agent-search-core 1.0.2

In-process SDK runtime for agent-search with optional callback-driven Langfuse tracing

agent-search core SDK

In-process Python SDK for agent-search.

The PyPI package is intentionally narrow: consumers should call advanced_rag(...) and treat that as the supported entrypoint.

The SDK always requires both:

• A chat model (for example langchain_openai.ChatOpenAI)
• A vector store that implements similarity_search(query, k, filter=None)

It does not auto-build these dependencies for you.

Install (PyPI)

python3.11 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install agent-search-core
python -c "import agent_search; print(agent_search.__file__)"

Quick start

from langchain_openai import ChatOpenAI
from agent_search import advanced_rag
from agent_search.vectorstore.langchain_adapter import LangChainVectorStoreAdapter

vector_store = LangChainVectorStoreAdapter(your_langchain_vector_store)
model = ChatOpenAI(model="gpt-4.1-mini", temperature=0.0)

response = advanced_rag(
    "What is pgvector?",
    vector_store=vector_store,
    model=model,
)
print(response.output)

Requirements

• Python >=3.11,<3.14
• A compatible vector store and chat model as shown above.

Build

cd sdk/core
python -m build

Supported API

The supported callable exported by agent_search is:

• advanced_rag

Notes about advanced_rag(...):

• It is a synchronous call that runs the full retrieval-and-answer workflow and returns a RuntimeAgentRunResponse.
• You supply the model and vector store; the SDK orchestrates the LangGraph-based runtime around them.
• Optional config={"thread_id": "..."} lets you pass a stable execution identity into the run.
• If you pass langfuse_callback=..., the SDK includes that callback in runtime tracing.
• langfuse_settings is accepted for compatibility but ignored unless you provide an explicit langfuse_callback.

advanced_rag(...) output schema:

RuntimeAgentRunResponse(
  main_question: str,
  thread_id: str,
  sub_qa: list[SubQuestionAnswer],
  output: str,
  final_citations: list[CitationSourceRow],
)

Vector store compatibility

Runtime SDK expects similarity_search(query, k, filter=None). For LangChain-backed stores, use:

• agent_search.vectorstore.langchain_adapter.LangChainVectorStoreAdapter

Notes

• This package is the SDK surface only. For the full app experience, run the repository with Docker Compose.
• The PyPI package is intentionally narrower than the backend internals; consumer integrations should rely on advanced_rag(...) only.
• For SDK-only use, install from PyPI and supply your own model + vector store.

Release guidance

Use the repository release script from project root:

./scripts/release_sdk.sh

The release script verifies the built wheel includes the agent_search package before upload.

Publish flow (requires TWINE_API_TOKEN):

PUBLISH=1 TWINE_API_TOKEN=*** ./scripts/release_sdk.sh

Tag format used by CI release workflow:

• agent-search-core-v<version>