Agent-Frameworks für Customer Support

Ein Agent beantwortet Tickets, greift auf deine Docs zu, eskaliert bei Bedarf an Menschen und schreibt konsistent im Stil deiner Marke.

Top 8 Frameworks für diesen Use-Case

Bewertet nach AGI Readiness Score (Autonomie · Multimodalität · Production).

1. 01

   LangChain

   19AGI

   Composable building blocks for LLM apps and agents.

   General-purpose agent and RAG apps

   Details 144,072 ★MITKurator-Pick
2. 02

   LangGraph

   18AGI

   Stateful Agenten als Graph-Workflows

   Produktions-Workloads, bei denen Determinismus zählt.

   Details 39,516 ★MITKurator-Pick
3. 03

   Mastra

   4AGI

   TypeScript framework for agents, RAG and workflows.

   TypeScript agent stacks

   Details 27,134 ★Elastic-2.0
4. 04

   Letta (MemGPT)

   4AGI

   Stateful agents with long-term memory.

   Long-running stateful assistants

   Details 24,210 ★Apache-2.0
5. 05

   Agno (Phidata)

   4AGI

   Lightweight multimodal agents toolkit.

   Multimodal agent prototypes

   Details 41,677 ★MPL-2.0
6. 06

   Haystack

   3AGI

   deepset's pipeline framework for search and agents.

   Search-heavy agent apps

   Details 26,188 ★Apache-2.0
7. 07

   Vercel AI SDK

   3AGI

   Provider-agnostic AI toolkit for TypeScript apps.

   TS web apps with embedded agents

   Details 26,145 ★Apache-2.0
8. 08

   smolagents

   3AGI

   Minimalistische Code-Agenten von Hugging Face

   Schlanke Setups, in denen Agenten Tools per Code orchestrieren.

   Details 28,771 ★Apache-2.0

graph = StateGraph()
graph.add_node("classify", classify_intent)
graph.add_node("retrieve", rag_lookup)
graph.add_node("respond", draft_reply)
graph.add_node("escalate", human_handoff)
graph.compile().invoke({"ticket": ticket})