🚀 Harvester SDK - Complete AI Processing Platform
"The unified interface for all AI providers with enterprise-grade reliability."
🌟 What is Harvester SDK?
Harvester SDK is a comprehensive AI processing platform that provides a unified interface to all major AI providers. Whether you need text generation, image creation, batch processing, agentic coding, or real-time conversations, Harvester SDK handles the complexity so you can focus on building.
⚡ Key Features
- Multi-Provider Support - OpenAI, Anthropic, Google AI Studio, Vertex AI, XAI, DeepSeek
- Agentic Coding Assistants - Grok Code Agent (fast) & Claude Code Agent (SDK-powered)
- Enhanced Chat Experience -
prompt_toolkitintegration with multi-line paste, command history, and professional line editing - Dual Authentication - API keys (GenAI) and service accounts (Vertex AI)
- Streaming & Turn-Based Chat - Real-time streaming or non-streaming conversations
- Batch Processing - Cost-effective bulk operations with 50% savings
- Template System - 30+ Jinja2 templates for AI-powered transformations
- Image Generation - DALL-E, Imagen, GPT Image support
- Enterprise Ready - Rate limiting, retries, error handling
🚀 Quick Start
Installation
# Install the SDK
pip install harvester-sdk
Basic Usage
# Main CLI conductor
harvester --help
# Turn-based conversation (non-streaming)
harvester message --model gemini-2.5-flash
harvester message --model sonnet-4-5 --system "You are a helpful assistant"
# Batch processing from CSV
harvester batch data.csv --model gpt-5 --template quick
# Process directory with templates
harvester process ./src --template refactor --model gemini-2.5-pro
# Generate images
harvester image "A beautiful sunset" --provider dalle3 --size 1024x1024
🔧 Provider Configuration
Google AI Studio (GenAI) - API Key Authentication
export GEMINI_API_KEY=your_api_key
harvester message --model gemini-2.5-flash
Google Vertex AI - Service Account Authentication
export GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.json
harvester message --model vtx-gemini-2.5-flash
Other Providers
export OPENAI_API_KEY=your_openai_key
export ANTHROPIC_API_KEY=your_anthropic_key
export XAI_API_KEY=your_xai_key
export DEEPSEEK_API_KEY=your_deepseek_key
📋 Available Commands
Core Commands
harvester chat- Interactive streaming chat with enhanced UX (multi-line paste, history, line editing)harvester message- Turn-based conversations (non-streaming)harvester batch- Batch process CSV filesharvester process- Directory processing with templatesharvester image- Image generation (single or batch)harvester search- AI-enhanced web search (Grok)
Agentic Commands
harvester agent-grok- Grok Code Agent - Fast / impressive agentic coding (grok-code-fast-1)harvester agent-claude- Claude Code Agent - Prone to hallucinations, be careful Claude will delete the Claude Agent SDKharvester computer- GPT Computer Use - AI agent that controls browser/computer
Utility Commands
harvester list-models- Show available modelsharvester config --show- Display configurationharvester templates- Manage batch processing templatesharvester status- Check batch job status
Chat Features
The harvester chat command provides a professional terminal experience:
- ✅ Multi-line paste support - Natural paste behavior, no special modes
- ✅ Command history - Use ↑/↓ arrows to recall previous messages
- ✅ Line editing - Ctrl+A, Ctrl+E, Ctrl+K, and other readline shortcuts
- ✅ Slash commands -
/help,/model,/search,/export, and more - ✅ Export conversations - Save to JSON or Markdown
🎯 Model Selection Guide
Google AI Models
| API Key (GenAI) | Service Account (Vertex) | Use Case |
|---|---|---|
gemini-2.5-flash |
vtx-gemini-2.5-flash |
Fast, cost-effective |
gemini-2.5-pro |
vtx-gemini-2.5-pro |
High-quality reasoning |
gemini-2.5-flash-lite |
vtx-gemini-2.5-flash-lite |
low latency |
Other Providers
- OpenAI:
gpt-5,gpt-5-mini,gpt-5-nano - Anthropic:
claude-sonnet-4-5,claude-sonnet-4,claude-opus-4-1 - XAI:
grok-code-fast-1,grok-4-fast-reasoning,grok-4-fast,grok-4-0709,grok-3,grok-3-mini - DeepSeek:
deepseek-chat,deepseek-reasoner
🤖 Agentic Coding Assistants
Harvester SDK includes two powerful agentic coding assistants that can autonomously handle complex multi-step coding tasks.
