Agentic Shared Memory provides a robust solution for managing contextual data in AI-driven multi-agent environments. Leveraging vector embeddings and efficient data structures, it stores agent observations, decisions, and state transitions, enabling seamless context retrieval and update. Agents can query the shared memory to access past interactions or global knowledge, fostering coherent behavior and collaborative problem-solving. The library supports plug-and-play integration with popular AI frameworks like LangChain or custom agent orchestrators, offering customizable retention strategies, context windowing, and search functions. By abstracting memory management, developers can focus on agent logic while ensuring scalable, consistent memory handling across distributed or centralized deployments. This improves overall system performance, reduces redundant computations, and enhances agent intelligence over time.

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.

Eunomia leverages a configuration-first approach to orchestrate AI agents. Through YAML, users define agent roles, prompt templates, tool integrations, memory stores, and branching logic. The framework supports synchronous/asynchronous tools, retrieval-augmented generation, and chain-of-thought prompting. An extensible plugin system allows custom tools, memory backends, and logging integrations. Eunomia’s CLI scaffolds projects, validates configs, and runs agents locally or in cloud environments. This enables teams to quickly prototype, iterate on conversational workflows, and maintain agent solutions without heavy custom development.

GPTMe provides a robust platform for orchestrating AI agents that retain conversational context, integrate external tools, and expose a consistent API. Developers install a lightweight Python package, define agents with plug-and-play memory backends, register custom tools (e.g., web search, database queries, file operations), and spin up a local or cloud service. GPTMe handles session tracking, multi-step reasoning, prompt templating, and model switching, delivering production-ready assistants for customer service, productivity, data analysis, and more.