#3354 Google Gemini 2.5 Pro: AnythingLLM Custom Agent

#3354 Google Gemini 2.5 Pro: AnythingLLM Custom Agent

#3354 Tell me about AnythingLLM

AnythingLLM is an all-in-one, privacy-focused AI application designed to allow users to easily download and run Large Language Models (LLMs) locally on their desktop with no additional setup or programs. It acts as a bridge between a user's preferred LLMs and their data, offering a user-friendly interface that doesn't require development expertise.

Here's a breakdown of its key aspects:

Core Functionality & Features:

• Local-by-Default: A major highlight is its emphasis on privacy. All models, documents, and chats are stored and run locally on your machine, with no account needed for the desktop version.
• Support for Various LLMs: AnythingLLM is highly flexible, working with a wide range of local (e.g., Ollama, LM Studio) and cloud-based LLM providers (e.g., OpenAI, Azure OpenAI, Anthropic, Google Gemini Pro). You can even configure multiple LLMs simultaneously.
• Retrieval-Augmented Generation (RAG): It enables RAG systems, allowing you to "chat with your documents." You can upload various document types (PDFs, Word files, codebases, CSVs, etc.) and use LLMs to query their content privately.
• AI Agents: AnythingLLM supports AI agents that have access to tools. These agents can perform tasks like web scraping, web Browse, summarizing documents, listing documents, saving files, and even generating charts or running SQL queries.
• Customization: It's open-source and highly customizable. Users can extend its functionality with custom agents, data loaders, and more.
• Multi-modal Support: It works with both text-only and multi-modal LLMs (text-to-text and image-to-text).
• Built-in Developer API: AnythingLLM can also be used as a powerful API for custom development or integrating AI features into existing products.
• One-click Install: The desktop application offers a simple, one-click installation process.
• Cross-platform Compatibility: It runs on macOS, Windows, and Linux.
• NVIDIA RTX AI PC Acceleration: AnythingLLM has added support for NVIDIA NIM microservices, accelerating LLM workflows on NVIDIA GeForce RTX and RTX PRO GPUs for faster performance.

Use Cases:

AnythingLLM is suitable for a variety of users and applications, including:

• Personal Knowledge Management: Organizing and interacting with personal documents and research materials.
• Business Document Analysis: Extracting insights from company reports, contracts, and other business files while maintaining privacy.
• Academic Research: Analyzing and synthesizing information from multiple academic papers and sources.
• Question Answering: Getting answers from LLMs without incurring costs (when using local models).
• Document Summarization: Generating summaries of lengthy documents.
• Data Analysis: Extracting insights by loading files and querying them with LLMs.
• Agentic Actions: Dynamically researching content, running generative tools, and executing actions based on user prompts.
• Private AI Interactions: Ensuring sensitive information stays on your device, mitigating risks associated with cloud-based solutions.

Pricing:

• AnythingLLM Desktop: This version is generally free and open-source, offering full privacy and local execution without requiring an account.
• AnythingLLM Hosted/Self-Hosted (Cloud): For teams and organizations, AnythingLLM offers paid plans with features like multi-user access, white-labeling, admin controls, and custom support. Pricing varies depending on the number of users, documents, and desired features, with plans ranging from Basic to Enterprise.

In essence, AnythingLLM aims to make advanced AI capabilities, particularly those involving LLMs and RAG, accessible to everyone, with a strong emphasis on privacy and ease of use, whether for individual desktop use or for team-based deployments.

Create a custom agent to find opportunities where innovative solutions are needed in a user-specified industry

Okay, let's create a conceptual custom agent for AnythingLLM that could identify opportunities for innovative solutions within a user-specified industry.

Since I cannot directly code and deploy a live agent within AnythingLLM from this environment, I will provide the conceptual design, the necessary components, and the logic. If you were implementing this in AnythingLLM, you would be using its Agent SDK or similar capabilities to define this.

Custom Agent: "Innovation Scout"

Agent Name: Innovation Scout

Agent Description: This agent is designed to identify and highlight areas within a specified industry where current solutions are lacking, inefficient, or ripe for disruption, thereby pinpointing opportunities for innovative approaches.

