cascadeflow

Smart AI model cascading for cost optimization

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cascadeflow is an intelligent AI model cascading library that dynamically selects the optimal model for each query or tool call through speculative execution. It helps reduce API costs by 40-85% through intelligent model cascading and speculative execution with automatic per-query cost tracking. The tool is based on the research that shows 40-70% of queries don't require slow, expensive flagship models, and domain-specific smaller models often outperform large general-purpose models on specialized tasks. cascadeflow automatically escalates to flagship models for advanced reasoning when needed. It supports multiple providers, low latency, cost control, and transparency, and can be used for edge and local-hosted AI deployment.

README:

Smart AI model cascading for cost optimization

Cost Savings Benchmarks: 69% (MT-Bench), 93% (GSM8K), 52% (MMLU) savings, retaining 96% GPT-5 quality.

Python • TypeScript • LangChain • n8n • 📖 Docs • 💡 Examples

Stop Bleeding Money on AI Calls. Cut Costs 30-65% in 3 Lines of Code.

40-70% of text prompts and 20-60% of agent calls don't need expensive flagship models. You're overpaying every single day.

cascadeflow fixes this with intelligent model cascading, available in Python and TypeScript.

pip install cascadeflow

npm install @cascadeflow/core

Why cascadeflow?

cascadeflow is an intelligent AI model cascading library that dynamically selects the optimal model for each query or tool call through speculative execution. It's based on the research that 40-70% of queries don't require slow, expensive flagship models, and domain-specific smaller models often outperform large general-purpose models on specialized tasks. For the remaining queries that need advanced reasoning, cascadeflow automatically escalates to flagship models if needed.

Use Cases

Use cascadeflow for:

Cost Optimization. Reduce API costs by 40-85% through intelligent model cascading and speculative execution with automatic per-query cost tracking.
Cost Control and Transparency. Built-in telemetry for query, model, and provider-level cost tracking with configurable budget limits and programmable spending caps.
Low Latency & Speed Optimization. Sub-2ms framework overhead with fast provider routing (Groq sub-50ms). Cascade simple queries to fast models while reserving expensive models for complex reasoning, achieving 2-10x latency reduction overall. (use preset speed_optimized)
Multi-Provider Flexibility. Unified API across OpenAI, Anthropic, Groq, Ollama, vLLM, Together, and Hugging Face, plus 17+ providers via the Vercel AI SDK with automatic provider detection and zero vendor lock-in. Optional LiteLLM integration for 100+ additional providers, plus LangChain integration for LCEL chains and tools.
Edge & Local-Hosted AI Deployment. Use best of both worlds: handle most queries with local models (vLLM, Ollama), then automatically escalate complex queries to cloud providers only when needed.

ℹ️ Note: SLMs (under 10B parameters) are sufficiently powerful for 60-70% of agentic AI tasks. Research paper

How cascadeflow Works

cascadeflow uses speculative execution with quality validation:

Speculatively executes small, fast models first - optimistic execution ($0.15-0.30/1M tokens)
Validates quality of responses using configurable thresholds (completeness, confidence, correctness)
Dynamically escalates to larger models only when quality validation fails ($1.25-3.00/1M tokens)
Learns patterns to optimize future cascading decisions and domain specific routing

Zero configuration. Works with YOUR existing models (7 Providers currently supported).

In practice, 60-70% of queries are handled by small, efficient models (8-20x cost difference) without requiring escalation

Result: 40-85% cost reduction, 2-10x faster responses, zero quality loss.

