The AI landscape has shifted dramatically in just a few years. What began with simple chatbots has now evolved into sophisticated AI agents capable of reasoning, planning, and collaborating in real-time. By 2027, 86% of organizations are expected to deploy AI agents, with many leaders projecting triple-digit ROI from these systems.
But the real challenge for enterprises in 2025 is this:
Which open-source AI agent framework should you choose?
With dozens of options available, we’ve narrowed it down to the top 9 open-source frameworks that stand out for enterprise readiness, scalability, and innovation. This guide explores their architectures, enterprise adoption, and the trade-offs you should know before deploying them.
1. LangGraph
LangGraph has quickly become the go-to framework for enterprises managing complex, stateful workflows. Built on LangChain, it models agent tasks as graphs with conditional branching, parallel execution, and human-in-the-loop support.
- Strengths: Handles high-throughput workflows (used by Uber & Klarna), enterprise-grade deployment options, superior debugging via LangSmith integration.
- Best for: Complex compliance workflows, real-time decision-making, and multi-agent orchestration.
- Limitations: Overhead makes it less suitable for lightweight conversational agents.
2. AG2 (AutoGen 2)
Evolving from the original AutoGen project, AG2 focuses on asynchronous, event-driven collaboration between agents. Agents can play roles like assistants, critics, or executors in modular group conversations.
- Strengths: High concurrency, role flexibility, and strong adoption in regulated industries (Novo Nordisk uses AG2 for pharma workflows).
- Best for: Teams requiring sophisticated agent collaboration with compliance-ready governance.
- Limitations: Requires careful orchestration design to avoid loops or inefficiencies.
3. CrewAI
CrewAI takes inspiration from organizational structures: agents are grouped into “crews” with specific roles and goals. This makes it easy to get started without deep expertise in orchestration.
- Strengths: Simple to implement, fastest time-to-deployment (1–4 weeks), ideal for proofs-of-concept.
- Best for: Rapid prototyping, small teams, startups testing multi-agent systems.
- Limitations: Limited scalability and weaker enterprise security compared to mature frameworks.
4. Semantic Kernel
Microsoft’s Semantic Kernel is the most enterprise-focused of all. It integrates natively with Microsoft 365, Azure, and enterprise APIs—offering a plugin-based skill system for seamless orchestration.
- Strengths: Deep enterprise compliance (HIPAA, GDPR, SOC 2), advanced authentication, and audit trails.
- Best for: Large enterprises already invested in Microsoft ecosystems.
- Limitations: Higher lock-in risk and longer implementation timelines.
5. Strands Agents
Strands Agents tackles one of the biggest enterprise concerns: vendor lock-in. With LiteLLM integration, it supports providers like OpenAI, Anthropic, Amazon Bedrock, and Ollama.
- Strengths: Cloud-native, multi-provider support, first-class observability with OpenTelemetry.
- Best for: Enterprises adopting multi-cloud strategies or optimizing costs by switching LLM providers.
- Limitations: A newer entrant, so less proven at scale than LangGraph or Semantic Kernel.
6. OpenAI Swarm
Swarm is an experimental framework from OpenAI for quickly spinning up lightweight agents. While not production-ready, it offers a fast way to experiment with agent interactions.
- Strengths: Extremely rapid prototyping, ideal for R&D teams.
- Best for: Organizations testing early ideas or exploring new agent interaction models.
- Limitations: Not suitable for production due to lack of maturity and support.
7. BeeAI
BeeAI is designed as a leaner orchestration alternative, balancing flexibility with simplicity. It supports role-based collaboration and API integrations but with less overhead than LangGraph.
- Strengths: Developer-friendly, modular, and lightweight for mid-scale deployments.
- Best for: Teams needing simpler orchestration without enterprise-heavy overhead.
- Limitations: Lacks advanced compliance tooling.
8. SuperAGI
SuperAGI is one of the most active open-source projects, designed to build autonomous agents with tool-use and memory capabilities.
- Strengths: Community-driven, extensive tool integrations, growing ecosystem.
- Best for: Developers exploring autonomous AI systems beyond workflow orchestration.
- Limitations: Still evolving—requires strong engineering support for enterprise deployments.
9. CopilotKit
CopilotKit focuses less on orchestration and more on embedding AI agents inside applications. It enables developers to add copilots that can reason, plan, and take actions directly within apps.
- Strengths: Great for AI-native product development, minimal overhead for integration.
- Best for: SaaS companies and startups embedding copilots into user-facing apps.
- Limitations: Not designed for large enterprise-scale orchestration.
Key Takeaways for 2025
- Enterprise-Ready Leaders: Semantic Kernel and LangGraph stand out for security, scale, and maturity.
- Best for Collaboration: AG2 leads in multi-agent conversations for regulated industries.
- Fast Prototyping: CrewAI and Swarm lower the entry barrier for teams moving fast.
- Avoiding Lock-In: Strands Agents is the best bet for multi-cloud and model-agnostic deployments.
The world of open-source AI agent frameworks in 2025 is diverse, fast-moving, and increasingly enterprise-ready. Choosing the right one depends on your scale, compliance needs, and innovation goals.
At InteligenAI, we help organizations navigate this evolving landscape—from selecting the right framework to building production-grade agentic systems that drive measurable business impact.
Looking to explore which framework fits your enterprise best? Get in touch with us and let’s design your AI agent strategy together.
FAQs
What is an AI agent framework?
An AI agent framework is a toolkit that provides the building blocks agent abstractions, orchestration (graphs, event loops or role-based crews), memory, tool integrations, monitoring and deployment patterns for creating autonomous, multi-step AI systems. Frameworks differ mainly by orchestration model, deployment approach, and enterprise features like security and auditability.
Which AI agent frameworks are most enterprise-ready in 2025?
Semantic Kernel and LangGraph are the most enterprise-ready choices. Semantic Kernel excels when you need deep Microsoft ecosystem integration and strict compliance (SSO, audit trails); LangGraph is a strong fit for stateful, complex workflows that need observability and parallel execution.
Which AI agent frameworks are best for rapid prototyping or PoCs?
CrewAI and OpenAI Swarm are best when speed is the priority. CrewAI’s role/crew model reduces design overhead (typical PoC: 1–4 weeks), while Swarm is useful for rapid experimental prototyping (not production).
How can I avoid vendor lock-in when building AI agents?
Prefer model-agnostic frameworks (e.g., Strands Agents), design an LLM abstraction layer, make your tool connectors and data stores portable, and keep critical knowledge in interoperable formats. Multi-provider strategies and runtime provider-switching reduce long-term dependency.
How should my company choose the right AI agent framework?
Match the framework to: use-case complexity; compliance & data-residency needs; existing cloud/vendor investments; expected scale; development team skills; and time-to-production. A common path: prototype with a lightweight framework (CrewAI), then migrate or redesign into LangGraph or Semantic Kernel as you scale and need stronger governance.