Agent Infrastructure
Agent infrastructure refers to the frameworks, toolkits, and orchestration layers used to build "AI Agents"—systems that can reason, plan, and execute multi-step tasks autonomously. Unlike a simple chatbot that answers a question, an agent uses this infrastructure to use tools, browse the web, and complete goals. Examples Agent Frameworks: Libraries that help developers chain together LLM prompts, memory, and tools (e.g., LangChain). Memory Systems: Databases that allow agents to remember past interactions and user preferences over long periods. Tool Use / Function Calling: Protocols that enable an AI to "click" buttons, send emails, or query APIs on its own. Multi-Agent Orchestration: Systems that manage teams of specialized agents (e.g., a "Coder" agent and a "Reviewer" agent) working together.
13 tools
Leena AI is a Gen AI Autonomous Agent that helps enterprises reduce 70% of employee tickets across functions like HR, IT, and Finance.
Mem0 provides a self-improving memory layer for AI agents, enabling personalized, cost-efficient, and continuously smarter interactions. It helps developers and enterprises reduce costs and enhance AI capabilities.
OpenPipe uses reinforcement learning to build agents for various applications. More details about their specific applications are not readily available on their website.
Memory for AI Agents; Announcing OpenMemory MCP - local and secure memory management.
Agent Reinforcement Trainer: train multi-step agents for real-world tasks using GRPO. Give your agents on-the-job training. Reinforcement learning for Qwen2.5, Qwen3, Llama, Kimi, and more!