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AI Agents Tools & Infrastructure

This article provides an overview of essential tools for building AI agents. Given the rapid advancements in this space, this is not an exhaustive list but rather a snapshot of tools available on Celo.

AI Agent Frameworks​

Frameworks define how AI agents interact, collaborate, and execute tasks. For a full list of frameworks, tools, and infrastructure, check out this comprehensive table.

On Celo:

  • Olas: Framework for autonomous economic agents in decentralized markets.
  • Gaia: Building intelligent ecosystems for evolving AI applications
  • EternalAI: A Decentralized Autonomous Agent protocol running AI agents on Solidity smart contracts — exactly as programmed — without censorship, interference, or downtime.
  • GT Protocol: AI Agents Builder, powered by GT Protocol AI Executive Technology, delivers customized AI agents tailored to enhance both business operations and personal daily tasks.
  • ElizaOS: TypeScript-based framework with multi-agent simulation capabilities.

Other:

  • LangChain: Framework for LLM-powered applications.
  • MetaGPT: Multi-agent meta programming framework, mimicks organizational roles at a software company.

Launchpads​

No-code platforms simplify AI agent deployment, making it easier to integrate social agents with tokens.

  • Virtuals: No-code AI Launchpad with LLP context system.
  • Vapor: Platform built on ai16z Eliza Framework

Essential Tools​

A variety of tools are available for building autonomous agents, including blockchain integration, machine learning, memory systems, simulation, monitoring, and security.

For a quick start, focus on:

  • Blockchain tools for onchain operations.
  • Memory systems for learning and adapting.
  • LLMs optimized for Web3 data.

When scaling, consider:

  • Frameworks for multi-agent systems.
  • Access controls and prompt verification.
  • Support for videos, PDFs, and research papers.
  • Effective use of LLMs, NLP, and RAG tools.

Intelligence Tools​

Machine Learning Tools:

  • Purpose: Training, deploying, debugging and managing ML models
  • Examples: LiteLLM, ModelZoo, TensorServe, GPT-Explorer
  • Use Cases: Pattern recognition, classification, prediction

Natural Language Processing Tools:

  • Purpose: Language understanding and processing
  • Examples: NeuralSpace, LangFlow
  • Use Cases: Text analysis, grammar checking, entity recognition

Retrieval Augmented Generation Tools:

  • Purpose: Combining LLMs with knowledge bases
  • Examples: Autonomous RAG, Agentic RAG, Local RAG Agent
  • Use Cases: Enhanced chatbots, documentation search, context-aware responses

Infrastructure​

Blockchain Tools:

  • GOAT: GOAT 🐐 (Great Onchain Agent Toolkit) is an open-source framework for adding blockchain capabilities like wallets and smart contracts to AI agents.
  • Nebula: Web3 LLM by thirdweb
  • Kaito: Unified crypto news data.
  • GOAT: GOAT 🐐 (Great Onchain Agent Toolkit) is an open-source framework for adding blockchain capabilities like wallets and smart contracts to AI agents.
  • ChainGPT: Is an advanced AI infrastructure that develops AI-powered technologies for the Web3, Blockchain, and Crypto space, developing solutions from Chatbots, NFT, Smart Contract Generators and AI Trading Assistants
  • EigenLayer: Autonomous Verifiable Service (AVS) on EigenLayer is a decentralized service built on Ethereum that provides custom verification mechanisms of off-chain operations.
  • Nebula: Web3 LLM by thirdweb
  • Safe: Smart Accounts for Agents
  • Kaito: Unified crypto news data.

Memory Systems:

  • Mem0: Intelligent memory layer for AI assistants.
  • Eliza Agent Memory: Knowledge graphing and document search.
  • Mem0: Intelligent memory layer for AI assistants.
  • Eliza Agent Memory: Knowledge graphing and document search.

Security and Policy:

  • Predicate: Define rules for onchain interactions
  • Functor Network: Policy framework for autonomous agents
  • Access Controls: Environmental permissions
  • Langfuse - Prompt Verification: Traces, evals, prompt management and metrics to debug and improve your LLM application.
  • LiteLLM - LLM Access: Manage LLM access for your developer

Data: When working with AI agents, it's essential to train models and collect the right data. For unique character creation, ensure you have sufficient training data. Some useful tools include: