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디렉토리 플랫폼 템플릿

Open-source multi-agent framework with vector memory support, tool integration for collaborative AI crews, and workflow orchestration ideal for agentic chatbots and task automation.

Leading framework for LLM applications with deep vector store integrations (e.g., Qdrant, Pinecone), tool calling, memory management, and agent orchestration for building chatbots and autonomous agents. Compared to LlamaIndex, it emphasizes general-purpose chains and multi-agent workflows over RAG-specific indexing.

AI agent framework featuring Observational Memory that achieves 95% on LongMemEval with 5-40x compression and stable, reproducible context windows.

Agentic Context Engineering framework for self-improving LLMs with structured context management, tool guides, and vector-based memory for agent behavior optimization.

Microsoft's open-source framework for multi-agent conversations with tool use, memory persistence, and vector retrieval integration for collaborative LLM agents and chat systems.

Rust crate for Self-Optimizing Neural Architecture (SONA) with LoRA adaptation, EWC++ plasticity, and ReasoningBank learning. Enables continuous improvement in LLM routers and agents without forgetting.

AI-native search framework that provides end-to-end neural search pipeline orchestration, supporting embedding models, vector indexing, and semantic search, with DocArray for data representation.

Multimodal memory system for lifelong learning agents capable of simultaneously understanding and remembering text, images, and video. Represents a step beyond traditional text-only memory systems toward multimodal context management for AI agents operating in diverse data environments.

Open-source multi-agent AI framework (formerly Microsoft AutoGen) with event-driven core, async-first execution, and pluggable orchestration strategies for building AI agent systems.

Enterprise agentic platform for automating workflows with self-improving agents using plan-execute-verify framework. Achieved 86% accuracy on LongMemEval benchmark.

Platform for building stateful AI agents with advanced memory that can learn and self-improve over time. Uses OS-inspired approach with main context as RAM and external storage as disk.

Local-first framework for building on-device personal AI agents with tools, memory, and learning capabilities. Runs entirely on-device with five composable primitives: intelligence, engine, agents, tools & memory, and learning.