Comparison Preset
Agno is the better fit here because its AgentOS runtime directly addresses core enterprise requirements. It provides multi-user isolated sessions, RBAC, audit logs, and runs in your own cloud, ensuring data ownership and simplifying compliance. While its two known vulnerabilities, one rated CRITICAL, must be mitigated, its feature set significantly de-risks the production deployment of agentic systems. LangGraph has a better immediate security posture with only one MODERATE vulnerability, but it lacks these built-in governance features, requiring more custom development. Agno's Apache-2.0 license and high bus factor (8/10) further support its suitability for long-term, maintainable systems.
Overview
The bottom line — what this framework is, who it's for, and when to walk away.
Bottom Line Up Front
Agno provides an SDK and runtime (AgentOS) for building, running, and managing agent platforms. It enables the creation of multi-agent systems and step-based workflows, offering production features like isolated sessions, RBAC, and data ownership. Teams can deploy intelligent software in their own cloud environments.
LangGraph is a low-level Python orchestration framework for building complex, stateful, and durable AI agents. It provides core benefits like persistence, human-in-the-loop capabilities, and comprehensive memory. While very powerful, it requires familiarity with agent components and offers less abstraction than other frameworks.
Best For
Building and productionizing multi-agent platforms, in-product copilots, and AI-driven data workflows.
Building low-level, long-running, stateful AI agents requiring durable execution and human oversight.
Avoid If
no data
You are new to agents or need a higher-level abstraction for simpler LLM application development.
Strengths
- +Provides an SDK for building agents, multi-agent teams, and step-based agentic workflows.
- +AgentOS runtime offers multi-user, isolated sessions with tracing, scheduling, RBAC, and audit logs.
- +Allows running agents as a service with a unified control plane for management.
- +Runs in your cloud using your database, ensuring ownership of session, memory, and trace data.
- +Natively typesafe and multi-modal, suitable for data labeling, extraction, and classification.
- +Can productionize agents built with any framework, model, or cloud.
- +Provides durable execution, allowing agents to persist through failures and resume operations.
- +Supports human-in-the-loop interaction, enabling inspection and modification of agent state at any point.
- +Offers comprehensive memory management for both short-term working memory and long-term cross-session memory.
- +Integrates with LangSmith for deep visibility, tracing, debugging, and production deployment of agent systems.
- +Acts as a low-level orchestration runtime, offering fine-grained control over agent workflow and state transitions.
Weaknesses
- −It is a very low-level framework, requiring engineers to manage prompts and architecture explicitly.
- −Does not abstract prompts or architecture, demanding familiarity with agent components like models and tools.
- −Has a higher learning curve for those just starting with agents or seeking a more abstract development experience.
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
AgentOS runtime manages state through multi-user, isolated sessions, allowing users to own their session, memory, and trace data.
LangGraph manages state through a graph-based structure allowing for persistence, human intervention, and comprehensive short-term/long-term memory.
Cost & Licensing
What does it actually cost? License type, pricing model, and hidden fees.
License
Perspective
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