As AI coding assistants evolve, the real challenge is no longer just generating code, it’s understanding your entire codebase, adapting to your workflow, and automating complex development tasks with safety and precision. LangCode is a newest open-source framework that unifies Gemini, Anthropic Claude, OpenAI, and Ollama into a single, powerful coding environment, all accessible directly from your terminal. If you want to analyze your code, implement new features, fix bugs, or refactor modules intelligently, LangCode handles it all through its interactive launcher and AI-powered deep code reasoning. Its ReAct and Deep modes let you toggle between fast, lightweight responses and in-depth multi-step reasoning, while Smart Routing automatically selects the most suitable LLM for your task based on cost, speed, or quality. With safe, reviewable diffs, customizable project instructions, and MCP-based extensibility, LangCode doesn’t just generate code, it thinks, plans, and acts alongside you like an intelligent engineering collaborator.
In short, LangCode transforms your terminal into an autonomous coding cockpit, one where multiple top-tier LLMs collaborate to write, debug, and understand your code seamlessly. In this guide, you’ll learn how to install and run LangCode locally, unlock its interactive launcher, and start building smarter, faster, and safer than ever before.
Prerequisites
The minimum system requirements for running this model are:
Step-by-step process to install and run LangCode
For the purpose of this tutorial, we’ll use a GPU-powered Virtual Machine by NodeShift since it provides high compute Virtual Machines at a very affordable cost on a scale that meets GDPR, SOC2, and ISO27001 requirements. Also, it offers an intuitive and user-friendly interface, making it easier for beginners to get started with Cloud deployments. However, feel free to use any cloud provider of your choice and follow the same steps for the rest of the tutorial.
Step 1: Setting up a NodeShift Account
Visit app.nodeshift.com and create an account by filling in basic details, or continue signing up with your Google/GitHub account.
If you already have an account, login straight to your dashboard.
Step 2: Create a GPU Node
After accessing your account, you should see a dashboard (see image), now:
- Navigate to the menu on the left side.
- Click on the GPU Nodes option.
- Click on Start to start creating your very first GPU node.
These GPU nodes are GPU-powered virtual machines by NodeShift. These nodes are highly customizable and let you control different environmental configurations for GPUs ranging from H100s to A100s, CPUs, RAM, and storage, according to your needs.
Step 3: Selecting configuration for GPU (model, region, storage)
- For this tutorial, we’ll be using 1x RTX A6000 GPU, however, you can choose any GPU as per the prerequisites.
- Similarly, we’ll opt for 200GB storage by sliding the bar. You can also select the region where you want your GPU to reside from the available ones.
Step 4: Choose GPU Configuration and Authentication method
- After selecting your required configuration options, you’ll see the available GPU nodes in your region and according to (or very close to) your configuration. In our case, we’ll choose a 1x RTX A6000 48GB GPU node with 64vCPUs/63GB RAM/200GB SSD.
2. Next, you’ll need to select an authentication method. Two methods are available: Password and SSH Key. We recommend using SSH keys, as they are a more secure option. To create one, head over to our official documentation.
Step 5: Choose an Image
The final step is to choose an image for the VM, which in our case is Nvidia Cuda.
That’s it! You are now ready to deploy the node. Finalize the configuration summary, and if it looks good, click Create to deploy the node.
Step 6: Connect to active Compute Node using SSH
- As soon as you create the node, it will be deployed in a few seconds or a minute. Once deployed, you will see a status Running in green, meaning that our Compute node is ready to use!
- Once your GPU shows this status, navigate to the three dots on the right, click on Connect with SSH, and copy the SSH details that appear.
As you copy the details, follow the below steps to connect to the running GPU VM via SSH:
- Open your terminal, paste the SSH command, and run it.
2. In some cases, your terminal may take your consent before connecting. Enter ‘yes’.
3. A prompt will request a password. Type the SSH password, and you should be connected.
Output:
Next, If you want to check the GPU details, run the following command in the terminal:
!nvidia-smi
Step 7: Set up the project environment with dependencies
- Create a virtual environment using Anaconda.
conda create -n langcode python=3.11 -y && conda activate langcode
Output:
2. Install ollama with its depencencies.
apt install pciutils lshw
curl -fsSL https://ollama.com/install.sh | sh
Output:
3. Start Ollama server in another terminal window.
ollama serve
4. Download model of your choice which you want to use as auditor, target and judge for evaluating safety of the model.
For this demo, we’re going to use GPT-OSS 20B that is available on Ollama.
ollama run gpt-oss:20b
Once done, confirm if it is successfully downloaded.
ollama list
Output:
5. Install langchain-code package.
pip install langchain-code
Output:
Step 8: Run the tool with your model
- Make sure Ollama server is running in the background and run the following command to launch LangCode CLI.
langcode
It’ll open up an interactive shell like this:
2. Select Ollama as the provider in LLM option.
3. Press Enter to start the langcode console where you can type in your prompts.
4. Enter prompt of your choice and wait for model to create a file in real-time inside your root directory and write the code.
5. As shown above, AI as created the file in our root directory. We can come out of the LangCode console by pressing CTRL+C and then we can test the python file by running it.
Conclusion
LangCode redefines what it means to work with AI-assisted development, bringing together Gemini, Anthropic, OpenAI, and Ollama under one unified, intelligent coding environment. From its interactive launcher and ReAct/Deep reasoning modes to safe code diffs, smart LLM routing, and MCP-based extensibility, the article covered how LangCode helps developers to automate tasks, analyze codebases, and refine workflows with precision and control. NodeShift Cloud makes running LangCode locally effortless, offering a streamlined installation, preconfigured environments, and full compatibility with Docker and Python setups. With NodeShift handling setup and environment management, developers can focus on utilizing powerful open source tools like LangCode with full potential, turning everyday coding into an intelligent, collaborative process powered by the world’s leading LLMs.
