Agentic Frameworks
What Works, What Doesn’t, and Why It Matters
Executive Summary
Agentic AI promises a new era of autonomy, intelligent orchestration, and operational efficiency, but much of the current discourse is dominated by buzzwords and promise rather than proven capability. This article cuts through the hype by subjecting popular agentic frameworks: LangChain, LangGraph, CrewAI, and AutoGen, to real-world benchmark testing. Two distinct tests were conducted to evaluate agentic frameworks:
Test 1 – AI Educational Coach
Structured content generation & complex-instruction compliance. LangGraph + Claude earned a perfect quality score and the fastest runtime, with LangChain a close second.
Test 2 – AI COO GTM Strategy
Multi-agent orchestration (8 runs per framework; 24 evaluations). LangGraph led on average quality; AutoGen placed second, CrewAI third. All frameworks flawlessly excluded the irrelevant “BaseballCoach” agent.
Bottom Line
No single framework wins every scenario. Framework choice must align with task needs, and true agentic success still depends on solid tool integration, sharp prompt design, and disciplined orchestration.
I. The Murky Waters of "Agentic Frameworks"
When I first set out to write about agentic frameworks I wanted to first understand what the term really meant, which led me to realize it kind of depends on who you ask, though a common denominator seems to be the idea that the agentic system should enable agents to act, reason and coordinate, with some level of independence. But as I looked into this I realized that there wasn’t a single industry leading consensus, there were simply numerous providers and innovators that were putting their version to practice. This experience highlighted a core problem: the term "agentic framework" is being widely used, often without a clear, consistent definition.
With years of experience as an Enterprise Architect, I instinctively think of a "framework" as a structured methodology—a comprehensive approach to organizing, planning, and implementing complex systems. However, in the fast-evolving tech landscape, "framework" can signify different things at different levels of abstraction. It might be a high-level conceptual guide, a set of best practices for a specific platform, or an executable software system. The common denominator is structure, but the scope and nature of that structure vary dramatically.
To bring clarity, I find it helpful to think of frameworks in layers:
Layer 1: Conceptual Frameworks
Layer 2: Platform-Specific Frameworks
Layer 3: Implementation Frameworks
Layer 4: Domain Specific Frameworks
The tools currently marketed as "agentic frameworks”, LangChain, CrewAI, AutoGen, LangGraph, and others predominantly belong to Layer 4. They are not (yet) comprehensive architectural practices or platform agnostic governance models. They are software toolkits and libraries for building applications with agentic capabilities. This realization is key: to evaluate them meaningfully, we must test them as such, as implementation tools, assessing their extensibility, real-world performance, and practical capabilities in orchestrating AI agents.
The reality, as these benchmarks will show, is that while current frameworks are powerful at facilitating agent reasoning and planning within their defined roles, the bridge to autonomous execution, especially interaction with diverse tools and environments, often requires significant, hands-on developer effort. The 'tools' for agents are not always pre-existing or auto-generated; they frequently need to be explicitly built and integrated by the developer. This distinction between the idealized autonomous system and the current state of developer-centric toolkits is crucial for setting realistic expectations.
II. Putting Frameworks to the Test: Methodology
To move beyond theoretical discussions and marketing claims, I designed and executed two distinct benchmark tests. The goal was to compare these agentic frameworks in scenarios that mimic real-world challenges, providing a data-driven basis for understanding their strengths and weaknesses.
Evaluation Criteria
Each framework's output for the benchmarks was systematically evaluated.
For Test 1 - AI Education Coach, evaluation used a 1-5 scale (5 being best) on:
Task Execution: Did the framework and its configured LLM successfully complete all aspects of the assigned task and adhere to all constraints and user needs?
Output Clarity: Was the generated output well-structured, readable, and presented in the requested format (e.g., Markdown tables)?
Error Recovery/Robustness: Did the system avoid major pitfalls? If ambiguities or potential errors arose, did it demonstrate an ability to manage them or produce a coherent result despite them?
Autonomy & Initiative: Did the framework go beyond the bare minimum? Did it demonstrate capabilities like self-correction, proactive information synthesis, or providing additional value?
For Test 2 - AI COO GTM Strategy, evaluation focused on:
Performance Metrics: Averaged across 8 runs per framework from the provided summary reports: Duration (seconds), Agent Turns, and Output Length (characters).
Agent Selection Capability: Assessed by analyzing the "Framework Behavior Analysis -> FINDING" and the agent's "Rationale" in 10 raw output files per framework, and cross-referencing with "BaseballCoach Handling" scores from detailed LLM evaluations.
Quality Assessment: Averaged across 8 summary reports per framework, using the average scores for Completeness, Rationale Quality, and Structure Quality (each out of 5, total out of 15).
Testing Variables
LLM Variation: For Testing 1, each framework was tested generating content with both OpenAI's GPT-4 and Anthropic's Claude 3 Opus. Benchmark 2 primarily used GPT-4.
Cross-Evaluation (Test 1): The output generated by one model (e.g., OpenAI) was then evaluated by the other model (e.g., Claude), and vice-versa, using a standardized evaluation prompt.
Time Tracking: The time taken for each test run to complete was recorded.
Agent Turns (Test 2): Recorded where provided.
III. Test 1: The AI Educational Coach
Objective: This benchmark was designed to test the foundational capabilities of agentic frameworks in generating complex, structured content.