┌─────────────────────────────────────────────────────────────┐
│                      cascadeflow Stack                      │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  ┌───────────────────────────────────────────────────────┐  │
│  │  Cascade Agent                                        │  │
│  │                                                       │  │
│  │  Orchestrates the entire cascade execution            │  │
│  │  • Query routing & model selection                    │  │
│  │  • Drafter -> Verifier coordination                   │  │
│  │  • Cost tracking & telemetry                          │  │
│  └───────────────────────────────────────────────────────┘  │
│                          ↓                                  │
│  ┌───────────────────────────────────────────────────────┐  │
│  │  Domain Pipeline                                      │  │
│  │                                                       │  │
│  │  Automatic domain classification                      │  │
│  │  • Rule-based detection (CODE, MATH, DATA, etc.)      │  │
│  │  • Optional ML semantic classification                │  │
│  │  • Domain-optimized pipelines & model selection       │  │
│  └───────────────────────────────────────────────────────┘  │
│                          ↓                                  │
│  ┌───────────────────────────────────────────────────────┐  │
│  │  Quality Validation Engine                            │  │
│  │                                                       │  │
│  │  Multi-dimensional quality checks                     │  │
│  │  • Length validation (too short/verbose)              │  │
│  │  • Confidence scoring (logprobs analysis)             │  │
│  │  • Format validation (JSON, structured output)        │  │
│  │  • Semantic alignment (intent matching)               │  │
│  └───────────────────────────────────────────────────────┘  │
│                          ↓                                  │
│  ┌───────────────────────────────────────────────────────┐  │
│  │  Cascading Engine (<2ms overhead)                     │  │
│  │                                                       │  │
│  │  Smart model escalation strategy                      │  │
│  │  • Try cheap models first (speculative execution)     │  │
│  │  • Validate quality instantly                         │  │
│  │  • Escalate only when needed                          │  │
│  │  • Automatic retry & fallback                         │  │
│  └───────────────────────────────────────────────────────┘  │
│                          ↓                                  │
│  ┌───────────────────────────────────────────────────────┐  │
│  │  Provider Abstraction Layer                           │  │
│  │                                                       │  │
│  │  Unified interface for 7+ providers                   │  │
│  │  • OpenAI • Anthropic • Groq • Ollama                 │  │
│  │  • Together • vLLM • HuggingFace • LiteLLM            │  │
│  └───────────────────────────────────────────────────────┘  │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Quick Start

Drop-In Gateway (Existing Apps)

If you already have an app using the OpenAI or Anthropic APIs and want the fastest integration, run the gateway and point your existing client at it:

python -m cascadeflow.server --mode auto --port 8084

Docs: docs/guides/gateway.md

Python

pip install cascadeflow[all]

from cascadeflow import CascadeAgent, ModelConfig

# Define your cascade - try cheap model first, escalate if needed
agent = CascadeAgent(models=[
    ModelConfig(name="gpt-4o-mini", provider="openai", cost=0.000375),  # Draft model (~$0.375/1M tokens)
    ModelConfig(name="gpt-5", provider="openai", cost=0.00562),         # Verifier model (~$5.62/1M tokens)
])

# Run query - automatically routes to optimal model
result = await agent.run("What's the capital of France?")

print(f"Answer: {result.content}")
print(f"Model used: {result.model_used}")
print(f"Cost: ${result.total_cost:.6f}")

💡 Optional: Use ML-based Semantic Quality Validation

For advanced use cases, you can add ML-based semantic similarity checking to validate that responses align with queries.

Step 1: Install the optional ML package:

pip install cascadeflow[ml]  # Adds semantic similarity via FastEmbed (~80MB model)

Step 2: Use semantic quality validation:

from cascadeflow.quality.semantic import SemanticQualityChecker

# Initialize semantic checker (downloads model on first use)
checker = SemanticQualityChecker(
    similarity_threshold=0.5,  # Minimum similarity score (0-1)
    toxicity_threshold=0.7     # Maximum toxicity score (0-1)
)

# Validate query-response alignment
query = "Explain Python decorators"
response = "Decorators are a way to modify functions using @syntax..."

result = checker.validate(query, response, check_toxicity=True)

print(f"Similarity: {result.similarity:.2%}")
print(f"Passed: {result.passed}")
print(f"Toxic: {result.is_toxic}")

What you get:

🎯 Semantic similarity scoring (query ↔ response alignment)
🛡️ Optional toxicity detection
🔄 Automatic model download and caching
🚀 Fast inference (~100ms per check)

Full example: See semantic_quality_domain_detection.py

⚠️ GPT-5 Note: GPT-5 streaming requires organization verification. Non-streaming works for all users. Verify here if needed (~15 min). Basic cascadeflow examples work without - GPT-5 is only called when needed (typically 20-30% of requests).