Grok Code Agent (agent-grok)
Powered by xAI's grok-code-fast-1 model - The fastest, most cost-effective agentic coding solution.
Features:
- ⚡ 4x faster than claude-code agents
- 💰 1/10th the cost of comparable solutions
- 🧰 11 tools: file operations, JSON tools, command execution, directory management
- 🔁 100 max iterations with loop detection on file reads
- 🎯 Streaming reasoning traces - Read the Agents thoughts after
- 🛡️ Safety first - Dangerous commands (rm -rf /, dd, fork bombs) automatically blocked
- 🚀 Ripgrep support - 10x faster code search when available
Example:
🤖 agent-grok "build programs from this list /path/to/file.md" --show-reasoning
🎯 Type: general
============================================================
🔄 Iteration 1/100
============================================================
🔧 Tool Calls (1)
→ read_file({'file_path': '/home/user/linux_c_programs/linux_c_program_taxonomy.md'})
✓ # Comprehensive Taxonomy of C Programs for Linux
## ~300 Feasible Program Ideas by Category
---
##...
============================================================
🔄 Iteration 2/100
============================================================
🔧 Tool Calls (1)
→ list_files({'path': '/home/user/linux_c_programs'})
✓ Success
============================================================
🔄 Iteration 3/100
============================================================
🔧 Tool Calls (1)
→ list_files({'path': '/home/user/linux_c_programs/libs'})
✓ Success
============================================================
🔄 Iteration 4/100
============================================================
🔧 Tool Calls (1)
→ read_file({'file_path': '/home/user/linux_c_programs/libs/common.h'})
✓ #ifndef COMMON_H
#define COMMON_H
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#inclu...
Claude Code Agent (agent-claude)
Built on Anthropic's official Claude Agent SDK - infrastructure from the team behind Claude Code CLI.
# Execute a coding task
harvester agent-claude "Implement a REST API endpoint with validation"
# Complex debugging
harvester agent-claude "Debug the memory leak in the worker pool"
Features:
- 🏗️ Production-tested agent loop - Anthropic's own implementation
- 🎯 Automatic context management - Built-in compaction and caching
- 🔧 Professional tooling - Same tools as Claude Code CLI
- 🤝 MCP protocol support - External service integrations
- ⚙️ Subagents - Parallel task execution (be careful, faceless Claude agents do not respect the project)
Example Output:
🤖 agent-claude
📋 Task: Implement REST API...
🎯 Type: feature
🧠 Model: claude-sonnet-4-5
🔧 Using tool: Write
✓ Created api/endpoints.py
🔧 Using tool: Bash
✓ Tests passed
📊 Status: completed
💰 Cost: $0.097
Agent Comparison
| Feature | Grok Agent | Claude Agent |
|---|---|---|
| Speed | ⚡⚡⚡⚡ Very Fast (3-5 iterations) | ⚡⚡ Thorough (10-15 iterations) |
| Cost | 💰 ~$0.002/task | 💰💰 ~$0.10/task |
| Use Case | Fast iteration, prototyping | you like Claude |
| Quality | ✅ Excellent | DEPENDS |
| Tools | 11 custom tools + safety | Full Claude Code SDK |
| Verification | Basic | Comprehensive |
When to use which:
- Grok Agent: general use
- Claude Agent: you like Claude
Examples
See practical examples in:
/example/agent-grok/- Output from Grok Code Agent/example/agent-claude/- Output from Claude Code Agent/example/batch-results...- Batch processing files
💼 Programming Interface
Python SDK Usage
from harvester_sdk import HarvesterSDK
# Initialize SDK
sdk = HarvesterSDK()
# Quick processing
result = await sdk.quick_process(
prompt="Explain quantum computing",
model="gemini-2.5-pro"
)
# Batch processing
results = await sdk.process_batch(
requests=["What is AI?", "Explain ML", "Define neural networks"],
model="claude-sonnet-4-20250514"
)
