Top 5 Open-Source Agentic AI Frameworks in 2026
We reviewed several popular open-source agentic AI frameworks, examining their performance, multi-agent orchestration capabilities, agent and function definitions, memory management, and human-in-the-loop features.
To evaluate their performance, we implemented four data analysis tasks on each framework: logistic regression, clustering, random forest classification, and descriptive statistical analysis. Each task was executed 100 times per framework to measure consistency, performance, and usability under realistic workloads.
Agentic AI frameworks benchmark
We benchmarked CrewAI, LangChain, OpenAI Swarm and LangGraph.We compared the latency and completion token usage of each framework across different data analysis tasks.
LangGraph is the fastest framework with the lowest latency values across all tasks, while LangChain has the highest latency and token usage.
OpenAI Swarm and CrewAI show very similar performance in both latency and token usage across all tasks. When examining task-level details, the OpenAI Swarm framework uses slightly fewer tokens than the CrewAI framework we prepared, yet it is slightly faster than CrewAI in two of the tasks.
You can see our methodology in detail here.
Potential reasons behind the performance differences
The key to understanding these performance differences lies in each framework’s architectural foundation:
CrewAI’s architecture
The primary reason for CrewAI’s performance lies in its architecture, which is fundamentally designed around multi-agent systems. Task delegation, inter-agent communication, and state management are handled naturally and centrally at the framework level. Additionally, tools are directly connected to agents, enabling data flow with minimal middleware, resulting in faster and more efficient execution. This architecture contributes to both lower latency and reduced token consumption.
LangChain’s design
On the other hand, LangChain is chain-first and built with a single-agent focus at its core. Multi-agent support was added later and is not a native part of the framework’s natural flow.
In LangChain, tool selection depends on the LLM’s natural language reasoning rather than direct function calls. Each step involves passing the task to the LLM through a natural language input, and the LLM analyzes this input to decide which tool to use. Since every invocation includes tool selection, LLM interpretation, and, if needed, output parsing, it increases both token consumption and execution time. While this approach offers more flexibility, it introduces more indirect steps, which leads to increased latency and higher token costs.
Efficiency-oriented approaches: Swarm and LangGraph
In contrast, Swarm and LangGraph are more efficiency-oriented.
Swarm distributes tasks among specialized agents, each working directly with its own toolset. Tools are connected as native Python functions, and the LLM is only involved when necessary. This leads to lower token usage and faster execution.
LangGraph, on the other hand, defines tasks as a graph (DAG), where the tool to be executed at each step is predetermined. It minimizes LLM involvement by only invoking it in ambiguous or decision-making nodes, depending on graph design. This approach enhances performance and simplifies debugging.
In this benchmark, we explained the frameworks along with their design approaches and how we used each individually. However, these frameworks have very different use cases, implementations, and design methods. All performance outcomes ultimately depend on the framework’s architecture, the specific use case, and the deployment environment, so results may vary according to the developer’s design choices and scenario needs.
Compare agentic AI frameworks
Agentic AI frameworks vary across several key dimensions, and understanding these differences is essential for making meaningful comparisons.
Best use cases by framework:
- LangGraph: Complex workflows needing detailed control and orchestration
- AutoGen: Research and prototyping with flexible agent behavior
- CrewAI: Production systems with role-based task delegation
- OpenAI Swarm: Lightweight experiments and simple task execution
- LangChain: General-purpose LLM apps with chains, tools, and RAG
Production readiness:
Here are the most important factors that distinguish one framework from another:
Multi-agent orchestration
Multi-agent orchestration coordinates multiple specialized AI agents to tackle complex workflows that exceed single-agent capabilities. Rather than building one monolithic agent, orchestration divides work among agents with distinct roles, tools, and expertise. Each framework offers different approaches to agent coordination.
LangGraph
LangGraph is a relatively well-known framework and stands out as a key option for developers building agent systems.
Explicit multi-agent coordination: You can model multiple agents as individual nodes or groups, each with its own logic, memory, and role in the system.
It creates AI workflows across APIs and tools. Thus, it is a good fit for RAG and custom pipelines.
AutoGen
AutoGen allows multiple agents to communicate by passing messages in a loop. Each agent can respond, reflect, or call tools based on its internal logic.
It has an asynchronous agent collaboration, making it particularly useful for research and prototyping scenarios where agent behavior requires experimentation or iterative refinement.
