Key takeaways
- LangChain is the market leader with 90M monthly downloads, but CrewAI is growing fastest with 60% Fortune 500 adoption
- Microsoft is consolidating AutoGen into Microsoft Agent Framework, signaling enterprise focus on unified agent platforms
- By 2027, expect 80% of production agent deployments to use frameworks that combine orchestration with observability platforms
FAQ
What is an agent framework?
An agent framework is a software library that provides abstractions for building AI agents — autonomous systems that use LLMs to reason, plan, and execute tasks using tools.
Which agent framework is best for production?
LangChain/LangGraph offers the most mature observability with LangSmith. CrewAI has the most Fortune 500 deployments. Choice depends on your use case and team expertise.
Are agent frameworks open source?
All major frameworks (LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex, Mastra) have open source cores with MIT or Apache licenses. Commercial platforms provide additional observability and deployment features.
What is the difference between agents and workflows?
Agents are autonomous and make decisions using LLMs. Workflows are deterministic sequences of steps. Modern frameworks like CrewAI Flows and LangGraph combine both for production reliability.
Executive Summary
The AI agent framework market has matured rapidly, with five major players emerging: AutoGen (Microsoft), CrewAI, LangChain/LangGraph, LlamaIndex, and Mastra. Each offers a distinct approach to building autonomous AI systems, from LangChain's comprehensive ecosystem to CrewAI's production-focused multi-agent teams.
Key Findings:
- LangChain dominates downloads — 90M monthly downloads and 100k+ GitHub stars make it the most adopted framework
- CrewAI leads enterprise adoption — 60% of Fortune 500 companies, 450M+ workflows monthly
- Microsoft is consolidating — AutoGen merging into Microsoft Agent Framework signals enterprise platform unification
- Specialization matters — LlamaIndex excels at RAG, CrewAI at multi-agent, Mastra at TypeScript
Strategic Planning Assumptions:
- By 2027, 80% of production agent deployments will require integrated observability platforms
- By 2028, framework consolidation will reduce the market to 3-4 major players
- By 2028, TypeScript agent frameworks will capture 30% market share (up from ~10% today)
Market Definition
Agent frameworks are software libraries that provide abstractions for building AI agents — autonomous systems that use LLMs to reason, plan, and execute multi-step tasks.
Inclusion Criteria:
- Provides agent orchestration primitives (not just LLM wrappers)
- Open source core (MIT, Apache, or equivalent)
- Active development with production users
- Multi-model support (not locked to single provider)
Exclusion Criteria:
- Pure LLM API wrappers without orchestration
- Proprietary-only frameworks
- Abandoned or pre-alpha projects
- Single-provider solutions (e.g., OpenAI Assistants API alone)
Comparison Matrix
| Framework | Primary Language | Architecture | Observability | Enterprise Focus | Maturity |
|---|---|---|---|---|---|
| AutoGen | Python, .NET | Multi-agent conversation | Basic → MS Agent Framework | Microsoft ecosystem | Transitioning |
| CrewAI | Python | Crews + Flows | AMP Cloud | Fortune 500 | GA |
| LangChain/LangGraph | Python, TypeScript | Chains + Graphs | LangSmith | Broad enterprise | GA |
| LlamaIndex | Python, TypeScript | RAG + Workflows | LlamaCloud | Document AI | GA |
| Mastra | TypeScript | Agents + Workflows | Integrated | TypeScript teams | Early GA |
| Vercel AI SDK | TypeScript | Agents + Tool Loops | AI Gateway | Vercel ecosystem | GA |
Product Profiles
AutoGen
Microsoft's pioneering multi-agent framework, now transitioning to Microsoft Agent Framework
| Quick Reference | |
|---|---|
| Website | microsoft.github.io/autogen |
| Founded | 2023 |
| Funding | Microsoft-backed |
Overview
AutoGen pioneered the multi-agent orchestration paradigm now adopted across the industry. In 2026, Microsoft announced its merger with Semantic Kernel into Microsoft Agent Framework, combining AutoGen's experimental patterns with enterprise-ready infrastructure.
