Agent Frameworks

The agent framework landscape has consolidated around four major approaches: graph-based workflows (LangGraph), role-based teams (CrewAI), conversational coordination (AutoGen/Microsoft Agent Framework), and harness-powered delegation (Claude Agent SDK). Each embodies fundamentally different philosophies about how agents should coordinate.

Framework Comparison Matrix

LangGraph

[2026-01-30]: LangGraph reached v1.0 as the first stable major release in the durable agent framework space. Powering production applications at Uber, LinkedIn, Klarna, and JP Morgan.

Core Architecture

LangGraph treats agent workflows as directed graphs with nodes (processing steps) and edges (transitions). State persists automatically across execution, enabling long-running workflows without custom database logic.

Key v1.0 Capabilities:

• Durable state: Agent execution state persists automatically
• Built-in persistence: Save and resume workflows at any point
• Human-in-the-loop: First-class API support for pausing execution for human review
• Real-time streaming: Token-by-token output showing agent reasoning

Design Patterns

Strengths

• Granular control: Every state transition is explicit and debuggable
• Performance: Lowest latency and token usage in benchmarks due to reduced redundant context passing
• Enterprise adoption: Production-proven at scale with observability built in
• LangChain integration: Seamless transition from chains to graphs when complexity requires

Trade-offs

• Scaling friction: High parallelism and distributed execution require external systems
• Missing batteries: Retries, fallbacks, and observability often need external tooling
• Verbose for simple cases: Graph definition overhead for linear workflows

When to Use

LangGraph fits when:

• Tasks require branching, error recovery, or conditional logic
• Long-running workflows need checkpoint and resume capabilities
• Production requirements demand durable execution guarantees
• Teams need visual representation of complex workflows

LangGraph does not fit when:

• Simple linear workflows suffice
• Rapid prototyping is the priority
• Teams lack graph-based thinking experience

CrewAI

[2026-01-30]: CrewAI operates as a standalone framework (independent of LangChain) with 100,000+ certified developers and adoption by 60% of Fortune 500 companies.

Core Architecture

CrewAI organizes agents into "crews" where each agent has a defined role, backstory, and tool access. Agents collaborate on tasks through structured delegation.

Two Primitives:

• Crews: Teams of autonomous agents with specialized roles
• Flows: Event-driven workflows that orchestrate crews and manage state

Design Philosophy

Role-based design mirrors human team organization:

┌─────────────────────────────────────┐
│              Flow                   │
│  ┌───────────────────────────────┐  │
│  │           Crew                │  │
│  │  ┌─────────┐  ┌─────────┐    │  │
│  │  │Researcher│  │Developer│    │  │
│  │  │  Agent  │  │  Agent  │    │  │
│  │  └─────────┘  └─────────┘    │  │
│  └───────────────────────────────┘  │
└─────────────────────────────────────┘

Strengths

• Intuitive model: Role-based design maps to natural team structures
• Performance: Executes 5.76x faster than LangGraph in certain task types
• Standalone: No dependency on LangChain, lighter resource demands
• Enterprise-ready: AWS partnership with Amazon Bedrock integration

Trade-offs

• Less granular control: Role abstraction hides coordination details
• Stochastic behavior: Agent interactions can be unpredictable
• Limited graph capabilities: Complex conditional logic harder to express

When to Use

CrewAI fits when:

• Teams have clear role separation (researcher, analyst, writer)
• Rapid prototyping is prioritized over fine-grained control
• Business users need to understand agent architecture
• Multi-agent collaboration is primary use case

CrewAI does not fit when:

• Workflows require complex branching logic
• Deterministic, reproducible behavior is critical
• Fine-grained state management is needed

Microsoft Agent Framework (AutoGen Evolution)

[2026-01-30]: Microsoft merged AutoGen with Semantic Kernel into a unified Microsoft Agent Framework, targeting GA by end of Q1 2026. AutoGen enters maintenance mode—existing projects require migration planning.

Core Architecture

The Agent Framework treats workflows as conversations between agents. Each agent can communicate with humans, tools, and other agents through natural language exchange.

Orchestration Patterns:

• Sequential: Step-by-step workflows
• Concurrent: Agents work in parallel
• Group chat: Agents brainstorm collaboratively
• Handoff: Responsibility transfers as context evolves
• Magentic: Manager agent coordinates via dynamic task ledger

Unique Capabilities

Request and Response: Workflows can pause execution and wait for external input before continuing—enabling sophisticated human-in-the-loop patterns not present in AutoGen's Team abstraction.

Enterprise Integration: Native Azure integration, multi-language support (C#, Python, Java), production SLAs, and compliance guarantees (SOC 2, HIPAA).