📖 Learn more: Python Documentation | Quickstart Guide | Providers Guide

TypeScript

npm install @cascadeflow/core

import { CascadeAgent, ModelConfig } from '@cascadeflow/core';

// Same API as Python!
const agent = new CascadeAgent({
  models: [
    { name: 'gpt-4o-mini', provider: 'openai', cost: 0.000375 },
    { name: 'gpt-4o', provider: 'openai', cost: 0.00625 },
  ],
});

const result = await agent.run('What is TypeScript?');
console.log(`Model: ${result.modelUsed}`);
console.log(`Cost: $${result.totalCost}`);
console.log(`Saved: ${result.savingsPercentage}%`);

💡 Optional: ML-based Semantic Quality Validation

For advanced quality validation, enable ML-based semantic similarity checking to ensure responses align with queries.

Step 1: Install the optional ML packages:

npm install @cascadeflow/ml @xenova/transformers

Step 2: Enable semantic validation in your cascade:

import { CascadeAgent, SemanticQualityChecker } from '@cascadeflow/core';

const agent = new CascadeAgent({
  models: [
    { name: 'gpt-4o-mini', provider: 'openai', cost: 0.000375 },
    { name: 'gpt-4o', provider: 'openai', cost: 0.00625 },
  ],
  quality: {
    threshold: 0.40,                    // Traditional confidence threshold
    requireMinimumTokens: 5,            // Minimum response length
    useSemanticValidation: true,        // Enable ML validation
    semanticThreshold: 0.5,             // 50% minimum similarity
  },
});

// Responses now validated for semantic alignment
const result = await agent.run('Explain TypeScript generics');

Step 3: Or use semantic validation directly:

import { SemanticQualityChecker } from '@cascadeflow/core';

const checker = new SemanticQualityChecker();

if (await checker.isAvailable()) {
  const result = await checker.checkSimilarity(
    'What is TypeScript?',
    'TypeScript is a typed superset of JavaScript.'
  );

  console.log(`Similarity: ${(result.similarity * 100).toFixed(1)}%`);
  console.log(`Passed: ${result.passed}`);
}

What you get:

🎯 Query-response semantic alignment detection
🚫 Off-topic response filtering
📦 BGE-small-en-v1.5 embeddings (~40MB, auto-downloads)
⚡ Fast CPU inference (~50-100ms with caching)
🔄 Request-scoped caching (50% latency reduction)
🌐 Works in Node.js, Browser, and Edge Functions

Example: semantic-quality.ts

📖 Learn more: TypeScript Documentation | Quickstart Guide | Node.js Examples | Browser/Edge Guide

🔄 Migration Example

Migrate in 5min from direct Provider implementation to cost savings and full cost control and transparency.

Before (Standard Approach)

Cost: $0.000113, Latency: 850ms

# Using expensive model for everything
result = openai.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What's 2+2?"}]
)

After (With cascadeflow)

Cost: $0.000007, Latency: 234ms

agent = CascadeAgent(models=[
    ModelConfig(name="gpt-4o-mini", provider="openai", cost=0.000375),
    ModelConfig(name="gpt-4o", provider="openai", cost=0.00625),
])

result = await agent.run("What's 2+2?")

🔥 Saved: $0.000106 (94% reduction), 3.6x faster

📊 Learn more: Cost Tracking Guide | Production Best Practices | Performance Optimization

n8n Integration

Use cascadeflow in n8n workflows for no-code AI automation with automatic cost optimization!

Installation

Open n8n
Go to Settings → Community Nodes
Search for: @cascadeflow/n8n-nodes-cascadeflow
Click Install

Quick Start

cascadeflow is a Language Model sub-node that connects two AI Chat Model nodes (drafter + verifier) and intelligently cascades between them:

Setup:

Add two AI Chat Model nodes (cheap drafter + powerful verifier)
Add cascadeflow node and connect both models
Connect cascadeflow to Basic LLM Chain or Chain nodes
Check Logs tab to see cascade decisions in real-time!

Result: 40-85% cost savings in your n8n workflows!

Features:

✅ Works with any AI Chat Model node (OpenAI, Anthropic, Ollama, Azure, etc.)
✅ Mix providers (e.g., Ollama drafter + GPT-4o verifier)
✅ Includes a CascadeFlow Agent node for tool-based agent workflows (drafter/verifier + tools + trace)
✅ Real-time flow visualization in Logs tab
✅ Detailed metrics: confidence scores, latency, cost savings

🔌 Learn more: n8n Integration Guide | n8n Documentation

LangChain Integration

Use cascadeflow with LangChain for intelligent model cascading with full LCEL, streaming, and tools support!