Strengths
- Pioneer status — Defined multi-agent patterns (debate, group chat, handoff)
- Microsoft backing — Corporate resources and Azure integration path
- Cross-language — Both Python and .NET SDKs
Cautions
- Transition state — Active development shifting to Microsoft Agent Framework
- Enterprise gaps — Better observability/durability in successor framework
- Learning curve — Multiple API layers can be confusing
Key Stats
| Metric | Value |
|---|---|
| GitHub Stars | 50.4k |
| Contributors | 559 |
| License | MIT |
| Status | Maintenance mode |
CrewAI
Production-ready multi-agent automation used by 60% of Fortune 500
| Quick Reference | |
|---|---|
| Website | crewai.com |
| Founded | 2024 |
| Funding | Series A (~$18M) |
Overview
CrewAI is a standalone Python framework built from scratch (not a LangChain wrapper). It combines autonomous agent teams (Crews) with deterministic workflows (Flows) for production-grade automation. 100,000+ certified developers and customer wins at DocuSign, IBM, and PwC validate its enterprise readiness.
Strengths
- Fastest growing — 60% Fortune 500, 450M+ workflows monthly
- Standalone architecture — No LangChain dependency, better performance
- Visual + code — CrewAI Studio for no-code, full APIs for developers
Cautions
- Python only — No TypeScript or .NET support
- Young company — Founded 2024, proving long-term viability
- Observability requires paid tier — Advanced tracing needs AMP Cloud
Key Stats
| Metric | Value |
|---|---|
| Fortune 500 Adoption | 60% |
| Monthly Workflows | 450M+ |
| Certified Developers | 100,000+ |
| License | MIT |
LangChain/LangGraph
#1 downloaded agent framework with comprehensive ecosystem
| Quick Reference | |
|---|---|
| Website | langchain.com |
| Founded | 2022 |
| Funding | ~$35M |
Overview
LangChain provides the most comprehensive LLM application development ecosystem, with LangChain for composable primitives, LangGraph for stateful orchestration, and LangSmith for observability. Trusted by Klarna, LinkedIn, Uber, and GitLab in production.
Strengths
- Market leader — 90M monthly downloads, 100k+ GitHub stars
- Ecosystem breadth — 1000+ integrations
- Full observability — LangSmith works with any stack
Cautions
- Complexity growth — Large ecosystem creates learning curve
- Abstraction overhead — Many layers between code and LLM calls
- LangSmith dependency — Best features require paid platform
Key Stats
| Metric | Value |
|---|---|
| Monthly Downloads | 90M |
| GitHub Stars | 100k+ |
| Integrations | 1000+ |
| License | MIT |
LlamaIndex
Leading framework for RAG and document AI with best-in-class parsing
| Quick Reference | |
|---|---|
| Website | llamaindex.ai |
| Founded | 2022 |
| Funding | ~$33M |
Overview
LlamaIndex specializes in context augmentation — making private data available to LLMs. LlamaParse handles complex documents (nested tables, images, handwriting), while Workflows provides event-driven agent orchestration. SOC 2 Type II, GDPR, and HIPAA certified.
Strengths
- Document AI leadership — LlamaParse handles complex formats
- Production scale — 500M+ documents processed
- Compliance ready — SOC 2, GDPR, HIPAA certified
Cautions
- RAG-focused identity — Less recognized for general agents
- Credit-based pricing — Can become expensive at scale
- Multiple doc sites — Framework, cloud, legacy docs create confusion
Key Stats
| Metric | Value |
|---|---|
| Documents Processed | 500M+ |
| Monthly Downloads | 25M+ |
| LlamaCloud Users | 300k+ |
| License | MIT |
Mastra
TypeScript-native AI framework with SOTA Observational Memory
| Quick Reference | |
|---|---|
| Website | mastra.ai |
| Founded | 2024 |
| Funding | Y Combinator W25 |
Overview
Mastra is a batteries-included TypeScript framework from the Gatsby team. Its standout feature is Observational Memory, achieving 94.9% on LongMemEval (SOTA) without RAG or knowledge graphs. Designed around "Python trains, TypeScript ships."
Strengths
- TypeScript-native — First-class TS experience, not a Python port
- SOTA memory — Observational Memory beats all published benchmarks
- Batteries included — Agents, workflows, RAG, memory, evals in one package
Cautions
- TypeScript only — Python developers have to switch stacks
- Young project — Limited production track record
- Gatsby ghost — Team's previous project was eclipsed by Next.js
Key Stats
| Metric | Value |
|---|---|
| LongMemEval Score | 94.87% |
| Model Providers | 40+ |
| License | Apache 2.0 |
Vercel AI SDK
Leading TypeScript toolkit with 20M+ monthly downloads and first-class agent support
| Quick Reference | |
|---|---|
| Website | ai-sdk.dev |
| Founded | 2023 |
| Funding | Vercel-backed |
Overview
The Vercel AI SDK is the most downloaded TypeScript toolkit for building AI applications. AI SDK 6 introduced the ToolLoopAgent abstraction, tool execution approval, and full MCP support — elevating it from a streaming library to a complete agent framework.