# Multi-provider council (get consensus)
consensus = await sdk.quick_council(
prompt="What is consciousness?",
models=["gemini-2.5-pro", "claude-sonnet-4-20250514", "gpt-4o"]
)
Provider Factory
from providers.provider_factory import ProviderFactory
# Create provider factory
factory = ProviderFactory()
# Get provider for specific model
provider = factory.get_provider("gemini-2.5-flash") # -> GenAI provider
provider = factory.get_provider("vtx-gemini-2.5-flash") # -> Vertex AI provider
# Generate completion
response = await provider.complete("Hello, world!", "gemini-2.5-flash")
🏗️ Architecture
┌───────────────────────────────────────────────────────────────────┐
│ HARVESTER SDK │
├───────────────────────────────────────────────────────────────────┤
│ Main CLI Conductor │
│ (harvester command) │
├────────┬────────┬────────┬────────┬────────┬────────┬────────────┤
│Message │ Batch │Process │ Image │ Search │ Grok │ Claude │
│(Chat) │ CSV │ Dir │ Gen │Enhanced│ Agent │ Agent │
├────────┴────────┴────────┴────────┴────────┴────────┴────────────┤
│ Provider Factory │
├────────┬────────┬────────┬────────┬────────┬──────────────────────┤
│ GenAI │Vertex │ OpenAI │Anthropic│ XAI │ DeepSeek │
│(APIKey)│(SA) │ │ │ (Grok) │ │
└────────┴────────┴────────┴────────┴────────┴──────────────────────┘
└──────────────────────────────────────┘
Agentic Tools Layer
┌─────────────────┬────────────────────┐
│ Grok Agent │ Claude Agent │
│ (Custom Loop) │ (Official SDK) │
│ - 9 tools │ - Full SDK tools │
│ - Challenger │ - previous champ │
└─────────────────┴────────────────────┘
🔒 Authentication Methods
Clear Separation for Google Services
Google AI Studio (GenAI):
- ✅ Simple API key:
GEMINI_API_KEY - ✅ Models:
gemini-2.5-flash,gemini-2.5-pro - ✅ Best for: Personal use, quick setup
Google Vertex AI:
- ✅ Service account:
GOOGLE_APPLICATION_CREDENTIALS - ✅ Models:
vtx-gemini-2.5-flash,vtx-gemini-2.5-pro - ✅ Best for: Enterprise, GCP integration
🌟 Open Source & Free
All features are completely free and open source under the MIT License. No tiers, no paywalls, no restrictions.
- ✅ Unlimited workers - Scale as much as you need
- ✅ All providers - Full access to every AI provider
- ✅ Advanced features - Structured output, function calling, multi-provider parallelism
- ✅ Enterprise ready - Production-grade reliability built-in
📖 Examples
Turn-Based Conversation
# Start a conversation with Gemini
harvester message --model gemini-2.5-flash
# Chat with Claude
harvester message --model claude-sonnet-4
# System prompt example
harvester message --model grok-4-0709 --system "You are an expert programmer"
Batch Processing
# Process CSV with AI
harvester batch questions.csv --model gemini-2.5-pro --template analysis
# Directory transformation
harvester process ./legacy_code --template modernize.j2 --model claude-sonnet-4-5
Image Generation
# DALL-E 3
harvester image "A futuristic city" --provider dalle-3 --quality hd
# Imagen 4
harvester image "Abstract art" --provider vertex_image --model imagen-4
🤝 Support & Contributing
- Documentation: Full guides in
/docs - Issues: Report bugs via GitHub issues
- Enterprise: Contact info@quantumencoding.io
- License: MIT - see LICENSE file
🌟 Why Harvester SDK?
- Unified Interface - One API for all providers
- Authentication Clarity - Clear separation of auth methods
- Production Ready - Error handling, retries, rate limiting
- Flexible Deployment - CLI tools + Python SDK
- Cost Optimization - Batch processing with 50% savings
- Multi-Modal - Text, images, and more
- Enterprise Grade - Open source, well-documented, production-ready
© 2025 QUANTUM ENCODING LTD
📧 Contact: info@quantumencoding.io
🌐 Website: https://quantumencoding.io
The complete AI processing platform for modern applications.
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