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Flashlearn

Flashlearn is an open-source Python framework for integrating and orchestrating LLM tasks in workflows and ETL pipelines. It uses compact JSON definitions for tasks and skills, offers a fit/predict-style interface, and supports concurrent task calls that return structured results for downstream processing.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Flashlearn — 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 Flashlearn when an existing Python workflow or ETL pipeline needs JSON-defined LLM tasks and structured results that can feed downstream processing.

Who it is for

  • Python developers adding LLM transformations to data workflows
  • Teams building ETL steps for classification, summarization or rewriting
  • Developers who need structured outputs from LLM tasks for subsequent processing

Documented uses

  • Define a reusable skill for transforming or classifying records.
  • Apply a skill to a list of dictionary-based inputs.
  • Run tasks concurrently and use structured outputs in later pipeline steps.
  • Connect workflows to supported LLM clients.
Before you choose

Requirements & limits

The README shows installation from pip. Flashlearn is identified as open source under the MIT license.

  • The framework expects Python-based integration and dictionary-style records in its examples.
  • The README lists OpenAI, DeepSeek, LiteLLM, Ollama and other OpenAI-compatible clients.
  • Credentials and client configuration depend on the provider.
  • The repository describes orchestration capacity of up to 1,000 calls per minute; this is a project-stated figure.

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 Flashlearn and prepare a provider connection

Install the Python package and configure credentials for an LLM provider.

Before you start

  • Python package installation with pip
  • Credentials for the selected LLM provider

Steps

  1. Install the package with the command below.

    Documented command
    pip install flashlearn

  2. Add the API key required by the chosen provider to a .env file, as shown for OPENAI_API_KEY in the README.

Expected outcome

The package is installed and provider credentials are prepared for use in a Flashlearn workflow.

Guide limitations

  • The setup instructions do not demonstrate execution or deployment.
  • Credential names and connection configuration depend on the selected provider.
Documentation 1 ↗
Source-based practical guide

Apply a saved skill to dictionary records

Load a previously saved skill, create tasks from records and obtain structured results.

Before you start

  • Flashlearn installed and provider credentials configured
  • A saved skill definition in JSON format
  • Input records represented as dictionaries

Steps

  1. Load the saved JSON skill definition with GeneralSkill.load_skill.

  2. Pass the input records to skill.create_tasks.

  3. Run the tasks with skill.run_tasks_in_parallel and use the returned results in downstream code.

Expected outcome

Structured results are returned with keys corresponding to the input record indexes.

Guide limitations

  • This example assumes a skill has already been saved.
  • The README does not specify provider-specific limits or guarantee a particular result quality.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Agent orchestration

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
FlashlearnDefine a reusable skill for transforming or classifying records.Linux, macOS, Server, WindowsThe README shows installation from pip. Flashlearn is identified as open source under the MIT license.
AgeniteDefine typed tools with JSON Schema validation and error handling.CLI
VeldraCreate an agent with a policy, tools, skills, knowledge base and reasoning method.Self-hostedThe documented deployment is self-hosted and uses a sign-in gate after a first-run administrator account is created.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
Pravko-Solutions/FlashLearn · Official homepage title · retained in its source language
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