Stacks/General production agent

A LangGraph + Langfuse + Claude 4.5 production stack (r/AI_Agents)

In a widely cited r/AI_Agents thread, the original poster shares the stack they landed on after a year and a half: Claude 4.5 Sonnet as the foundation model (they say it was consistently better with complex JSON schemas than the OpenAI models they tried), LangGraph for orchestration, Langfuse for traces, Green flash for conversation-level product insight, and PostHog for session replay around agent triggers.

Use case

A production AI agent stack described by the original poster after about a year and a half of trial and error, with LangGraph orchestration, Claude for structured JSON, and a layered observability setup

Category

General production agent

Team size

not stated

Orchestrator / framework

LangGraph, chosen because the team needed control over state routing and cyclical agent loops without building a state machine from scratch.

Memory

not stated

Evals

not stated as a formal eval product. Green flash is described as a way to read conversations at scale, find unanticipated edge cases, and see whether the user got what they needed.

Observability

Langfuse for observability, with trace visualizations used to debug which step of a multi-prompt agent chain timed out. PostHog for click analytics and session replays, to see what UI elements a user interacted with just before the agent triggered. Green flash for reading conversations at scale.

Monthly cost

not stated

Models

Claude 4.5 Sonnet

Observability tools

LangfusePostHogGreen flash

Other tools

not stated

Source

https://www.reddit.com/r/AI_Agents/comments/1ukzs4m/what_does_your_production_stack_look_like/

Original poster on r/AI_Agents