An open-source multi-agent orchestration framework that uses a role-based paradigm where developers define AI 'crews' of agents, each with specific roles, goals, and tools. CrewAI simplifies building collaborative agent teams—agents can delegate tasks to each other and coordinate toward shared objectives. It integrates with MCP for tool connections and LangChain tools.
Empieza por la explicación más corta y útil antes de profundizar.
An open-source multi-agent orchestration framework that uses a role-based paradigm where developers define AI 'crews' of agents, each with specific roles, goals, and tools. CrewAI simplifies building collaborative agent teams—agents can delegate tasks to each other and coordinate toward shared objectives. It integrates with MCP for tool connections and LangChain tools.
Usa primero la analogía corta para razonar mejor sobre el término cuando aparezca en código, docs o prompts.
Piensa en esto como una pieza de la pila de contexto o inferencia usada en productos con agentes o LLMs.
Ubica el término dentro de la capa de Solana en la que vive para razonar mejor sobre él.
LLMs, RAG, embeddings, inferencia y primitivas orientadas a agentes.
Convierte el término de vocabulario en algo operacional para producto e ingeniería.
Este término desbloquea conceptos adyacentes rápido, así que funciona mejor cuando lo tratas como un punto de conexión y no como una definición aislada.
Usa este bloque compacto cuando quieras dar contexto sólido a un agente o asistente sin volcar toda la página.
CrewAI (crewai) Categoría: IA / ML Definición: An open-source multi-agent orchestration framework that uses a role-based paradigm where developers define AI 'crews' of agents, each with specific roles, goals, and tools. CrewAI simplifies building collaborative agent teams—agents can delegate tasks to each other and coordinate toward shared objectives. It integrates with MCP for tool connections and LangChain tools. Aliases: Crew AI Relacionados: Agente de IA, LangChain / LangGraph, Model Context Protocol (MCP)
Usa contexto del glosario, relaciones entre términos, modelos mentales y builder paths para recibir respuestas estructuradas en vez de output genérico.
Opcional: pega código Anchor, Solana o Rust para que el Copilot mapee primitivas de vuelta al glosario.
El Copilot responderá usando el término actual, conceptos relacionados, modelos mentales y el grafo alrededor del glosario.
Estas ramas muestran qué conceptos toca este término directamente y qué existe una capa más allá de ellos.
An autonomous AI system that can plan, use tools, and take actions to accomplish goals. Agents use LLMs as the reasoning core and have access to tools (APIs, code execution, web browsing, database queries). In blockchain: agents can analyze smart contracts, execute transactions, monitor DeFi positions, and automate trading strategies. Frameworks: LangChain, CrewAI, Claude Agent SDK.
LangChain is a popular open-source framework for building LLM-powered applications, providing abstractions for chains, tools, memory, and retrieval. LangGraph extends it with a graph-based runtime for building stateful, multi-step agent workflows with precise control over execution flow, state persistence, and error recovery. LangGraph is the production-grade choice for complex agentic applications requiring fine-grained state management.
An open standard introduced by Anthropic in November 2024 for connecting AI applications to external data sources, tools, and workflows via a unified protocol. Often described as 'USB-C for AI,' MCP eliminates the need for custom integrations per data source. Adopted by OpenAI in March 2025 and donated to the Linux Foundation's Agentic AI Foundation. MCP handles standardized tool/data connections while agent frameworks handle orchestration.
Estos son los siguientes conceptos que vale la pena abrir si quieres que este término tenga más sentido dentro de un workflow real de Solana.
Estas entradas son fáciles de mezclar cuando lees rápido, haces prompting a un LLM o estás entrando en una nueva capa de Solana.
An AI-native code editor (VS Code fork) with integrated LLM capabilities including multi-file editing, Composer mode for autonomous agent workflows, and natural-language commands. With 360K+ paying users by 2025, Cursor popularized the concept of an 'agentic IDE' where AI can reason across an entire codebase, propose and apply multi-file changes, and run iterative coding loops.
Las entradas del glosario se vuelven útiles cuando están conectadas. Estos enlaces son el camino más corto hacia ideas adyacentes.
An autonomous AI system that can plan, use tools, and take actions to accomplish goals. Agents use LLMs as the reasoning core and have access to tools (APIs, code execution, web browsing, database queries). In blockchain: agents can analyze smart contracts, execute transactions, monitor DeFi positions, and automate trading strategies. Frameworks: LangChain, CrewAI, Claude Agent SDK.
LangChain is a popular open-source framework for building LLM-powered applications, providing abstractions for chains, tools, memory, and retrieval. LangGraph extends it with a graph-based runtime for building stateful, multi-step agent workflows with precise control over execution flow, state persistence, and error recovery. LangGraph is the production-grade choice for complex agentic applications requiring fine-grained state management.
An open standard introduced by Anthropic in November 2024 for connecting AI applications to external data sources, tools, and workflows via a unified protocol. Often described as 'USB-C for AI,' MCP eliminates the need for custom integrations per data source. Adopted by OpenAI in March 2025 and donated to the Linux Foundation's Agentic AI Foundation. MCP handles standardized tool/data connections while agent frameworks handle orchestration.
Estas entradas viven junto al término actual y ayudan a que la página se sienta parte de un grafo de conocimiento más amplio en lugar de un callejón sin salida.
A neural network trained on vast text corpora to understand and generate human language. LLMs (GPT-4, Claude, Llama, Gemini) use transformer architectures with billions of parameters. They power chatbots, code generation, summarization, and reasoning tasks. In blockchain development, LLMs assist with smart contract writing, audit review, documentation, and code explanation.
The neural network architecture underlying modern LLMs, introduced in 'Attention Is All You Need' (2017). Transformers use self-attention mechanisms to process input sequences in parallel (unlike recurrent networks). Key components: multi-head attention, positional encoding, feedforward layers, and layer normalization. Variants include encoder-only (BERT), decoder-only (GPT), and encoder-decoder (T5).
A neural network component that allows models to weigh the relevance of different parts of the input when producing output. Self-attention computes query-key-value dot products across all positions, enabling each token to 'attend' to every other token. Multi-head attention runs multiple attention functions in parallel. Attention is O(n²) in sequence length, driving context window research.
A large AI model trained on broad data that can be adapted for many downstream tasks. Foundation models (GPT-4, Claude, Llama 3, Gemini) are pre-trained on internet-scale text/code and can be fine-tuned, prompted, or used via APIs for specific applications. The term emphasizes that one base model serves as the foundation for diverse use cases rather than training task-specific models.