Fetch.ai Developer Walkthrough (AI Agent Products)
TLDRFetch AI's video guide introduces an advanced AI system that connects agents, APIs, and databases to dynamically solve tasks. The system includes the AI engine, Agentverse platform, microagents library, and a blockchain product. The AI engine leverages LLMs to understand human input and execute tasks by coordinating with AI agents. Agentverse allows users to build and run AI agents, which can be registered as services for the AI engine to discover. The microagents library is an open-source Python library for building agents, and the blockchain supports the almac contract for messaging fees. The video demonstrates how to create and interact with agents, services, and tasks, showcasing the power of Fetch AI's platform in connecting independent services and APIs.
Takeaways
- 🤖 Fetch AI is developing an advanced AI system that connects agents, APIs, and databases to dynamically solve tasks.
- 🔍 The AI engine uses LLMs (Language Models) to understand human input, software code, and schemas, enabling it to discover and execute tasks through AI agents.
- 💬 Delta V is a web chat interface that allows communication with AI agents and the AI engine.
- 🛠️ AgentVerse is a platform for building and running AI agents, which can be registered as services to be discovered by the AI engine.
- 🔗 AI agents can connect to the Fetch AI system as a hook to execute tasks.
- 📊 The microagents library is an open-source Python library for building AI engine-compatible agents and multi-agent systems.
- 📚 Documentation and guides are available on the Fetch AI website for developers to learn how to build and integrate agents.
- 🔗 The blockchain product at Fetch AI supports the ALAC (Agent Ledger and Access Control) contract, handling messaging fees for agents.
- 🔄 The AI engine can chain tasks between independent services, allowing for complex interactions without direct API connections.
- 📝 Services in AgentVerse are created to represent agents, and their field descriptions can reference values from other tasks or services.
- 📱 The mailroom feature allows agents to store messages when offline, to be retrieved when they come back online.
Q & A
What is the primary goal of Fetch AI's advanced AI system?
-The primary goal is to connect agents, APIs, and databases dynamically to solve tasks using the power of LLMs (Large Language Models).
What are the four core products offered by Fetch AI?
-The four core products are the AI Engine, AgentVerse, the Microagents Library, and the Blockchain product supporting the ALAC contract.
How does the AI Engine in Fetch AI work?
-The AI Engine leverages LLMs to understand human input and software code, discovers services that can execute tasks, and then contacts the relevant AI agents to execute those tasks.
What is the purpose of AgentVerse?
-AgentVerse is a web platform where users can build, run, and register AI agents as services, making them discoverable by the AI Engine for task execution.
What is the Microagents Library, and where can it be found?
-The Microagents Library is an open-source Python library that allows users to build AI engine-compatible agents. It can be found on GitHub at github.com/fetchai/agents.
How does the AI Engine execute tasks on top of LLMs?
-The AI Engine uses the understanding of human input and software code from LLMs to discover and execute tasks by interacting with the appropriate AI agents.
What is the role of the Mail Room in AgentVerse?
-The Mail Room is a feature that allows agents to store messages when they are offline, which can be retrieved when the agent comes back online.
How does the AMAC contract on Fetch AI's blockchain support agents?
-The AMAC contract is a ledger of registered agents, discovered by the AI Engine, and it handles messaging fees for the agents on the blockchain.
What is the significance of the 'service group' in Fetch AI's system?
-Service groups help categorize services, making it easier for the AI Engine to look them up faster and understand their relationships, which is crucial for efficient task execution.
How can developers get started with building projects using Fetch AI's tools?
-Developers can refer to the guides on Fetch AI's documentation, which cover AI agents, AgentVerse, Delta V, and the blockchain layer, as well as tutorials for building and communicating with agents.
Where can users seek help if they encounter issues while working with Fetch AI's tools?
-Users can seek help on Discord, where they can ask questions and receive assistance from the Fetch AI community.
