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Chatbase vs LangGraph

ChatbaseLangGraph

Bottom line: Chatbase for support teams automating common questions; LangGraph for engineering teams building production agents.

ChatbaseChatbase

Build AI support agents trained on your own data, no code required.

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LangGraphLangGraph

Graph-based orchestration for stateful, controllable LLM agents.

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Votes00
PricingFreemiumFreemium
CategoryAgent FrameworksAgent Frameworks
Tags
ai-chatbotcustomer-supportno-coderagai-agents
agentsorchestrationllm-frameworkopen-sourceworkflow
Best for
  • Support teams automating common questions
  • SMBs adding chat to their website
  • E-commerce brands on WhatsApp and Instagram
  • Engineering teams building production agents
  • Developers needing controllable, resumable workflows
  • Teams already invested in LangChain
Pros
  • Fast, no-code setup from your own content
  • Multiple deployment channels including WhatsApp and Instagram
  • Mature product with a large user base
  • API available for custom integrations
  • Free plan for testing
  • Explicit, debuggable control over agent state and flow
  • MIT-licensed core, free to self-host
  • Model-agnostic, not tied to one LLM vendor
  • Strong support for cycles, checkpointing, and human-in-the-loop
  • Backed by the widely used LangChain ecosystem
Cons
  • Credit-based pricing can escalate with volume
  • Premium models consume more credits per response
  • Extra agents and branding removal are paid add-ons
  • Free plan agents are deleted after inactivity
  • Advanced customization can hit platform limits
  • Steeper learning curve than high-level agent builders
  • Requires comfort with graph and state concepts
  • Paid platform and observability are separate products
  • Documentation and APIs have evolved quickly
  • Can be overkill for simple single-shot LLM tasks

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