Build Anything with AI Agents

AgentDock is a framework for building sophisticated AI agents that deliver complex tasks with configurable determinism. It consists of two main components:

1. AgentDock Core: An open-source, backend-first framework for building and deploying AI agents. It's designed to be framework-agnostic and provider-independent, giving you complete control over your agent's implementation.

2. Open Source Client: A complete Next.js application that serves as a reference implementation and consumer of the AgentDock Core framework. You can see it in action at https://hub.agentdock.ai

Built with TypeScript, AgentDock emphasizes simplicity, extensibility, and configurable determinism - making it ideal for building reliable and predictable AI systems that can operate with minimal supervision.

🌐 AgentDock Pro Coming Soon: Experience the future of AI automation with our comprehensive cloud platform featuring visual workflow builders, advanced orchestration, and enterprise-grade infrastructure. Sign up at AgentDock.ai to secure early access and earn free platform credits when we launch.

📖 AI Agents Book: Master the complete methodology for building production-ready AI agents with our comprehensive guide at AI Agents Book - covering everything from fundamentals to enterprise deployment patterns.

🎯 Comprehensive Prompt Library: We're building the most extensive prompt library covering everyday automation needs and highly specialized vertical requirements. From general productivity to industry-specific workflows, our curated collection empowers agents with battle-tested prompts for any scenario.

Demos

Dr. Gregory House: A diagnostic reasoning powerhouse that orchestrates search, deep_research, and pubmed tools in a multi-stage workflow to tackle complex medical cases using methodical investigation techniques that rival expert diagnosticians.

https://github.com/user-attachments/assets/50c766dc-fc65-481c-aad2-9a71169c7b28

Cognitive Reasoner: Multi-stage reasoning engine that orchestrates seven specialized cognitive tools (search, think, reflect, compare, critique, brainstorm, debate) in configurable workflows to systematically deconstruct and solve complex problems with human-like reasoning patterns.

https://github.com/user-attachments/assets/279a4e48-a980-4f83-becb-5e039fe10c56

History Mentor: Immersive educational agent combining vectorized historical knowledge with search capabilities and dynamic Mermaid diagram rendering to create authentic learning experiences that visualize complex historical relationships and timelines on demand.

https://github.com/user-attachments/assets/56e80a15-eac3-452b-aa8b-efe7b7f3360c

Calorie Vision: Vision-based nutritional analysis system that combines computer vision with structured data extraction to deliver precise macro and micronutrient breakdowns from food images, functioning like a nutritionist that can instantly quantify meal composition without relying on manual input.

https://github.com/user-attachments/assets/6b4e71cf-accc-4c18-bb42-7bc5ad2f37e4

🌐 README Translations

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🧠 Design Principles

AgentDock is built on these core principles:

• Simplicity First: Minimal code required to create functional agents
• Node-Based Architecture: All capabilities implemented as nodes
• Tools as Specialized Nodes: Tools extend the node system for agent capabilities
• Configurable Determinism: Control the predictability of agent behavior
• Type Safety: Comprehensive TypeScript types throughout

Configurable Determinism

Configurable determinism is a cornerstone of AgentDock's design philosophy, enabling you to balance creative AI capabilities with predictable system behavior:

• AgentNodes are inherently non-deterministic as LLMs may generate different responses each time
• Workflows can be made more deterministic through defined tool execution paths
• Developers can control the level of determinism by configuring which parts of the system use LLM inference
• Even with LLM components, the overall system behavior remains predictable through structured tool interactions
• This balanced approach enables both creativity and reliability in your AI applications

Deterministic Workflows

AgentDock fully supports the deterministic workflows you're familiar with from typical workflow builders. All the predictable execution paths and reliable outcomes you expect are available, with or without LLM inference:

flowchart LR
  Input[Input] --> Process[Process]
  Process --> Database[(Database)]
  Process --> Output[Output]
  
  style Input fill:#f9f9f9,stroke:#333,stroke-width:1px
  style Output fill:#f9f9f9,stroke:#333,stroke-width:1px
  style Process fill:#d4f1f9,stroke:#333,stroke-width:1px
  style Database fill:#e8e8e8,stroke:#333,stroke-width:1px

