A2A SDK is a developer toolkit for building, chaining, and managing AI agents in Python. It provides APIs to define agent behaviors via prompts or code, connect agents into pipelines or workflows, and enable asynchronous message passing. Integrations with OpenAI, Llama, Redis, and REST services allow agents to fetch data, call functions, and store state. A built-in UI monitors agent activity, while the modular design ensures you can extend or replace components to fit custom use cases.

Agentic Framework is a versatile, open-source framework designed to streamline the creation of autonomous AI agents that leverage large language models and MongoDB. It equips developers with modular components for managing agent memory, defining toolsets, orchestrating multi-step workflows, and templating prompts. The integrated MongoDB-backed memory store enables agents to maintain persistent context across sessions, while pluggable tool interfaces allow seamless interaction with external APIs and data sources. Built on Node.js, the framework includes logging, monitoring hooks, and deployment examples to rapidly prototype and scale intelligent agents. With customizable configuration, developers can tailor agents for tasks such as knowledge retrieval, automated customer support, data analysis, and process automation, reducing development overhead and accelerating time-to-production.

Aladin provides a modular architecture that allows developers to define autonomous agents powered by large language models (LLMs). Each agent can load memory backends (e.g., SQLite, in-memory), utilize dynamic prompt templates, and integrate custom plugins for external API calls or local command execution. It features a task planner that breaks high-level goals into sequenced actions, executing them in order and iterating based on LLM feedback. Configuration is managed through YAML files and environment variables, making it adaptable to various use cases. Users can deploy Aladin via Docker Compose or pip installation. The CLI and FastAPI-based HTTP endpoints let users trigger agents, monitor execution, and inspect memory states, facilitating integration with CI/CD pipelines, chat interfaces, or custom dashboards.

Flyte is a flexible, scalable open-source workflow orchestration platform. It integrates seamlessly into your data and ML stack, allowing you to define, deploy, and manage robust data and ML workflows effortlessly. Its powerful and extensible features help in creating production-grade workflows that are reproducible and highly concurrent, making it an essential tool for data scientists, engineers, and analysts.

LangGraphJS API provides a programmatic interface to design AI agent workflows using directed graphs. Each node in the graph represents an LLM call, decision logic, or data transformation. Developers can chain nodes, handle branching logic, and manage asynchronous execution seamlessly. With TypeScript definitions and built-in integrations for popular LLM providers, it streamlines development of conversational agents, data extraction pipelines, and complex multi-step processes without boilerplate code.

rag-services is an extensible platform that breaks down RAG pipelines into discrete microservices. It offers a document store service, a vector index service, an embedder service, multiple LLM inference services, and an orchestrator service to coordinate workflows. Each component exposes REST APIs, allowing you to mix and match databases and model providers. With Docker and Docker Compose support, you can deploy locally or in Kubernetes clusters. The framework enables scalable, fault-tolerant RAG solutions for chatbots, knowledge bases, and automated document Q&A.