Dhiren Mathur

This list features open-source AI agent frameworks and tools designed for various applications, from workflow automation to software development. It includes popular projects like CrewAI, which orchestrates role-playing AI agents, LangChain, a framework for building LLM-powered applications, and Potpie AI, an agent system specialized in codebase understanding. Whether you're looking for infrastructure tools, productivity boosters, or AI-powered development assistants, this list covers a wide range of options.

Unlock the power of multiple AI models with Potpie’s Multi-LLM Support feature. Seamlessly integrate OpenAI, Gemini, Claude, and more to optimize performance, cost, and flexibility. Learn how to set up and configure your preferred LLM for smarter AI Agents today!

PotpieAI has launched Open-Source Custom AI Agents, enabling developers to build AI assistants that deeply understand their codebases. These agents leverage a Neo4j-based knowledge graph and CrewAI-powered RAG agents for intelligent, context-aware automation. Developers can create and manage agents via the Potpie dashboard or API, optimizing workflows with customizable AI assistance.

PotpieAI introduces Web Access, enabling AI Agents to fetch real-time data from the internet, enhancing context-awareness and accuracy in responses. This feature leverages Firecrawl for structured web data extraction and tool calling to dynamically interact with specialized tools like GitHub issue retrieval. Developers can provide live links or let the AI autonomously fetch relevant data, streamlining workflows and improving AI-driven decision-making.

Potpie's Debugging Agent is an AI tool that mirrors how developers debug code, using a knowledge graph of the codebase to understand relationships between functions, files, and classes. It features Knowledge Graph Queries, Code Retrieval, Node Neighbor Analysis, and Tag-based Retrieval to provide precise debugging assistance. The agent acts as an experienced pair programmer, analyzing stacktraces, providing debugging directions, and helping developers iteratively reach the root cause of issues.

This blog explores how knowledge graphs can help language models generate code with better context by understanding the entire codebase. It discusses challenges like duplicating functionality, violating design patterns, and missing business requirements. The approach involves mapping function relationships, providing runtime code fetches, and delivering function-level explanations via a vector database for agents to query. This system optimizes code generation by providing detailed, dynamic context to avoid redundant, inconsistent code and ensure alignment with design and business logic across complex codebases.