OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
Agent Frameworks

Hydra — hassenhamdi

HyDRA is an MIT-licensed Python framework for agentic retrieval-augmented generation, combining hierarchical agents, iterative planning and retrieval with a learning loop that stores policies from past interactions.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Hydra — hassenhamdi — 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 HyDRA when developing a Python RAG prototype that needs iterative retrieval decisions, a terminal interaction loop and Milvus-backed knowledge and agent memory.

Who it is for

  • Developers prototyping retrieval-augmented applications.
  • Teams building question-answering or research tools over a document collection.
  • Practitioners exploring multi-agent retrieval, persistent agent memory and adaptive delegation.

Documented uses

  • Ingest Markdown documents into a searchable knowledge base.
  • Build question-answering or research workflows that iteratively choose retrieval actions.
  • Combine local knowledge-base search with live web research through specialist agents.
  • Save generated reports and add saved reports back into the knowledge base.
  • Use policies learned from earlier interactions to guide later agent delegation.
Before you choose

Requirements & limits

The repository is public and identifies an MIT license. Local setup requires Python 3.10+, a Gemini API key and Docker with Docker Compose for Milvus.

  • The documented model credential is a Google Gemini API key; other model providers are not specified.
  • The documented ingestion flow currently accepts Markdown files.
  • Milvus is central to the stated hybrid-search and memory architecture.
  • The documented user interface is an interactive terminal interface.

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 HyDRA and prepare a Markdown knowledge base

Install the framework, configure its documented services and ingest Markdown files before using the terminal interface.

Before you start

  • Python 3.10 or later
  • Google Gemini API key
  • Docker and Docker Compose for Milvus
  • Git and pip

Steps

  1. Start a standalone Milvus instance using the README's operating-system-specific guidance.

  2. Clone the HyDRA repository, enter its directory and install dependencies with the command below.

    Documented command
    pip install -r requirements.txt

  3. Copy .env.example to .env and add the Gemini API key.

  4. Create the Milvus collections with the command below.

    Documented command
    python -m src.services.milvus_setup --profile development

  5. Put Markdown documents in data/ and run the command below.

    Documented command
    python -m data_processing.ingest --path ./data --profile development

Expected outcome

Milvus collections are prepared and Markdown documents are ingested into the knowledge base.

Guide limitations

  • The README says the ingestion process currently supports Markdown files.
  • The guide documents setup and ingestion, not a successful run or deployment.
Documentation 1 ↗
Source-based practical guide

Launch the HyDRA terminal interface

Open the documented interactive interface and start a personalized conversation.

Before you start

  • HyDRA repository and dependencies installed
  • Milvus configured and running
  • Gemini API key set in .env

Steps

  1. From the repository directory, launch the command below.

    Documented command
    python main.py --profile production_balanced --user_id alex

  2. Chat naturally or use /help to view available commands.

  3. Use /save to save the last generated report, /ingest to add a saved report to the knowledge base, or /new to start another conversation.

Expected outcome

The README describes an interactive terminal conversation with commands for managing sessions and reports.

Guide limitations

  • The README does not specify supported models beyond the Gemini API key prerequisite.
  • The command and interface steps describe documented usage, not an execution test.
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
Hydra — hassenhamdiIngest Markdown documents into a searchable knowledge base.CLI, Self-hosted, ServerThe repository is public and identifies an MIT license. Local setup requires Python 3.10+, a Gemini API key and Docker with Docker Compose for Milvus.
AgentnexusConfigure agents from stored models, tools, prompts, memory, and knowledge sources.Docker, Self-hosted, ServerThe project is documented as open source, and its README states that it uses the MIT License.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
17 October 2026
Original website title
hassenhamdi/HyDRA · Official homepage title · retained in its source language
How we review sourcesSuggest a correction