Strengths

• Conversational flexibility: Agents adapt roles based on context
• Microsoft ecosystem: Deep Azure AI Foundry integration
• Research backing: #1 accuracy on GAIA benchmark
• A2A Protocol: Agent-to-Agent interoperability across runtimes

Trade-offs

• Stochastic behavior: Conversation-driven agents may loop or go off-track
• Migration overhead: AutoGen users must migrate to Agent Framework
• Complexity: Conversational dynamics harder to debug than explicit graphs
• Azure-centric: Full capabilities require Azure investment

When to Use

Microsoft Agent Framework fits when:

• Azure is the target deployment environment
• Dynamic, context-dependent role assignment is valuable
• Enterprise compliance requirements exist
• Conversational multi-agent patterns match the use case

Microsoft Agent Framework does not fit when:

• Deterministic workflow behavior is required
• Teams want vendor-neutral solutions
• Simple delegation suffices over conversation

Claude Agent SDK

[2026-01-30]: The Claude Agent SDK (renamed from Claude Code SDK in late 2025) provides the agent harness powering Claude Code, now available for custom agent development.

Core Architecture

The SDK provides an agentic loop where Claude autonomously reads files, runs commands, searches the web, and edits code. Tools are first-class citizens in Claude's context window.

Key Components:

• Built-in tools: File operations, command execution, code editing
• Custom tools via MCP: In-process MCP servers without separate processes
• Tool Search Tool: Access thousands of tools without context bloat
• Programmatic tool calling: Invoke tools in code execution environment

Advanced Tool Use (2026)

Three features for large-scale tool libraries:

• Tool Search Tool: Search over tools dynamically (85% token reduction)
• Programmatic Tool Calling: Reduces context window impact
• Tool Use Examples: Universal standard for demonstrating tool usage

Performance: Opus 4.5 accuracy improved from 79.5% to 88.1% with Tool Search enabled.

Strengths

• Tool-native: Tools are the primary building blocks, not an afterthought
• Same harness as Claude Code: Battle-tested in production
• Python and TypeScript: SDK available in both languages
• MCP ecosystem: Access to growing tool library

Trade-offs

• Single-vendor: Deep Anthropic coupling
• Emerging patterns: Multi-agent coordination still evolving
• Subagent constraints: Task tool unavailable to subagents (flat orchestration)

When to Use

Claude Agent SDK fits when:

• Deep tool integration is the primary requirement
• Autonomous code execution and file manipulation are needed
• Anthropic models are the target
• Teams want Claude Code's proven patterns

Claude Agent SDK does not fit when:

• Multi-vendor model support is required
• Complex multi-agent coordination is primary use case
• Teams need established multi-agent patterns out of the box

Decision Framework

Quick Selection Guide

Decision Tree

Is model-agnostic critical?
├─ Yes → LangGraph or CrewAI
└─ No
   ├─ Azure ecosystem? → Microsoft Agent Framework
   └─ Anthropic models? → Claude Agent SDK

Is workflow complexity high?
├─ Yes (branching, loops, checkpoints) → LangGraph
└─ No (sequential, role-based) → CrewAI

Is rapid prototyping priority?
├─ Yes → CrewAI
└─ No → Match to deployment environment

Hybrid Approaches

Frameworks are not mutually exclusive. Common patterns:

• Prototype in CrewAI, productionize in LangGraph: Use CrewAI's intuitive model for exploration, then implement in LangGraph for production durability
• ADK tool wrappers: Google ADK's LangchainTool and CrewaiTool enable cross-framework composition
• MCP as integration layer: Connect agents across frameworks via shared tool servers

Framework Evolution Trends

2026 Landscape Shifts

• Production focus: Frameworks compete on enterprise features (durability, compliance, observability)
• Agent interoperability: A2A protocol and MCP enable cross-framework coordination
• Consolidation: Microsoft merged AutoGen and Semantic Kernel; expect similar consolidation
• Multi-modal expansion: Audio/video streaming becoming framework differentiator

Market Context

Agent-based systems now represent 86% of copilot spending ($7.2B). Framework selection has moved from technical evaluation to strategic platform decision.

Connections

• To Claude Code: Claude Agent SDK powers Claude Code's subagent system and tool execution
• To Google ADK: ADK provides similar multi-agent coordination with Google Cloud integration
• To Orchestrator Pattern: Frameworks implement orchestrator patterns differently—LangGraph via graphs, CrewAI via crews, AutoGen via conversations
• To Tool Use: Framework tool integration patterns vary significantly—MCP support, tool filtering, context management
• To Multi-Agent Context: Each framework handles context isolation differently

Sources

• LangChain and LangGraph v1.0 Milestones
• LangGraph Overview - LangChain Docs
• CrewAI - The Leading Multi-Agent Platform
• CrewAI Introduction
• Microsoft Agent Framework Overview
• Introducing Microsoft Agent Framework - Azure Blog
• AutoGen to Microsoft Agent Framework Migration Guide
• Building agents with the Claude Agent SDK
• Advanced Tool Use - Anthropic
• Agent Orchestration 2026 - Iterathon
• CrewAI vs LangGraph vs AutoGen - DataCamp
• Top AI Agent Frameworks - Turing