For AI agents: The documentation index is at https://docs.digitalocean.com/llms.txt. Markdown versions of pages use the same URL with index.html.md in place of the HTML page (for example, append index.html.md to the directory path instead of opening the HTML document).

DigitalOcean AI Platform is a comprehensive suite of tools and features designed to help you build, manage, and deploy AI-powered agents. This includes a variety of foundation models to choose from and a range of features to make your agents more effective and efficient, including agent routing, knowledge bases, guardrails, and more.

Models

Models are large language models (LLMs) trained on large datasets to perform a variety of tasks. You can choose from multiple foundation models, including commercial and open-source options, depending on your use case. These models generate responses for agents you create using the DigitalOcean Control Panel, CLI, and API, or build and deploy using the Agent Development Kit.

You can interact with models in the Model Playground before building an agent.

DigitalOcean Knowledge Bases

• The MCP server endpoint uses the standard API rate limits. If you need higher limits for production workloads, contact support.

• Reranking is not supported through the knowledge bases MCP endpoint.

• You cannot edit attributes of a knowledge base using the DigitalOcean API. Instead, edit your knowledge base using the DigitalOcean Control Panel.

• You cannot change embeddings models after creating a knowledge base.

• You cannot change the OpenSearch database for an existing knowledge base. To use a different database, create a new knowledge base with the same data sources, and then choose the new OpenSearch database you want to use.

• Each team can create up to 120 knowledge bases. To increase this limit, contact support.

Data Source Limits

• Estimates are available only for locally uploaded files and Spaces buckets. External sources cannot be estimated.

• Estimates are approximate and may differ from the final extractable text due to file structure, parsing behavior, or non-text (binary) content.

• For web crawling data sources, the crawler indexes up to 5,500 pages and skips inaccessible or disallowed links to prevent excessively large indexing jobs.

• The size of S3 buckets is unavailable in the Control Panel. You can view the size of S3 buckets on Amazon.

• You cannot currently reindex a previously crawled seed URL. To reindex the content, delete the seed URL, and then add it again to start a new crawl.

• Knowledge bases partially support indexing PowerPoint files (.ppt, .pptx). Text is extracted, but images and other visual content are not processed.

• Indexing image files (such as .png, .jpeg, .tiff, and .bmp) are not currently supported.

Indexing Limits

• Indexable size cannot be predicted for web crawls, GitHub repositories, external URLs, APIs, or any source that cannot be inspected beforehand.

• You cannot re-index specific data sources within a knowledge base. To re-index any changed data sources, you need to reindex all the data sources.

• Auto-indexing your data sources currently runs only once per day, up to seven days a week.

Chunking Limits

• Chunk sizes (max_chunk_size, parent_chunk_size, child_chunk_size) must remain within the token limits of the selected embeddings model.

• All chunking strategies enforce a minimum chunk size of approximately 100 tokens.

• Chunking settings apply per data source, not globally.

• Changing the chunking strategy after a data source is created is not supported. To change strategies, you must remove the data source, and then re-add it with the preferred strategy.

Activity Limits

• Only the 15 most recent activities are listed in a knowledge base’s Activity tab. If you want to keep a copy of the past indexing job logs, download the CSV file after running your indexing job.

• You cannot access your knowledge base’s activity logs through the DigitalOcean API. Activity logs are only available in the Control Panel.

• A knowledge base’s activity logs currently track only indexing jobs.

Guardrails

Guardrails scan an agent’s input and output for sensitive and inappropriate content and override the agent’s output when it detects the specified problematic content. For example, they help prevent an agent from sharing login credentials or credit card information when tuned correctly for your specific use case.

We offer the following guardrails that you can attach to your agent:

• Sensitive Data: Identifies and anonymizes various categories of sensitive information, including credit card numbers, personally identifiable information, and location data.

• Jailbreak: Helps your agent maintain proper functionality by preventing malicious inputs.

• Content Moderation: Controls agent output by filtering responses related to inappropriate content categories, including violence and hate, sexual content, weapons, regulated substances, self-harm, and illegal activities.

Agents

Agents are AI-powered tools that can perform a wide range of tasks, like answering questions or generating text content. Agents can use a combination of foundation models, knowledge bases, functions, and guardrails to inform their responses to user queries.

