Stellar Chat empowers teams to build conversational AI agents by providing a robust framework that abstracts LLM interactions, memory management, and tool integrations. At its core, it features an extensible pipeline that handles user input preprocessing, context enrichment through vector-based memory retrieval, and LLM invocation with configurable prompting strategies. Developers can plug in popular vector storage solutions like Pinecone, Weaviate, or FAISS, and integrate third-party APIs or custom plugins for tasks like web search, database queries, or enterprise application control. With support for streaming outputs and real-time feedback loops, Stellar Chat ensures responsive user experiences. It also includes starter templates and best-practice examples for customer support bots, knowledge search, and internal workflow automation. Deployed with Docker or Kubernetes, it scales to meet production demands while remaining fully open-source under the MIT license.

AgentsFlow abstracts each AI agent as a node in a directed graph, enabling developers to visually and programmatically design complex pipelines. Each node can represent an LLM call, data preprocessing task, or decision logic, and can be connected to trigger subsequent actions based on outputs or conditions. The framework supports branching, loops, and parallel execution, with built-in error handling, retries, and timeout controls. AgentsFlow integrates with major LLM providers, custom models, and external APIs. Its monitoring dashboard offers real-time logs, metrics, and flow visualization, simplifying debugging and optimization. With a plugin system and REST API, AgentsFlow can be extended and integrated into CI/CD pipelines, cloud services, or custom applications, making it ideal for scalable, production-grade AI workflows.

QueryCraft is a Python-based prompt engineering toolkit designed to streamline the development of AI agents. It enables users to define structured prompts through a modular pipeline, connect seamlessly to multiple LLM APIs, and conduct automated evaluations against custom metrics. With built-in logging of token usage and costs, developers can measure performance, compare prompt variations, and identify inefficiencies. QueryCraft also includes debugging tools to inspect model outputs, visualize workflow steps, and benchmark across different models. Its CLI and SDK interfaces allow integration into CI/CD pipelines, supporting rapid iteration and collaboration. By providing a comprehensive environment for prompt design, testing, and optimization, QueryCraft helps teams deliver more accurate, efficient, and cost-effective AI agent solutions.

At its core, AI Orchestra offers a modular orchestration engine that lets developers define nodes representing AI agents, tools, and custom modules. Each node can be configured with specific LLMs (e.g., OpenAI, Hugging Face), parameters, and input/output mapping, enabling dynamic task delegation. The framework supports composable pipelines, concurrency controls, and branching logic, allowing complex flows that adapt based on intermediate results. Built-in telemetry and logging capture execution details, while callback hooks handle errors and retries. AI Orchestra also includes a plugin system for integrating external APIs or custom functionalities. With YAML or Python-based pipeline definitions, users can prototype and deploy robust multi-agent systems in minutes, from chat-based assistants to automated data analytics workflows.

NeXent is a flexible AI agent framework that lets you define custom digital workers via YAML or Python SDK. You can integrate multiple LLMs, external APIs, and toolchains into modular pipelines. Built-in memory modules enable stateful interactions, while a monitoring dashboard provides real-time insights. NeXent supports local and cloud deployment, Docker containers, and scales horizontally for enterprise workloads. The open-source design encourages extensibility and community-driven plugins.