Non-technical people have suggested using other tools like n8n or make.
Being able to write tests, use version control, and make full use of a programming language I am proficient with are perks.
It is also enshittification resistant unlike other platforms. I still might use them for something lightweight.
I have rolled my own solutions in previous roles and it worked well for very simple tasks (analyze this output and make sure it meets this criteria or try again...) I would be concerned about complexity if there were more steps, tool calls, or the need to compose multiple agents out of the same nodes, tools, state, etc...
Curious to hear more what you mean about compacting histories? Langgraph state management is simple enough and a custom reducer function gives you full control of context management...
I wrote my own agent state machine in pretty much pure async Python (no libs). Running successfully in prod with very few issues.
I use the OpenAI messages spec, and have the messages be an append only list, to make it easy to reason about.
Don’t bother compacting histories imo. worse case just summarise and spin up a new agent with the context.
good luck!
I am enjoying langgraph.
Non-technical people have suggested using other tools like n8n or make.
Being able to write tests, use version control, and make full use of a programming language I am proficient with are perks.
It is also enshittification resistant unlike other platforms. I still might use them for something lightweight.
I have rolled my own solutions in previous roles and it worked well for very simple tasks (analyze this output and make sure it meets this criteria or try again...) I would be concerned about complexity if there were more steps, tool calls, or the need to compose multiple agents out of the same nodes, tools, state, etc...
Curious to hear more what you mean about compacting histories? Langgraph state management is simple enough and a custom reducer function gives you full control of context management...