Enterprise AI Analysis
An Empirical Study of Agent Developer Practices in AI Agent Frameworks
Executive Impact: Key Findings at a Glance
This study provides the first large-scale empirical analysis of how developers engage with and adapt AI agent frameworks to develop agent throughout the software development lifecycle (SDLC). By examining ten representative frameworks and analyzing data from thousands of real-world repositories and community discussions, we reveal a comprehensive picture of both their strengths and persistent challenges in practical adoption. Our findings show that the ten LLM-based agent frameworks serve functional roles in four categories: basic orchestration, multi-agent collaboration, data processing, and experimental exploration, and are applied across ten domains including software development. And 96% of top-starred projects adopt multiple frameworks, highlighting that a single framework can no longer meet the complex needs of agent systems. We further identify two widely adopted collaboration patterns among agent frameworks. what's more, we propose a taxonomy of agent development challenges in the software development lifecycle (SDLC), covering four domains and nine categories. Finally, we construct a five-dimensional evaluation framework based on agent developer needs to compare the performance of the ten frameworks.
1575
GitHub Repos Analyzed
8710
Developer Discussions
10
Agent Frameworks Studied
96%
Projects Use Multiple Frameworks
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Challenges
Frameworks
Protocols
96%
of top-starred projects use multiple frameworks, highlighting the complexity of modern agent systems.
Enterprise Process Flow
Requirements Analysis & Design
→
Implementation (Coding, Model Integration)
→
Testing (Functionality, Performance, Security)
→
Deployment (Production Environment)
→
Maintenance (Updates, Issues, Features)
25.61%
of issues involve API limitations, permission errors, and missing dynamic libraries, highlighting major challenges in API integration and third-party service interfacing.
23.53%
of technical obstacles are directly related to version dependencies, underscoring version compatibility traps.
| Dimension | LangChain & CrewAI | AutoGen | LlamaIndex & LangGraph | MetaGPT & BabyAGI |
|---|---|---|---|---|
| Learning Cost |
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| Development Efficiency |
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| Functional Abstraction |
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AutoGen & LangChain
excel at rapid prototyping, yet face challenges with scalability and concurrent processing under high load or multi-agent scenarios.
AutoGen & LangChain
face the highest maintenance complexity despite their mature ecosystems.
Model Context Protocol (MCP) Adoption
MCP has been widely adopted and is crucial for engineering workflows, but faces challenges with excessive prompt overhead, insecure credential storage, and limited multi-tenant scalability. For instance, in Codename Goose, MCP seamlessly connects APIs and data sources, boosting engineer productivity by approximately 20%.
Calculate Your Potential AI ROI
Estimate the efficiency gains and cost savings your enterprise could realize by implementing AI agent frameworks.
Estimated Annual Savings
$0
Hours Reclaimed Annually
0
Your AI Implementation Roadmap
A phased approach to integrate AI agent frameworks into your enterprise, ensuring a smooth transition and maximum impact.
Phase 1: Discovery & Strategy
Assess current processes, identify AI opportunities, define clear objectives, and select suitable frameworks.
Phase 2: Pilot & Prototyping
Develop initial AI agent prototypes, test core functionalities, and validate technical feasibility with a small team.
Phase 3: Integration & Expansion
Integrate pilot agents into existing systems, refine workflows, and expand to broader departments or use cases.
Phase 4: Optimization & Scaling
Monitor performance, optimize resource utilization, address scalability challenges, and enhance agent capabilities.
Phase 5: Governance & Continuous Improvement
Establish AI governance policies, ensure compliance, and implement a feedback loop for ongoing innovation and maintenance.
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