---
title: "dynamiq"
type: "tool"
slug: "dynamiq-ai-dynamiq"
canonical_url: "https://www.graphcanon.com/tools/dynamiq-ai-dynamiq"
github_url: "https://github.com/dynamiq-ai/dynamiq"
homepage_url: "https://getdynamiq.ai"
stars: 1056
forks: 129
primary_language: "Python"
license: "Apache-2.0"
categories: ["llm-frameworks", "ai-agents"]
tags: ["llmops", "agents", "llm", "ai", "rag", "generative-ai", "gpt"]
updated_at: "2026-07-07T18:48:03.679231+00:00"
---

# dynamiq

> Orchestration framework for agentic AI and LLM applications

Dynamiq is an orchestration framework designed to facilitate the development of generative AI applications, especially those involving retrieval-augmented generation (RAG) and large language model agents.

## Facts

- Repository: https://github.com/dynamiq-ai/dynamiq
- Homepage: https://getdynamiq.ai
- Stars: 1,056 · Forks: 129 · Open issues: 7 · Watchers: 8
- Primary language: Python
- License: Apache-2.0
- Last pushed: 2026-07-07T17:24:39+00:00

## Categories

- [LLM Frameworks](/categories/llm-frameworks.md)
- [AI Agents](/categories/ai-agents.md)

## Tags

llmops, agents, llm, ai, rag, generative-ai, gpt

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## README (excerpt)

```text
<p align="center">
  <a href="https://www.getdynamiq.ai/"><img src="https://github.com/dynamiq-ai/dynamiq/blob/main/docs/img/Dynamiq_Logo_Universal_Github.png?raw=true" alt="Dynamiq"></a>
</p>


<p align="center">
    <em>Dynamiq is an orchestration framework for agentic AI and LLM applications</em>
</p>

<p align="center">
  <a href="https://getdynamiq.ai">
    <img src="https://img.shields.io/website?label=website&up_message=online&url=https%3A%2F%2Fgetdynamiq.ai" alt="Website">
  </a>
  <a href="https://github.com/dynamiq-ai/dynamiq/releases" target="_blank">
    <img src="https://img.shields.io/github/release/dynamiq-ai/dynamiq" alt="Release Notes">
  </a>
  <a href="#" target="_blank">
    <img src="https://img.shields.io/badge/Python-3.10%2B-brightgreen.svg" alt="Python 3.10+">
  </a>
  <a href="https://github.com/dynamiq-ai/dynamiq/blob/main/LICENSE" target="_blank">
    <img src="https://img.shields.io/badge/License-Apache_2.0-blue.svg" alt="License">
  </a>
  <a href="https://dynamiq-ai.github.io/dynamiq" target="_blank">
    <img src="https://img.shields.io/website?label=documentation&up_message=online&url=https%3A%2F%2Fdynamiq-ai.github.io%2Fdynamiq" alt="Documentation">
  </a>
</p>


Welcome to Dynamiq! 🤖

Dynamiq is your all-in-one Gen AI framework, designed to streamline the development of AI-powered applications. Dynamiq specializes in orchestrating retrieval-augmented generation (RAG) and large language model (LLM) agents.

## Getting Started

Ready to dive in? Here's how you can get started with Dynamiq:

### Installation

First, let's get Dynamiq installed. You'll need Python, so make sure that's set up on your machine. Then run:

```sh
pip install dynamiq
```

Or build the Python package from the source code:
```sh
git clone https://github.com/dynamiq-ai/dynamiq.git
cd dynamiq
uv sync
```

## Documentation
For more examples and detailed guides, please refer to our [documentation](https://dynamiq-ai.github.io/dynamiq).

## Examples

### Simple LLM Flow

Here's a simple example to get you started with Dynamiq:

```python
from dynamiq.nodes.llms.openai import OpenAI
from dynamiq.connections import OpenAI as OpenAIConnection
from dynamiq.prompts import Prompt, Message

# Define the prompt template for translation
prompt_template = """
Translate the following text into English: {{ text }}
"""

# Create a Prompt object with the defined template
prompt = Prompt(messages=[Message(content=prompt_template, role="user")])

# Setup your LLM (Large Language Model) Node
llm = OpenAI(
    id="openai",  # Unique identifier for the node
    connection=OpenAIConnection(api_key="OPENAI_API_KEY"),  # Connection using API key
    model="gpt-4o",  # Model to be used
    temperature=0.3,  # Sampling temperature for the model
    max_tokens=1000,  # Maximum number of tokens in the output
    prompt=prompt  # Prompt to be used for the model
)

# Run the LLM node with the input data
result = llm.run(
    input_data={
        "text": "Hola Mundo!"  # Text to be translated
    }
)

# Print the result of the translation
print(result.output)
```


### Simple ReAct Agent with asynchronous execution
An agent that has the access to E2B Code Interpreter and is capable of solving complex coding tasks.

```python
from dynamiq.nodes.llms.openai import OpenAI
from dynamiq.connections import OpenAI as OpenAIConnection, E2B as E2BConnection
from dynamiq.nodes.agents import Agent
from dynamiq.nodes.tools.e2b_sandbox import E2BInterpreterTool

# Initialize the E2B tool
e2b_tool = E2BInterpreterTool(
    connection=E2BConnection(api_key="E2B_API_KEY")
)

# Setup your LLM
llm = OpenAI(
    id="openai",
    connection=OpenAIConnection(api_key="OPENAI_API_KEY"),
    model="gpt-4o",
    temperature=0.3,
    max_tokens=1000,
)

# Create the agent
agent = Agent(
    name="react-agent",
    llm=llm, # Language model instance
    tools=[e2b_tool],  # List of tools that the agent can use
    role="Senior Data Scientist",  # Role of the agent
    max_loops=10
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/dynamiq-ai-dynamiq`](/api/graphcanon/tools/dynamiq-ai-dynamiq)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_