Feature development pipeline with code review
  and automated testing

  Generating agents.yaml with Claude (project analysis + YAML spec reference)...

  Generated agents.yaml:
  ─────────────────────────────────────
  apiVersion: aqm/v0.1
  agents:
    - id: planner
      system_prompt: |
        You are a senior Next.js/React architect...
      ...
    - id: developer
      runtime: claude_code
      mcp:
        - github
      ...
    - id: qa
      runtime: claude_code
      system_prompt: |
        Run Vitest unit tests and Playwright e2e tests...
      ...
  ─────────────────────────────────────

  [1] Use this pipeline  [2] Regenerate  [3] Use default template
  Choice: 1

✓ .aqm/ initialized with AI-generated pipeline

Creates:

.aqm/
├── agents.yaml    ← Pipeline configuration (edit this)
├── tasks/         ← Task context directories (auto-generated)
└── queue.db       ← SQLite queue (auto-generated)

aqm run

Create a task and run it through the pipeline.

# Basic usage
aqm run "Build a login feature"

# Specify starting agent
aqm run "Fix the payment bug" --agent developer

# With verbose logging
aqm -v run "Add user registration"

Output:

✓ Task created: T-A3F2B1
  Starting agent: planner

  stage 1 planner → Feature specification written...
  stage 2 reviewer → APPROVED...
  stage 3 developer → Code implemented...

✓ Completed T-A3F2B1

If a human gate is encountered:

⏸ Awaiting gate T-A3F2B1
  Proceed with 'aqm approve T-A3F2B1' or
  'aqm reject T-A3F2B1 -r "reason"'.

aqm fix

Follow-up on a previous task. Carries over the full context.md so agents understand the previous work and can make targeted corrections.

aqm fix T-A3F2B1 "The login button color should be blue, not red"
aqm fix T-A3F2B1 "Authentication fails on mobile"
aqm fix T-A3F2B1 "Update API endpoint to use v2" --agent developer

Output:

✓ Fix task created: T-C4D5E6 (from T-A3F2B1)
  Starting agent: planner

  stage 1 planner → Analyzed previous context, fixing color...
  stage 2 developer → Updated button color to blue...

✓ Completed T-C4D5E6

Use fix for any follow-up — bug reports, corrections, refinements. One command covers all iteration.

aqm status

View task status.

# Summary of all tasks
aqm status

# Detailed view of a specific task
aqm status T-A3F2B1

Detailed output:

T-A3F2B1  Add JWT authentication
  Status: awaiting_gate
  Current agent: reviewer
  Created: 2026-03-24 10:22
  Stages: 2

  stage 1: planner
  stage 2: reviewer [rejected] (missing security requirements)

aqm list

List tasks with optional status filtering.

# All tasks
aqm list

# Filter by status
aqm list --filter completed
aqm list --filter failed
aqm list --filter awaiting_gate
aqm list --filter pending

aqm approve

Approve a task waiting at a human gate.

aqm approve T-A3F2B1
aqm approve T-A3F2B1 -r "Looks good, proceed with implementation"

aqm reject

Reject a task waiting at a human gate. Reason is required.

aqm reject T-A3F2B1 -r "Missing error handling for edge cases"

aqm agents

Display all agents and their handoff graph.

aqm agents

Output:

Agent Pipeline

  planner (Planning Agent) [api] (MCP: github, filesystem)
    → reviewer (always)

  reviewer (Review Agent) [api]
    gate: llm
    → developer (on_approve)
    → planner (on_reject)

  developer (Development Agent) [claude_code] (MCP: github)
    → qa (always)

aqm context

Print the full context.md for a task.

aqm context T-A3F2B1

aqm serve

Launch the local web dashboard.

aqm serve
aqm serve --port 3000
aqm serve --host 0.0.0.0 --port 8080

Requires: pip install aqm[serve]

Dashboard features:

• Task list — status, current agent, elapsed time
• Agent diagram — auto-generated connection graph from YAML
• Task detail — stage-by-stage input/output, gate results
• Gate actions — approve/reject buttons directly in the UI

aqm pull

Pull a pipeline from the registry and install it into .aqm/agents.yaml.

