CrewAI

Creates autonomous agents with defined roles, goals, and backstories through a declarative Agent class that encapsulates identity, capabilities, and behavioral constraints. Each agent maintains internal state including assigned tools, memory context, and execution parameters. The framework uses composition to bind agents to specific tasks and crews, enabling role-specific behavior without code duplication across agent instances.

Maps discrete tasks to specific agents and orchestrates their execution in defined sequences through a Task class that specifies expected outputs, execution context, and agent assignments. The framework manages task dependencies, handles inter-task communication, and coordinates agent handoffs. Execution flow is determined by task ordering within a Crew, with built-in support for task callbacks and result aggregation.

Allows developers to implement custom execution processes by extending the Process base class, enabling completely custom agent coordination patterns beyond sequential and hierarchical. Custom processes have full access to crew state, agents, and tasks, allowing arbitrary execution logic. The framework provides hooks for process initialization, task execution, and result aggregation.

Handles execution errors through configurable error handling strategies that determine whether tasks retry, skip, or fail the entire crew. The framework catches agent execution errors, tool invocation failures, and LLM errors, providing detailed error context. Error recovery can be configured per task with custom retry logic and fallback strategies.

Enables dynamic discovery of agent capabilities and assignment of tools based on agent roles and task requirements. The framework can introspect agent configurations to determine available tools and capabilities, allowing tasks to be routed to agents with appropriate skills. Tool assignment can be dynamic based on task context rather than static per-agent configuration.

Orchestrates multiple agents and tasks through a Crew class that manages overall execution flow, process selection (sequential, hierarchical, or custom), and result aggregation. The framework supports different process types including sequential execution, manager-based hierarchical delegation, and custom process implementations. Crews handle agent coordination, manage shared context, and provide unified execution interfaces for complex multi-agent workflows.

Integrates external tools and APIs into agent execution through a schema-based function calling system that maps tool definitions to agent capabilities. Tools are registered with the framework, their schemas are automatically extracted and provided to LLMs, and function calls are dispatched back to tool implementations. The framework handles tool invocation, error handling, and result formatting to feed back into agent reasoning loops.

Manages agent memory through configurable memory systems that track execution history, task context, and inter-agent communication. The framework maintains memory state across task executions within a crew, allowing agents to reference previous outputs and maintain conversational context. Memory can be configured per agent with different retention policies and storage backends.

Implements hierarchical multi-agent coordination through a manager agent that receives high-level objectives and delegates subtasks to specialized agents. The manager agent is responsible for task decomposition, agent selection, and result aggregation. This process type enables complex workflows where a central coordinator orchestrates multiple specialized agents without explicit task ordering.

Abstracts LLM provider interactions through a unified interface that supports multiple providers (OpenAI, Anthropic, Ollama, local models) and model families. The framework handles provider-specific API differences, token counting, and model configuration. Agents can be configured with different LLM providers independently, enabling cost optimization and model selection per agent.

Enables agents to delegate tasks to other agents and collaborate through structured inter-agent communication. Agents can request assistance from peers, share context, and coordinate on complex problems. The framework manages delegation chains, prevents circular dependencies, and tracks collaboration patterns. Communication is mediated through the crew's execution context.

Structures task outputs through expected output specifications that define format, schema, and validation rules. Tasks can specify expected output formats (JSON, markdown, structured data) and the framework guides agents toward producing outputs matching these specifications. Output validation ensures results conform to defined schemas before being passed to downstream tasks.

Provides callback mechanisms for monitoring crew and task execution through event hooks that fire at key execution points (task start, completion, error). Callbacks enable logging, monitoring, debugging, and integration with external systems. The framework supports task-level and crew-level callbacks with access to execution context and results.