agent-search-core 0.1.2

In-process SDK runtime for agent-search

agent-search core SDK

In-process Python SDK for agent-search. This package lets you call the runtime directly inside your own app.

The SDK always requires both:

• A chat model (e.g. 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)

python3.13 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install agent-search-core

Quick start

from langchain_openai import ChatOpenAI
from agent_search import run
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 = run("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

Runtime API surface

Primary functions exposed by agent_search:

• run
• run_async
• get_run_status
• cancel_run

Config and errors exposed by agent_search:

• RuntimeConfig, RuntimeTimeoutConfig, RuntimeRetrievalConfig, RuntimeRerankConfig
• SDKError, SDKConfigurationError, SDKRetrievalError, SDKModelError, SDKTimeoutError

Vector store compatibility

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

• agent_search.vectorstore.langchain_adapter.LangChainVectorStoreAdapter

Notes

• For the full app (API, DB, UI), run this repo with Docker Compose.
• 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

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>