> ## Documentation Index
> Fetch the complete documentation index at: https://celo-64ac69bd-martinvol-fix-smart-contracts-release-docs.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# 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](https://www.aiagenttoolkit.xyz/).

**On Celo:**

* [**Olas**](https://docs.autonolas.network/open-autonomy/): Framework for autonomous economic agents in decentralized markets.
  * [Implement MECH client into your dApp](https://www.youtube.com/watch?v=fuDteQqsf2A)
  * [Celo Trader Agent](https://www.youtube.com/watch?v=WSB0H0dDc78\&t=1740s)
    * can do transfers (for advanced Python developers)
* [**Gaia**](https://www.gaianet.ai/): Building intelligent ecosystems for evolving AI applications
  * Meme Token Generator - an AI Agent that autonomously deploys tokens on Celo.
  * [Twitter Thread](https://github.com/harishkotra/celo-token-agent) - short intro
  * [Video Tutorial](https://www.youtube.com/watch?v=-7Bcgpj79LM)
  * [Example Repository](https://github.com/harishkotra/celo-token-agent/)
* [EternalAI](https://eternalai.org/): A Decentralized Autonomous Agent protocol running AI agents on Solidity smart contracts — exactly as programmed — without censorship, interference, or downtime.
* [GT Protocol](https://www.gt-protocol.io/): 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**](https://elizaos.github.io/eliza/): TypeScript-based framework with multi-agent simulation capabilities.

**Other:**

* [**LangChain**](https://www.langchain.com/): Framework for LLM-powered applications.
* [**MetaGPT**](https://github.com/geekan/MetaGPT): Multi-agent meta programming framework, mimicks organizational roles at a software company.
* [**OpenAgents**](https://openagents.org): 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**](https://www.crewai.com): 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**](https://www.microsoft.com/en-us/research/project/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)**](https://ai-sdk.dev/): 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.

## Launchpads

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

* [**Virtuals**](https://app.virtuals.io/): No-code AI Launchpad with LLP context system.
* [**Vapor**](https://alpha.vaporware.fun/): 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](https://ohmygoat.dev/introduction)**: GOAT 🐐 (Great Onchain Agent Toolkit) is an open-source framework for adding blockchain capabilities like wallets and smart contracts to AI agents.
* **[thirdweb AI](https://portal.thirdweb.com/ai/chat?utm_source=celo\&utm_medium=documentation\&utm_campaign=chain_docs)**: Web3 LLM by thirdweb (formerly Nebula). Natural language model optimized for blockchain interactions with autonomous transaction capabilities.
  * [Tutorial](https://www.youtube.com/watch?v=FeubfHwfJcM)
  * [Example Repository](https://github.com/thirdweb-example/thirdweb-ai-mini-app)
  * [Documentation](https://portal.thirdweb.com/ai/chat)
* **[ChainGPT](https://www.chaingpt.org/)**: 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](https://www.eigenlayer.xyz/): Autonomous Verifiable Service (AVS) on EigenLayer is a decentralized service built on Ethereum that provides custom verification mechanisms of off-chain operations.
* [Safe](https://safe.global/safenet): Smart Accounts for Agents
* **[Kaito](https://www.kaito.ai/)**: Unified crypto news data.

**Memory Systems:**

* **[Mem0](https://github.com/mem0ai/mem0)**: Intelligent memory layer for AI assistants.
* **[Eliza Agent Memory](https://github.com/elizaOS/agentmemory)**: Knowledge graphing and document search.

**Security and Policy:**

* **[Predicate](https://x.com/0xPredicate)**: Define rules for onchain interactions
* **[Functor Network](https://www.functor.sh/)**: Policy framework for autonomous agents
* **Access Controls**: Environmental permissions
* **[Langfuse](https://langfuse.com/) - Prompt Verification**: Traces, evals, prompt management and metrics to debug and improve your LLM application.
* **[LiteLLM](https://www.litellm.ai/#features) - 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](https://github.com/datasphere/datasphere)**: Visualizes large datasets for analysis.
* **[JinAI's LLM-friendly Markdown Tool](https://github.com/jina-ai/serve)**: Converts websites into LLM-friendly markdown.
* **[Masa](https://www.masa.ai/)**: The #1 real-time data network for AI Agents & Apps
* **[Vana](https://www.vana.org/)**: The first open protocol for data sovereignty. User-owned AI through user-owned data. Growing the DataDAO ecosystem.