anlyon-langchain
LangChain integration for Anlyon: AnlyonVectorStore and AnlyonRetriever over Anlyon Memory, AnlyonChatMessageHistory over Anlyon Sessions, and AnlyonTracer for nested run/span tracing.
Trace a LangChain invocation
from anlyon import Client
from anlyon_langchain import AnlyonTracer
client = Client(api_key="anlyon_live_...")
tracer = AnlyonTracer(client, application="support-agent")
# Pass the callback to any LangChain runnable/agent invocation.
result = chain.invoke({"question": "Where is my order?"}, config={"callbacks": [tracer]})
The handler creates one Anlyon run for the root callback and parent-linked
workflow_step, model_call, and tool_call spans for nested callbacks. It is
best-effort: Anlyon transport failures never change the application result.
Installation
pip install anlyon-langchain
Retrieval
from anlyon import Client
from anlyon_langchain import AnlyonVectorStore
client = Client() # uses ANLYON_API_KEY
store = AnlyonVectorStore(client, collection="docs")
retriever = store.as_retriever()
Chat history
AnlyonChatMessageHistory puts a chain's conversation in an Anlyon Session durable across processes, and summarized automatically once the thread gets long.
from anlyon import Client
from anlyon_langchain import AnlyonChatMessageHistory
from langchain_core.runnables.history import RunnableWithMessageHistory
client = Client()
chain_with_history = RunnableWithMessageHistory(
chain,
lambda session_id: AnlyonChatMessageHistory(client, session_id),
input_messages_key="input",
history_messages_key="history",
)
# `session_id` is an Anlyon session id create one with client.sessions.create()
chain_with_history.invoke(
{"input": "Where is my order?"},
config={"configurable": {"session_id": "ses_abc123"}},
)
.messages reads the thread back as LangChain messages, .add_messages() appends a turn in one round-trip, and .clear() truncates the thread while keeping the session id usable.
See the Anlyon docs for the full guide.
License
MIT
1 maintainer
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