You can interact with agents in the following ways:

• Agent endpoints: Each agent has an endpoint that allows you to interact with it through an API. You can integrate endpoints into your applications, customize requests to the agent, and authenticate them using access keys.

• Chatbot embed: We provide a code snippet for each agent that allow you to embed a chatbot interface into your website or application.

• Agent playground: We provide a web-based interface for interacting with agents, allowing you to test and refine agents.

• Agent tracing: View a step-by-step timeline of how your agent processes prompts, including token usage, processing time, and resource access. Each trace also includes the full input and output for every interaction, giving you a complete record of the conversation flow.

  • Insights: Analyze trace data to generate recommendations for improving efficiency and accuracy. Insights send trace data to a third-party model for processing, and you receive data-driven suggestions to reduce latency, optimize token usage, and improve agent behavior. These recommendations help you troubleshoot issues, enhance performance, and lower costs.

• Agent feedback: End users and agent developers can provide feedback on the quality and helpfulness of agent responses. The feedback is collected through the chatbot interface, agent playground, and log stream traces.

• Agent templates: We provide templates for common use cases, such as a customer support and business analysis. Templates have predefined instructions and foundation models that allow you to quickly create an agent.

Tool (Function) Calling

Tool (function) calling enables foundation models to interact with external tools to access real-time data, perform actions, and extend their capabilities beyond their internal knowledge. The model identifies that a user request requires external information or action, and decides which tool to use. The model doesn’t execute the tool itself. Instead, it provides the necessary parameters to the external tool using a structured request to provide the required information or perform the action. The application runs the tool, and feeds the results back to the model. The model uses this information to produce a complete response.

All commercial models from Anthropic and OpenAI available on the DigitalOcean AI Platform support tool (function) calling.

You can also call DigitalOcean Functions from your agents to enhance agent responses with real-time or external data and get more relevant responses.

Agent Evaluations

Agent evaluations are automated tests that can provide insight into how well your agents are responding to prompts you’ve provided. Workspaces let you run evaluations on multiple agents at once.

There are 19 evaluation metrics available you can use to evaluate your agents, including checking for factual correctness, instruction adherence, tone, and toxicity.

The test results are percentage pass/fail scores with visualizations so you can see your agents’ performance over time.

Agent and Function Routing

You can use agent and function routing to create more complex and dynamic responses to user queries.

• Agent Routing directs queries to the right agent based on context.

• Function Routing enhances agent responses with real-time or external data.

For example, you may have one agent to answer general travel questions and another to managing booking. Agent routing automatically sends booking-related requests to the booking agent for a more accurate response. Function routing can then call a function to retrieve weather information which the booking agent can include in its reply to provide more relevant travel recommendations.

Agent Development Kit (ADK) public

The DigitalOcean AI Agent Development Kit (ADK) is a Python SDK and CLI that lets you deploy agent code as a hosted, production-ready service. The ADK is in public preview. You can opt in from the Feature Preview page.

The ADK has the following key features:

• Quick Setup: Build agents quickly with existing models that DigitalOcean provides, including commercial models from OpenAI and Anthropic, or third-party models. You can use any model key, even if the model isn’t hosted on DigitalOcean or the model key is not provided by DigitalOcean.

• Python package gradient-adk: The ADK includes a Python package gradient-adk that you can install using pip to integrate with your existing code.

• Framework Agnostic: Use LangGraph, LangChain, CrewAI, or custom code for your agent code.

• Local Testing: Test on your machine before deploying. You can have multiple versions of your agent deployed to different environments in the same workspace simultaneously. This lets you test agent configurations and workflows exhaustively before you deploy the workflow to production.

• One-Command Deploy: Use the gradient agent deploy command to deploy your agent and make it live.

• Automatic Monitoring: Use built-in traces and logs for monitoring agent performance. Capture calls to LangGraph nodes automatically to create traces to debug and evaluate agent performance when you use LangGraph to add nodes.

• Streaming Support: View real-time responses.

• Evaluation Framework: Run comprehensive evaluations with custom metrics.

• Knowledge Base Integration: Connect to Knowledge Bases for RAG.

See Build Agents Using the Agent Development Kit to learn more about using the ADK to create agents.