hexdag 0.7.0.dev12

Lightweight DAG orchestration framework with enterprise pipeline capabilities

hexDAG -- Operating System for AI Agents

Just as Linux provides processes, syscalls, drivers, and /lib so programs don't reinvent the wheel, hexDAG provides pipelines, ports, drivers, and a standard library so AI agents don't reinvent orchestration.

hexDAG transforms complex AI workflows into deterministic, testable, and maintainable systems through declarative YAML pipelines and DAG-based execution. It is async-first, type-safe with Pydantic, and built on hexagonal architecture so you can swap any infrastructure dependency without touching business logic.

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The OS Analogy

Linux	hexDAG	Purpose
Kernel	kernel/	Core execution engine, system call interfaces (Protocols), domain models
System calls	kernel/ports/	Contracts for external capabilities (LLM, Memory, Database, etc.)
Drivers	drivers/	Low-level infrastructure (executor, observer manager, pipeline spawner)
/lib	stdlib/	Standard library -- built-in nodes, adapters, macros, system libs
Processes	Pipeline runs	Tracked by ProcessRegistry (like ps)
fork/exec	PipelineSpawner	Launch sub-pipelines from within a running pipeline
Process scheduler	Scheduler	Delayed and recurring pipeline execution
State machines	EntityState	Business entity lifecycle management
/usr/bin	api/	User-facing tools (MCP + Studio REST)
Shell	cli/	Command-line interface (hexdag init, hexdag lint, etc.)

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Quick Start

Installation

# Install from PyPI
pip install hexdag

# Or with uv (recommended)
uv pip install hexdag

# With optional dependencies
pip install hexdag[openai]      # OpenAI adapter
pip install hexdag[anthropic]   # Anthropic adapter
pip install hexdag[all]         # Everything

Development

git clone https://github.com/omniviser/hexdag.git
cd hexdag
uv sync

Your First Pipeline

Define a workflow in YAML:

# research_agent.yaml
apiVersion: hexdag/v1
kind: Pipeline
metadata:
  name: research-workflow
  description: AI-powered research assistant
spec:
  ports:
    llm:
      adapter: hexdag.stdlib.adapters.openai.OpenAIAdapter
      config:
        model: gpt-4

  nodes:
    - kind: agent_node
      metadata:
        name: researcher
      spec:
        initial_prompt_template: "Research the topic: {{topic}}"
        max_steps: 5
        available_tools: ["web_search", "summarize"]
      dependencies: []

    - kind: llm_node
      metadata:
        name: analyst
      spec:
        prompt_template: |
          Analyze the research findings: {{researcher.results}}
          Provide actionable insights.
      dependencies: [researcher]

    - kind: function_node
      metadata:
        name: formatter
      spec:
        fn: "myapp.reports.format_report"
        input_mapping:
          findings: "researcher.results"
          insights: "analyst.output"
      dependencies: [researcher, analyst]

Run it:

from hexdag import PipelineRunner

runner = PipelineRunner()
result = await runner.run("research_agent.yaml", input_data={"topic": "AI trends 2025"})

With port overrides for testing:

from hexdag import PipelineRunner, MockLLM

runner = PipelineRunner(port_overrides={"llm": MockLLM(responses="Mock analysis result")})
result = await runner.run("research_agent.yaml", input_data={"topic": "AI trends 2025"})

Or validate without executing:

runner = PipelineRunner()
issues = await runner.validate(pipeline_path="research_agent.yaml")
# [] means valid

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Architecture

hexdag/
  kernel/   -- Core primitives. Protocols, domain models, orchestration.     (/kernel)
  stdlib/   -- Standard library. Built-in nodes, adapters, macros, libs.     (/lib)
  drivers/  -- Low-level infrastructure. Executor, observer, spawner.        (/drivers)
  api/      -- Public API. MCP tools + Studio REST endpoints.                (/usr/bin)
  cli/      -- CLI commands (hexdag init, hexdag lint, hexdag studio, etc.)

The kernel defines contracts and depends on nothing external. The stdlib ships implementations users interact with directly. Drivers provide low-level infrastructure the orchestrator needs internally. The API exposes everything to users via MCP and REST.

Decision Rules

When adding something new:

1. Defines a Protocol or domain model? --> kernel/
2. Implements a Protocol for users to use or extend? --> stdlib/
3. Low-level infrastructure the orchestrator needs internally? --> drivers/
4. User-facing function (MCP tool or REST endpoint)? --> api/

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Uniform Entity Pattern

All framework entities follow one pattern: kernel defines contract, stdlib ships builtins, users write their own.

Entity	Kernel Contract	Stdlib Builtins	User Custom
Ports	kernel/ports/llm.py	--	myapp.ports.X
Adapters	Protocol in kernel/ports/	stdlib/adapters/openai/	myapp.adapters.X
Nodes	NodeSpec + BaseNodeFactory	LLMNode, AgentNode, FunctionNode	myapp.nodes.X
Macros	(convention)	ReasoningAgent, ConversationAgent	myapp.macros.X
Prompts	(convention)	tool_prompts, error_correction	myapp.prompts.X
Libs	HexDAGLib base class	ProcessRegistry, EntityState, Scheduler	myapp.lib.X

Components are referenced by full Python module path in YAML:

nodes:
  - kind: hexdag.stdlib.nodes.LLMNode      # Full path
  - kind: llm_node                          # Built-in alias
  - kind: myapp.nodes.CustomNode            # User custom

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System Libraries (Libs)

Libs are system-level capabilities for multi-pipeline coordination. Every public async method on a HexDAGLib subclass auto-becomes an agent-callable tool.

