Benchmarking Agentic AI Frameworks in Analytics Workflows
While agentic frameworks share the goal of empowering LLMs with tool usage and reasoning, their architectures reveal critical differences in decision-making, error handling, and data processing.
We had previously benchmarked agentic frameworks across different use cases, but we wanted to observe how these frameworks would behave and perform on a more complex task.
We benchmarked CrewAI, LangChain, OpenAI Swarm and LangGraph, examining their decision-making efficiency, tool integration capabilities, recovery mechanisms, execution speed, token usage, and how each handles data inconsistencies and tool failures in analytical workflows.
LangGraph vs LangChain vs CrewAI vs Swarm
For this benchmark, we provided frameworks with a dataset containing issues like null prices, missing Product_IDs, and empty Categories. The frameworks were evaluated on how accurately and efficiently they handled these critical data quality challenges, allowing us to compare their decision-making performance under realistic error conditions.
Decision accuracy and decision efficiency
- Decision Accuracy measures how successful the framework’s each decision is in handling nulls, defaults, field mappings, failure recovery.
- Decision Efficiency evaluates the proportion of critical issues addressed per total decisions. A value of 100% indicates that each critical issue was resolved with exactly one decision, representing the most efficient scenario. Lower percentages reflect additional attempts, retries, or meta-decisions taken to resolve issues, which can improve robustness and allow the framework to adapt to failures, but reduce efficiency and increase overhead, such as higher token usage and processing cost.
- Decision Accuracy and tool call success measure different dimensions of performance. A framework can fail tool calls but still achieve high decision accuracy if it compensates with alternative reasoning or recovery strategies (e.g., CrewAI).
You can see benchmark methodology.
CrewAI: Low Efficiency, High Accuracy (21%, 87%) CrewAI takes a “whatever it takes” approach, making extensive decisions and exploring problems from multiple angles. When tools fail, it switches to alternative methods like manual calculations and adds extra validation steps. This resulted in numerous decisions but also helped reach the correct outcome. This creates substantial decision overhead but delivers reliable results. The approach burns through resources and time but ensures problems get solved, making it suitable for high-stakes scenarios where accuracy trumps efficiency.
LangGraph: High Efficiency, High Accuracy (60%, 100%) LangGraph uses graph-based coordination with confidence scoring systems that streamline decision-making. Its architecture prevents wasted effort by mapping out execution paths and coordinating tool usage. This results in clean, direct problem-solving without redundant attempts or recovery mechanisms needed. The framework makes fewer decisions overall but each one tends to be correct and purposeful.
Swarm: High Efficiency, High Accuracy (60%, 90%) Swarm utilizes specialized agents working in assembly-line fashion, where each agent handles specific tasks like KPI analysis or competitor research. This division of labor creates efficiency through focused expertise and generally achieves successful coordination. The multi-agent system made minimal errors while maintaining high accuracy, demonstrating that specialized agent collaboration works effectively.
LangChain: Medium Efficiency, Low Accuracy (42%, 78%) LangChain follows sequential execution patterns with limited tool coordination capabilities. When tools fail, it tends to retry the same approach multiple times rather than exploring alternatives. This creates moderate decision overhead without the recovery benefits seen in CrewAI – while LangChain made fewer decisions than CrewAI, CrewAI’s decisions led to successful outcomes through logical alternative strategies, whereas LangChain’s repeated attempts often resulted in the same failures. The framework lacks in error handling, leading to repeated failures and suboptimal resource utilization, demonstrating wasted resources and lower reliability compared to other frameworks.
Tool integration performance
Framework | Tool Calls | Direct Successes | Failures | Recovery Success | Success Rate | Recovery Capability |
|---|---|---|---|---|---|---|
Swarm | 5 | 5 | 0 | – | 100% | – |
LangGraph | 5 | 5 | 0 | – | 100% | – |
CrewAI | 8 | 3 | 5 | 5 | 37% | 100% |
LangChain | 7 | 4 | 3 | 0 | 51% | 0% |
Swarm & LangGraph: Both frameworks achieved flawless tool coordination without any failures. Swarm’s specialized agent architecture (KPI Analyst, Competitor Analyst, Currency Analyst) created seamless handoffs between different analytical tasks. LangGraph’s graph-based orchestration mapped tool dependencies effectively, preventing coordination issues before they occurred.
CrewAI: Low Success, High Recovery CrewAI struggled with direct tool execution, experiencing multiple failures particularly with the KPI calculator tool. However, its recovery mechanisms proved robust – when tools crashed, the framework automatically pivoted to manual calculations and alternative approaches. This adaptive problem-solving ensured all tasks were ultimately completed despite the initial tool failures.