CrewAI
CrewAI handles most of the low-level logic for you and provides multi-agent orchestration:
- Integrates with monitoring tools for tracing and debugging
- Built-in execution control through Flows with conditional logic, loops, and state management
- Supports hierarchical (manager-worker) and structured multi-agent coordination
OpenAI Swarm
Swarm is a lightweight, experimental multi-agent framework for prototyping. Agents work sequentially through handoffs, transferring tasks while maintaining shared context. It uses natural language routines and Python tools for flexible workflows.
LangChain
LangChain is a framework for building single-agent LLM applications with RAG tooling. It provides modular components including chains, tools, memory, and retrieval for document processing workflows.
LangChain operates primarily through single-agent execution patterns where one agent manages the workflow.
Agent and function definition
LangGraph
LangGraph takes a graph-based approach to agent design, where each agent is represented as a node that maintains its own state. These nodes are connected through a directed graph, enabling conditional logic, multi-team coordination, and hierarchical control. This enables you build and visualize multi-agent graphs with supervisor nodes for scalable orchestration.
LangGraph uses annotated, structured functions that attach tools to agents. You can build out nodes, connect them to various supervisors, and visualize how different teams interact. Think of it like giving each team member a detailed job description. This makes it easier to build and test agents that work together.
AutoGen
AutoGen defines agents as adaptive units capable of flexible routing and asynchronous communication. Agents interact with each other (and optionally with humans) by exchanging messages, allowing for collaborative problem-solving. Like LangGraph uses annotated, structured functions.
CrewAI
CrewAI takes a role-based design approach. Each agent is assigned a role (e.g., Researcher, Developer) and a set of skills, functions or tools it can access. Function definition is through structured annotations.
OpenAI Swarm
OpenAI Swarm uses a routine-based model where agents are defined through prompts and function docstrings. It doesn’t have formal orchestration or state models, relying instead on manually structured workflows. Functions behavior is inferred by the LLM through docstrings (Swarm identifies what a function does by reading its description) making this setup flexible but less precise.
LangChain
LangChain uses a chain-based architecture where a single orchestrator agent manages calls to language models and various tools. It defines functions through explicit interfaces like toolkits and prompt templates.
While primarily focused on centralized workflows, LangChain supports extensions for multi-agent setups but lacks built-in agent-to-agent communication.
Memory
Memory capabilities:
- Stateful: Whether the framework supports persistent memory across executions.
- Contextual: Whether it supports short-term memory via message history or context passing.
Memory features is a key part of building agentic systems to remember context and adapt over time:
- Short-term memory: Keeps track of recent interactions, enabling agents to handle multi-turn conversations or step-by-step workflows.
- Long-term memory: Stores persistent information across sessions, such as user preferences or task history.
- Entity memory: Tracks and updates knowledge about specific objects, people, or concepts mentioned during interactions (e.g., remembering a company name or project ID mentioned earlier).
LangGraph
LangGraph uses two types of memory: in-thread memory, which stores information during a single task or conversation, and cross-thread memory, which saves data across sessions. Developers can use MemorySaver to save the flow of a task and link it to a specific thread_id. For long-term storage, LangGraph supports tools like InMemoryStore or other databases. This provides flexible control over how memory is scoped and retained across executions.
AutoGen
AutoGen uses a contextual memory model. Each agent maintains short-term context through a context_variables object, which stores interaction history. It doesn’t have built-in persistent memory.
CrewAI
CrewAI provides layered memory out of the box. It stores short-term memory in a ChromaDB vector store, recent task results in SQLite, and long-term memory in a separate SQLite table (based on task descriptions). Additionally, it supports entity memory using vector embeddings. This memory setup is automatically configured when memory=True is enabled,
OpenAI Swarm
Swarm is stateless and does not manage memory natively. Developers can pass short-term memory through context_variables manually, and optionally integrate external tools or third-party memory layers (e.g., mem0) to store longer-term context.
LangChain
LangChain supports both short-term and long-term memory through flexible components. Short-term memory is typically managed via in-memory buffers that track conversation history within a session. For long-term memory, LangChain integrates with external vector stores or databases to persist embeddings and retrieval data.
Developers can customize memory scopes and strategies using built-in memory classes, enabling efficient management of contextual and entity-specific memory across interactions.
Human-in-the-loop
LangGraph
LangGraph supports custom breakpoints (interrupt_before) to pause the graph and wait for user input mid-execution.