Installation

TypeScript

npm install @cascadeflow/langchain @langchain/core @langchain/openai

Python

pip install cascadeflow[langchain]

Quick Start

TypeScript - Drop-in replacement for any LangChain chat model

import { ChatOpenAI } from '@langchain/openai';
import { ChatAnthropic } from '@langchain/anthropic';
import { withCascade } from '@cascadeflow/langchain';

const cascade = withCascade({
  drafter: new ChatOpenAI({ modelName: 'gpt-5-mini' }),      // $0.25/$2 per 1M tokens
  verifier: new ChatAnthropic({ modelName: 'claude-sonnet-4-5' }),  // $3/$15 per 1M tokens
  qualityThreshold: 0.8, // 80% queries use drafter
});

// Use like any LangChain chat model
const result = await cascade.invoke('Explain quantum computing');

// Optional: Enable LangSmith tracing (see https://smith.langchain.com)
// Set LANGSMITH_API_KEY, LANGSMITH_PROJECT, LANGSMITH_TRACING=true

// Or with LCEL chains
const chain = prompt.pipe(cascade).pipe(new StringOutputParser());

Python - Drop-in replacement for any LangChain chat model

from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic
from cascadeflow.integrations.langchain import CascadeFlow

cascade = CascadeFlow(
    drafter=ChatOpenAI(model="gpt-4o-mini"),      # $0.15/$0.60 per 1M tokens
    verifier=ChatAnthropic(model="claude-sonnet-4-5"),  # $3/$15 per 1M tokens
    quality_threshold=0.8,  # 80% queries use drafter
)

# Use like any LangChain chat model
result = await cascade.ainvoke("Explain quantum computing")

# Optional: Enable LangSmith tracing (see https://smith.langchain.com)
# Set LANGSMITH_API_KEY, LANGSMITH_PROJECT, LANGSMITH_TRACING=true

# Or with LCEL chains
chain = prompt | cascade | StrOutputParser()

💡 Optional: Cost Tracking with Callbacks (Python)

Track costs, tokens, and cascade decisions with LangChain-compatible callbacks:

from cascadeflow.integrations.langchain.langchain_callbacks import get_cascade_callback

# Track costs similar to get_openai_callback()
with get_cascade_callback() as cb:
    response = await cascade.ainvoke("What is Python?")

    print(f"Total cost: ${cb.total_cost:.6f}")
    print(f"Drafter cost: ${cb.drafter_cost:.6f}")
    print(f"Verifier cost: ${cb.verifier_cost:.6f}")
    print(f"Total tokens: {cb.total_tokens}")
    print(f"Successful requests: {cb.successful_requests}")

Features:

🎯 Compatible with get_openai_callback() pattern
💰 Separate drafter/verifier cost tracking
📊 Token usage (including streaming)
🔄 Works with LangSmith tracing
⚡ Near-zero overhead

Full example: See langchain_cost_tracking.py

💡 Optional: Model Discovery & Analysis Helpers (TypeScript)

For discovering optimal cascade pairs from your existing LangChain models, use the built-in discovery helpers:

import {
  discoverCascadePairs,
  findBestCascadePair,
  analyzeModel,
  validateCascadePair
} from '@cascadeflow/langchain';

// Your existing LangChain models (configured with YOUR API keys)
const myModels = [
  new ChatOpenAI({ model: 'gpt-3.5-turbo' }),
  new ChatOpenAI({ model: 'gpt-4o-mini' }),
  new ChatOpenAI({ model: 'gpt-4o' }),
  new ChatAnthropic({ model: 'claude-3-haiku' }),
  // ... any LangChain chat models
];

// Quick: Find best cascade pair
const best = findBestCascadePair(myModels);
console.log(`Best pair: ${best.analysis.drafterModel} → ${best.analysis.verifierModel}`);
console.log(`Estimated savings: ${best.estimatedSavings}%`);

// Use it immediately
const cascade = withCascade({
  drafter: best.drafter,
  verifier: best.verifier,
});

// Advanced: Discover all valid pairs
const pairs = discoverCascadePairs(myModels, {
  minSavings: 50,              // Only pairs with ≥50% savings
  requireSameProvider: false,  // Allow cross-provider cascades
});