LangChain: Moderate Success, Zero Recovery LangChain achieved reasonable direct success rates but showed critical weakness in error handling. When the currency conversion tool failed with different input formats, the framework made repeated attempts using the same failed approach rather than exploring alternatives. This lack of adaptive capability left several tasks unresolved, demonstrating poor resilience compared to other frameworks.
Agentic frameworks execution time and completion token
Swarm: Speed & Efficiency Leader 20 seconds execution time and 1000 token usage delivers the most efficient performance. The specialized agent architecture proves effective – each agent focuses on their expertise area, reducing unnecessary processing overhead. The 100% tool integration success rate and minimal token consumption support this efficiency.
CrewAI: Resource-Intensive Problem Solver 32 seconds and 4500 tokens demonstrate the cost of the “whatever it takes” approach. Recovery from tool failures consumes significant tokens – switching to manual calculations and adding validation layers explains this overhead. Slow but reliable.
LangGraph: Balanced Performance 24 seconds and 1600 tokens show a middle-ground approach. Graph-based coordination is efficient but not as streamlined as Swarm. Despite perfect tool integration scores, there’s more planning and coordination overhead.
LangChain: Verbose & Inefficient 48 seconds execution time makes it the slowest performer. 2100 token usage reflects sequential retry patterns – repeatedly attempting the same failed approaches wastes both time and tokens. Without recovery capability, failed attempts provide no value.
Agentic frameworks error handling approaches
To evaluate error handling strategies, we conducted another test allowing each framework to use its native error processing approach rather than shared data processing logic. This revealed fundamental differences in how frameworks handle data integrity versus processing completeness.
LangChain implemented data imputation, calculating missing values from available data. When encountering null price fields, it computed values based on original prices and discount rates. While demonstrating intelligent gap-filling, this resulted in modified revenue calculations that differed from expected values.
LangGraph applied conservative skip logic, excluding records with null or invalid data from calculations. Records with missing critical fields were properly omitted, maintaining calculation accuracy and data integrity by processing only verified complete records.
CrewAI followed comprehensive data inclusion under a “zero data loss” philosophy, attempting to process all records regardless of completeness. This approach included null and empty fields in calculations, affecting accuracy in favor of data retention.
Swarm implemented proper skip logic with technical optimizations. After core API patching to resolve compatibility issues, the framework correctly identified and excluded invalid records while maintaining processing integrity through specialized agent coordination.
When to use each framework
CrewAI: When unexpected problems are likely and autonomous problem-solving is required.
LangGraph: For balanced reasoning and structure. Suitable for general-purpose use cases.
Swarm: In production environments where speed and reliability are critical. Fastest and most consistent.
LangChain: When detailed traceability and transparency are needed. Logs every step but slower than alternatives.
Framework architecture impact on analytics workflows
Different agentic frameworks employ fundamentally different architectural approaches that directly influence how analytics systems handle complex data processing scenarios. Understanding these architectural philosophies becomes crucial when designing enterprise analytics solutions that can autonomously analyze data from multiple data streams. This new paradigm represents a significant shift from traditional approaches, requiring key considerations around how AI agents work within existing data infrastructure.
Memory management strategies across frameworks
Context persistence patterns
Agentic frameworks handle analytical context differently, creating distinct implications for business intelligence workflows:
Conversation-based memory: Frameworks like LangChain maintain analytical context through conversation history. While this enables incremental query building, the strategy of filling missing data can lead to inaccurate results and context pollution in long analytical sessions, requiring careful monitoring by data engineers.
Graph-based state management: LangGraph’s stateful approach allows AI agents to maintain complex analytical workflows across multiple steps. This proves valuable for data analysis requiring multiple data transformations and validation steps, particularly when processing raw data from various sources. The system enables AI agents handle complex data pipelines while maintaining state consistency across operations.
Agent-specific memory: Multi-agent frameworks like CrewAI and Swarm maintain separate memory contexts for different analytical tasks. This isolation prevents interference between parallel analytical processes but can complicate coordination among data teams working with sensitive data. Each agent operates with specialized memory that supports contextual insights generation while requiring human AI collaboration for complex decisions.
