The main differences between llama-index and langchain, according to the community members, are:
- LlamaIndex is focused on data-centric pipelines and query engines, with highly customizable components and advanced querying techniques. LangChain, on the other hand, is a general-purpose toolkit for building LLM-based applications, particularly suited for chat interfaces.
- The community members suggest that tools from the LlamaHub can be used within LangChain agents, as LangChain is somewhat limited in pre-defined tools. They provide an example of how to convert a LlamaIndex tool to a LangChain tool.
There's definitely some differences between the two. I think most of it comes down to use cases, and ability to customize.
LlamaIndex - data focused pipelines/query engines, plus data agents using the tools from llamahub. Query engine components are fully customizable, with some advanced querying techniques (routers, retrievers, sub-question query engines, etc.)
LangChain -- General purpose LLM tool kit, best for chat interfaces. Any query engine in llama-index can be used as a tool in langchain agents, which is a common pattern I've seen
Since Langchain is a bit limited in terms of pre-defined tools, but it also useful in terms of its level of abstraction of calling chat-query-engines. I'd like to pull some tools from Lamma-hub to plugin those to agent from Langchain
Hmm, all the llama-hub tools should be compatible with langchain
Just have to do tool.to_langchain_tool()
For example
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from llama_hub.tools.arxiv.base import ArxivToolSpec
from llama_index.agent import OpenAIAgent
tool_spec = ArxivToolSpec()
tools = tool_spec.to_tool_list()
tools = [x.to_langchain_tool() for x in tools]