// Validate specific pair
const validation = validateCascadePair(drafter, verifier);
console.log(`Valid: ${validation.valid}`);
console.log(`Warnings: ${validation.warnings}`);

What you get:

🔍 Automatic discovery of optimal cascade pairs from YOUR models
💰 Estimated cost savings calculations
⚠️ Validation warnings for misconfigured pairs
📊 Model tier analysis (drafter vs verifier candidates)

Full example: See model-discovery.ts

Features:

✅ Full LCEL support (pipes, sequences, batch)
✅ Streaming with pre-routing
✅ Tool calling and structured output
✅ LangSmith cost tracking metadata
✅ Cost tracking callbacks (Python)
✅ Works with all LangChain features

🦜 Learn more: LangChain Integration Guide | TypeScript Package | Python Examples

Resources

Examples

Python Examples:

Basic Examples - Get started quickly

Example	Description	Link
Basic Usage	Simple cascade setup with OpenAI models	View
Preset Usage	Use built-in presets for quick setup	View
Multi-Provider	Mix multiple AI providers in one cascade	View
Reasoning Models	Use reasoning models (o1/o3, Claude 3.7, DeepSeek-R1)	View
Tool Execution	Function calling and tool usage	View
Streaming Text	Stream responses from cascade agents	View
Cost Tracking	Track and analyze costs across queries	View

Advanced Examples - Production & customization

Example	Description	Link
Production Patterns	Best practices for production deployments	View
FastAPI Integration	Integrate cascades with FastAPI	View
Streaming Tools	Stream tool calls and responses	View
Batch Processing	Process multiple queries efficiently	View
Multi-Step Cascade	Build complex multi-step cascades	View
Edge Device	Run cascades on edge devices with local models	View
vLLM Example	Use vLLM for local model deployment	View
Multi-Instance Ollama	Run draft/verifier on separate Ollama instances	View
Multi-Instance vLLM	Run draft/verifier on separate vLLM instances	View
Custom Cascade	Build custom cascade strategies	View
Custom Validation	Implement custom quality validators	View
User Budget Tracking	Per-user budget enforcement and tracking	View
User Profile Usage	User-specific routing and configurations	View
Rate Limiting	Implement rate limiting for cascades	View
Guardrails	Add safety and content guardrails	View
Cost Forecasting	Forecast costs and detect anomalies	View
Semantic Quality Detection	ML-based domain and quality detection	View
Profile Database Integration	Integrate user profiles with databases	View
LangChain Basic	Simple LangChain cascade setup	View
LangChain Streaming	Stream responses with LangChain	View
LangChain Model Discovery	Discover and analyze LangChain models	View
LangChain LangSmith	Cost tracking with LangSmith integration	View
LangChain Cost Tracking	Track costs with callback handlers	View
LangChain Benchmark	Comprehensive cascade benchmarking	View

TypeScript Examples:

Basic Examples - Get started quickly

Example	Description	Link
Basic Usage	Simple cascade setup (Node.js)	View
Tool Calling	Function calling with tools (Node.js)	View
Multi-Provider	Mix providers in TypeScript (Node.js)	View
Reasoning Models	Use reasoning models (o1/o3, Claude 3.7, DeepSeek-R1)	View
Cost Tracking	Track and analyze costs across queries	View
Semantic Quality	ML-based semantic validation with embeddings	View
Streaming	Stream responses in TypeScript	View

Advanced Examples - Production, edge & LangChain

Example	Description	Link
Production Patterns	Production best practices (Node.js)	View
Multi-Instance Ollama	Run draft/verifier on separate Ollama instances	View
Multi-Instance vLLM	Run draft/verifier on separate vLLM instances	View
Browser/Edge	Vercel Edge runtime example	View
LangChain Basic	Simple LangChain cascade setup	View
LangChain Cross-Provider	Haiku → GPT-5 with PreRouter	View
LangChain LangSmith	Cost tracking with LangSmith	View
LangChain Cost Tracking	Compare cascadeflow vs LangSmith cost tracking	View