Searches in order: GitHub registry → local registry → bundled examples.

# Pull from GitHub registry (default)
aqm pull software-dev-pipeline

# Pull from a custom registry repo
aqm pull my-pipeline --repo myorg/my-registry

# Offline mode — skip GitHub, use local/bundled only
aqm pull software-feature-pipeline --offline

aqm publish

Share your pipeline by creating a PR to the GitHub registry.

# Publish to GitHub registry (creates a PR)
aqm publish --name "my-pipeline" --description "Custom workflow"

# Publish to a custom registry repo
aqm publish --name "my-pipeline" --repo myorg/my-registry

# Save to local registry only (no PR)
aqm publish --name "my-pipeline" --local

Requires GitHub CLI (gh) for GitHub publishing. Local publish (--local) works without it.

aqm search

Search for available pipelines across all sources.

# List all available pipelines
aqm search

# Filter by keyword
aqm search "code review"

# Offline search (local + bundled only)
aqm search --offline

agents.yaml Complete Reference

The agents.yaml file is the single source of truth for your pipeline. It defines all agents, their roles, how they connect, and how tasks flow between them.

Top-Level Structure

# .aqm/agents.yaml
params:          # (optional) Parameterization for reusability
  model: claude-sonnet-4-20250514
  project_path:
    type: string
    required: true
    description: "Path to the project root"

imports:         # (optional) Import agents from external files
  - from: ./shared/reviewers.yaml
    agents: [code_reviewer]

agents:
  - id: ...      # First agent (pipeline entry point)
  - id: ...      # Second agent
  - id: ...      # ...

The file has three top-level keys: params (variable declarations), imports (external agent files), and agents (the pipeline definition). The first non-abstract agent is used as the default starting point.

---

Params — Parameterization for Reusability

Params make pipelines portable. Declare variables with types and defaults, then reference them anywhere using ${{ params.var_name }} syntax. Add prompt and auto_detect to create interactive setup flows.

params:
  # Shorthand — just a default value
  model: claude-sonnet-4-20250514

  # Full declaration
  project_path:
    type: string        # string | number | boolean
    required: true
    description: "Path to the project root"

  # Interactive param with auto-detection
  primary_color:
    type: string
    required: true
    description: "Primary brand color hex code"
    prompt: "What is the primary brand color?"                    # question shown during aqm run
    auto_detect: "Analyze CSS/config files for primary color"     # LLM instruction for auto-fill

  max_retries:
    type: number
    default: 3

agents:
  - id: developer
    model: ${{ params.model }}
    mcp:
      - server: filesystem
        args: ["${{ params.project_path }}"]

Interactive setup with prompt + auto_detect:

When you pull a shared pipeline, params can ask questions interactively:

params:
  primary_color:
    type: string
    required: true
    prompt: "What is the primary brand color?"
    auto_detect: "Analyze the project's CSS/tailwind config and extract the primary color"

  project_name:
    type: string
    required: true
    prompt: "What is the project name?"
    auto_detect: "Read package.json or pyproject.toml and extract the project name"

When you run aqm run, each unresolved param with a prompt shows an interactive setup:

? What is the primary brand color?
  [1] Enter manually
  [2] Auto-detect from project
  Choice [1]: 2
  Detecting...
  Detected: #3B82F6
  Use this value? [Y/n]: Y

This makes pipelines truly portable — pull a design pipeline, and it asks the right questions for your project.

Override params at runtime:

# Via CLI flags (skips interactive prompts)
aqm run "Build feature" --param model=claude-opus-4-6 --param project_path=/my/project

# Via overrides file (.aqm/params.yaml)
echo "model: claude-opus-4-6" > .aqm/params.yaml
echo "project_path: /my/project" >> .aqm/params.yaml
aqm run "Build feature"

Resolution priority: CLI flags > params.yaml file > interactive prompt > default values.