Non-Deterministic Agent Behavior

With AgentDock, you can also leverage AgentNodes with LLMs when you need more adaptability. The creative outputs may vary based on your needs, while maintaining structured interaction patterns:

flowchart TD
  Input[User Query] --> Agent[AgentNode]
  Agent -->|"LLM Reasoning (Non-Deterministic)"| ToolChoice{Tool Selection}
  ToolChoice -->|"Option A"| ToolA[Deep Research Tool]
  ToolChoice -->|"Option B"| ToolB[Data Analysis Tool]
  ToolChoice -->|"Option C"| ToolC[Direct Response]
  ToolA --> Response[Final Response]
  ToolB --> Response
  ToolC --> Response
  
  style Input fill:#f9f9f9,stroke:#333,stroke-width:1px
  style Agent fill:#ffdfba,stroke:#333,stroke-width:1px
  style ToolChoice fill:#ffdfba,stroke:#333,stroke-width:1px
  style ToolA fill:#d4f1f9,stroke:#333,stroke-width:1px
  style ToolB fill:#d4f1f9,stroke:#333,stroke-width:1px
  style ToolC fill:#d4f1f9,stroke:#333,stroke-width:1px
  style Response fill:#f9f9f9,stroke:#333,stroke-width:1px

Non-Deterministic Agents with Deterministic Sub-Workflows

AgentDock gives you the best of both worlds by combining non-deterministic agent intelligence with deterministic workflow execution:

flowchart TD
  Input[User Query] --> Agent[AgentNode]
  Agent -->|"LLM Reasoning (Non-Deterministic)"| FlowChoice{Sub-Workflow Selection}
  FlowChoice -->|"Decision A"| Flow1[Deterministic Workflow 1]
  FlowChoice -->|"Decision B"| Flow2[Deterministic Workflow 2]
  FlowChoice -->|"Decision C"| DirectResponse[Generate Response]
  
  Flow1 --> |"Step 1 → 2 → 3 → ... → 200"| Flow1Result[Workflow 1 Result]
  Flow2 --> |"Step 1 → 2 → 3 → ... → 100"| Flow2Result[Workflow 2 Result]
  
  Flow1Result --> Response[Final Response]
  Flow2Result --> Response
  DirectResponse --> Response
  
  style Input fill:#f9f9f9,stroke:#333,stroke-width:1px
  style Agent fill:#ffdfba,stroke:#333,stroke-width:1px
  style FlowChoice fill:#ffdfba,stroke:#333,stroke-width:1px
  style Flow1 fill:#c9e4ca,stroke:#333,stroke-width:1px
  style Flow2 fill:#c9e4ca,stroke:#333,stroke-width:1px
  style Flow1Result fill:#c9e4ca,stroke:#333,stroke-width:1px
  style Flow2Result fill:#c9e4ca,stroke:#333,stroke-width:1px
  style DirectResponse fill:#ffdfba,stroke:#333,stroke-width:1px
  style Response fill:#f9f9f9,stroke:#333,stroke-width:1px

This approach enables complex multi-step workflows (potentially involving hundreds of deterministic steps implemented within tools or as connected node sequences) to be invoked by intelligent agent decisions. Each workflow executes predictably despite being triggered by non-deterministic agent reasoning.

For more advanced AI agent workflows and multi-stage processing pipelines, we're building AgentDock Pro - a powerful platform for creating, visualizing, and running complex agent systems.

TL;DR on Configurable Determinism

Think of it like driving. Sometimes you need the AI's creativity (like navigating city streets - non-deterministic), and sometimes you need reliable, step-by-step processes (like following highway signs - deterministic). AgentDock lets you build systems that use both, choosing the right approach for each part of a task. You get the AI's smarts and predictable results where needed.

🏗️ Core Architecture

The framework is built around a powerful, modular node-based system, serving as the foundation for all agent functionality. This architecture uses distinct node types as building blocks:

• BaseNode: The fundamental class establishing the core interface and capabilities for all nodes.
• AgentNode: A specialized core node orchestrating LLM interactions, tool usage, and agent logic.
• Tools & Custom Nodes: Developers implement agent capabilities and custom logic as nodes extending BaseNode.

These nodes interact through managed registries and can be connected (leveraging the core architecture's ports and potential message bus) to enable complex, configurable, and potentially deterministic agent behaviors and workflows.

For a detailed explanation of the node system's components and capabilities, please see the Node System Documentation.

🚀 Getting Started

For a comprehensive guide, see the Getting Started Guide.

