Agent overview

The Agent class wraps the VectorShift agent platform: declarative tools, typed inputs/outputs, and multi-turn conversations via Session.

Prerequisites: Installed SDK · API key set · Python 3.10+.

Mental model

An agent is an LLM + tools + instructions + typed I/O.
Two flavours: Functional (single call, you get outputs back — designed to be invoked from inside a Pipeline) and Conversational (multi-turn, runs behind a Session). Pick with type=AgentType.FUNCTIONAL or AgentType.CONVERSATIONAL.
Tools are declared with Tool + ToolInput. ~200 integration tools are auto-generated under vectorshift.agent.tools.
Every method has an async variant (anew, arun, acreate_session, …).

Where functional agents live. A functional agent is most useful as a node inside a Pipeline — drop it into a graph the same way you’d drop an LLM node, and it handles tool selection autonomously while the surrounding pipeline handles wiring, branching, and orchestration. Standalone agent.run({...}) is fine for one-off calls; pipeline-embedded is the common production shape.

Quick start

from vectorshift import Agent, LlmInfo

agent = Agent.new(
    name="Researcher",
    llm_info=LlmInfo(provider="openai", model_id="gpt-5.1"),
    instructions="You are a helpful research assistant.",
)
agent.save()

result = agent.run({"input": "What is the population of Tokyo?"})
print(result.outputs)

import asyncio
from vectorshift import Agent, LlmInfo

async def main():
    agent = Agent.new(
        name="Researcher",
        llm_info=LlmInfo(provider="openai", model_id="gpt-5.1"),
        instructions="You are a helpful research assistant.",
    )
    await agent.asave()

    async with await agent.create_session() as session:
        await session.send("What is the population of Tokyo?")
        async for event in session.listen():
            if event.is_complete:
                print(event.text)
                break

asyncio.run(main())

Sessions require an async context. If you paste a Session snippet into a .py file, wrap it in async def main(): … and call asyncio.run(main()).

Functional vs Conversational

	Functional	Conversational
Shape	One call, one result	Multi-turn, streaming events
Entry point	`agent.run({...})` — typically called from inside a Pipeline node	`agent.create_session()` then `session.send()` / `session.listen()`
Use when	You want autonomous tool selection as one step of a larger pipeline (extraction, classification, summarisation, multi-tool research)	You’re building a chatbot, want token-level streaming, or need human-in-the-loop approvals
State	Stateless — each call is independent	Stateful per `session_id` (memory carries across turns when `MemoryConfig.enable_session_memory=True`)

Recent additions

The recent agent revamp introduced the Tool / ToolInput system, an auto-generated catalogue of ~200 integration tools (vectorshift.agent.tools), and Session objects with streamed events.

What’s next

Reference

Every public method, grouped by topic.

Examples

Runnable examples to copy-paste and adapt.

Session

Multi-turn streaming on top of an Agent.

Get started

Guides

Pipeline

Agent

Knowledge Base

Integrations

Table

Transformation

Session

Analytics

Workspace

Mental model

Quick start

Functional vs Conversational

Recent additions

What’s next

Reference

Examples

Session

​Mental model

​Quick start

​Functional vs Conversational

​Recent additions

​What’s next

Reference

Examples

Session

Mental model

Quick start

Functional vs Conversational

Recent additions

What’s next