Core Goal: To analyze information about a given industry and generate a report of potential problem areas and corresponding innovation opportunities.

---

Agent Components & Logic:

This agent would primarily leverage AnythingLLM's RAG capabilities (to access industry data) and its ability to define "tools" that the LLM can use to gather more information or perform specific actions.

1. Agent Definition (Conceptual - how you'd define it in AnythingLLM's agent config):

{

  "name": "Innovation Scout",
  "description": "Identifies opportunities for innovative solutions in a user-specified industry.",
  "tools_enabled": ["web_search", "document_qa", "industry_trend_analyzer"], // Custom tools we'll define
  "initial_prompt": "You are an expert innovation consultant. Your task is to analyze a specified industry and identify key problems, inefficiencies, or unmet needs that represent opportunities for innovative solutions. Focus on areas where new technologies, business models, or approaches could create significant value.",
  "trigger_phrases": ["find innovation opportunities in", "innovation scout for", "identify problems in"]
}

2. Required Tools (These would need to be either built-in AnythingLLM tools or custom tools you'd define):

• web_search (Built-in/Common):
  • Description: Performs a web search for current events, news, industry reports, and company information.
  • Usage: Crucial for getting up-to-date information on the industry, competitor activities, emerging technologies, and recent challenges.
• document_qa (Built-in/RAG):
  • Description: Queries uploaded documents (e.g., industry reports, market analyses, academic papers, company filings) to extract specific information.
  • Usage: The user would pre-load relevant industry documents into AnythingLLM's workspace for the agent to reference. This is critical for deep, proprietary analysis.
• industry_trend_analyzer (Custom Tool - Python/JavaScript function):
  • Description: Analyzes retrieved text to identify recurring themes, emerging trends, common pain points, and areas of high customer dissatisfaction or operational inefficiency within an industry.
  • Input: Textual data (e.g., search results, document excerpts).
  • Output: Structured summary of trends, problems, and unmet needs.
  • Example Internal Logic:
    • NLP techniques to extract keywords, entities (companies, technologies), and sentiment.
    • Pattern matching for phrases indicating challenges ("struggles with," "lack of," "inefficient," "costly," "customer complaints").
    • Aggregation of common topics.

3. Agent Workflow / Decision-Making Process:

When the user prompts the "Innovation Scout" (e.g., "Innovation Scout for the healthcare industry"), the agent's internal logic (orchestrated by the LLM) would follow these steps:

1. Understand User Request: Parse the industry specified by the user.

2. Initial Information Gathering (using web_search):

   • "What are the current major challenges in [user_specified_industry]?"
   • "Recent news and developments in [user_specified_industry]."
   • "Emerging technologies impacting [user_specified_industry]."
   • "Common customer complaints or inefficiencies in [user_specified_industry]."
   • "Key players and their reported challenges in [user_specified_industry]."

3. Deep Dive into User-Provided Documents (using document_qa):

   • "Summarize the main challenges and opportunities mentioned in the uploaded [user_specified_industry] reports."
   • "Extract any sections describing unmet needs or pain points from the documents related to [user_specified_industry]."
   • "Identify technological gaps or areas for improvement highlighted in the documents."

4. Synthesize and Analyze Trends (using industry_trend_analyzer):

   • Feed the gathered information (from web searches and documents) into the industry_trend_analyzer tool.
   • The tool identifies recurring themes, validates perceived problems, and quantifies (if possible) the prevalence of certain issues.

5. Identify Innovation Opportunities (LLM's reasoning):

   • Based on the synthesized problems and trends, the LLM identifies specific areas where innovation is needed.
   • It might categorize these opportunities (e.g., process innovation, product innovation, business model innovation, technological innovation).
   • For each opportunity, it will articulate:
     • The Problem: Clearly state the unmet need or inefficiency.
     • Current State: Briefly describe how the problem is currently addressed (if at all).
     • Innovation Opportunity: Suggest the type of solution needed (e.g., "a decentralized platform," "AI-driven automation," "a sustainable material alternative").
     • Potential Impact: Briefly explain the value a solution in this area could bring (e.g., "cost reduction," "improved customer experience," "increased efficiency").

