awslabs/agent-squad
Agent Squad
Agent Squad is a multi-agent system orchestrator and language model agent orchestration framework. It serves as an AI workflow automation engine and tool integration layer designed to coordinate teams of specialized agents to solve complex tasks through routing, parallel execution, and state management.
The project is distinguished by its ability to dynamically compose purpose-specific agents on-demand and route requests based on intent, language, or domain expertise. It supports advanced coordination patterns, including parallel subtask distribution, sequential task pipelines, and the ability to escalate complex interactions to human operators.
The framework covers a broad range of capabilities, including retrieval-augmented generation, conversation state preservation across storage backends, and the integration of external APIs and serverless functions. It also provides utilities for automatic language detection, text translation, content moderation filtering, and incremental response streaming.
The orchestration logic is designed for cross-environment deployment, supporting local machines, cloud platforms, and event-driven serverless environments.
Features
- Agent Orchestration Layers - Implements a control plane that orchestrates multiple agents and their toolsets within complex workflows.
- Multi-Agent Orchestration Frameworks - Provides a framework for coordinating multiple specialized autonomous agents to execute complex collaborative tasks.
- Multi-Agent Orchestrators - Coordinates teams of specialized AI agents to solve complex tasks through intent-based routing and task distribution.
- Agent Session Memory - Maintains session context using in-memory storage to track interaction history between users and agents.
- Agent Orchestration Frameworks - Integrates pre-built agents into a control layer to handle routed queries with streaming and tracing.
- Multi-Agent Routing Systems - Directs user inputs to domain-specific agents to ensure accurate and specialized responses.
- Control Flow & Workflows - Integrates structured control flows and workflows to handle cross-region invocation and data transformation.
- On-Demand Agent Composition - Generates purpose-specific agents on-demand by dynamically selecting relevant action groups and knowledge bases.
- Agent Tool Execution - Enables agents to invoke external functions and tools to perform actions or fetch live data during conversations.
- Agent Tool Integrations - Provides mechanisms for connecting autonomous agents to external software tools and APIs to extend their functional capabilities.
- Query Routing - Implements intent-based routing to direct queries to specialized agents based on domain expertise and language.
- Parallel Persona Coordinators - Manages groups of specialized agents in parallel to distribute subtasks and synthesize a single comprehensive response.
- AI Workflow Automation - Acts as an automation engine that coordinates sequential or parallel AI tasks with intent-based routing.
- LLM Tooling Integrations - Provides connectors and interfaces that allow language models to access external APIs and serverless functions.
- Conversation History Management - Provides tools for storing and retrieving interaction logs to maintain coherence across multi-turn AI sessions.
- Conversation State Management - Implements systems for tracking context and history to ensure follow-up inputs remain coherent across diverse topics.
- Conversational AI Frameworks - Provides a framework for building multi-turn chat systems that maintain session state and history across models.
- Conversational State Managers - Tracks multi-turn interaction history across storage backends to maintain coherence between users and agent teams.
- External Tool Integration - Links agents to external APIs and data sources to perform real-time tasks such as order lookups.
- Retrieval Augmented Generation - Grounds agent responses in external data sources to provide specific information for accurate answers.
- LLM Integration Layers - Implements a standardized middleware layer connecting language models to external APIs and retrieval systems.
- Multi-Agent Orchestration Layers - Provides a coordination layer for managing collaborative workflows and task distribution between specialized agents.
- Multi-Agent Orchestration Systems - Manages teams of AI agents by distributing subtasks and synthesizing responses from multiple providers.
- Multi-Agent System Composition - Generates purpose-specific agents on-demand by associating tasks with specific action groups and knowledge bases.
- Agent-Specific Query Routing - Analyzes requests and conversation history to direct each query to the most appropriate specialized agent.
- Serverless Function Orchestration - Executes custom backend orchestration logic automatically in response to specific system events or API requests in serverless environments.
- Hierarchical Agent Routing - Directs queries through a classifier to specific teams or individual agents based on the nature of the request.
- Unified Model Interfaces - Standardizes execution across different language model providers through a common API for processing and streaming.
- Intent-Based Routing - Uses classification and semantic similarity to direct user requests to the most suitable agent based on intent.
- Sequential Agent Execution - Passes the output of one agent as the input to the next to create linear processing pipelines.
- Agent Development - Provides platforms to build and customize AI agents with specialized logic for model interactions and API calls.
- Language Model Integrations - Provides adapters and interfaces to connect the system to various hosted or local language model providers.
- Human Approval - Provides mechanisms to pause workflows for human review and transfer complex cases to supervisors for resolution.
- Language Detection Tools - Includes utilities for automatically identifying the language of incoming text to ensure accurate content analysis.
- LLM Response Streaming - Implements incremental token-by-token delivery of AI model responses to reduce perceived latency.
- Text Translation Services - Converts text between source and target languages using models with dynamic selection and custom parameters.
- Multi-Tier Memory Systems - Uses a multi-tier memory system to record communications between users, supervisors, and agent teams.
- Concurrent Agent Messaging - Executes communications to multiple agents simultaneously to accelerate task completion and data gathering.
- Hybrid Local-Cloud Deployments - Supports running orchestration logic across diverse environments, including local machines and cloud platforms.
- Serverless Deployment - Deploys orchestration logic into event-driven cloud environments to execute workflows based on triggers.
- Content Moderation - Analyzes text for toxicity and private information to block processing based on safety and compliance rules.
- AI Agent Frameworks - Flexible framework for multi-agent management.