This is what makes the "pull and customize" workflow possible:

aqm pull software-dev-pipeline
# Interactive prompts fill in project-specific values
aqm run "Build login feature"

---

Imports — Reuse Agents Across Pipelines

Import agent definitions from external YAML files to avoid duplication:

# .aqm/agents.yaml
imports:
  - from: ./shared/reviewer.yaml          # relative path
    agents: [security_reviewer]            # import specific agents (optional — omit to import all)

agents:
  - id: planner
    runtime: api
    handoffs:
      - to: security_reviewer
        condition: always

# .aqm/shared/reviewer.yaml
agents:
  - id: security_reviewer
    runtime: api
    system_prompt: "Review for security vulnerabilities: {{ input }}"
    gate:
      type: llm
      prompt: "Are there any security issues?"

---

Extends — Agent Inheritance

Define a base agent and extend it to create specialized variants:

agents:
  - id: base_reviewer
    abstract: true          # Not instantiated — only used as a base
    runtime: api
    gate:
      type: llm
    system_prompt: "Review: {{ input }}"

  - id: code_reviewer
    extends: base_reviewer  # Inherits runtime, gate from parent
    system_prompt: "Review this CODE for bugs and style: {{ input }}"

  - id: security_reviewer
    extends: base_reviewer
    system_prompt: "Review for security vulnerabilities: {{ input }}"

• abstract: true agents are excluded from the pipeline (base-only)
• Child fields override parent fields (shallow merge)
• Works with imports: import a base, extend it locally

---

Agent Definition

Each agent supports the following fields:

agents:
  - id: planner                      # (required) Unique identifier, used in handoff routing
    name: Planning Agent             # (required) Display name for CLI output and dashboard
    runtime: api                     # (optional) api | claude_code — default: api
    model: claude-sonnet-4-20250514  # (optional) Model to use, omit for CLI default
    system_prompt: |                 # (optional) Jinja2 template for the system prompt
      You are a software planner.
      Analyze: {{ input }}
    handoffs:                        # (optional) List of handoff rules
      - to: reviewer
        task: review_spec
        condition: always
    gate:                            # (optional) Quality gate configuration
      type: llm
      prompt: "Is this ready?"
    mcp:                             # (optional) MCP server connections
      - server: github
    claude_code_flags:               # (optional) Extra CLI flags, claude_code runtime only
      - "--allowedTools"
      - "Edit,Write,Bash,Read"

Field Reference

Field	Type	Required	Default	Description
id	string	Yes	—	Unique identifier. Used as handoff target. Must not duplicate.
name	string	Yes	—	Human-readable display name.
runtime	"api" | "claude_code"	No	"api"	Execution runtime. See Runtime section.
model	string	No	CLI default	Claude model ID (e.g. claude-opus-4-6, claude-sonnet-4-20250514).
system_prompt	string	No	""	Jinja2 template. Available variables: {{ input }}, {{ output }}.
handoffs	list[Handoff]	No	[]	Where to send results after this agent completes.
gate	GateConfig	No	null	Quality gate evaluated before handoff routing.
mcp	list[MCPServer]	No	[]	MCP servers to attach to this agent.
claude_code_flags	list[string]	No	null	Additional CLI flags passed to claude. Only used with claude_code runtime.
abstract	boolean	No	false	If true, agent is a base template only — excluded from pipeline execution.
extends	string	No	null	ID of a parent agent to inherit fields from (shallow merge).

---

Runtime

Value	Description	Use Case
api	Runs claude -p <prompt> --print. Text-only, no tool access.	Planning, reviewing, summarizing, analysis
claude_code	Runs Claude Code CLI with full tool access. Can read/write files, execute shell commands, use MCP tools.	Implementation, testing, file manipulation

Both runtimes invoke the claude CLI as a subprocess. The difference is that api mode disables tool use, while claude_code mode enables full Claude Code capabilities.

model values — Any valid Claude model ID:

• claude-opus-4-6 — Most capable, best for complex reasoning
• claude-sonnet-4-20250514 — Balanced speed and quality (recommended default)
• claude-haiku-4-5-20251001 — Fastest, best for simple tasks

---

Handoff

Handoffs define how tasks flow from one agent to the next.

handoffs:
  - to: reviewer             # (required) Target agent ID
    task: review_spec        # (optional) Task label, default: ""
    condition: always        # (optional) When to trigger, default: "always"
    payload: "{{ output }}"  # (optional) Data to pass, default: "{{ output }}"

Handoff Fields

Field	Type	Required	Default	Description
to	string	Yes	—	Target agent id. Comma-separated for fan-out (e.g. "qa, docs"). Ignored when condition: auto.
task	string	No	""	Label describing what the next agent should do.
condition	string	No	"always"	When this handoff triggers. See conditions below.
payload	string	No	"{{ output }}"	Jinja2 template for data passed to the next agent.

