AI agentic frameworks provide the infrastructure for building autonomous AI agents that can perceive, reason, and act to achieve goals. With the rapid growth of large language models (LLMs), these frameworks extend LLMs with orchestration, planning, memory, and tool-use capabilities. This blog compares prominent frameworks from a 2025 perspective – including LangChain, Microsoft AutoGen, Semantic Kernel, CrewAI, LlamaIndex AgentWorkflows, Haystack Agents, SmolAgents, PydanticAI, and AgentVerse – across their internal execution models, agent coordination mechanisms, scalability, memory architecture, tool use abstraction, and LLM interoperability. I will also cover emerging frameworks in my next blog (e.g. Atomic Agents, LangGraph, OpenDevin, Flowise, CAMEL) and analyze their design principles, strengths, and limitations relative to existing solutions.
Comparison of Established Agentic Frameworks (2025)
The table below summarizes core characteristics of each major framework.
Table 1. Key Features of Prominent AI Agent Frameworks (2025)
| Framework | Execution Model | Agent Coordination | Scalability Strategies | Memory Architecture | Tool Use & Plugins | LLM Interoperability |
|---|---|---|---|---|---|---|
| LangChain | Chain-of-thought sequences (ReAct loops) using prompts. Chains modularly compose LLM calls, memory, and actions. | Primarily single-agent, but supports multi-agent interactions via custom chains. No built-in agent-to-agent messaging. | Designed for integration rather than distributed compute. Concurrency handled externally. | Pluggable Memory modules (short-term context, long-term via vector stores). | Abstraction for Tools as functions. Implements ReAct and OpenAI function calling. Rich API/DB connectors. | Model-agnostic: supports OpenAI, Azure, HuggingFace, etc. |
| AutoGen (Microsoft) | Event-driven asynchronous agent loop. Agents converse via messages, generating code or actions executed asynchronously. | Multi-agent conversation built-in – e.g., AssistantAgent and UserProxyAgent chat to solve tasks. | Scalable by design: async messaging for non-blocking execution. Supports distributed networks. | Relies on message history for context. Can integrate external memory if needed. | Tools and code execution via messages. Easy integration with Python tools and custom functions. | Multi-LLM support (OpenAI, Azure, etc.), optimized for Microsoft’s stack. |
| Semantic Kernel | Plan-and-execute model using skills (functions) and planners. High-level SDK for embedding AI into apps. | Concurrent agents supported via planner/orchestrator. Multi-agent collaboration via shared context. | Enterprise-grade scalability: async and parallel calls, integration with cloud infrastructure. | Robust Memory system: supports volatile and non-volatile memory stores. Vector memory supported. | Plugins (Skills) as first-class tools. Secure function calling for C#/Python functions. | Model-flexible: OpenAI, Azure OpenAI, HuggingFace. Multi-language support. |
| CrewAI | Role-based workflow execution. Pre-defined agent roles run in sequence or parallel. Built atop LangChain. | Multi-agent teams (“crews”) with structured coordination. Sequential, hierarchical, and parallel pipelines supported. | Focuses on orchestrating multiple agents. Enterprise version integrates with cloud for production deployment. | Inherits LangChain memory. Context passed through crew steps. Conflict resolution supported. | Flexible tool integration per agent role. Open-source version integrates LangChain tools. | Any LLM via LangChain: OpenAI, Anthropic, local models supported. |
| LlamaIndex AgentWorkflows | Workflow graph execution. Agents (nodes) execute in a graph, handing off state via shared Context. | Built for both single and multi-agent orchestration. Supports cyclic workflows and human-in-the-loop. | Parallelizable workflows. Checkpointing for intermediate results. Scales to large data volumes. | Shared memory context via WorkflowContext. Integration with vector stores. | Tools integrated as functions or pre-built tools. Strong retrieval-generation combination. | Model-agnostic via LlamaIndex: OpenAI, HF, local LLMs. |
| Haystack Agents | Tool-driven ReAct agents. LLM planner selects tools iteratively until task completion. | Primarily single-agent. Can be extended to multi-agent via connected pipelines. | Designed for production Q&A. Scalability via batching and pipeline parallelism. | Emphasis on retrieval-augmented memory. Uses embedding stores and indexes. | Abstracts services as Tools. Modular pipeline design for swapping components. | Pluggable LLMs via PromptNode: OpenAI, Azure, Cohere, etc. |
| SmolAgents (HF) | Minimalist ReAct implementation. Agents write/execute code or call structured tools. | Single-agent, multi-step. Can run multiple agents in parallel if needed. | Lightweight for rapid prototyping. Can embed in larger systems. No built-in distribution. | No built-in long-term memory. External vector DBs can be integrated manually. | Direct code execution with secure sandbox options. Minimal abstractions. | Highly model-flexible: OpenAI, HuggingFace, Anthropic, local models. |
| PydanticAI | Structured agent loop with output validation. Supports async execution. Pythonic control flow. | Single-agent by default. Supports multi-agent via delegation and composition. | Async & scalable: handles concurrent API calls or tools. Production-grade error handling. | Structured state passed via Pydantic models. External stores can be integrated. | Tools as Python functions with Pydantic I/O models. Dependency injection supported. | Model-agnostic: OpenAI, Anthropic, Cohere, Azure, Vertex AI, etc. |
| AgentVerse (Fetch.ai) | Modular multi-agent environment simulation. Agents register in a decentralized registry. | Multi-agent by design. Agents discover each other and collaborate dynamically. | Supports large agent populations. Agent Explorer UI for monitoring. Distributed deployment supported. | Environment state as shared memory. Agents may also have private memory/state. | Tools as environment-specific actions. Emphasizes communication protocols. | Model-agnostic. LLM-based agents supported via wrappers. |
