FlashLearn

Source#

• Repository: Pravko-Solutions/FlashLearn
• Last source update: 2026-02-17
• Last verified: 2026-03-01

Tags#

Integration notes#

Framework-level abstraction; derive bounded worker contracts from concrete tasks and APIs in docs/examples.

worker.md example#

Starter worker.md contract mapped from this registry entry. Copy this file and adapt schemas, constraints, and statuses for your task.

---
id: flashlearn-repo-derived-worker
name: FlashLearn Repo-Derived Worker
version: 1.0.0
source_registry_url: https://worker.md/registry/flashlearn/
source_repository: https://github.com/Pravko-Solutions/FlashLearn
repository_default_branch: main
repository_language: Python
repository_license: MIT
repository_updated_at: 2026-02-17
worker_mode: agent-orchestration-worker
derivation_method: github_repository_metadata_plus_raw_readme
derivation_confidence: 0.95
derived_on: 2026-03-01
tags:
  - agentic-ai-development
  - ai
  - ai-agents
  - ai-agents-framework
  - concurrency
  - etl-pipeline
---

# FlashLearn Repo-Derived Worker

## Repo-derived summary
- Registry summary: Integrate LLM in any pipeline - fit/predict pattern, JSON driven flows, and built in concurency support.
- Repository description: Integrate LLM in any pipeline - fit/predict pattern, JSON driven flows, and built in concurency support.
- Stars (snapshot): 608
- Primary language: Python
- Worker mode classification: agent-orchestration-worker

## Extracted from
- https://github.com/Pravko-Solutions/FlashLearn
- https://github.com/Pravko-Solutions/FlashLearn/blob/main/README.md
- https://img.shields.io/github/languages/code-size/Pravko-Solutions/FlashLearn
- https://github.com/Pravko-Solutions/FlashLearn/tree/main/examples
- https://flashlearn.tech/index.php/docs/

## Evidence notes (from repository text)
- README summary paragraph: FlashLearn provides a simple interface and orchestration **(up to 1000 calls/min)** for incorporating **Agent LLMs** into your typical workflows and ETL pipelines. Conduct data transformations, classifications, summarizations, rewriting, and custom multi-step tasks, just like you’d do with any standard ML library, harnessing the power of LLMs under the hood. Each **step and task has a compact JSON definition** which makes pipelines simple to understand and maintain. It supports **LiteLLM**, **Ollama**, **OpenAI**, **DeepSeek**, and all other OpenAI-compatible clients.
- 3. Send them to another tool for further analysis (for example, rewriting the “reason” in a formal tone)
- - [Research assistant](examples/Personal%20asistant/research_assistant.md)
- ## “All JSON, All the Time”: Example Classification Workflow
- from flashlearn.skills.toolkit import ClassifyReviewSentiment
- from flashlearn.skills.toolkit import HumorizeText

## Installation hints found in README
- `pip install flashlearn`

## worker.md contract (derived starter)
Purpose: Execute one orchestrated agent task as a bounded worker step.

### Input schema
```json
{
  "type": "object",
  "additionalProperties": false,
  "required": [
    "run_id",
    "task",
    "context"
  ],
  "properties": {
    "run_id": {
      "type": "string"
    },
    "task": {
      "type": "string"
    },
    "context": {
      "type": "object"
    }
  }
}
```

### Output schema
```json
{
  "type": "object",
  "additionalProperties": false,
  "required": [
    "run_id",
    "status",
    "result"
  ],
  "properties": {
    "run_id": {
      "type": "string"
    },
    "status": {
      "type": "string",
      "enum": [
        "ok",
        "retryable_error",
        "invalid_request",
        "invalid_output"
      ]
    },
    "result": {
      "type": "object"
    }
  }
}
```

### Constraints
- timeout_seconds: 30
- max_attempts: 2
- idempotency_key: run_id
- status_enum: [ok, retryable_error, invalid_request, invalid_output]
- notes: adapt to concrete APIs/classes documented in this repository before production use

## How this should be used
1. Treat this file as a repo-derived starter profile, not a claim of an official repository API contract.
2. Replace schemas with exact interfaces from code/docs you adopt.
3. Keep execution bounded and auditable using worker protocol constraints.

How to use#

• Save this as a worker spec file (for example: flashlearn-my-task.worker.md).
• Replace the input/output schemas and purpose with your real bounded task.
• Enforce schema validation + timeout + retry policy in your runtime before production use.

Citation#

Reference URL: https://worker.md/registry/flashlearn/

Source URL: https://github.com/Pravko-Solutions/FlashLearn