Condition Values

Condition	Triggers When
always	Always triggers (no gate needed)
on_approve	Gate decision is approved
on_reject	Gate decision is rejected
on_pass	No gate exists, or gate approved
auto	Agent decides at runtime — parses HANDOFF: <id> from agent output
Custom expression	Expression evaluates to true (e.g. "severity == critical", "severity in [major, minor]")

Three Routing Strategies

1. Static routing — fixed target, simplest form:

handoffs:
  - to: reviewer
    condition: always

2. Fan-out — send to multiple agents simultaneously:

handoffs:
  - to: qa, docs, deploy       # all three run in parallel as child tasks
    condition: on_approve

The first target continues in the current task; additional targets spawn independent child tasks that run concurrently.

3. Agent-decided routing (auto) — the agent itself chooses where to route:

# Triage agent analyzes the input and decides which specialist to hand off to
- id: triage
  name: Triage Agent
  runtime: api
  system_prompt: |
    Analyze the request and decide which agent should handle it.
    End your response with: HANDOFF: <agent_id>
    Available agents: developer, designer, analyst
  handoffs:
    - to: "*"                   # 'to' is ignored when condition is auto
      condition: auto

The agent includes a HANDOFF: directive in its output:

This request requires code changes to the payment module.
HANDOFF: developer

Multiple targets are also supported:

This needs both code changes and documentation updates.
HANDOFF: developer, docs

Note: When using auto, the agent must include HANDOFF: <agent_id> in its output. If the directive is missing, the handoff is skipped and a warning is logged.

Payload Template Variables

Variable	Description
{{ output }}	Current agent's output text
{{ input }}	Current agent's input text
{{ reject_reason }}	Gate rejection reason (empty if approved)
{{ gate_result }}	Gate decision: "approved" or "rejected"

Example — Passing reject reason back to planner:

handoffs:
  - to: planner
    task: revise_spec
    condition: on_reject
    payload: "REJECTED: {{ reject_reason }}\nOriginal plan: {{ output }}"

Example — Intelligent triage with fan-out:

agents:
  - id: triage
    name: Triage Agent
    runtime: api
    system_prompt: |
      Analyze this customer request. Determine which teams should handle it.
      If multiple teams are needed, list them all.
      End with: HANDOFF: team1, team2
    handoffs:
      - to: "*"
        condition: auto

  - id: billing
    name: Billing Agent
    runtime: api
    system_prompt: "Handle billing issues: {{ input }}"

  - id: technical
    name: Technical Agent
    runtime: claude_code
    system_prompt: "Investigate technical issues: {{ input }}"

  - id: account
    name: Account Agent
    runtime: api
    system_prompt: "Handle account issues: {{ input }}"

---

Gate

Gates evaluate an agent's output before handoff routing occurs. If a gate exists, handoff conditions like on_approve / on_reject are resolved based on the gate result.

gate:
  type: llm                          # (required) llm | human
  prompt: "Is this plan actionable?" # (optional) Extra evaluation criteria for LLM gate
  model: claude-sonnet-4-20250514    # (optional) Model for LLM gate evaluation

Gate Fields

Field	Type	Required	Default	Description
type	"llm" | "human"	No	"llm"	Gate type.
prompt	string	No	""	Additional evaluation criteria. Jinja2 template (variables: {{ output }}, {{ input }}).
model	string	No	claude-sonnet-4-20250514	Model used for LLM gate evaluation.

Gate Types

Type	Behavior
llm	Claude CLI automatically evaluates the output and returns {"decision": "approved"/"rejected", "reason": "..."}. Pipeline continues immediately.
human	Pipeline pauses and waits for manual approval. Resume with aqm approve <task-id> or aqm reject <task-id> -r "reason".

