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OSAI - Open Source AI Documentation

Comprehensive documentation on the state of the art in open source AI tooling (2025)

Overview

This repository contains research and documentation on the current landscape of open source artificial intelligence, covering models, coding agents, frameworks, and infrastructure. The gap between open source and proprietary AI has narrowed dramatically, with projections showing parity by Q2 2026.

Key Findings

Performance Gap Narrowing

  • October 2024: 15-20 point gap between best open source and proprietary models
  • Early 2025: Just 7 points separate them
  • Projection: Parity expected by Q2 2026

Cost Advantage

  • 86% average cost savings with open source models
  • 7.3x better price-to-performance ratio
  • Sweet spot: Qwen3-235B, DeepSeek V3.2, Llama 3.3 70B at $0.17-0.42/M tokens

Ecosystem Maturity

Open source AI has reached production-ready status across all layers:

  • World-class models matching GPT-4o and o1-pro
  • Autonomous coding agents with full IDE integration
  • Battle-tested frameworks with 100K+ GitHub stars
  • Optimized infrastructure with sub-second inference

Documentation Structure

Open Source Models

State of the art in open source LLMs, including:

  • DeepSeek-R1: Matching OpenAI o1-pro in reasoning
  • Qwen3: Alibaba’s MoE models beating GPT-4o on benchmarks
  • Llama 3.3/4: Meta’s widely-deployed model family
  • Performance analysis and cost comparisons

Coding Agents

Open source autonomous coding assistants:

  • Cline: VS Code agent with Plan & Act modes, MCP support
  • Aider: Terminal-native AI pair programmer with Git integration
  • Continue.dev: Fully local IDE extension via Ollama
  • OpenHands: Full-capability autonomous software developer
  • Cost analysis: $1-3/hour typical sessions

AI Frameworks

Leading open source frameworks for building AI applications:

  • LangChain/LangGraph: 1M+ builders, 100K GitHub stars
  • LlamaIndex: Leading RAG and data connection platform
  • AutoGen: Microsoft’s multi-agent framework (MIT license)
  • CrewAI: Role-based agent orchestration
  • Comparison matrix and selection guidance

Infrastructure

The foundation layer for deploying open source AI:

  • Model Context Protocol (MCP): Anthropic’s open standard, adopted by OpenAI
  • Ollama: 95K+ stars, local model runtime with 100+ models
  • Groq: 18x faster inference with custom LPU hardware
  • OpenRouter: Unified API gateway for all models
  • Deployment patterns and cost optimization strategies

Quick Start

Running Models Locally (Free)

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh
 
# Run DeepSeek-R1 (reasoning model)
ollama run deepseek-r1
 
# Run Llama 3.3 70B
ollama run llama3.3:70b
 
# Run Qwen2.5
ollama run qwen2.5

Setting Up a Coding Agent

Cline (VS Code)

  1. Install from VS Code marketplace
  2. Configure with your API key (OpenRouter, Anthropic, etc.)
  3. Or use local models via Ollama

Aider (Terminal)

# Install
pip install aider-chat
 
# Run with Claude
aider --model claude-3.5-sonnet
 
# Run with local Ollama model
aider --model ollama/llama3.3:70b

Building with Frameworks

LangChain

pip install langchain langchain-openai
# Use with any LLM provider or local models

LlamaIndex

pip install llama-index
# Optimized for RAG and document integration

AutoGen

pip install pyautogen
# MIT licensed, zero fees from Microsoft

Why Open Source AI in 2025?

1. Performance Parity

Open source models now compete at the highest levels, with DeepSeek-R1 matching o1-pro and Qwen3 beating GPT-4o on many benchmarks.

2. Cost Efficiency

86% cost savings on average, with quality models at 15-60/M for proprietary alternatives.

3. Privacy and Control

Run models locally with Ollama or in your own infrastructure. Your data never leaves your control.

4. No Vendor Lock-in

Open source frameworks and models provide freedom to switch providers, customize behavior, and avoid proprietary dependencies.

5. Rapid Innovation

Community-driven development means features and improvements appear quickly. The gap is closing at an accelerating rate.

6. Enterprise Compliance

Many Fortune 500 companies now use open source AI tools like Cline specifically for compliance requirements.

Cost Comparison

Proprietary Models (GPT-4, Claude)

  • $15-60 per million tokens
  • Vendor lock-in
  • Data sent to third parties
  • Rate limits and usage restrictions

Open Source via Cloud (OpenRouter, Groq)

  • $0.17-0.42 per million tokens (86% savings)
  • Provider flexibility
  • Performance comparable to proprietary
  • Typical coding session: $1-3/hour

Open Source Local (Ollama)

  • $0 per token (infrastructure only)
  • Complete privacy
  • Offline capability
  • One-time hardware investment

Startup/Prototype

  • Models: Llama 3.3 or Qwen3 via OpenRouter
  • Coding: Cline with cloud models
  • Framework: LangChain
  • Cost: Minimal, pay-as-you-go

Enterprise/Production

  • Models: DeepSeek V3.2, Llama 4, Qwen3-235B
  • Coding: Cline (client-side, BYOK)
  • Framework: LangChain or LlamaIndex
  • Infrastructure: Groq (speed) + OpenRouter (redundancy)
  • Observability: LangSmith or Langfuse

Privacy-Critical

  • Models: Ollama with local models
  • Coding: Continue.dev or Aider (local mode)
  • Framework: Self-hosted LangChain
  • Infrastructure: Air-gapped deployment
  • Cost: Hardware only, zero API fees

Research/Experimentation

  • Models: Ollama for all latest open source
  • Coding: Multiple agents (Cline, Aider, Continue)
  • Framework: All options (compare and learn)
  • Cost: Minimal (primarily local)

Technology Adoption Timeline

November 2024

Anthropic releases Model Context Protocol (MCP) as open standard

Early 2025

  • Performance gap narrows to 7 points
  • DeepSeek-R1 matches o1-pro
  • Qwen3 beats GPT-4o on benchmarks
  • Ollama reaches 95K+ GitHub stars

March 2025

OpenAI announces MCP adoption across all products

April 2025

Llama 4 released under Community License

Q2 2026 (Projected)

Open source models reach full parity with proprietary alternatives

Key Statistics

  • 1M+ developers using LangChain
  • 95K+ GitHub stars for Ollama
  • 100K+ GitHub stars for LangChain
  • 86% cost savings vs proprietary models
  • 7 point gap (down from 15-20 in October 2024)
  • 18x faster inference with Groq LPU vs traditional GPUs
  • 100+ models available in Ollama
  • $0.17-0.42/M tokens for top open source models

Future Outlook

The trajectory is clear and accelerating:

  1. Q2 2026: Projected parity with proprietary models
  2. MCP Standardization: Universal adoption by late 2025
  3. Edge Deployment: Local inference expanding to mobile and edge devices
  4. Specialized Hardware: More LPU-style chips for optimized inference
  5. Framework Convergence: Best features spreading across ecosystems

Open source AI has moved from “promising alternative” to “default choice” for most use cases in 2025.

Contributing

This documentation is maintained as a living resource. To contribute:

  1. Research developments in open source AI
  2. Update relevant documentation files
  3. Submit pull requests with sources

License

This documentation is provided for educational and informational purposes.

Resources

Last Updated: November 2025

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