Documentation

augLab is a no-code platform for building AI agents, teams, and workflows. You bring your own API keys, pick from 110+ models and 60+ tools, and configure everything through the dashboard -- no code required.

What you can build

• Agents -- A single AI assistant with tools. Good for: research, content writing, data analysis, image generation, email management, code execution.
• Teams -- Multiple agents working together. A coordinator assigns tasks to specialists. Good for: complex projects that need different expertise (e.g. a researcher + a writer).
• Workflows -- Step-by-step pipelines. Each step runs an agent or team in sequence. Good for: repeatable processes like content pipelines, data ETL, report generation.

An agent is an AI assistant powered by a large language model (LLM) that can use tools to take actions. You configure it once, then interact with it through chat, API, or schedules.

Creating an agent

Go to Agents > Create Agent. The form has these sections:

Running an agent

• Chat -- Click the agent from your Agents list to open the chat interface. Type a message and hit send.
• API -- Use the REST API to run agents programmatically. See API Access.
• Schedule -- Set up a cron schedule or webhook to run the agent automatically. See Schedules.

Files and media

When an agent generates files (images, PDFs, CSVs, etc.), they appear in the chat and are saved to your Files library. You can download, view, and delete files from there. Files are shared within organizations but personal files stay private.

Teams are multiple agents working together under a coordinator. Use teams when a task needs different kinds of expertise that a single agent cannot handle well.

When to use a team vs. a single agent

• A single agent with web search can research and write -- you do not always need a team.
• Use a team when members need different models, different tools, or different instructions.
• Example: a "Researcher" agent with search tools + a "Writer" agent with file generation.

Team modes

When creating a team, choose a mode that determines how the coordinator works with members. This is the most important setting for your team's behavior:

How to create a team

1. Create member agents first. Each member is a regular agent. Build them in Agents > Create Agent with their own model, tools, and instructions.
2. Create the team. Go to Teams > Create Team. Give it a name, choose a mode (see above), pick a coordinator model, and write team-level instructions.
3. Add members. Select existing agents as team members. For each member, write a role description so the coordinator knows when to delegate to them. Good role descriptions are critical -- they tell the coordinator what each member specializes in.
4. Run it. Chat with the team. The coordinator handles delegation and response synthesis based on the mode you chose.

Example: Research + Writing Team

Workflows are step-by-step pipelines. Unlike teams (where the coordinator decides what happens), workflows follow a fixed sequence that you define. Each step runs an agent or team.

When to use a workflow

• You need steps to run in a specific order (Step 1 output feeds into Step 2).
• You want a repeatable process you can trigger with different inputs.
• Examples: content pipelines, data processing, report generation, multi-step analysis.

How to create a workflow

1. Create the agents you need for each step. Each step will run one agent or team.
2. Create the workflow. Go to Workflows > Create Workflow. Name it and optionally add an input template.
3. Add steps. For each step, select an agent or team, give the step a name, and optionally add an input template. Steps run in order from top to bottom. You can drag to reorder.
4. Set parallel or sequential. Between each step, click the connector label to toggle between then (sequential) and parallel. See below for details.
5. Run it. Enter your input and the workflow executes the steps, passing context forward.

Sequential vs. parallel steps

By default, steps run sequentially -- each step waits for the previous one to finish before starting. You can mark steps as parallel to run them at the same time as the step before them, which speeds up workflows where steps are independent.

Input templates

Input templates let you parameterize workflows. Use curly braces for variables:Write an article about: {topic}. When you run the workflow, you fill in the variables.

Example: SEO Content Pipeline

Knowledge bases let your agent reference your own documents and data. Instead of relying only on the model's training data, the agent searches your uploaded files and uses the relevant content in its responses. This is called RAG (Retrieval-Augmented Generation).

How to set up a knowledge base

1. Create a knowledge base. Go to Knowledge > Create. Name it and choose a vector database (LanceDB is the default and works well for most cases).
2. Upload files. Add PDFs, text files, CSVs, Markdown, or URLs. augLab splits them into chunks and indexes them for search.
3. Connect to an agent. When creating or editing an agent, select your knowledge base in the Knowledge Sources section at the bottom of the form.
4. Use it. When the agent runs, it automatically searches the knowledge base for relevant content and includes it in the context.