---

MCP Servers

Attach Model Context Protocol servers to give agents real-world capabilities.

mcp:
  # Simple format — auto-resolves to npx -y @modelcontextprotocol/server-{name}
  - server: github

  # With arguments
  - server: filesystem
    args: ["/path/to/dir"]

  # Custom server with full configuration
  - server: custom-db
    command: node
    args: ["./my-mcp-server.js"]
    env:
      DATABASE_URL: "postgres://..."
      API_KEY: "sk-..."

MCP Server Fields

Field	Type	Required	Default	Description
server	string	Yes	—	Server name. Used as identifier and for auto-resolution.
command	string	No	"npx"	Command to launch the server.
args	list[string]	No	[]	Arguments passed to the command.
env	dict[string, string]	No	null	Environment variables for the server process.

Auto-resolution: When only server name is provided (no command), it resolves to:

npx -y @modelcontextprotocol/server-{name} [args...]

Common MCP servers:

Server Name	Capabilities
github	Read/write repos, create PRs, manage issues
filesystem	Read/write local files and directories
postgres	Execute SQL queries against PostgreSQL
slack	Send messages, read channels
browsertools	Browser automation, E2E testing
sentry	Error tracking and lookup

---

claude_code_flags

Extra CLI flags passed directly to the claude command. Only applies when runtime: claude_code.

claude_code_flags:
  - "--allowedTools"
  - "Edit,Write,Bash,Read"

This is useful for restricting which tools an agent can use, or passing other Claude Code CLI options.

---

Complete Example

# .aqm/agents.yaml
agents:
  - id: planner
    name: Planning Agent
    runtime: api
    model: claude-sonnet-4-20250514
    system_prompt: |
      You are a software planner.
      Analyze the requirements and write a detailed specification.
      Requirements: {{ input }}
    handoffs:
      - to: reviewer
        task: review_spec
        condition: always
        payload: "{{ output }}"

  - id: reviewer
    name: Review Agent
    runtime: api
    model: claude-sonnet-4-20250514
    system_prompt: |
      Review this specification. Decide approve or reject.
      If rejecting, always include the reason.
      Spec: {{ input }}
    gate:
      type: llm
      prompt: "Is this spec clear, complete, and ready for implementation?"
      model: claude-sonnet-4-20250514
    handoffs:
      - to: developer
        task: implement
        condition: on_approve
        payload: "{{ output }}"
      - to: planner
        task: revise_spec
        condition: on_reject
        payload: "REJECTED: {{ reject_reason }}\nOriginal: {{ output }}"

  - id: developer
    name: Development Agent
    runtime: claude_code
    model: claude-sonnet-4-20250514
    mcp:
      - server: filesystem
        args: ["/path/to/project"]
    system_prompt: |
      Implement the approved specification.
      Plan: {{ input }}
    claude_code_flags:
      - "--allowedTools"
      - "Edit,Write,Bash,Read"
    handoffs:
      - to: qa
        task: test_implementation
        condition: always

  - id: qa
    name: QA Agent
    runtime: claude_code
    mcp:
      - server: browsertools
    system_prompt: |
      Run tests and evaluate quality. If issues are found,
      decide severity and route accordingly.
      End with: HANDOFF: <agent_id>
    handoffs:
      # Agent decides based on analysis — no static condition needed
      - to: "*"
        condition: auto

In this example, the QA agent analyzes test results and autonomously decides:

• HANDOFF: developer for minor bugs
• HANDOFF: planner for critical design issues
• HANDOFF: developer, docs if both code and docs need updates

Pipelines for Any Domain

Domain	Pipeline Example
Software Dev	Planning → Review → Implementation → QA → PR
Content Creation	Research → Draft → Edit → Publish
Legal Documents	Extract → Summarize → Risk Flag → Approval
Data Analysis	Collect → Clean → Analyze → Report
Customer Support	Classify → Lookup → Respond → Escalate

Comparison with Existing Frameworks

	LangGraph	CrewAI	OpenSWE	aqm
Pipeline definition	Python code	Python code	Code	YAML
Pipeline sharing	❌	Paid platform	❌	Open registry
Explicit queue	❌	❌	❌	SQLite default
Approve/Reject gate	Interrupt pattern	❌	❌	First-class
Reverse feedback loop	Manual	Limited	❌	Built-in
Fan-out (parallel branches)	Manual	❌	❌	Declarative
Agent-decided routing	Manual	❌	❌	condition: auto
File-based context	❌	❌	❌	context.md
MCP agent connection	Manual	❌	❌	Declarative
Local/offline	❌	❌	❌	Default

