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vectara-agentic

vectara-agentic is an Apache-2.0 Python library that helps developers build AI assistants connected to Vectara corpora using agentic RAG. It provides helpers for creating retrieval and search tools, configuring assistant instructions, streaming responses, applying hallucination-correction steps to eligible tools, and building multi-step workflows.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

vectara-agentic — OpenViST project profile in Agent Frameworks.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use vectara-agentic when developing a Python assistant that should retrieve from a Vectara corpus and may need custom tools, streamed responses, or structured workflows.

Who it is for

  • Python developers creating assistants grounded in information stored in Vectara corpora.
  • Teams that need to define assistant tools, corpus filters, or structured multi-step interactions.

Documented uses

  • Create RAG or search tools that connect an assistant to a Vectara corpus.
  • Define tool arguments that map questions to corpus filters, such as year or company ticker.
  • Build assistants with custom instructions and streaming responses.
  • Structure multi-step tasks with validated workflow inputs and outputs.
  • Add hallucination-correction analysis for tools marked eligible.
Before you choose

Requirements & limits

The project is an open-source Python package published under the Apache-2.0 license and installed with pip. The documented Vectara-connected setup requires a Vectara account and corpus credentials.

  • The library is built around Vectara corpus access and uses the LlamaIndex Agent framework.
  • A Vectara API key and corpus key are needed for the documented corpus-connected tool setup.
  • Python 3.10 or later is required.
  • The README lists multiple model-provider options; AWS Bedrock requires enabled models and AWS profile and region settings.
  • The documented workflow approach uses Pydantic models to validate inputs and outputs.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up a Vectara RAG tool for an assistant

Install the library and define a RAG tool connected to a Vectara corpus.

Before you start

  • Python 3.10 or higher
  • A Vectara account and corpus
  • A Vectara API key and corpus key
  • An API key for a supported model provider when building the assistant

Steps

  1. Install the package with the command below.

    Documented command
    pip install vectara-agentic

  2. Make the Vectara API key and corpus key available to the Python environment.

  3. Initialize VectaraToolFactory with those credentials.

  4. Create a RAG tool with create_rag_tool; define any additional arguments, such as corpus filter attributes, in a Pydantic model.

Expected outcome

A configured Vectara RAG tool that can query the corpus and provide summarized, corpus-grounded responses for use by an assistant.

Guide limitations

  • This covers tool setup; the README’s complete agent-creation and response flow is not included here.
  • A model-provider API key is also required for an assistant; AWS Bedrock has additional account and environment requirements.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
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Source availability
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Sources checked
13 October 2026
Original website title
vectara/py-vectara-agentic · Official homepage title · retained in its source language
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