📂 View All Python Examples → | View All TypeScript Examples →

Documentation

Getting Started - Core concepts and basics

Guide	Description	Link
Quickstart	Get started with cascadeflow in 5 minutes	Read
Providers Guide	Configure and use different AI providers	Read
Presets Guide	Using and creating custom presets	Read
Streaming Guide	Stream responses from cascade agents	Read
Tools Guide	Function calling and tool usage	Read
Cost Tracking	Track and analyze API costs	Read

Advanced Topics - Production, customization & integrations

Guide	Description	Link
Production Guide	Best practices for production deployments	Read
Performance Guide	Optimize cascade performance and latency	Read
Custom Cascade	Build custom cascade strategies	Read
Custom Validation	Implement custom quality validators	Read
Edge Device	Deploy cascades on edge devices	Read
Browser Cascading	Run cascades in the browser/edge	Read
FastAPI Integration	Integrate with FastAPI applications	Read
LangChain Integration	Use cascadeflow with LangChain	Read
n8n Integration	Use cascadeflow in n8n workflows	Read

📚 View All Documentation →

Features

Feature	Benefit
🎯 Speculative Cascading	Tries cheap models first, escalates intelligently
💰 40-85% Cost Savings	Research-backed, proven in production
⚡ 2-10x Faster	Small models respond in <50ms vs 500-2000ms
⚡ Low Latency	Sub-2ms framework overhead, negligible performance impact
🔄 Mix Any Providers	OpenAI, Anthropic, Groq, Ollama, vLLM, Together + LiteLLM (optional) + LangChain integration
👤 User Profile System	Per-user budgets, tier-aware routing, enforcement callbacks
✅ Quality Validation	Automatic checks + semantic similarity (optional ML, ~80MB, CPU)
🎨 Cascading Policies	Domain-specific pipelines, multi-step validation strategies
🧠 Domain Understanding	15 domains auto-detected (code, medical, legal, finance, math, etc.), routes to specialists
🤖 Drafter/Validator Pattern	20-60% savings for agent/tool systems
🔧 Tool Calling Support	Universal format, works across all providers
📊 Cost Tracking	Built-in analytics + OpenTelemetry export (vendor-neutral)
🚀 3-Line Integration	Zero architecture changes needed
🏭 Production Ready	Streaming, batch processing, tool handling, reasoning model support, caching, error recovery, anomaly detection

License

MIT © see LICENSE file.

Free for commercial use. Attribution appreciated but not required.

Contributing

We ❤️ contributions!

📝 Contributing Guide - Python & TypeScript development setup

Roadmap

Cascade Profiler - Analyzes your AI API logs to calculate cost savings potential and generate optimized cascadeflow configurations automatically
User Tier Management - Cost controls and limits per user tier with advanced routing
Semantic Quality Validators - Optional lightweight local quality scoring (200MB CPU model, no external API calls)
Code Complexity Detection - Dynamic cascading based on task complexity analysis
Domain Aware Cascading - Multi-stage pipelines tailored to specific domains
Benchmark Reports - Automated performance and cost benchmarking

Support

📖 GitHub Discussions - Searchable Q&A
🐛 GitHub Issues - Bug reports & feature requests
📧 Email Support - Direct support

Citation

If you use cascadeflow in your research or project, please cite:

@software{cascadeflow2025,
  author = {Lemony Inc., Sascha Buehrle and Contributors},
  title = {cascadeflow: Smart AI model cascading for cost optimization},
  year = {2025},
  publisher = {GitHub},
  url = {https://github.com/lemony-ai/cascadeflow}
}

Ready to cut your AI costs by 40-85%?

pip install cascadeflow

npm install @cascadeflow/core

Read the Docs • View Python Examples • View TypeScript Examples • Join Discussions

About

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⭐ Star us on GitHub if cascadeflow helps you save money!

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Lynkr is a self-hosted proxy server that unlocks various AI coding tools like Claude Code CLI, Cursor IDE, and Codex Cli. It supports multiple LLM providers such as Databricks, AWS Bedrock, OpenRouter, Ollama, llama.cpp, Azure OpenAI, Azure Anthropic, OpenAI, and LM Studio. Lynkr offers cost reduction, local/private execution, remote or local connectivity, zero code changes, and enterprise-ready features. It is perfect for developers needing provider flexibility, cost control, self-hosted AI with observability, local model execution, and cost reduction strategies.

