Langchain Exa

LangChain Exa Skill

Expert assistance for langchain-exa: Exa's neural/semantic web search as a LangChain retriever and agent tools.

Install: pip install -U langchain-exa
Setup: export EXA_API_KEY=your_api_key
API: https://exa.ai

Reference: references/api.md (500 KB — full API reference).

When to Use This Skill

Activate when:

• Semantic web search for RAG — using ExaSearchRetriever to retrieve live web results as documents
• Search as an agent tool — using ExaSearchResults for tool-calling in a ReAct agent
• Finding similar content — using ExaFindSimilarResults to find pages similar to a URL
• Getting per-result summaries — setting summary=True for LLM-generated page summaries
• Getting highlighted excerpts — setting highlights=True for relevant snippets from each page
• Real-time crawling — setting livecrawl="always" to bypass index and crawl live
• LLM query rewriting — setting use_autoprompt=True for Exa to optimize the query
• Filtering by domain — using include_domains or exclude_domains
• Filtering by date — using start_published_date, end_published_date, start_crawl_date, end_crawl_date
• Choosing search type — setting type="neural", "keyword", or "auto"

Quick Reference

ExaSearchRetriever — semantic search for RAG

from langchain_exa import ExaSearchRetriever

retriever = ExaSearchRetriever(
    k=5,                                    # number of results
    # exa_api_key="...",                    # or set EXA_API_KEY env var
    use_autoprompt=True,                    # Exa rewrites query for better results
    type="neural",                          # "neural" | "keyword" | "auto"
    highlights=True,                        # include relevant excerpts
    summary=True,                           # include LLM-generated page summary
    livecrawl="always",                     # "always" | "fallback" | "never"
)

docs = retriever.invoke("How does LangGraph handle state persistence?")
for doc in docs:
    print(doc.page_content[:300])
    print(doc.metadata)   # url, title, score, published_date, highlights, summary

Filter by domain and date

from langchain_exa import ExaSearchRetriever

retriever = ExaSearchRetriever(
    k=3,
    include_domains=["arxiv.org", "github.com"],   # only these domains
    # exclude_domains=["reddit.com"],              # block these domains
    start_published_date="2024-01-01",             # ISO date
    end_published_date="2026-01-01",
    # start_crawl_date="2025-01-01",               # filter by when Exa crawled
    use_autoprompt=True,
)

docs = retriever.invoke("LLM agent memory architectures")

ExaSearchResults — search as an agent tool

from langchain_exa import ExaSearchResults
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

search_tool = ExaSearchResults()

# Invoke directly
result = search_tool.invoke({
    "query": "latest LangChain releases 2026",
    "num_results": 3,
})
print(result)   # SearchResponse with Result objects

# Use in a ReAct agent
agent = create_react_agent(
    ChatOpenAI(model="gpt-4o-mini"),
    tools=[search_tool],
)
response = agent.invoke({"messages": [("human", "What happened in AI this week?")]})

ExaFindSimilarResults — find similar URLs

from langchain_exa import ExaFindSimilarResults

find_similar = ExaFindSimilarResults()

# Find pages similar to a given URL
result = find_similar.invoke({
    "url": "https://blog.langchain.dev/langgraph/",
    "num_results": 5,
})
print(result)   # Similar pages with title, url, score

Use in a RAG chain

from langchain_exa import ExaSearchRetriever
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI
from langchain_core.runnables import RunnablePassthrough

retriever = ExaSearchRetriever(
    k=3,
    use_autoprompt=True,
    summary=True,    # get summaries instead of raw text for clean context
)
llm = ChatOpenAI(model="gpt-4o-mini")

prompt = ChatPromptTemplate.from_template("""
Answer based on the following web search results:

{context}

Question: {question}
""")

def format_docs(docs):
    return "\n\n".join(
        f"**{doc.metadata.get('title', 'Untitled')}**\n{doc.page_content}"
        for doc in docs
    )

chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

answer = chain.invoke("What are the latest LangGraph features?")
print(answer)

Combine ExaSearchResults with other tools

from langchain_exa import ExaSearchResults, ExaFindSimilarResults
from langchain_community.tools import WikipediaQueryRun
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

tools = [
    ExaSearchResults(),
    ExaFindSimilarResults(),
    WikipediaQueryRun(),
]

agent = create_react_agent(ChatOpenAI(model="gpt-4o"), tools=tools)

API Reference

ExaSearchRetriever key parameters

Search result fields

Each result includes: url, id, title, score, published_date, author, text, highlights, highlight_scores, summary

Exa vs other search tools

Reference Files

Source: https://reference.langchain.com/python/langchain-exa
API key: https://exa.ai
GitHub: https://github.com/langchain-ai/langchain/tree/main/libs/partners/exa