Feedback Rivers is a platform designed to collect and streamline customer feedback from multiple sources into one cohesive system. With integrations for various tools like customer service, NPS, user feedback, and app reviews, the platform provides businesses with a comprehensive view of customer sentiments. Using AI, it measures and analyzes feedback to highlight key issues and areas for improvement. Feedback Rivers can also send you all your feedback in an easily digestible format via Slack channels, ensuring that valuable insights are transparent and actionable across your team.

GenAI Agents SDK is an open-source Python library designed to help developers create self-driven AI agents using large language models. It offers a core agent template with pluggable modules for memory storage, tool interfaces, planning strategies, and execution loops. You can configure agents to call external APIs, read/write files, run searches, or interact with databases. Its modular design ensures easy customization, rapid prototyping, and seamless integration of new capabilities, empowering the creation of dynamic, autonomous AI applications that can reason, plan, and act in real-world scenarios.

Gems is a robust AI-powered knowledge assistant that connects with your preferred tools like Notion, Gmail, and Slack to provide precise answers to your queries. Its core function is to extract and present ready-to-use responses from your consolidated information, eliminating the need for manual searching and organization. Simply activate Gems, type your natural language question, and get the answers you need instantly, making it an invaluable tool for increasing productivity and efficiency in managing your digital workspace.

LangChain is a developer-focused framework designed to streamline the creation of intelligent AI agents and applications. It provides abstractions for chains of LLM calls, agentic behavior with tool integrations, memory management for context persistence, and customizable prompt templates. With built-in support for document loaders, vector stores, and various model providers, LangChain allows you to construct retrieval-augmented generation pipelines, autonomous agents, and conversational assistants that can interact with APIs, databases, and external systems in a unified workflow.

LangChain serves as a comprehensive toolkit for building advanced LLM-powered applications, abstracting away low-level API interactions and providing reusable modules. With its prompt template system, developers can define dynamic prompts and chain them together to execute multi-step reasoning flows. The built-in agent framework combines LLM outputs with external tool calls, allowing autonomous decision-making and task execution such as web searches or database queries. Memory modules preserve conversational context, enabling stateful dialogues over multiple turns. Integration with vector databases facilitates retrieval-augmented generation, enriching responses with relevant knowledge. Extensible callback hooks allow custom logging and monitoring. LangChain’s modular architecture promotes rapid prototyping and scalability, supporting deployment on both local environments and cloud infrastructure.

Nestor offers a modular architecture to assemble AI agents that maintain conversation state, invoke external tools, and customize processing pipelines. Key features include session-based memory stores, a registry for tool functions or plugins, flexible prompt templating, and unified LLM client interfaces. Agents can execute sequential tasks, perform decision branching, and integrate with REST APIs or local scripts. Nestor is framework-agnostic, enabling users to work with OpenAI, Azure, or self-hosted LLM providers.

The LangGraph Cookbook provides ready-to-use recipes for constructing sophisticated AI agents by representing workflows as directed graphs. Each node can encapsulate prompts, tool invocations, data connectors, or post-processing steps. Recipes cover tasks such as question answering over documents, summarization, code generation, and multi-tool coordination. Developers can study and adapt these patterns to rapidly prototype custom LLM-powered applications, improving modularity, reusability, and execution transparency.

LeanAgent is a Python-based framework designed to streamline the creation of autonomous AI agents. It offers built-in planning modules that leverage large language models for decision making, an extensible tool integration layer for calling external APIs or custom scripts, and a memory management system that retains context across interactions. Developers can configure agent workflows, plug in custom tools, iterate quickly with debugging utilities, and deploy production-ready agents for a variety of domains.

LLMWare serves as a comprehensive toolkit for constructing AI agents powered by large language models. It allows you to define reusable chains, integrate external tools via simple interfaces, manage contextual memory states, and orchestrate multi-step reasoning across language models and downstream services. With LLMWare, developers can plug in different model backends, set up agent decision logic, and attach custom toolkits for tasks like web browsing, database queries, or API calls. Its modular design enables rapid prototyping of autonomous agents, chatbots, or research assistants, offering built-in logging, error handling, and deployment adapters for both development and production environments.

MCP Agent Tool Adapter acts as a middleware layer between language model-based agents and external tool implementations. By registering function signatures or tool descriptors, the framework automatically parses agent outputs that specify tool calls, dispatches the appropriate adapter, handles input serialization, and returns the result back to the reasoning context. Features include dynamic tool discovery, concurrency control, logging, and error handling pipelines. It supports defining custom tool interfaces and integrating cloud or on-premise services. This enables building complex, multi-tool workflows such as API orchestration, data retrieval, and automated operations without modifying underlying agent code.