Design Principles

• The ecosystem is the product — The YAML sharing ecosystem is the core value, not the execution engine
• Queue is the contract — No direct calls between agents. Communication only through queues
• Context is a file — Accumulated in human-readable context.md
• Local first — SQLite default. Runs without external infrastructure
• Declarative first — Define in YAML, code is the escape hatch
• Gate is first-class — approve/reject is a core feature

Documentation

Document	Description
Core Concepts	Task, Queue, Handoff, Gate, Condition, Context, Pipeline — with LangGraph/CrewAI comparison tables
YAML Specification	Independent agents.yaml format spec (apiVersion: aqm/v0.1), field reference, processing order, versioning policy
JSON Schema	Machine-readable schema for validation and IDE autocomplete
Competitive Analysis	Positioning vs. LangGraph, CrewAI, AutoGen, OpenSWE, Copilot, Vertex AI
Seed Pipelines	10 ready-to-use pipelines with feature matrix
Contributing	How to contribute pipelines (equal to code!), submission template, review process

Validate your pipeline against the spec:

aqm validate .aqm/agents.yaml

Architecture

aqm/
├── aqm/
│   ├── core/
│   │   ├── task.py           # Task, StageRecord, TaskStatus
│   │   ├── agent.py          # AgentDefinition, params, extends, imports
│   │   ├── pipeline.py       # Pipeline execution loop
│   │   ├── gate.py           # LLMGate / HumanGate
│   │   ├── context_file.py   # File-based context accumulation
│   │   ├── context.py        # Prompt builder
│   │   └── project.py        # Project root detection
│   ├── queue/
│   │   ├── base.py           # AbstractQueue interface
│   │   ├── sqlite.py         # SQLiteQueue (default)
│   │   └── file.py           # FileQueue (testing)
│   ├── runtime/
│   │   ├── base.py           # AbstractRuntime interface
│   │   ├── api.py            # Claude CLI runtime (text-only)
│   │   └── claude_code.py    # Claude Code CLI runtime (tools + MCP)
│   ├── web/
│   │   └── app.py            # FastAPI web dashboard
│   └── cli.py                # Click CLI
├── schema/
│   └── agents-schema.json    # JSON Schema for agents.yaml
├── docs/
│   ├── concepts.md           # Core concepts guide
│   ├── spec.md               # YAML format specification
│   └── competitive-analysis.md
├── examples/                  # 10 seed pipelines
└── tests/

Roadmap

v0.1 — Core

• SQLite-based task queue
• YAML agent declarations
• Handoff routing (static, fan-out, agent-decided)
• LLM / Human approval gates
• File-based context accumulation (context.md)
• Claude Code runtime with MCP support
• Local web UI dashboard
• Fan-out: parallel child tasks from comma-separated targets
• condition: auto: agent-decided routing via HANDOFF: directive
• params: parameterization with ${{ params.X }} for portable pipelines
• extends / abstract: agent inheritance for DRY definitions
• imports: reuse agents across pipelines from external files
• 10 seed pipelines covering software, content, legal, data, and more
• Interactive params: prompt + auto_detect for guided pipeline setup
• Follow-up tasks: aqm fix for multi-turn iteration with context carry-over

v0.2 — Connections

• Enhanced per-agent MCP server support
• GitHub / Slack webhook triggers
• Context summarization (prevent token explosion)

v0.3 — Ecosystem

• GitHub-based pipeline registry (aqm publish / pull / search)
• registry.aqm.dev launch (dedicated registry server)
• YAML version control and forking

v1.0 — Stabilization

• Task dependencies (DAG)
• Redis / Postgres queue backends
• Full web dashboard with real-time updates

Contributing

git clone https://github.com/aqm-framework/aqm
cd aqm
pip install -e ".[dev]"
pytest tests/

Creating and sharing pipelines is also a great contribution.

License

MIT