Comparison Preset
LangGraph is the more prudent choice due to its stronger security posture, with only one MODERATE known vulnerability compared to Agno's CRITICAL one. While both frameworks have excellent bus factor scores (8/10), maintainer counts (100), and enterprise-friendly licenses, the security risk in Agno is a significant concern. LangGraph's focus on durable, stateful execution combined with observability through LangSmith provides the control needed for mission-critical systems. The lower-level nature of the framework allows for more explicit control, which can reduce long-term maintenance risks for complex, stateful applications.
Overview
The bottom line ā what this framework is, who it's for, and when to walk away.
Bottom Line Up Front
Agno provides an SDK, a stateless FastAPI runtime (AgentOS), and a control plane for building and deploying production-ready AI agent platforms. It supports agents with memory, knowledge, and integrations, offering cloud-agnostic deployment.
LangGraph is a low-level Python framework designed for orchestrating complex, stateful LLM agents. It provides essential infrastructure for durable execution, streaming, persistence, and human-in-the-loop capabilities. Engineers gain fine-grained control over agent logic and state management.
Best For
Deploying, monitoring, and managing production-grade AI agent platforms with structured workflows.
Orchestrating long-running, stateful agent workflows requiring durable execution, persistence, and human-in-the-loop.
Avoid If
no data
Requiring high-level abstractions for agents or when first learning about agent development.
Strengths
- +Provides a rich SDK for building agents, teams, and complex workflows.
- +Offers extensive integrations (100+) for agent capabilities.
- +Features a production-ready runtime (AgentOS) based on a stateless FastAPI backend.
- +Includes an integrated control plane with a UI for monitoring and management.
- +Supports deployment across multiple cloud providers and containerization options, including AWS, GCP, Azure, Kubernetes, and Docker.
- +Provides durable execution, allowing agents to persist through failures and resume from where they left off.
- +Supports human-in-the-loop workflows, enabling inspection and modification of agent state at any point.
- +Offers comprehensive memory, supporting both short-term working memory and long-term memory across sessions.
- +Integrates with LangSmith for deep visibility, tracing execution paths, and debugging complex agent behavior.
- +Designed for production-ready deployment, handling challenges of stateful, long-running workflows.
- +Offers a low-level framework for maximum control over agent orchestration.
Weaknesses
- āRequires familiarity with core agent components like models and tools before effective use.
- āDoes not abstract prompts or architectural details, demanding manual management.
- āIt is very low-level, which can increase complexity for beginners or those seeking higher abstractions.
Project Health
Is this project alive, well-maintained, and safe to bet on long-term?
Bus Factor Score
Maintainers
Open Issues
Fit
Does it support the workflows, patterns, and capabilities your team actually needs?
State Management
The SDK enables agents to manage internal state via its 'memory' feature, while the AgentOS runtime itself operates statelessly.
LangGraph manages state by providing persistence, comprehensive memory, and durable execution for long-running agent workflows.
Cost & Licensing
What does it actually cost? License type, pricing model, and hidden fees.
License
Perspective
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