MCP Ollama Agent leverages the Ollama local LLM runtime to provide a versatile agent framework for task automation. It integrates multiple tool interfaces, including web search via SERP API, file system operations, shell command execution, and Python environment management. By defining custom prompts and tool configurations, users can orchestrate complex workflows, automate repetitive tasks, and build specialized assistants tailored to various domains. The agent handles tool invocation and context management, maintaining conversation history and tool responses to generate coherent actions. Its CLI-based setup and modular architecture make it easy to extend with new tools and adapt to different use cases, from research and data analysis to development support.

Nexus Agents is a modular framework for constructing AI-driven multi-agent systems with large language models at their core. Developers can define custom agents, integrate external tools, and orchestrate workflows through declarative YAML or Python configurations. It supports dynamic task routing, memory management, and inter-agent communication, ensuring scalable and reliable automation. With built-in logging, error handling, and CLI support, Nexus Agents streamlines building complex pipelines spanning data retrieval, analysis, content generation, and customer interactions. Its architecture allows easy extension with custom tools or LLM providers, empowering teams to automate business processes, research tasks, and operational workflows in a consistent and maintainable manner.

Odyssey provides a flexible architecture for building collaborative multi-agent systems. It includes core components such as the Task Manager for defining and distributing subtasks, Memory Modules for storing context and conversation histories, Agent Controllers for coordinating LLM-powered agents, and Tool Managers for integrating external APIs or custom functions. Developers can configure workflows via YAML files, select prebuilt LLM kernels (e.g., GPT-4, local models), and seamlessly extend the framework with new tools or memory backends. Odyssey logs interactions, supports asynchronous task execution, and enables iterative refinement loops, making it ideal for research, prototyping, and production-ready multi-agent applications.

Notte is a developer-centric Python framework designed for orchestrating AI agents powered by large language models. It provides built-in memory modules to store and retrieve conversational context, flexible tool integration for external APIs or custom functions, and a planning engine that sequences tasks. With Notte, you can rapidly prototype conversational assistants, data analysis bots, or automated workflows, while benefiting from open-source extensibility and cross-platform support.

PulpSense offers AI-powered solutions to optimize your business's operational efficiency. Our services include bespoke project management systems, custom CRM builds, hiring systems, lead generation, automated service fulfillment, and consultation. Our solutions are designed to remove sales bottlenecks, automate crucial operations, and seamlessly integrate with existing tools, enabling your business to scale up effortlessly and achieve 8-figure growth.

Surfsite is an AI-driven platform that helps businesses centralize their tools and data for streamlined operations and decision-making. It provides secure, adaptive AI assistants that enhance customer experiences, ensure compliance, and improve overall efficiency. With Surfsite, businesses can integrate their favorite tools, customize workflows, and access real-time insights, all within a protected environment. Whether you are a product manager, growth marketer, or founder, Surfsite aids in making faster data-driven decisions and scaling your business effectively.

Routine is an all-in-one productivity application that brings together tasks, calendar events, notes, and contacts. Say goodbye to manual data entry and app switching by seamlessly integrating your existing tools and services. Routine offers quick data capture, hierarchical note organization, and media embedding. Its minimalist design ensures a smooth user experience, helping you maintain control over your daily tasks and long-term projects. Whether you're a student, professional, or entrepreneur, Routine is tailored to fit your productivity needs.

Semi Agent provides a modular architecture for building AI agents that can plan, execute actions, and remember context over time. It integrates with popular language models, supports tool definitions for custom functionality, and maintains conversational or task-oriented memory. Developers can define step-by-step plans, connect external APIs or scripts as tools, and leverage built-in logging to debug and optimize agent behavior. Its open-source design and Python basis allow easy customization, extensibility, and integration into existing pipelines.

Silo Team is a comprehensive developer onboarding platform designed to streamline and automate the onboarding process. Utilizing AI-powered templates, it creates tailored onboarding plans, ensuring new developers can quickly become productive team members. The platform offers real-time progress tracking to identify and address bottlenecks, integrates essential tools and documentation in one location, and fosters a smooth and efficient onboarding journey.

Smooth Operator is an advanced AI agent that specializes in automating communication processes, managing schedules, and integrating seamlessly with tools to optimize business workflows. It can assist in scheduling meetings, sending reminders, generating reports, and performing routine administrative tasks, allowing users to focus on more strategic activities. With its intuitive interface, Smooth Operator adapts to user preferences, making it an essential tool for businesses looking to improve efficiency and productivity through automation.