Which is more popular, LLM Agents or LLMFlows?

LLM Agents has more GitHub stars (1,055 vs 708).

Which is more actively developed, LLM Agents or LLMFlows?

LLM Agents had more commits in the last 90 days (0 vs 0).

Should I use LLM Agents or LLMFlows?

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.

LLM Agents vs LLMFlows

Side-by-side comparison of two AI agent tools

Short answer

LLM Agents is growing faster: +2 GitHub stars in the last 30 days vs +0 for LLMFlows.
Pick LLM Agents for: build agents which are controlled by LLMs. Pick LLMFlows for: lLMFlows - Simple, Explicit and Transparent LLM Apps.

From GitHub data refreshed daily.

LLM Agentsopen-source

Build agents which are controlled by LLMs

LLMFlowsopen-source

LLMFlows - Simple, Explicit and Transparent LLM Apps

Metrics

	LLM Agents	LLMFlows
Stars	1.1k	708
Star velocity /mo	2.0526315789473686	0.15789473684210523
Commits (90d)	0	0
Releases (6m)	0	0
Downloads (30d, npm + PyPI)	14	43
Overall score	0.1665593033584882	0.1343349139130593

Pros

+Educational transparency with minimal abstraction layers for understanding agent mechanics
+Easy customization and extension with simple tool integration API
+Lightweight codebase that's easy to modify and debug

+Complete transparency with no hidden prompts or LLM calls, making debugging and monitoring straightforward
+Minimalistic design with clear abstractions that don't compromise on flexibility or capabilities
+Explicit API design that promotes clean, readable code and easy maintenance of complex LLM workflows

Cons

-Limited built-in tools compared to comprehensive frameworks like LangChain
-Requires manual setup of API keys for OpenAI and optional SERPAPI services
-Lacks advanced features like memory management, conversation history, or production optimizations

-Relatively small community with 707 GitHub stars, which may limit community support and resources
-Minimalistic approach might require more manual setup compared to more feature-rich frameworks
-Limited built-in integrations compared to larger LLM frameworks, requiring more custom implementation

Use Cases

•Learning how LLM agents work by studying and modifying a simple implementation
•Rapid prototyping of custom agent workflows with specific tool combinations
•Building educational demos or simple automation tasks where transparency matters more than features

•Building transparent chatbots where every LLM interaction needs to be traceable and debuggable
•Creating question-answering systems that combine multiple LLMs with vector stores for document retrieval
•Developing AI agents with complex multi-step workflows that require explicit control over each LLM call

View LLM Agents Details View LLMFlows Details

FAQ

Which is more popular, LLM Agents or LLMFlows?: LLM Agents has more GitHub stars (1,055 vs 708).
Which is more actively developed, LLM Agents or LLMFlows?: LLM Agents had more commits in the last 90 days (0 vs 0).
Should I use LLM Agents or LLMFlows?: 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.