Capabilities
- Specialized agent roles with defined scopes
- Cross-agent handoffs and message passing
- Task arbitration and conflict resolution
- Shared memory and context pools
- Supervisor-based orchestration patterns
Agent type
Networks of specialist agents coordinated by a supervisor or orchestrator, enabling role-based task delegation, cross-agent handoffs, and shared memory for complex workflows.
Single-agent systems break down for complex tasks that require specialist knowledge across multiple domains. One agent cannot be expert at research, coding, analysis, and communication simultaneously, leading to shallow results on multi-step workflows.
Open GuideEngineering teams spend 20-30% of their review cycle on repetitive style, security, and performance checks that could be automated. At scale, manual reviews become a bottleneck that slows deployment velocity.
Open GuideResearchers spend 3-5 hours filtering through sources, cross-referencing claims, and organizing conclusions for a single research question. Manual synthesis is error-prone, sources get lost, and findings are hard to reproduce.
Open GuideMicrosoft's multi-agent conversation framework (autogen-agentchat). Now in maintenance mode as it merges into the unified Microsoft Agent Framework targeting Q1 2026 GA.
open-source
Open ToolMulti-agent platform with open-source framework and Agent Management Platform (AMP). Visual editor, AI copilot, and enterprise deployment used by 60% of Fortune 500.
freemium
Open ToolAgent framework (v1.1) with create_agent abstraction, LangGraph stateful orchestration, middleware for retries and moderation, and model profiles.
open-source
Open ToolIn-memory data store with Vector Sets (Redis 8 preview) for native vector search, semantic caching, JSON document storage, and session management for AI agents.
open-source-or-cloud
Open ToolOpen-source vector engine with built-in Weaviate Agents (Query, Transformation, Personalization), Hybrid Search 2.0, and multi-tenant architecture.
open-source-or-cloud
Open ToolSingle-agent systems break down for complex tasks that require specialist knowledge across multiple domains. One agent cannot be expert at research, coding, analysis, and communication simultaneously, leading to shallow results on multi-step workflows.
Open GuideResearchers spend 3-5 hours filtering through sources, cross-referencing claims, and organizing conclusions for a single research question. Manual synthesis is error-prone, sources get lost, and findings are hard to reproduce.
Open GuideEngineering teams spend 20-30% of their review cycle on repetitive style, security, and performance checks that could be automated. At scale, manual reviews become a bottleneck that slows deployment velocity.
Open Guide