Strengths
- Market leader — 20M+ monthly downloads, 21k+ GitHub stars
- Complete agent support — Tool loops, approval, MCP in SDK 6
- Type-safe UI — End-to-end TypeScript from agent to components
Cautions
- TypeScript only — No Python SDK
- Vercel-optimized — Best experience on Vercel platform
- Newer agent features — SDK 6 agent abstractions are recent (Dec 2025)
Key Stats
| Metric | Value |
|---|---|
| Monthly Downloads | 20M+ |
| GitHub Stars | 21.7k |
| Model Providers | 40+ (via AI Gateway) |
| License | Apache 2.0 |
Architecture/Pattern Analysis
Orchestration Approaches
| Approach | Frameworks | Pros | Cons |
|---|---|---|---|
| Graph-based | LangGraph | Visual, deterministic | Rigid structure |
| Event-driven | LlamaIndex Workflows, Mastra | Flexible, async | Harder to visualize |
| Crews + Flows | CrewAI | Separates autonomy/control | Framework-specific |
| Conversation-based | AutoGen | Natural interaction | Less structured |
Memory Approaches
| Approach | Frameworks | Pros | Cons |
|---|---|---|---|
| Vector RAG | LangChain, LlamaIndex | Proven, scalable | Context loss |
| Observational Memory | Mastra | SOTA benchmarks | New, untested at scale |
| Conversation history | AutoGen, CrewAI | Simple | Limited context window |
Gap Analysis
| Feature | AutoGen | CrewAI | LangChain | LlamaIndex | Mastra | Vercel AI SDK |
|---|---|---|---|---|---|---|
| Python SDK | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
| TypeScript SDK | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ |
| .NET SDK | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ |
| Visual Builder | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
| Integrated Observability | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ |
| Document Parsing | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ |
| MCP Support | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| Human-in-the-loop | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
Bottom line: No single framework covers all needs. LangChain has broadest coverage but misses document parsing. LlamaIndex leads document AI but lacks visual builder. Mastra and Vercel AI SDK are TypeScript-only.
Strategic Recommendations
By Use Case
| Use Case | Recommended | Runner-Up |
|---|---|---|
| Multi-agent teams | CrewAI | AutoGen |
| Document AI / RAG | LlamaIndex | LangChain |
| Maximum integrations | LangChain | LlamaIndex |
| TypeScript development | Mastra | LangChain |
| Microsoft ecosystem | AutoGen → MS Agent Framework | — |
| Production observability | LangChain + LangSmith | CrewAI AMP |
By Buyer Profile
Enterprise with complex documents: → LlamaIndex + LlamaCloud for document parsing, consider LangChain for broader agent needs
Fortune 500 automating business processes: → CrewAI with AMP Cloud — proven at scale with enterprise support
TypeScript-first development team: → Mastra for native experience, LangChain as fallback for breadth
Microsoft shop evaluating agents: → Start with AutoGen to understand patterns, plan migration to Microsoft Agent Framework
Startup needing fastest time-to-production: → LangChain + LangSmith — largest community, most examples, best observability
Market Outlook
Near-Term (2026-2027)
- Microsoft Agent Framework launch will consolidate Microsoft's agent story
- CrewAI likely to expand beyond Python (TypeScript SDK anticipated)
- LangChain will deepen LangSmith platform with more deployment features
- Expect 2-3 frameworks to emerge as clear production leaders
Medium-Term (2027-2028)
- Framework consolidation — smaller players absorbed or marginalized
- Observability becomes table stakes — all major frameworks will require platforms
- Enterprise agent orchestration standardizes around 2-3 patterns
- TypeScript frameworks capture significant market share
Long-Term (2028+)
- Agent frameworks become infrastructure layer (like databases)
- Major cloud providers launch competing offerings
- Open standards (MCP, A2A) enable framework interoperability
- Specialization increases — document agents, coding agents, etc.
Bottom Line
Current Leaders:
- LangChain/LangGraph for ecosystem breadth and observability
- CrewAI for production multi-agent automation
- LlamaIndex for document understanding and RAG
The agent framework market is maturing but not yet consolidated. Expect significant changes as Microsoft Agent Framework launches, CrewAI expands language support, and observability platforms become differentiators.
Strategic advice: Choose based on primary use case (documents vs. multi-agent vs. general LLM), language preference (Python vs. TypeScript), and observability needs. All major frameworks are viable; differentiation is in specialization and platform features.
Research by Ry Walker Research • methodology