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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.

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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.
  • OpenAgents: Open-source framework for building large-scale networks of AI agents. Focus on interoperability, decentralized collaboration, and payment flows. Best for scalable cross-domain agent systems, fintech, and collaborative tasks.
  • CrewAI: Python framework for orchestrating multiple AI agents as teams. Features role-based architecture, memory management, and workflow automation. Best for business process automation, research, and content analysis.
  • Microsoft AutoGen: Multi-agent AI systems with autonomous and human-in-the-loop collaboration. Features asynchronous messaging, MCP protocol support, and observability. Best for enterprise automation, software development, and data analysis.
  • Vercel AI SDK (ai-sdk): TypeScript-first toolkit for AI-powered applications. Provides unified interface for multiple LLM providers and agent abstraction. Best for web applications, Next.js/React apps, and production-ready AI integration.

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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

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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.

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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

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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.
  • thirdweb AI: Web3 LLM by thirdweb (formerly Nebula). Natural language model optimized for blockchain interactions with autonomous transaction capabilities.
  • 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.
  • 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.
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:
  • DataSphere: Visualizes large datasets for analysis.
  • JinAI’s LLM-friendly Markdown Tool: Converts websites into LLM-friendly markdown.
  • Masa: The #1 real-time data network for AI Agents & Apps
  • Vana: The first open protocol for data sovereignty. User-owned AI through user-owned data. Growing the DataDAO ecosystem.