Getting Started with GNT: Building an Autonomous Agent Memory Layer

Begin by cloning the GNT repository from GitHub to your local development environment. GNT is an open-source platform designed to act as a memory layer for AI agents. Open your terminal and run the following command to download the latest stable version: ```bash git clone https://github.com/gnt-ai/gnt.git cd gnt ``` Once inside the directory, you will need to set up the Python environment. GNT relies on modern Python packages for vector storage and agent orchestration. Create a virtual environment to avoid dependency conflicts with your existing projects: ```bash python3 -m venv venv source venv/bin/activate # On Windows, use venv\Scripts\activate pip install -r requirements.txt ``` This process may take a few minutes as it installs core dependencies including vector database clients and API connectors. Ensure you have Python 3.9 or higher installed. After installation, verify the setup by checking the version of the core GNT module. This initialization step is crucial as it prepares the foundational libraries required for the agent's 'brain' to function correctly.

GNT requires access to various data sources to build its knowledge base. You need to configure your environment variables to authenticate with these sources. Create a `.env` file in the root of the `gnt` directory. This file will store sensitive credentials and configuration keys. Typical configurations include API keys for internal wikis, database connections for customer data, and LLM provider keys for processing. Here is an example structure for your `.env` file: ```env # LLM Provider Configuration LLM_PROVIDER=openai OPENAI_API_KEY=sk-your-key-here # Vector Database Configuration VECTOR_DB_URL=http://localhost:6333 VECTOR_DB_COLLECTION=gnt_memory # Knowledge Source Configurations CONFLUENCE_API_KEY=your-confluence-key NOTION_API_KEY=your-notion-secret ``` Make sure to replace the placeholder values with your actual credentials. GNT uses these variables to connect to your organizational knowledge bases. Without proper configuration, the agent will lack the context it needs to make informed decisions. Always keep your `.env` file out of version control by adding it to your `.gitignore` file to prevent accidental exposure of sensitive keys.

The core value of GNT is its ability to ingest and structure internal knowledge. Use the provided CLI tool to index your documents. GNT supports multiple formats including Markdown, PDFs, and direct API integrations with tools like Notion or Confluence. Run the ingestion command to start the indexing process. For example, to index a local directory of documentation: ```bash gnt ingest --source ./docs --format markdown ``` Or, to connect to a remote knowledge base: ```bash gnt ingest --source notion --space-id 'your-space-id' ``` During ingestion, GNT will parse documents, chunk them into manageable segments, and generate embeddings for vector storage. This process transforms unstructured data into a queryable memory layer. You will see progress logs in the terminal indicating how many chunks were processed and stored. This step is critical because the quality of the agent's decisions depends directly on the relevance and completeness of this ingested knowledge. Ensure your source documents are clean and well-structured for optimal results.

Now that your knowledge base is populated, you need to define how your agent will interact with it. GNT allows you to configure 'retrieval strategies' that determine which context is fetched before an agent executes a task. Create a `agent_config.yaml` file in your project root. In this file, specify the retrieval parameters such as the number of relevant chunks to fetch and the semantic similarity threshold. Here is a basic configuration: ```yaml agent: name: "CustomerSupportAgent" model: "gpt-4o" retrieval: top_k: 5 similarity_threshold: 0.75 sources: - "confluence" - "notion" ``` This configuration tells GNT to fetch the top 5 most relevant chunks from Confluence and Notion when the agent processes a query, provided they meet the similarity threshold. This ensures that the agent only uses highly relevant context, reducing hallucinations and improving response accuracy. You can also define custom filters to restrict access to certain knowledge domains based on user roles or query intent, adding a layer of security and relevance control.

With the knowledge base indexed and the agent configured, you can now start the GNT service. Run the following command to launch the local server: ```bash gnt serve --config agent_config.yaml ``` The service will start on a default port (usually 8000). Your AI agent can now send requests to this endpoint to retrieve context. Here is a Python example of how your agent code might interact with GNT: ```python import requests def get_context(query: str) -> dict: response = requests.post('http://localhost:8000/retrieve', json={"query": query}) return response.json() context = get_context("How do I reset my password?") print(context['relevant_chunks']) ``` This snippet demonstrates how to query the GNT memory layer. The agent sends a natural language query, and GNT returns the relevant contextual chunks from your ingested knowledge. This decoupling allows your agent to remain lightweight while leveraging the deep organizational context provided by GNT. Ensure your agent's main loop calls this retrieval function before generating any final output to the user.

Finally, test your setup by sending a query through your agent. Use a known question from your ingested documents to verify that the correct context is retrieved and used. For example, if you ingested a 'Troubleshooting Guide,' ask the agent a specific question from that guide. Observe the logs in the terminal where GNT is running. You should see the retrieval process executing, fetching the specific chunks, and returning them to your agent. Then, check the final output generated by your LLM. Does it accurately reflect the ingested knowledge? Is it concise and relevant? If the answers are incorrect, review your `similarity_threshold` and `top_k` values. You may also need to refine your document ingestion process to ensure better chunking. GNT provides a debug mode that allows you to inspect the raw embeddings and retrieval scores, which is invaluable for tuning the system. Iterate on these parameters until the agent consistently provides accurate, context-aware responses. This validation step is crucial for ensuring reliability in production environments.