Which is more popular, Agent or LangGraph?

LangGraph has more GitHub stars (42,656 vs 472).

Which is more actively developed, Agent or LangGraph?

Agent had more commits in the last 90 days (310 vs 132).

Should I use Agent or LangGraph?

Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.

Agent vs LangGraph

Side-by-side comparison of two AI agent tools

Short answer

LangGraph is growing faster: +2,365 GitHub stars in the last 30 days vs +21 for Agent.
Pick Agent for: create state-machine-powered LLM agents using XState. Pick LangGraph for: build resilient language agents as graphs.

From GitHub data refreshed daily.

Agentopen-source

Create state-machine-powered LLM agents using XState

LangGraphopen-source

Build resilient language agents as graphs.

Metrics

	Agent	LangGraph
Stars	472	42.7k
Star velocity /mo	20.68421052631579	2.4k
Commits (90d)	310	132
Releases (6m)	10	10
Downloads (30d, npm + PyPI)	—	43.7M
Overall score	0.6245788355727497	0.8091319530692536

Pros

+State machine structure provides predictable, auditable agent behavior with clear transition logic
+Learning capabilities through observations and feedback enable agents to improve performance over time
+Flexible model provider support via Vercel AI SDK integration allows switching between different LLMs

+Durable execution ensures agents automatically resume from exactly where they left off after failures or interruptions
+Comprehensive memory system with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions
+Seamless human-in-the-loop capabilities allow for inspection and modification of agent state at any point during execution

Cons

-Higher complexity compared to simple prompt-based agents, requiring knowledge of both XState and AI concepts
-Documentation appears incomplete with placeholder sections for key setup instructions
-State machine approach may be overkill for simple conversational agents or basic AI tasks

-Low-level framework requires more technical expertise and setup compared to high-level agent builders
-Graph-based agent design paradigm may have a steeper learning curve for developers new to agent orchestration
-Production deployment complexity may be overkill for simple chatbot or single-turn use cases

Use Cases

•Customer service chatbots that need to follow specific escalation workflows and remember interaction history
•Game AI characters that must exhibit consistent behavior patterns while adapting to player actions
•Automated support systems requiring structured decision trees with learning from resolution outcomes

•Long-running autonomous agents that need to persist through system failures and operate over days or weeks
•Complex multi-step workflows requiring human oversight, approval, or intervention at specific decision points
•Stateful agents that must maintain context and memory across multiple sessions and interactions

View Agent Details View LangGraph Details

FAQ

Which is more popular, Agent or LangGraph?: LangGraph has more GitHub stars (42,656 vs 472).
Which is more actively developed, Agent or LangGraph?: Agent had more commits in the last 90 days (310 vs 132).
Should I use Agent or LangGraph?: Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.