github

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mcp-memory-service

The MCP Memory Service is a universal memory service designed for AI assistants, providing semantic memory search and persistent storage. It works with various AI applications and offers fast local search using SQLite-vec and global distribution through Cloudflare. The service supports intelligent memory management, universal compatibility with AI tools, flexible storage options, and is production-ready with cross-platform support and secure connections. Users can store and recall memories, search by tags, check system health, and configure the service for Claude Desktop integration and environment variables.

github

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mimiclaw

MimiClaw is a pocket AI assistant that runs on a $5 chip, specifically designed for the ESP32-S3 board. It operates without Linux or Node.js, using pure C language. Users can interact with MimiClaw through Telegram, enabling it to handle various tasks and learn from local memory. The tool is energy-efficient, running on USB power 24/7. With MimiClaw, users can have a personal AI assistant on a chip the size of a thumb, making it convenient and accessible for everyday use.

github

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aippt_PresentationGen

A SpringBoot web application that generates PPT files using a llm. The tool preprocesses single-page templates and dynamically combines them to generate PPTX files with text replacement functionality. It utilizes technologies such as SpringBoot, MyBatis, MySQL, Redis, WebFlux, Apache POI, Aspose Slides, OSS, and Vue2. Users can deploy the tool by configuring various parameters in the application.yml file and setting up necessary resources like MySQL, OSS, and API keys. The tool also supports integration with open-source image libraries like Unsplash for adding images to the presentations.

github

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starknet-agentic

Open-source stack for giving AI agents wallets, identity, reputation, and execution rails on Starknet. `starknet-agentic` is a monorepo with Cairo smart contracts for agent wallets, identity, reputation, and validation, TypeScript packages for MCP tools, A2A integration, and payment signing, reusable skills for common Starknet agent capabilities, and examples and docs for integration. It provides contract primitives + runtime tooling in one place for integrating agents. The repo includes various layers such as Agent Frameworks / Apps, Integration + Runtime Layer, Packages / Tooling Layer, Cairo Contract Layer, and Starknet L2. It aims for portability of agent integrations without giving up Starknet strengths, with a cross-chain interop strategy and skills marketplace. The repository layout consists of directories for contracts, packages, skills, examples, docs, and website.

github

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morgana-form

MorGana Form is a full-stack form builder project developed using Next.js, React, TypeScript, Ant Design, PostgreSQL, and other technologies. It allows users to quickly create and collect data through survey forms. The project structure includes components, hooks, utilities, pages, constants, Redux store, themes, types, server-side code, and component packages. Environment variables are required for database settings, NextAuth login configuration, and file upload services. Additionally, the project integrates an AI model for form generation using the Ali Qianwen model API.

github

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pluely

Pluely is a versatile and user-friendly tool for managing tasks and projects. It provides a simple interface for creating, organizing, and tracking tasks, making it easy to stay on top of your work. With features like task prioritization, due date reminders, and collaboration options, Pluely helps individuals and teams streamline their workflow and boost productivity. Whether you're a student juggling assignments, a professional managing multiple projects, or a team coordinating tasks, Pluely is the perfect solution to keep you organized and efficient.

github

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claudex

Claudex is an open-source, self-hosted Claude Code UI that runs entirely on your machine. It provides multiple sandboxes, allows users to use their own plans, offers a full IDE experience with VS Code in the browser, and is extensible with skills, agents, slash commands, and MCP servers. Users can run AI agents in isolated environments, view and interact with a browser via VNC, switch between multiple AI providers, automate tasks with Celery workers, and enjoy various chat features and preview capabilities. Claudex also supports marketplace plugins, secrets management, integrations like Gmail, and custom instructions. The tool is configured through providers and supports various providers like Anthropic, OpenAI, OpenRouter, and Custom. It has a tech stack consisting of React, FastAPI, Python, PostgreSQL, Celery, Redis, and more.

github

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dewhale

Dewhale is a GitHub-Powered AI tool designed for effortless development. It utilizes prompt engineering techniques under the GPT-4 model to issue commands, allowing users to generate code with lower usage costs and easy customization. The tool seamlessly integrates with GitHub, providing version control, code review, and collaborative features. Users can join discussions on the design philosophy of Dewhale and explore detailed instructions and examples for setting up and using the tool.

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ai-guide

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

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classifai

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

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chatbot-ui

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

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BricksLLM

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

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uAgents

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

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griptape

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.

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