6. Generate Report: Compile the findings into a structured, readable report.

---

Example Interaction:

User Prompt: "Hey Innovation Scout, can you find opportunities for innovation in the sustainable agriculture industry?"

Agent's Internal Process (Simplified):

1. Web Search:
   • Searches for "challenges in sustainable agriculture," "innovations in sustainable farming," "unmet needs in agritech."
   • Finds articles about water scarcity, soil degradation, high input costs, lack of precision tools for small farms, efficient carbon sequestration.
2. Document QA (if user has uploaded relevant docs):
   • Queries uploaded "Sustainable Farming 2024 Report.pdf" for sections on "water management challenges" and "soil health solutions."
   • Extracts data about nutrient runoff and pesticide usage.
3. Industry Trend Analyzer:
   • Identifies "resource efficiency" (water, nutrients) and "environmental impact reduction" as key recurring themes and problems.
   • Notes "data accessibility for farmers" as a consistent pain point.
4. LLM Reasoning:
   • Connects water scarcity with inefficient irrigation.
   • Connects soil degradation with current fertilization methods.
   • Connects high costs with lack of automation and precise resource application.
   • Identifies the need for user-friendly data platforms.

Agent's Output Report:

Innovation Opportunities in the Sustainable Agriculture Industry

Based on analysis, the following key areas present significant opportunities for innovative solutions:

1. Precision Resource Management for Small-to-Medium Farms:

   • The Problem: While large-scale farms benefit from advanced precision agriculture, smaller farms often lack access to affordable, user-friendly tools for precise water, nutrient, and pest management, leading to waste and environmental impact.
   • Current State: Manual monitoring, generalized application rates, or expensive, complex enterprise solutions.
   • Innovation Opportunity: Develop low-cost, easy-to-deploy IoT sensors combined with AI-driven recommendations (e.g., mobile app-based dashboards) for hyper-localized resource application, optimized for smaller plot sizes.
   • Potential Impact: Reduced input costs, minimized environmental runoff, improved yields for a broader segment of farmers.

2. Advanced Soil Health Regeneration Solutions:

   • The Problem: Widespread soil degradation due to conventional farming practices threatens long-term agricultural productivity and carbon sequestration potential.
   • Current State: Limited adoption of regenerative practices; reliance on synthetic fertilizers and pesticides.
   • Innovation Opportunity: Novel biological inputs (e.g., advanced microbial inoculants), automated soil testing and remediation robotics, or incentive-based platforms for farmers adopting advanced regenerative techniques.
   • Potential Impact: Enhanced biodiversity, improved soil fertility, increased carbon capture, long-term sustainability.

3. Decentralized Agri-Food Supply Chain Transparency:

   • The Problem: Lack of transparency from farm to table makes it difficult for consumers to verify sustainability claims and for farmers to capture value from eco-friendly practices.
   • Current State: Limited traceability, often relying on paper trails or opaque certifications.
   • Innovation Opportunity: Blockchain-enabled platforms for immutable traceability, AI-powered systems to verify sustainable practices, or consumer-facing apps that link product QR codes directly to farm-level environmental data.
   • Potential Impact: Increased consumer trust, premium pricing for sustainable products, empowered farmers through direct market access.

4. AI-Powered Crop Disease & Pest Early Detection for Bio-Control:

   • The Problem: Late detection of crop diseases and pests leads to significant yield losses and reliance on broad-spectrum chemical interventions.
   • Current State: Manual inspection, reactive chemical spraying.
   • Innovation Opportunity: Drone-based multispectral imaging combined with AI for early disease/pest identification, integrated with automated biological control release systems or precision robotic weeding.
   • Potential Impact: Reduced crop loss, minimized pesticide use, healthier ecosystems.

---

This framework provides a robust method for an AnythingLLM agent to move beyond simple question-answering and actively assist users in strategic problem identification for innovation.

How do I program in this agent in AnythingLLM?

