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  • 👋Welcome to Groclake
  • ⏩Jump right in
  • 🗣️Introduction to Groclake
  • 🧠High level Concepts
    • Agent Discovery
    • Agent Registry
    • Agent Communication
      • Agent Text Transfer Protocol - ATTP
    • Agent Security
      • Agent Private Cloud - APC
      • Authentication & Encryption
      • Zero Trust Policy
  • 💽Installation & Guide
  • 🏗️Groclake Use Cases
  • 📰Groclake Records
  • Example Codes
  • GrocAgent
    • What is GrocAgent?
    • Example Chat Agent
    • Reflections in GrocAgent
      • Workflow of Reflection Handler
  • Lakes
    • 💾Data & Model Management
      • Datalake
        • Create Datalake
        • Retrieve Document
        • Upload Documents
        • Datalake Connections
          • Snowflake integration
      • Vectorlake
        • Creating vector
        • Generating Vector
        • Pushing Vector
        • Retrieve Document
        • Searching Vector
      • Modellake
        • Create Modellake
        • Language Translation
        • Conversation AI
        • Text to Speech
        • Chat Completion
      • Knowledgelake
        • Create Knowledge Base
        • Push Documents from a URL
        • Push Documents from Local Storage
        • Searching for Information
    • ⚒️Tool Management & Gateway
      • Toollake
        • Tools
        • Salesforce CRM Integration
        • Slack Communication Module
        • New Relic Integration
        • Google Calendar Integration
          • Check Slot Availability
          • Get Available Slots
          • Delete Event
          • Create new event
          • Create new calendar event
    • 🤖Agent Management & Deployment
      • Agentlake
        • Register your agent
        • Fetch agent details & categories
        • Create Agent Private Cloud (APC)
        • Assign Agent Private Cloud (APC) to an Agent
      • Promptlake
        • Setting Connection & Initializing
        • Storing a Prompt
        • Fetching a Prompt
        • Example API Calls
      • Memorylake
        • Context Component Examples
        • Value Structure
        • Setup & Guide
        • Storing & Retrieving Memory
        • Wildcard Search
        • Updating Memory Quality
    • 🗃️Index Stores
      • Cataloglake
        • Create catalog
        • Generate Product Data
        • Fetch Catalog Data
        • Push Product Data
        • Optimize Data Retrieval with Catalog Caching
        • Search for Products
        • Filter Product Search
        • Update Product Data
        • Recommend Products Based on Product Name
        • Update Inventory in Catalog
        • Fetch Inventory Details from Catalog
        • Fetch Product Price
        • Update Product Price in Catalog
        • Cache Image in Catalog
        • Sync Your Catalog with external ecomm platforms
        • Deleting items
        • Address Parsing and Intent Extraction
        • Creating Mapper
        • Convert Mapper's Metadata
        • Fetching Mapper
        • Updating Mapper
        • Example use case of Cataloglake
      • Joblake
        • Joblake Mapping
        • Creating a Joblake
      • Resumelake
        • Resumelake Mapping
        • Creating a Resumelake
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On this page
  • Key Features
  • Why Choose Agentlake?
  • Applications
  • Flow of Agentlake
  1. Lakes
  2. Agent Management & Deployment

Agentlake

Agentlake is a Python-based integration library designed to streamline the registration, management, and utilization of intelligent agents within diverse workflows. By interfacing with the Agentlake API, businesses can create, fetch, and categorize agents tailored to their specific needs, ranging from coding assistants to customer support bots. The library offers a developer-friendly framework for implementing intelligent agent solutions, making it ideal for dynamic and AI-driven applications.

Key Features

  1. Agent Registration Effortlessly register new intelligent agents with metadata such as descriptions, categories, and public keys, enabling seamless integration into existing systems.

  2. Agent Retrieval Fetch detailed information about specific agents using their unique identifiers, ensuring transparency and easy updates.

  3. Category Management Access and manage a list of available agent categories to organize and optimize agent deployment.

Why Choose Agentlake?

  • Versatile: Suitable for a wide range of use cases, from coding bots to commerce agents.

  • Scalable: Supports the management of large agent repositories, catering to growing organizational needs.

  • AI-Integrated: Prepares agents for context-aware and AI/ML-driven tasks.

  • User-Friendly: Simplifies agent management with well-documented APIs for quick implementation.

Applications

  • Automated Coding Assistance: Deploy agents to convert and debug code across languages.

  • Customer Support: Integrate responsive bots for enhanced customer interactions.

  • AI-Driven Commerce: Leverage intelligent agents to personalize e-commerce experiences.

Refer to the Diagram below for better Understanding

Flow of Agentlake

Agentlake is a lake of agents or a collection of agents. Each Agentlake has a agent registry and agent name server. Each Agentlake can have multiple APCs. Each APC can have multiple agents Each agent can talk to another agent using uuid and /query endpoint The name server can ping each agent on this uuid/query endpoint and check if it up using periodic pings. Multiple agents can be running on a single server in cloud on multiple ports. Apache router routes all messages to appropriate port as it knows which uuid is running on which port. Agents can also run on multiple server and Apache router can reach those agents too

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Last updated 1 month ago

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