AutoGen
AutoGen natively supports human agents via UserProxyAgent, allowing humans to review, approve, or modify steps during agent collaboration.
CrewAI:
CrewAI enables feedback after each task by setting human_input=True; the agent pauses to collect natural language input from the user.
OpenAI Swarm
OpenAI Swarm offers no built-in HITL.
LangChain
LangChain allows inserting custom breakpoints within chains or agents to pause execution and request human input. This supports review, feedback, or manual intervention at defined points in the workflow.
Model Context Protocol (MCP) integration in agentic AI frameworks
AI agents need to interact with external tools like databases, APIs, file systems, and business applications. Without a standard, each framework had to build custom integrations for every tool, creating a fragmented ecosystem. MCP solves this by providing a universal protocol that lets any agent connect to any tool through a single interface.
How each framework integrates with MCP
LangGraph
LangGraph connects to MCP servers through an adapter that automatically discovers available tools and converts them into LangChain-compatible format. Agents can then use these tools seamlessly alongside their native capabilities.
AutoGen
AutoGen provides built-in MCP integration through its extension module. Developers can connect to MCP servers and make all their tools available to AutoGen agents with just a few lines of code.
CrewAI
CrewAI agents can directly reference MCP servers in their configuration using simple URLs or structured settings. The framework handles connection lifecycle and error management automatically.
OpenAI Swarm
Swarm benefits from OpenAI’s native MCP support across its ecosystem. Since OpenAI integrated MCP into ChatGPT and its Agents SDK, Swarm can leverage this infrastructure directly.
LangChain
LangChain offers MCP tool-calling capabilities where Python functions act as bridges to MCP servers. This enables pulling tools from various sources and integrating them into chains, agents, and other LangChain components without custom wrappers.
What agentic AI frameworks actually do?
Agentic AI frameworks assist with prompt engineering and managing how data flows to and from LLMs. At a basic level, they help structure prompts so the LLM responds in a predictable format and route responses to the right tool, API, or document.
If building from scratch, you would manually define the prompt, extract the tool the LLM wants to use, and trigger the corresponding API call. Frameworks streamline this by:
- Prompt orchestration: Building, managing, and routing complex prompts to LLMs
- Tool integration: Letting agents call external APIs, databases, code functions, etc.
- Memory: Maintaining state across turns or sessions (short- and long-term)
- RAG integration: Enabling knowledge retrieval from external sources
- Multi-agent coordination: Structuring how agents collaborate or delegate tasks
Agentic AI frameworks: Real life use cases
LangGraph – Multi-agent travel planner
A production project built with LangGraph demonstrates a stateful, multi-agent travel assistant that pulls flight and hotel data (using Google Flights & Hotels APIs) and generates travel recommendations.4
CrewAI – Agentic content creator
CrewAI’s official examples repository includes flows like trip planning, marketing strategy, stock analysis, and recruitment assistants, where role-specific agents (e.g., “Researcher”, “Writer”) collaborate on tasks.5
CrewAI turns a high-level content brief into a complete article using Groq.
Core features of agentic AI frameworks
Model support:
- Most are model-agnostic, supporting multiple LLM providers (e.g., OpenAI, Anthropic, open-source models).
- However, system prompt structures vary by framework and may perform better with some models than others.
- Access to and customization of system prompts is often essential for optimal results.
Tooling:
- All frameworks support tool use, a core part of enabling agent actions.
- Offer simple abstractions to define custom tools.
- Most support Model-Context-Protocol (MCP), either natively or through community extensions.
Memory / State:
- Use state tracking to maintain short-term memory across steps or LLM calls.
- Some helps agents retain prior interactions or context within a session.
RAG (Retrieval-Augmented Generation):
- Most include easy setup options for RAG, integrating vector databases or document stores.
- This allows agents to reference external knowledge during execution.
Other common features
- Support for asynchronous execution, enabling concurrent agent or tool calls.
- Built-in handling for structured outputs (e.g., JSON).
- Support for streaming outputs where the model generates results incrementally.
- Basic observability features for monitoring and debugging agent runs.
Benchmark methodology
In this benchmark, we designed fully equivalent pipelines to fairly and directly compare the LangChain and CrewAI frameworks. We executed four fundamental data analysis tasks: Random Forest, Clustering, Descriptive Statistics, and Logistic Regression, on both frameworks using the same dataset, the same tools, identical task definitions, and identical prompts.