Let's break down how you would program the "Innovation Scout" agent in AnythingLLM. AnythingLLM is constantly evolving, but the core principles for creating agents involve defining their behavior, providing them with tools, and training them on your data.

Key Concepts in AnythingLLM for Agents:

1. Workspaces: These are your isolated environments where you upload documents, define tools, and manage agents.
2. Documents/Embeddings: You upload your industry-specific data (PDFs, text files, web pages) into a workspace. AnythingLLM processes these into embeddings, allowing the LLM to perform RAG (Retrieval-Augmented Generation) on them.
3. LLM Selection: You choose the Large Language Model that your agent will use (e.g., GPT-4 via OpenAI, a local Ollama model, Gemini Pro, etc.). The agent's performance heavily depends on the capabilities of the chosen LLM.
4. Agents: These are pre-configured personas or workflows that the LLM follows. They can use tools and interact with your uploaded documents.
5. Tools: These are external functionalities that the LLM can call upon. AnythingLLM provides some built-in tools (like web searching), and you can define custom tools.

---

Steps to Program the "Innovation Scout" Agent in AnythingLLM:

Step 1: Set Up Your AnythingLLM Environment

1. Download & Install AnythingLLM: If you haven't already, download the desktop application from the AnythingLLM website (https://anythingllm.com/).
2. Choose Your LLM Provider:
   • Local (Recommended for Privacy/Cost): Set up Ollama, LM Studio, or another local LLM server. Then configure AnythingLLM to connect to it. For example, if using Ollama, you'd specify http://localhost:11434 as the API endpoint. You'll also need to download a model (e.g., llama3, mixtral).
   • Cloud: Configure your API keys for OpenAI, Azure OpenAI, Anthropic, Google Gemini, etc., within AnythingLLM's settings.

Step 2: Create a Workspace

1. Open AnythingLLM and click "New Workspace."
2. Give it a name, e.g., "Innovation Scout Project."
3. Select your desired LLM for this workspace.

Step 3: Upload Industry-Specific Documents (RAG Data)

This is crucial for the "Innovation Scout" to have deep, domain-specific knowledge.

1. Within your "Innovation Scout Project" workspace, click the "Upload" button.
2. Upload various documents related to the industries you want to analyze:
   • Industry reports (e.g., "Future of Energy Report 2025," "Global Healthcare Trends").
   • Market research analyses.
   • Academic papers on industry challenges.
   • News articles about industry pain points or emerging technologies.
   • Company annual reports (sometimes they discuss challenges).
   • Web pages (AnythingLLM can scrape URLs).
3. Let AnythingLLM process these documents into embeddings. This makes them searchable and understandable by the LLM.

Step 4: Define Custom Tools (if needed)

AnythingLLM often has built-in tools like Web Search. For industry_trend_analyzer, you might need to create a custom tool.

How custom tools work in AnythingLLM (general concept):

Custom tools in AnythingLLM are typically defined as JavaScript or Python functions that the LLM can call. They interact with external APIs or perform specific data processing.

• web_search: AnythingLLM usually provides a built-in web search tool (e.g., via Brave Search API, Google Search API, etc.). Make sure it's enabled and configured in your settings.

• document_qa: This is inherent to AnythingLLM's RAG capabilities. When an agent has access to a workspace with documents, it implicitly has "document QA" ability. You don't usually define this as a separate tool per se but rather ensure the agent can access workspace documents.

• industry_trend_analyzer (Custom Tool Example - Conceptual):

  • This would be the most complex part. You'd likely write a small web service or a local script that exposes an API endpoint.
  • Functionality: This tool would take a chunk of text (e.g., search results, document excerpts) as input. It would then use more advanced NLP techniques (like topic modeling, sentiment analysis, entity extraction) to summarize key trends, common problems, and unmet needs.
  • AnythingLLM Integration: You would then configure AnythingLLM to recognize and call this external tool. This usually involves defining the tool's name, description, and the endpoint it communicates with.