Requirements

• Node.js ≥ 20.11.0 (LTS)
• pnpm ≥ 9.15.0 (Required)
• API keys for LLM providers (Anthropic, OpenAI, etc.)

Installation

1. Clone the Repository:

   git clone https://github.com/AgentDock/AgentDock.git
   cd AgentDock
   

2. Install pnpm:

   corepack enable
   corepack prepare pnpm@latest --activate
   

3. Install Dependencies:

   pnpm install
   

   For a clean reinstallation (when you need to rebuild from scratch):

   pnpm run clean-install
   

   This script removes all node_modules, lock files, and reinstalls dependencies correctly.

4. Configure Environment:

   Create an environment file (.env or .env.local) based on .env.example:

   # Option 1: Create .env.local
   cp .env.example .env.local
   
   # Option 2: Create .env
   cp .env.example .env
   

   Then add your API keys to the environment file.

5. Start Development Server:

   pnpm dev
   

Deploy the Open Source Client

Click the button above to deploy the AgentDock Open Source Client directly to your Vercel account.

Advanced Capabilities

Capability	Description	Documentation
Session Management	Isolated, performant state management for conversations	Session Documentation
Orchestration Framework	Control agent behavior and tool availability based on context	Orchestration Documentation
Storage Abstraction	Flexible storage system with pluggable providers for KV, Vector, and Secure storage	Storage Documentation
Evaluation Framework	Systematically measure and improve agent quality with diverse evaluators	Evaluation Documentation

The storage system is currently evolving with key-value storage (Memory, Redis, Vercel KV providers) and secure client-side storage, while vector storage and additional backends are in development.

📕 Documentation

Documentation for the AgentDock framework is available at hub.agentdock.ai/docs and in the /docs/ folder of this repository. The documentation includes:

• Getting started guides
• API references
• Node creation tutorials
• Integration examples

For comprehensive ecosystem documentation and guides, visit agentdock.ai/docs.

📂 Repository Structure

This repository contains:

1. AgentDock Core: The core framework located in agentdock-core/
2. Open Source Client: A complete reference implementation built with Next.js, serving as a consumer of the AgentDock Core framework.
3. Example Agents: Ready-to-use agent configurations in the agents/ directory
4. AI Agents Book: Comprehensive guide content in the content/ai-agents-book/ directory

You can use AgentDock Core independently in your own applications, or use this repository as a starting point for building your own agent-powered applications.

📝 Agent Templates

AgentDock includes several pre-configured agent templates. Explore them in the agents/ directory or read the Agent Templates Documentation for details on configuration.

🔧 Example Implementations

Example implementations showcase specialized use cases and advanced functionality:

Implementation	Description	Status
Orchestrated Agent	Example agent using orchestration to adapt behavior based on context	Available
Cognitive Reasoner	Tackles complex problems using structured reasoning & cognitive tools	Available
Agent Planner	Specialized agent for designing and implementing other AI agents	Available
Code Playground	Sandboxed code generation and execution with rich visualization capabilities	Planned

🔐 Environment Configuration Details

The AgentDock Open Source Client requires API keys for LLM providers to function. These are configured in an environment file (.env or .env.local) which you create based on the provided .env.example file.

LLM Provider API Keys

Add your LLM provider API keys (at least one is required):

# LLM Provider API Keys - at least one is required
ANTHROPIC_API_KEY=sk-ant-xxxxxxx  # Anthropic API key
OPENAI_API_KEY=sk-xxxxxxx         # OpenAI API key
GEMINI_API_KEY=xxxxxxx            # Google Gemini API key
DEEPSEEK_API_KEY=xxxxxxx          # DeepSeek API key
GROQ_API_KEY=xxxxxxx              # Groq API key

API Key Resolution

The AgentDock Open Source Client follows a priority order when resolving which API key to use:

1. Per-agent custom API key (set via agent settings in the UI)
2. Global settings API key (set via the settings page in the UI)
3. Environment variable (from .env.local or deployment platform)

Tool-specific API Keys

Some tools also require their own API keys:

# Tool-specific API Keys
SERPER_API_KEY=                  # Required for search functionality
FIRECRAWL_API_KEY=               # Required for deeper web search

For more details about environment configuration, see the implementation in src/types/env.ts.