Data and workflow
For data processing, we used the Telco Churn dataset on both frameworks, downloaded via the DownloadDatasetTool from the same GitHub source. Data preprocessing, loading, and overall data flow were structured identically across both platforms.
To ensure secure data sharing between agents and tasks, we implemented thread-safe global state management using Python’s threading.Lock mechanism in both frameworks.
All experiments were executed with the same OpenAI API key, the same LLM model, and identical configuration parameters, including timeout and maximum iterations.
Tool alignment
The tools used in frameworks: DownloadDatasetTool, LoadDataTool, TrainModelTool, and EvaluateModelTool were designed to be functionally identical. Error handling, input/output structures, and global state integrations were consistently implemented across both platforms. In addition, fail-safe mechanisms were developed in parallel to ensure that both systems could terminate gracefully in case of errors.
Tools in CrewAI are directly linked to specific agents, allowing straightforward and efficient task execution. In LangChain, however, tool usage depends on the LLM’s natural language understanding to select and invoke tools since multi-agent orchestration is manually implemented and not native to the framework. In LangGraph, tool usage follows a predefined graph (DAG) structure. The tool to run at each step is fixed; the LLM only intervenes in ambiguous or branching scenarios. Tool selection is managed by the graph flow, not the LLM. Although LangGraph can work with LangChain Base tools, for the benchmark, we chose to use LangGraph independently.
In Swarm, tools are connected directly to each agent as Python functions. Each agent only has access to the tools relevant to its task and can call these functions directly.
Task definitions and execution flow
Task definitions and execution order were also designed to be exactly the same. All systems employed two agents: “Data Scientist” and “ML Engineer”. These agents executed the same tasks in the same order, with the same dependency structure. Task descriptions and role definitions were made to match word-for-word to ensure consistency.
Adapting to framework-native architectures
CrewAI is built around a role-based, declarative architecture that naturally supports multi-agent systems. In this setup, each agent is clearly defined with a role and a specific goal. Tasks are explicitly assigned to agents, and for each task, a detailed description and an expected output must be provided. The entire workflow is organized under a centralized Crew structure and executed with a single command. In CrewAI, parameters like task order and expected outputs are mandatory and form an integral part of how the framework operates.
In contrast, LangChain adopts a tool-centric and procedural approach. Agents are created based on a set of tools and a general task type. Unlike CrewAI’s declarative flow, LangChain requires the developer to manually control the task execution step-by-step. Each task must be explicitly invoked by the developer, with no automatic orchestration between steps. For this benchmark, we manually orchestrated a multi-agent setup in LangChain to simulate the same task flow as other frameworks. While this isn’t native to LangChain’s architecture, we ensured equivalent task logic and execution order to allow for a fair comparison.
In Swarm, tools are indeed defined as simple Python functions, and agents are created using the agent class, where task instructions guide the agent’s behavior. Agents have access to and can directly call the tools relevant to their specific tasks. Swarm operates through message-based communication, where tasks are executed based on user inputs (messages), and tools are invoked as direct function calls. However, Swarm offers limited built-in automatic step-by-step execution or workflow management; the developer typically controls the process flow. Being more functional and agent-centric, Swarm’s natural operation fits this description.
LangGraph is built around a graph-based, declarative architecture where tasks are represented as nodes in a directed acyclic graph (DAG). Each node has a predetermined, static tool assigned, defining a fixed execution path. The LLM is only engaged in cases of ambiguity or branching, minimizing its use to improve performance and simplify debugging.
Unlike CrewAI’s role-based multi-agent setup or LangChain’s procedural, tool-centric flow requiring manual orchestration, we used LangGraph’s automatic orchestration through its graph structure. Tasks flow naturally from node to node without explicit developer intervention at each step.
Preserving architectural differences
This methodology preserves the natural architectural differences across the frameworks. While maintaining equivalent logic and task definitions across frameworks, we allowed each framework’s native architecture to guide the execution flow, be it sequential, declarative, or graph-based. This approach guarantees that the benchmark results reflect how each framework performs in its realistic, idiomatic usage scenario.
Python environment and execution logic
All implementations were carried out in a Python environment. We integrated the tasks into agent-based architectures on both frameworks using Python-based tools. Tasks were executed in a sequential flow with a multi-agent architecture on both platforms. We intentionally chose a sequential setup because steps like data downloading, data cleaning, model training, and model evaluation are inherently dependent on each other, making sequential execution the most logical and realistic approach for this benchmark.
Reference Links
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.
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