Outlines
🤖 Introduction to Fetch AI and Core Products
This paragraph introduces the audience to Fetch AI's video technical guide aimed at developers, hackers, and entrepreneurs. It highlights Fetch AI's mission to build an advanced AI system that connects agents, APIs, and databases to solve tasks dynamically. The speaker, Josh, a software engineer, outlines Fetch AI's four core products: the AI engine (best showcased by Delta V), Agentverse (a platform for building and running AI agents), the microagents library (a Python library for creating agents), and the blockchain product (supporting the almanac contract for messaging fees). The AI engine's ability to execute tasks using the power of LLMs and AI agents is emphasized, as well as the importance of the service feature in Agentverse for agent discovery and task execution.
📚 Exploring Agentverse and Microagents Library
The second paragraph delves into the Agentverse platform, where users can build and deploy AI agents. It explains how agents can be registered as services to be discovered by the AI engine. The speaker demonstrates creating a new agent that fetches BTC price data from an API every 10 seconds. The paragraph also introduces the microagents library, an open-source Python library for building AI engine-compatible agents. A quick demo is provided, showing how to install the library and create a simple agent named Alice that logs a message every two seconds. The importance of the agent's address for communication within a local setting is also discussed.
🔗 Agent Communication and Blockchain Integration
This paragraph focuses on how agents communicate with each other and the role of the blockchain in Fetch AI's ecosystem. The speaker creates a new agent named Bob that sends a message to Alice every four seconds. The process of updating Alice's script to respond to messages is demonstrated. The blockchain product is introduced as supporting the almanac contract for messaging fees, with the AMAC contract serving as a ledger of registered agents. The speaker also explains how to define tasks and service groups in Delta V, using the example of a dice roll for a board game called Settlers of Tartan.
📝 Understanding Task Creation and Service Definition
The fourth paragraph explains the process of creating tasks and defining services in Agentverse. It details how the Settlers of Tartan task is set up to require a dice roll, which is then fulfilled by another service. The importance of specifying the protocol and response type for the AI engine to understand and work with the agent is emphasized. The paragraph also discusses how services can be created based on agent addresses and how the AI engine uses the information provided in the field description to execute tasks and return results.
📖 Guides, Examples, and Community Support
The final paragraph provides guidance on where to find information for building projects with Fetch AI. It highlights the guides available for AI agents, Agentverse, Delta V, and the blockchain layer. The speaker encourages users to explore the tutorials and examples provided on the Fetch AI documentation website. The paragraph concludes with a reminder that the Fetch AI community, particularly the Discord channel, is a valuable resource for help and support.
Mindmap
Keywords
💡Fetch AI
💡AI Engine
💡Agentverse
💡Microagents Library
💡Blockchain
💡LLMs (Large Language Models)
💡Service
💡Delta V
💡Task Execution
💡Agent Communication
Highlights
Fetch AI is building an advanced AI system that connects agents, APIs, and databases to dynamically solve tasks.
The AI engine leverages LLMs (Large Language Models) to understand human input and software code, discovering and executing tasks.
Delta V is a web chat interface for communicating with AI agents and the AI engine.
AgentVerse is a platform for building and running AI agents, which can be registered as services to be found by the AI engine.
The AI engine allows executing tasks on top of the power of LLMs, with AI agents connecting as hooks to execute tasks.
The microagents library is an open-source Python library for building AI engine-compatible agents.
The blockchain product at Fetch AI supports the alac (agent ledger and contract) system, handling messaging fees for agents.
KMP (Keeper Multi-Party) is a Python library for sending and validating transactions in inter smart contracts on any Cosmos-based blockchain.
Agents can communicate with each other, with the AI engine acting as a platform connecting independent services.
The AI engine can chain independent APIs and data silos represented by agents, allowing information to be exchanged in one command.
Agents can be run locally and also represented as a service on the AI, allowing for local execution and connectivity on Delta V.
Fetch AI provides guides for AI agents, AgentVerse, Delta V, and the blockchain layer to help users build their projects.
Fetch AI's documentation includes examples for the code being run, with more being added daily.
For help with projects, users are directed to Fetch AI's Discord server.
The AI engine uses protocols to understand and execute tasks, with agents needing to register to be discoverable.
Services are created in AgentVerse, with field descriptions defining how the AI engine should interact with them.
Agents can be chained together, with one agent's output being used as input for another, creating a graph of communication.
Fetch AI's tools aim to help developers, hackers, and entrepreneurs reach their goals by providing a dynamic and interconnected AI system.