External tool orchestration
Intelligent agents need access to various analytical tools and platforms for comprehensive data analysis:
BI tool integration: Frameworks must coordinate with existing traditional BI tools, enabling AI agents to generate relevant charts and dashboards using established visualization libraries. This integration ensures that agentic analytics platforms can work alongside conventional business intelligence systems without replacing existing investments while reducing manual effort required for routine reporting.
Statistical computing: Complex data analysis often requires specialized statistical tools that agentic frameworks must orchestrate seamlessly, moving beyond the static rules that traditional bi systems typically employ. These AI powered systems can automatically select appropriate analytical methods based on data characteristics and business requirements.
Machine learning pipelines: AI agents increasingly need to trigger and monitor machine learning model training and inference processes, creating a more dynamic approach to data prep and analysis than conventional systems offer. Machine learning models integrate seamlessly with agentic frameworks to provide predictive analytics capabilities and support proactive insights generation.
Error handling and recovery mechanisms
Framework resilience patterns
Agentic analytics systems must handle various failure modes gracefully:
Graceful degradation: Well-designed frameworks continue operating when individual AI agents encounter errors, providing partial results rather than complete failure. This resilience is crucial when working with multiple data sources that may have varying availability or when dealing with missing data scenarios.
Automatic retry logic: Frameworks implement retry mechanisms that account for different failure types, from temporary network issues to data quality problems that affect how systems retrieve data from external sources. These mechanisms reduce manual effort while ensuring reliable data access.
Human escalation protocols: When automated recovery fails, frameworks must seamlessly transfer control to human analysts with sufficient context for effective intervention, ensuring that business users can take over when needed. This require human approval for critical decisions while maintaining system continuity.
Data quality error management
Agentic frameworks differ significantly in handling data quality issues:
Validation-first approaches: Some frameworks validate data quality before processing, preventing downstream errors but potentially rejecting useful partial data from various data streams. This approach prioritizes accuracy while potentially limiting analytical scope.
Progressive quality assessment: Other frameworks assess data quality incrementally during analysis, enabling more flexible handling of mixed-quality datasets while maintaining the integrity of data analytics processes. This adaptive approach enables more comprehensive analysis of imperfect data.
Quality-aware processing: Some frameworks adjust analytical approaches based on data quality assessments, providing appropriate confidence levels for results and ensuring that business context influences how AI agents interpret questionable data. This capability supports better decision-making by clearly communicating uncertainty levels.
Developer experience
Different frameworks demonstrate varying levels of compatibility and performance with specific LLM providers. For instance, LangChain exhibits superior integration and accuracy when paired with OpenAI’s ChatGPT models, delivering more precise results through optimized prompt handling and response processing.
However, while frameworks may utilize LLMs more efficiently, their fundamental purpose remains consistent across different models. The characteristic behaviors we observed – such as decision-making patterns, recovery handling and alternative reasoning capabilities – are primarily dependent on their underlying architectural design rather than the specific LLM employed. This suggests that framework-LLM combinations can impact performance metrics, but the core behavioral patterns like CrewAI’s “whatever it takes” approach or Swarm’s specialized agent coordination remain consistent regardless of the language model used.
We encountered notable integration challenges when attempting to connect CrewAI with Anthropic’s Claude models. Despite multiple configuration attempts, persistent environment setup errors prevented successful deployment. Our research indicates this is not an isolated issue – numerous developers in the community have reported similar integration difficulties between CrewAI and Anthropic services, suggesting potential architectural incompatibilities or API handling limitations.
Based on these findings, we recommend evaluating different framework-LLM combinations when selecting frameworks for your specific use case.
Benchmark methodology
We aimed to objectively compare four AI agent frameworks (LangGraph, LangChain, CrewAI, Swarm) using identical datasets and measurement systems. We evaluated the frameworks’ decision-making accuracy, resource efficiency, and tool integration capabilities under realistic error conditions.
We ensured identical test conditions for each framework. We used the same JSON dataset , same ground truth KPIs, same mock APIs and timing delays across all frameworks. We used a dataset of 100 records, which was sufficient to observe the decision capacities. We reset tracking systems before each test (decision_tracker, perf_tracker reset). We used the same tool functions across all frameworks but adapted naming conventions to each framework (_swarm_tool, crewaitool).
E-commerce purchase data was utilized. The dataset contains the following fields: User_ID (Customer identifier), Product_ID (Product identifier), Category (Product category), Price (Rs.) (Original price), Discount (%) (Discount percentage), Final_Price(Rs.) (Final price after discount), Payment_Method (Payment method), Purchase_Date (Purchase date).
