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Dynamiq is an orchestration framework for agentic AI and LLM applications
| Date | Stars |
|---|---|
| 2026-07-24 | 1061 |
| 2026-07-25 | 1061 |
| 2026-07-28 | 1061 |
| 2026-07-30 | 1061 |
| 2026-07-31 | 1062 |
| 2026-08-06 | 1062 |
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<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">
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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=10Excerpt of 18,940 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f127063d330d9306, topic:llm, topic:gpt
matched fp:f127063d330d9306, topic:llmops
matched fp:f127063d330d9306, topic:agents, desc:agentic, readme:agentic
matched fp:f127063d330d9306, topic:rag, readme:retrieval-augmented generation, readme:retrieval augmented