Lib	Linux Analogy	Purpose
ProcessRegistry	ps	Track pipeline runs -- status, duration, results, parent/child relationships
EntityState	State machines	Declarative state machines for business entities with validated transitions and audit trails
Scheduler	cron / at	Delayed and recurring pipeline execution via asyncio timers
DatabaseTools	sqlite3 CLI	Agent-callable SQL query tools wrapping any SupportsQuery adapter

from hexdag.kernel.lib_base import HexDAGLib
from hexdag.kernel.ports.data_store import SupportsKeyValue

class OrderManager(HexDAGLib):
    def __init__(self, store: SupportsKeyValue) -> None:
        self._store = store

    async def acreate_order(self, customer_id: str, items: list[dict]) -> str:
        """Create a new order. Auto-exposed as agent tool."""
        ...

    async def aget_order(self, order_id: str) -> dict:
        """Get order by ID. Auto-exposed as agent tool."""
        ...

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Key Features

YAML-First Pipelines

Declarative workflows versioned in git. Environment-specific configs for dev/staging/prod. Jinja2 templating, !include tags for modular composition, and automatic field mapping between nodes.

Pipeline Linting

Static analysis for YAML pipelines: cycle detection, hardcoded secret scanning, missing timeout/retry warnings, naming conventions, and unused output detection.

hexdag lint my_pipeline.yaml

Macro System

Reusable pipeline templates that expand into full DAG subgraphs at build time. Built-in macros: ReasoningAgent, ConversationAgent, LLMMacro.

Event-Driven Observability

Comprehensive event system (PipelineStarted, NodeCompleted, NodeFailed, etc.) with pluggable observers for logging, cost profiling, and custom monitoring.

Hexagonal Architecture (Ports & Adapters)

Swap OpenAI for Anthropic, PostgreSQL for SQLite, or any adapter -- with one config line. Business logic stays pure. Test everything with mock adapters.

Pydantic Validation Everywhere

Type safety at every layer. Automatic schema compatibility checking between connected nodes. Runtime type coercion and validation.

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MCP Server

hexDAG includes a built-in Model Context Protocol server that exposes pipeline building capabilities to Claude Code, Cursor, and other LLM-powered editors:

# Install MCP dependencies
uv sync --extra mcp

# Configure in Claude Desktop

{
  "mcpServers": {
    "hexdag": {
      "command": "uv",
      "args": ["run", "python", "-m", "hexdag", "--mcp"]
    }
  }
}

The MCP server provides tools to:

• List available nodes, adapters, tools, and macros from your registry
• Build and validate YAML pipelines interactively
• Get component schemas and documentation
• Manage processes (spawn, schedule, track pipeline runs)
• Auto-discover custom plugins from HEXDAG_PLUGIN_PATHS

Custom Plugin Discovery

hexDAG supports three levels of component discovery:

1. Builtin -- Core adapters and nodes from hexdag.stdlib
2. Plugins -- Community plugins from the hexdag_plugins namespace
3. User-authored -- Your custom components via HEXDAG_PLUGIN_PATHS

export HEXDAG_PLUGIN_PATHS="./my_adapters:./my_nodes"

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CLI

Command	Purpose
hexdag init	Initialize a new hexDAG project
hexdag pipeline validate	Validate a YAML pipeline
hexdag pipeline execute	Execute a pipeline
hexdag lint	Lint YAML for best practices and security
hexdag create	Create pipeline templates from schemas
hexdag build	Build Docker containers for pipelines
hexdag studio	Launch the visual pipeline editor
hexdag plugins	Manage plugins and adapters
hexdag docs	Generate and serve documentation

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Documentation & Learning

Interactive Notebooks

Notebook	Topic	Time
01. Introduction	Your first pipeline	15 min
02. YAML Pipelines	Declarative workflows	25 min
03. Practical Workflow	Real-world patterns	30 min
06. Dynamic Reasoning Agent	Advanced agent patterns	--
YAML Includes & Composition	Modular pipeline composition	--

Docs

• Architecture -- System architecture and the OS analogy
• Roadmap -- Development roadmap and planned kernel extensions
• Quick Start -- Build your first workflow
• All Documentation -- Full documentation index

Examples

• MCP Research Agent -- Deep research agent with environment configs
• Demo: Startup Pitch -- Live demo with rich terminal UI

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Development

# Setup
uv sync
uv run pre-commit install

# Test
uv run pytest
uv run pytest --cov=hexdag --cov-report=term-missing

# Code quality
uv run ruff check hexdag/ --fix
uv run pyright ./hexdag
uv run pre-commit run --all-files

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The Six Pillars

1. Async-First Architecture -- Non-blocking execution for maximum performance
2. Event-Driven Observability -- Real-time monitoring via comprehensive event system
3. Pydantic Validation Everywhere -- Type safety at every layer
4. Hexagonal Architecture -- Clean separation of business logic and infrastructure
5. Composable Declarative Files -- Complex workflows from simple YAML components
6. DAG-Based Orchestration -- Intelligent dependency management and parallelization

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License

Apache License 2.0 -- see LICENSE for details.

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Built for the AI community by the hexDAG team.