We employed deliberately corrupted e-commerce data:
- Null values
- Empty fields – “Product_ID”: “”, “User_ID”: “”, “Category”: “”
- Mixed field names – “cost”: 1200.0, “revenue”: 150.0
- Data inconsistency – Date format variations (“07/01/2024” vs “dd-mm-yyyy”)
- Zero/negative values
Each framework was assigned 5 identical tasks:
- Data processing – Enhanced data processing with framework-specific execution for cleaning and transformation
- KPI calculation – Apply identical KPI calculation algorithms using enhanced_kpi_calculator tool
- Competitor analysis – Perform competitor analysis for top 3 products using CompetitorAPI
- Currency conversion – Convert total revenue to USD using CurrencyAPI
- Error handling – Implement native error management strategies for data inconsistencies
Key Decision Points Expected:
- Null handling decision – How to handle null Final_Price
- Empty field default decision – How to fill empty fields
- Field mapping decision – Field transformations
- Data inconsistency decision – Format normalization
- Zero value skip decision – Include/exclude zero values
- Tool execution decision: Which tool to use when? Whether it will be successful? What to do in case of error? How to handle tool failures and fallback strategies?
We executed each framework pipeline 10 times and took the median values for all metrics.
We implemented the same measurement infrastructure across all frameworks: AccuracyLatencyTracker for timing measurement (start_timer/end_timer), DecisionTracker for decision logging with categorization, EnhancedAnalyticsDataProcessor for identical data cleaning logic, and Mock APIs including CompetitorAPI (0.05s delay) and CurrencyAPI (0.1s delay).
We maintained framework-specific configurations: LangGraph used graph-based orchestration with confidence scoring and intelligent routing. LangChain employed sequential ReAct agent with ConversationBufferMemory and detailed logging. CrewAI utilized multi-agent collaboration with autonomous problem-solving.
All frameworks (CrewAI, LangGraph, LangChain, and Swarm) were tested using GPT-4.1 to ensure consistent model performance and fair comparison across the evaluation metrics.
Decision Accuracy measures how reliably a framework resolves critical data issues and is calculated as (Correct Decisions ÷ Total Decisions) × 100. Decision Accuracy was calculated by evaluating the frameworks’ decisions against predefined business logic criteria. Each decision was classified as correct or incorrect in a binary manner based on null handling (skipping invalid records), empty field defaults (assigning UNKNOWN values), field mapping intelligence (e.g., transforming cost → Price), and tool failure recovery strategies with alternative reasoning.
Decision Efficiency evaluates the efficiency of decisions in addressing critical issues and is calculated as (Critical Data Points ÷ Total Decisions) × 100. Critical points were established as the minimum steps/obstacles that a framework must resolve to reach the correct answer – such as null handling, empty field defaults, and field mapping, each representing mandatory decision points that frameworks cannot bypass, with the ideal scenario requiring exactly one decision per critical point, while making additional decisions indicates inefficiency (over-processing).
In terms of Tool Performance, Primary Success Rate measures the proportion of direct tool calls completed successfully and is calculated as (Direct Successes ÷ Total Tool Calls) × 100.
Recovery Capability assesses a framework’s ability to successfully recover from failed tool calls and is calculated as (Successful Recoveries ÷ Total Failures) × 100.
We followed a standardized workflow: tracker reset → dataset load → enhanced data processing → framework-specific execution → result extraction → metrics calculation.
We ensured validation through controlled variables with identical ground truth, same expected KPIs, and standardized measurement instruments. We preserved framework unique strengths while ensuring fair comparison conditions. We provided validation through cross-framework consistency checks and decision pattern analysis.
For error handling evaluation, we removed standardized data processing infrastructure, allowing each framework to apply native error management strategies. The test dataset contained strategically placed errors including null Final_Price values, empty Product_ID fields, and missing User_ID entries across multiple records.
Framework execution followed native preprocessing approaches rather than shared logic. LangChain applied autonomous data correction attempts, LangGraph implemented conservative filtering mechanisms, CrewAI pursued comprehensive data retention strategies, and Swarm maintained technical accuracy through specialized agent coordination.
LangChain maintained zero-shot-react-description patterns with Tool class implementations and conversation context management. LangGraph continued using create_react_agent with MemorySaver checkpointer and thread-based conversation management. CrewAI preserved structured output approaches with Pydantic validation, role definitions, and backstory configurations for agent personality establishment. Swarm retained SwarmCorePatcher functionality with parallel_tool_calls parameter removal and sequential agent workflows.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
Be the first to comment
Your email address will not be published. All fields are required.