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Documentation Index

Fetch the complete documentation index at: https://docs.neuralfactory.ai/llms.txt

Use this file to discover all available pages before exploring further.

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Built-in Tools

Neural Factory includes several built-in tools that agents can use out of the box. These tools are available to all agents and can be enabled or disabled per agent in the configuration. The default tool for every agent. When enabled, the agent searches connected knowledge bases to find relevant document chunks for answering questions. Results include citations back to the source documents. Allows the agent to search the internet for current information. Useful when:
  • The question is about recent events not in your knowledge base
  • The agent needs to supplement KB results with public information
  • The user asks about something outside your organization’s documents

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Code execution

Runs Python code in a secure, sandboxed environment (Docker container or Azure Dynamic Sessions). The agent can:
  • Perform calculations and data analysis
  • Generate charts and visualizations
  • Process and transform data
  • Execute custom logic
Code execution is completely isolated — it cannot access your organization’s systems or data outside the sandbox.

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Bash

Executes shell commands in the same sandboxed environment as code execution. Useful for:
  • File manipulation tasks
  • Running command-line tools
  • System-level operations within the sandbox

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Browser automation

Navigates to web pages and extracts content. The agent can:
  • Visit a URL and read the page content
  • Follow links and navigate multi-page sites
  • Extract structured data from web pages

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File editor

Reads and modifies files within the sandbox environment. Commonly used alongside code execution for tasks that involve creating or editing files.

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Image generation

Generates images from text descriptions using AI image generation models. Useful for creating diagrams, illustrations, or visual content during conversations.

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Memory

Allows the agent to store and recall information across conversations. The agent can:
  • Save important facts or preferences mentioned by the user
  • Recall stored information in future conversations
  • Build up knowledge about recurring topics or user preferences
Memory is scoped to the user and agent — different users have separate memory stores.