Use VectorSmith in an agent¶

Two paths, one YAML. Hub: documentation home · Python API · integrations.

Python app — import one function. No MCP subprocess.

pip install "vectorsmith[qdrant,langchain]"

from vectorsmith import load_tools
from langchain.agents import create_agent

tools = load_tools("tools.invoices.yaml", "tools.tickets.yaml")
agent = create_agent("openai:gpt-4.1", tools)

Chat / IDE host (Claude Desktop, Claude Code, Codex, Cursor) — those products spawn a process. They cannot import Python.

{
  "mcpServers": {
    "invoices": {
      "command": "vectorsmith",
      "args": ["serve", "tools.invoices.yaml", "--name", "invoices"]
    }
  }
}

Codex uses TOML instead of JSON. Full copy-paste for every host and framework: docs/integrations/.

Framework extras¶

Extra	Import
`vectorsmith[langchain]`	`from vectorsmith import load_tools`
`vectorsmith[langgraph]`	same tools; `create_react_agent` / `ToolNode`
`vectorsmith[openai-agents]`	`from vectorsmith.openai_agents import load_tools`
`vectorsmith[anthropic]`	`from vectorsmith.anthropic import load_tools`

Any other stack: pip install "vectorsmith[qdrant]" then from vectorsmith import connect and await vs.call(name, args). That path does not need the LangChain extra.

While authoring¶

Write and check the YAML first — tools.yaml reference (connections, tools, filters, built-ins).

vectorsmith validate and vectorsmith test check a file before you wire load_tools or serve.