  Example (Conceptual Tool Definition within AnythingLLM's settings/tool configuration - syntax may vary):

  {

    "name": "industry_trend_analyzer",
    "description": "Analyzes text to identify recurring industry trends, common problems, and unmet needs. Input is raw text.",
    "type": "custom_api", // Or 'javascript_file' if it runs locally
    "endpoint": "http://localhost:5000/analyze_trends", // Your custom tool's API endpoint
    "input_schema": {
      "type": "object",
      "properties": {
        "text_to_analyze": { "type": "string", "description": "The textual data to be analyzed for trends." }
      },
      "required": ["text_to_analyze"]
    }
  }
  

  You would need to write the Python/Node.js Flask/Express app that runs on http://localhost:5000/analyze_trends.

Step 5: Create the "Innovation Scout" Agent

This is where you define the agent's persona and how it uses tools.

1. In your workspace, go to the "Agents" tab.
2. Click "New Agent."
3. Agent Name: Innovation Scout
4. Agent Description: This agent is designed to identify and highlight areas within a specified industry where current solutions are lacking, inefficient, or ripe for disruption, thereby pinpointing opportunities for innovative approaches.
5. Agent Persona (System Prompt): This is the most critical part. It tells the LLM how to behave.

   You are an expert innovation consultant and strategic analyst named "Innovation Scout". Your primary goal is to dissect a given industry and identify significant pain points, inefficiencies, technological gaps, or unmet needs that represent prime opportunities for innovative solutions.
   
   Your process involves:
   1.  Thoroughly researching the specified industry using available tools (web search and internal documents).
   2.  Synthesizing information to identify recurring problems, customer frustrations, and areas ripe for disruption.
   3.  Proposing concrete innovation opportunities, detailing the problem, current approaches (if any), and the potential impact of a new solution.
   4.  Always output your findings in a structured report format.
   
   Prioritize solutions that leverage emerging technologies, sustainable practices, or novel business models. If asked about an industry, immediately initiate a research process.
   

6. Tools Enabled: Select the tools your agent can use.
   • Web Search (or whatever it's named in your AnythingLLM instance)
   • industry_trend_analyzer (your custom tool, if you implemented it)
   • (Crucially, ensure the agent has access to the workspace documents for document_qa.)
7. Temperature: (LLM creativity) Set to a moderate level (e.g., 0.5-0.7) to allow for some creativity in identifying opportunities but still grounded in facts.
8. Vector Similarity (Context Window): This determines how much relevant document context the LLM sees. Adjust based on your LLM's context window size.
9. Click "Create Agent."

Step 6: Interact with Your Agent

1. Go back to your workspace chat interface.
2. Select your "Innovation Scout" agent from the agent dropdown.
3. Start asking it questions:
   • Find innovation opportunities in the renewable energy storage industry.
   • What are the biggest problems facing urban logistics, and how can they be solved innovatively?
   • Using the uploaded documents, identify unmet needs in the personalized education sector.

Important Considerations & Troubleshooting:

• LLM Capabilities: The quality of the "Innovation Scout's" output will heavily depend on the underlying LLM's reasoning abilities. More powerful LLMs (e.g., GPT-4, Claude 3 Opus, Llama 3 70B) will perform better.
• Data Quality: Garbage in, garbage out. The better and more relevant your uploaded industry documents are, the more insightful the agent's analysis will be.
• Custom Tool Reliability: If you implement a custom tool, ensure it's robust and provides consistent, accurate outputs. Debug its API endpoint independently if you encounter issues.
• Prompt Engineering: You might need to iterate on the agent's "System Prompt" (persona) to fine-tune its behavior and output format.
• Tool Usage: Sometimes, LLMs need a little guidance to use tools effectively. If the agent isn't using a tool when it should, adjust its persona prompt to emphasize when and why to use that tool. For example, "When a new industry is mentioned, immediately use the web_search tool to gather initial context."
• AnythingLLM Documentation: Always refer to the official AnythingLLM documentation and community resources for the most up-to-date and detailed instructions on agent creation and tool integration. The exact UI and steps can change with updates.

By following these steps, you can program your "Innovation Scout" agent within AnythingLLM to leverage its powerful RAG and tool-use capabilities to find valuable innovation opportunities.

From <https://gemini.google.com/app/>  Google Gemini 2.5 Pro (preview)