Using Your Own API Keys (BYOK)

AgentDock follows a BYOK (Bring Your Own Key) model:

1. Add your API keys in the settings page of the application
2. Alternatively, provide keys via request headers for direct API usage
3. Keys are securely stored using the built-in encryption system
4. No API keys are shared or stored on our servers

📦 Package Manager

This project requires the use of pnpm for consistent dependency management. npm and yarn are not supported.

💡 What You Can Build

1. AI-Powered Applications

   • Custom chatbots with any frontend
   • Command-line AI assistants
   • Automated data processing pipelines
   • Backend service integrations

2. Integration Capabilities

   • Any AI provider (OpenAI, Anthropic, etc.)
   • Any frontend framework
   • Any backend service
   • Custom data sources and APIs

3. Automation Systems

   • Data processing workflows
   • Document analysis pipelines
   • Automated reporting systems
   • Task automation agents

Key Features

Feature	Description
🔌 Framework Agnostic (Node.js Backend)	Core library integrates with Node.js backend stacks.
🧩 Modular Design	Build complex systems from simple nodes
🛠️ Extensible	Create custom nodes for any functionality
🔒 Secure	Built-in security features for API keys and data
🔑 BYOK	Use your own API keys for LLM providers
📦 Self-Contained	Core framework has minimal dependencies
⚙️ Multi-Step Tool Calls	Support for complex reasoning chains
📊 Structured Logging	Detailed insights into agent execution
🛡️ Robust Error Handling	Predictable behavior and simplified debugging
📝 TypeScript First	Type safety and enhanced developer experience
🌐 Open Source Client	Complete Next.js reference implementation included
🔄 Orchestration	Dynamic control of agent behavior based on context
💾 Session Management	Isolated state for concurrent conversations
🎮 Configurable Determinism	Balance AI creativity & predictability via node logic/workflows
📊 Evaluation Framework	Robust tools to define, run, and analyze agent performance evaluations

🧰 Components

AgentDock's modular architecture is built upon these key components:

• BaseNode: The foundation for all nodes in the system
• AgentNode: The primary abstraction for agent functionality
• Tools & Custom Nodes: Callable capabilities and custom logic implemented as nodes.
• Node Registry: Manages the registration and retrieval of all node types
• Tool Registry: Manages tool availability for agents
• CoreLLM: Unified interface for interacting with LLM providers
• Provider Registry: Manages LLM provider configurations
• Evaluation Framework: Core components for agent assessment
• Error Handling: System for handling errors and ensuring predictable behavior
• Logging: Structured logging system for monitoring and debugging
• Orchestration: Controls tool availability and behavior based on conversation context
• Sessions: Manages state isolation between concurrent conversations

For detailed technical documentation on these components, see the Architecture Overview.

🗺️ Roadmap

Below is our development roadmap for AgentDock. Most improvements listed here relate to the core AgentDock framework (agentdock-core), which is currently developed locally and will be published as a versioned NPM package upon reaching a stable release. Some roadmap items may also involve enhancements to the open-source client implementation.

Feature	Description	Category
Storage Abstraction Layer	Flexible storage system with pluggable providers	In Progress
Advanced Memory Systems	Long-term context management	In Progress
Vector Storage Integration	Embedding-based retrieval for documents and memory	In Progress
Evaluation for AI Agents	Comprehensive testing and evaluation framework	In Progress
Platform Integration	Support for Telegram, WhatsApp, and other messaging platforms	Planned
Multi-Agent Collaboration	Enable agents to work together	Planned
Model Context Protocol (MCP) Integration	Support for discovering and using external tools via MCP	Planned
Voice AI Agents	AI agents using voice interfaces and phone numbers via AgentNode	Planned
Telemetry and Traceability	Advanced logging and performance tracking	Planned
Workflow Runtime & Nodes	Core runtime, node types, and orchestration logic for complex automations	Planned
AgentDock Pro	Comprehensive enterprise cloud platform for scaling AI agents & workflows	Cloud
Natural Language AI Agent Builder	Visual builder + natural language agent and workflow construction	Cloud
Agent Marketplace	Monetizable agent templates	Cloud

👥 Contributing

We welcome contributions to AgentDock! Please see the CONTRIBUTING.md for detailed contribution guidelines.

📜 License

AgentDock is released under the MIT License.

✨ Build Anything!

AgentDock provides the foundation to build almost any AI-powered application or automation you can imagine. We encourage you to explore the framework, build innovative agents, and contribute back to the community. Let's build the future of AI interaction together!