AgentDock
Build Anything with AI Agents
Install / Use
/learn @AgentDock/AgentDockREADME
AgentDock is a framework for building sophisticated AI agents that deliver complex tasks with configurable determinism. It consists of two main components:
-
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.
-
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.
<br>🌐 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
</br>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
</br>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
</br>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
Français • 日本語 • 한국어 • 中文 • Español • Deutsch • Italiano • Nederlands • Polski • Türkçe • Українська • Ελληνικά • Русский • العربية
🧠 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](docs/agentdock-p