The Task: "You are an AI educational coach. A user has asked for help designing a personalized learning plan to become proficient in AI and machine learning. They already know Python programming and basic statistics. They want to learn core ML foundations, understand LLMs, and build hands-on projects. Constraints: ~10 hours/week, 12-week total plan. Your task is to: Design a 12-week curriculum, include hands-on projects every 3-4 weeks, recommend specific resources (with links), ensure difficulty increases progressively, output as a Markdown table (Week, Topics, Resources, Project), and provide a summary explaining the plan's rationale."
The Code:
AutoGen - Claude generating OpenAI evaluating
AutoGen - OpenAI generating Claude evaluating
CrewAI - Claude generating OpenAI evaluating
CrewAI - OpenAI generating Claude evaluating
LangChain - Claude generating OpenAI evaluating
LangChain - OpenAI generating Claude evaluating
LangGraph - Claude generating OpenAI evaluating
LangGraph - OpenAI generating Claude evaluating
Top Scorer & Speedster: LangGraph, when using Claude for generation and OpenAI for evaluation, achieved a perfect score of 20/20 and was the fastest, completing in an impressive 30 seconds.
Strong Performers: LangChain (OpenAI generating, Claude evaluating) also performed well, scoring 19/20 in 180 seconds. AutoGen (OpenAI generating, Claude evaluating) achieved a score of 18/20, though it took longer (312.47 seconds).
CrewAI Performance: CrewAI (OpenAI generating, Claude evaluating) scored 16/20 in 420 seconds. The run with Claude generating had data issues.
IV. Test 2: The AI COO
Objective: This benchmark aimed to test advanced agentic capabilities: dynamic orchestration, inter-agent communication, shared context, and rationale generation in a multi-step planning scenario. A key aspect was the inclusion of an irrelevant "BaseballCoachAgent" to test the frameworks' ability to filter or correctly handle extraneous information/agents.
The Task: The orchestrator agent's goal: "Plan the go-to-market strategy for the AI note-taking app using outputs from all agents." Specialist agents included Research, Product, Marketing, and Project Management, plus the irrelevant BaseballCoachAgent.
Benchmark 2: Aggregated Key Findings & Analysis (Based on 8 Runs)
Agent Selection Capabilities (Handling of Baseball Coach Agent):
A review of the 8 summary reports for each framework reveals a consistent pattern:
All three frameworks consistently demonstrated the correct identification and explicit exclusion of the irrelevant BaseballCoachAgent.
The detailed LLM evaluations awarded perfect scores (5/5) for "Baseball Coach Handling" across all 8 runs for all frameworks. The rationale provided by the agents for exclusion was noted as clear and appropriate.
Framework Insights (Benchmark 2)
LangGraph (Rank 1):
Quality: Achieved a perfect aggregated average quality score, demonstrating exceptional consistency and reliability across all 8 runs.
AutoGen (Rank 2):
Quality: Achieved the second-highest aggregated average quality score. Showed excellent Rationale Quality. Completeness was high but impacted by one run (Run 8) where it failed to generate content.
CrewAI (Rank 3):
Performance: Fastest average duration.
Quality: Scored well overall, showing strong Structure and Completeness. Its scores showed some variability, notably a lower completeness score in one run.
V. Synthesizing the Results
The two benchmarks paint a clear picture: the effectiveness of an agentic framework is not absolute but highly dependent on the nature of the task.
Structured Content Generation (Test 1): LangGraph (especially with Claude) stands out for both exceptional quality and speed. LangChain also demonstrates strong, reliable performance.
Multi-Agent Orchestration (Test 2): Based on aggregated quality scores from 8 runs, LangGraph ranked highest with a perfect score, followed by AutoGen, then CrewAI. All three frameworks demonstrated the ability to correctly identify and exclude an irrelevant agent.
VI. How to Choose the Right Agentic Framework
Based on the latest benchmark findings:
For Top-Tier Quality, Consistency, and Reliability:
Recommended: LangGraph
Why: Achieved a perfect aggregated average quality score in Benchmark 2 across 8 runs, demonstrating flawless performance. It is a highly reliable choice for critical applications. Also a top performer in Test 1.
For High Potential Quality (with Reliability Risks):
Recommended: AutoGen
Why: Achieved the second-highest aggregated average quality score in Test 2. However, its average was impacted by one critical failure.
For Speed in Multi-Agent Collaboration:
Recommended: CrewAI
Why: Fastest average execution time in Test 2. Ideal when agent roles are clear and speed is a priority, but expect more variability in output quality.
For Rapid Prototyping & Broad Tool Integration:
Recommended: LangChain
Why: Strong Test 1 performer. Its versatility and large ecosystem make it excellent for quick experiments.
VII. Conclusive Findings & The Road Ahead
This investigation into agentic frameworks, grounded in empirical benchmark testing over 8 runs for Test 2, yields several key takeaways:
No One-Size-Fits-All: The "best" agentic framework is context-dependent. LangGraph excelled in consistent quality for Test 2, while CrewAI was fastest.
Reliability is Paramount: AutoGen's single failure in Test 2, despite otherwise strong scores, underscores that even high-performing frameworks can have critical failure points. LangGraph's perfect consistency across 8 runs in Test 2 highlights its robustness.
Developer Orchestration Remains Paramount: While agents within these frameworks can exhibit impressive reasoning and planning, the broader orchestration still largely falls to the developer.
Evaluation is Key (and Hard): Meaningfully evaluating agentic systems is difficult. Aggregating data from multiple runs provides a more robust picture.