Introduction

Project Overview

The MuleSoft AI Chain (MAC) Project consist of multiple solutions. While the project has started with MuleSoft AI Chain as MuleSoft Custom Connector, the vision evolved and it transitioned to a project. We will be introducing more AI related capabilities to the MuleSoft Developers to have a No Code / Low Code experience in Anypoint Studio / Platform.

MAC Project Connectors Support

The MAC Project offers a suite of AI-powered connectors integrated with MuleSoft, including MuleSoft AI Chain, MAC Vectors, MAC Einstein AI, MAC AWS Bedrock, MAC Whisperer and a MAC WebCrawler for easing the way autonomous agents are built, by providing complete coverage of capabilities, by connecting LLMs, Vector Databases, AI Services, Chains, Tooling and Conversations. Through the unification layer of MuleSoft AI Chain Connectors, customers can easily switch between LLMs and Vector Databases with zero effort.

MAC Project Model Support

With the integration of key AI frameworks into MuleSoft, we make sure to support a broad variety of Large Language Models (LLM) providers to cover the need of the MuleSoft Community. We will continuously invest in adding new model providers to our connectors.

Released Connectors

MuleSoft AI Chain

Our flagship project integrates LangChain4j capabilities into MuleSoft, allowing for:

• Easier interaction with LLMs and Vector Store (In-memory only, external Vector Databases are supported with MAC Vectors)
• Optimized usage in MuleSoft applications
• Access to a wide range of AI Services, Tools, and Chains

MuleSoft AI Chain leverages the MuleSoft ecosystem to provide additional capabilities on top of the LangChain4j project. These include:

MAC Einstein AI

MAC Connector for Salesforce Einstein AI to interact with the models API of the Salesforce platform and benefit from its trust layer and automation capabilities. It allows you to:

• Leverage the Salesforce Trustlayer
• Generate Embeddings with Embedding Models
• Build Prompt Templates in your workflow
• Perform adhoc RAG using Embedding Models
• Perform RAG using MAC Vectors Connector
• Build Chat Memory capabilities
• Optimized usage in MuleSoft applications

MAC Vectors

MAC Vectors Connector for external vector databases (i.e. Milvus, Chroma, Elastic, PGVector, Pinecone, etc.) and for embedding models (i.e. OpenAI, MistralAI, etc.).

• Leverage various external Vector Databases / Stores
  • Pinecone
  • Postgres PGVector
  • Elasticsearch
  • Milvus
  • Chroma
  • Weaviate
• Ingest files into the Vector Database
• Ingest folders into the Vector Database
• Ingest text into the Vector Database
• Parse Documents
• Split Documents

MAC AWS Bedrock

MAC Connector for AWS Bedrock to design, build and manage AWS bedrock agent workflows, llm interaction, knowledgebase and RAG directly in the MuleSoft Anypoint Platform. The AWS Bedrock connector allows you to:

• Build and Manage AI Agents
• Dynamic and Flexible Tooling
• Leverage the AWS Bedrock Security Capabilities
• Ingest files into the supported Embedding Models
• Build Prompt Templates in your workflow
• Perform adhoc RAG using the Vector Database
• Build Chat Memory capabilities
• Optimized usage in MuleSoft applications

MAC Whisperer

A connector offering Speech-to-Text and Text-to-Speech capabilities. In the beginning it will only support OpenAI.

Work in Progress

MAC WebCrawler

A connector offering web crawling capabilities to extract and process data from web pages based on their structure.

Future Plans

Our goal is to continuously enhance the MAC project by releasing new connectors and improving existing ones. We aim to provide comprehensive AI capabilities within the MuleSoft ecosystem, empowering developers to build more intelligent and versatile applications.

Getting Involved