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RAG & Knowledge Systems

Trustrag

TrustRAG is a configurable Python framework for building retrieval-augmented generation systems, with modules for document processing, retrieval, ranking, generation and citations, plus a recursive DeepResearch workflow.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Trustrag — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use TrustRAG when implementing or adapting a RAG pipeline in Python, particularly when configurable retrieval components or a recursive deep-search workflow are useful.

Who it is for

  • Developers building document-grounded question-answering systems.
  • Teams that want to configure and combine RAG pipeline components.
  • Developers exploring recursive search and research-report workflows.

Documented uses

  • Parse and chunk documents for use in a retrieval pipeline.
  • Build and query BM25, dense or hybrid retrievers.
  • Combine retrieved material with generated answers and citations.
  • Run a DeepResearch workflow that decomposes queries and iterates through search and reasoning actions.
Before you choose

Requirements & limits

  • The README documents both pip and editable source installation.
  • Python 3.10 is identified in the repository badge; the optional Conda example uses Python 3.12.
  • The framework exposes multiple modules, so users select and configure pipeline components rather than relying on one fixed application flow.
  • The README describes a DeepResearch CLI requiring LLM API and search configuration.

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

Install TrustRAG from source

Prepare a local editable installation of the Python framework.

Before you start

  • Python environment and pip
  • Git

Steps

  1. Clone the TrustRAG repository.

  2. From the project directory, install its dependencies with pip install -e .

Expected outcome

An editable source installation, as described in the repository README.

Guide limitations

  • This covers installation only, not configuration or execution of a RAG application.
Documentation 1 ↗
Source-based practical guide

Configure and start the DeepResearch CLI

Set up the documented DeepResearch CLI configuration and launch its pipeline.

Before you start

  • A local TrustRAG checkout
  • LLM API configuration
  • Search configuration

Steps

  1. Change to trustrag/modules/deepsearch.

  2. Copy .env.example to .env.

  3. Configure the LLM API and search in .env.

  4. Run python pipeline.py.

Expected outcome

The README’s documented command sequence for starting the DeepResearch pipeline.

Guide limitations

  • The README does not specify provider credentials or search configuration values.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Retrieval-augmented applications

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
TrustragParse and chunk documents for use in a retrieval pipeline.Linux, macOS, Windows
LangsearchCrawl website or local data for a knowledge base.CLI, Self-hosted, Server
Hybrid Search RagCombine document retrieval and language-model generation in a configurable workflow.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
16 October 2026
Original website title
gomate-community/TrustRAG · Official homepage title · retained in its source language
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