Supported file types

PDF, TXT, Markdown, CSV, JSON, DOCX, HTML, and web URLs.

When to use knowledge bases

• Your agent needs to answer questions about your company docs, policies, or product information.
• You want to ground the agent in specific data rather than general knowledge.
• You need the agent to cite specific sources from your documents.

augLab uses a Bring Your Own Keys (BYOK) model. You provide your own API keys for AI providers and third-party services. You pay providers directly with zero markup from augLab. Keys are AES-256 encrypted at rest and only decrypted in-memory during agent execution.

How to add a credential

1. Go to Credentials in the sidebar.
2. Click Add Credential.
3. Select the credential type from the dropdown (e.g. openai_api_key).
4. Paste your API key and save.

Your credential is now available to all agents that need it. If you are in an organization, credentials are shared with org members.

Credential types and where to get them

Google Account (Gmail, Calendar, Sheets)

Google tools use a one-click OAuth connection instead of manual API keys. On the Credentials page, click Sign in with Google to authorize access to your Gmail, Calendar, and Sheets. This is a personal connection tied to your Google account -- each user in an organization connects their own account. Once connected, any agent you create with Gmail, Google Calendar, or Google Sheets tools will automatically use your Google credentials.

Schedules automate agent, team, and workflow runs so they execute without manual intervention.

Cron schedules

Run on a recurring schedule using cron expressions. Go to Schedules > Create Schedule, select the resource (agent, team, or workflow), define the input message, and set the cron expression.

Webhook triggers

Each schedule also gets a unique webhook URL. Send a POST request to that URL to trigger the run on demand. This is useful for integrating with CI/CD pipelines, Zapier, or other external systems. The request body becomes the input to the agent.

Viewing results

All scheduled runs appear in Run History with their status, duration, and output. You can click any run to see the full conversation and any generated files.

Every agent, team, and workflow execution is automatically traced using OpenTelemetry. Traces capture the full execution flow -- every LLM model call, tool invocation, and team coordination step -- giving you deep visibility into what your agents are doing and how they are performing.

What gets traced

Viewing traces

Navigate to Tracing in the sidebar to see all your traces. The main page shows summary statistics (total traces, average duration, error rate) and a filterable list of traces. You can filter by time range (1 hour to 30 days) and status (OK or Error).

Trace detail and span waterfall

Click any trace to see its detail view. This shows a hierarchical span waterfall -- a tree of every operation that happened during the execution. Each span displays its duration as a proportional bar, making it easy to identify bottlenecks. Expand any span to see detailed attributes like token counts, model names, tool parameters, and error messages.

Use cases

Run agents, teams, and workflows programmatically from your own applications.

Getting an API key

Go to API Keys in the sidebar, click Create API Key, and give it a name. Copy the key immediately -- it is only shown once.

Making requests

Include the key in the Authorization header:

Endpoints

For the full endpoint reference with request/response schemas, visit API Docs in the dashboard.

Skills are reusable knowledge and instruction packages that augment an agent's behavior. They give your agent domain-specific expertise, standard operating procedures, or specialized know-how that goes beyond its base instructions. Think of them as "plug-in expertise" you can attach to any agent.

What's in a skill?

Each skill has three parts:

How to create a skill

1. Go to Skills > Create Skill.
2. Give it a name and optional description so you can identify it later.
3. Write instructions -- detailed guidance for the agent on how to use this skill. Be specific about processes, rules, formatting, and expected behavior.
4. Optionally add reference documents -- supporting materials like style guides, templates, or product data.
5. Optionally add scripts -- code files the agent can reference or execute.
6. Save the skill.

How to attach a skill to an agent

When creating or editing an agent, scroll to the Skills section at the bottom of the form. Select one or more skills to attach. The agent will have access to the skill's instructions and reference materials during every conversation.

Example use cases

Skills vs. instructions vs. knowledge bases

• Agent instructions -- general behavior and personality for a specific agent.
• Skills -- reusable expertise packages you can attach to multiple agents. Good for processes and domain knowledge.
• Knowledge bases -- large document collections the agent can search. Good for FAQs, documentation, and reference data.