QVeris Python SDK

Empower LLM agents with QVeris: dynamically search for thousands of live tools (APIs, data sources, automations) and execute them in a tight agent loop.

What QVeris is

QVeris provides a tool search + tool execution layer for agents:

• search: the agent can call search_tools with a natural-language capability query (e.g. “stock price”, “web search”, “send email”).
• execute: after selecting a tool from search results, the agent calls execute_tool with the tool id + parameters.

The qveris.Agent wraps this into a loop that:

• searches for suitable tools to complete the task,
• executes proper tools (and your extra tools, if provided),
• feeds tool outputs back into the LLM until it reaches a final answer.

Configuration

Set the following environment variables (or pass them via QverisConfig / provider configs):

• QVERIS_API_KEY: Your QVeris API key. (Get it from QVeris)
• OPENAI_API_KEY: Your OpenAI (or OpenAI-compatible) provider API key.
• OPENAI_BASE_URL: Base URL for OpenAI-compatible providers (e.g. OpenAI, OpenRouter etc).

Quick Start

import asyncio
from qveris import Agent, Message

async def main():
    # Uses env vars automatically (QVERIS_API_KEY / OPENAI_API_KEY / ...)
    agent = Agent()

    messages = [
        Message(role="user", content="Find a weather tool and check New York weather.")
    ]

    print("Assistant: ", end="", flush=True)
    async for event in agent.run(messages):  # streaming by default
        if event.type == "content" and event.content:
            print(event.content, end="", flush=True)
        elif event.type == "tool_result" and event.tool_result:
            tr = event.tool_result
            name = tr.get("name", "unknown")
            is_error = tr.get("is_error", False)
            result = tr.get("result")
            print(f"\n← tool_result: {name} (error={is_error})", flush=True)

if __name__ == "__main__":
    asyncio.run(main())

Examples

This repo includes two example scripts that show different integration styles:

• Interactive streaming chat (examples/interactive_chat.py): a terminal chat UI that streams tokens and prints tool calls/results as they happen (great for debugging and demos).
• Stock debate (examples/stock_debate.py): two agents debate NVIDIA using non-streaming turns while still surfacing tool calls/results (demonstrates multi-agent orchestration and different models).

Integration patterns

QVeris supports several ways to integrate, depending on how much control you want over events and UI.

(a) Built-in streaming agent

Use Agent.run(messages) (streaming is the default). You’ll receive StreamEvents as the model streams content and as tools are invoked.

(b) Built-in non-streaming agent, and you need events (tool calls/results, metrics, etc.)

Use Agent.run(messages, stream=False) and handle StreamEvents:

• content (full assistant message, non-streaming)
• tool_call (what the model asked to run)
• tool_result (what actually ran + output)
• metrics (token usage if available)
• error

(c) Built-in non-streaming agent, final message only

If you only need the final assistant text (no tool-calls), use:

final_text = await agent.run_to_completion(messages)

(d) Bring your own agent loop (use QVeris client + tool definitions directly)

If you already have an agent framework (or want full control), you can directly use:

• qveris.client.tools.SEARCH_TOOL_DEF / EXECUTE_TOOL_DEF to expose tool schemas to your LLM
• qveris.client.tools.DEFAULT_SYSTEM_PROMPT as a starting system prompt
• qveris.client.api.QverisClient to handle QVeris tool calls

Your loop is responsible for:

• sending messages + tool schemas to the LLM,
• detecting tool calls,
• calling QverisClient.handle_tool_call(...),
• appending tool results back into messages until completion.

Custom LLM providers (non-OpenAI compatible APIs)

By default, Agent() uses an internal OpenAI-compatible provider. If your model API is not OpenAI-compatible, implement a provider that follows LLMProvider (qveris/llm/base.py) and pass it to Agent:

from typing import AsyncGenerator, List
from qveris.llm.base import LLMProvider
from qveris.types import Message, StreamEvent, ChatResponse
from openai.types.chat import ChatCompletionToolParam
from qveris.config import AgentConfig

class MyProvider(LLMProvider):
    async def chat_stream(
        self,
        messages: List[Message],
        tools: List[ChatCompletionToolParam],
        config: AgentConfig,
    ) -> AsyncGenerator[StreamEvent, None]:
        # Yield StreamEvent(type="content", content="...") and/or tool_call/metrics/etc.
        ...

    async def chat(
        self,
        messages: List[Message],
        tools: List[ChatCompletionToolParam],
        config: AgentConfig,
    ) -> ChatResponse:
        # Return ChatResponse(content="...", tool_calls=[...], metrics={...})
        ...

agent = Agent(llm_provider=MyProvider())

Features

• Provider Agnostic: Works with OpenAI, OpenRouter, LocalAI, etc.
• Auto-Tool Execution: The agent automatically searches for tools, executes them, and returns results.
• Smart Context: Automatically prunes old tool results to save tokens (enable_history_pruning=True).
• Multiple Agents: Configure different agents with unique system prompts and temperatures.
• Reasoning Support: Captures reasoning traces from models like Gemini (via OpenRouter) or DeepSeek.