ashishpatel26

ashishpatel26 / omnicache-ai

Public

Unified multi-layer caching library for AI/agent pipelines — LangChain, LangGraph, AutoGen, CrewAI, Agno, A2A

ashishpatel26.github.ioomnicache-ai agent agno aiagents autogen caching-memory
10
3
100% credibility
Found Mar 23, 2026 at 10 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

OmniCache-AI is a Python library that adds multi-layer caching to AI agent workflows across popular frameworks to reuse computations and reduce latency and costs.

How It Works

1
📰 Discover OmniCache-AI

You hear about a helpful tool that makes AI conversations faster and cheaper by remembering repeated questions.

2
📥 Add it to your project

You easily bring this remembering feature into the app you're building for chatting with AI.

3
🔗 Connect your AI helpers

You link it to your AI thinking steps so it starts saving common answers and calculations.

4
⚡ See instant speedups

The first chat takes normal time, but the next identical one pops back immediately, feeling magical.

5
🧠 Handle similar questions too

You turn on smart matching so even slightly different questions pull from saved wisdom.

6
🌐 Share across your setup

You make the memory available everywhere your app runs, keeping everything in sync.

💰 Save time and money forever

Your AI app now runs super efficiently, cutting wait times and costs with every use.

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AI-Generated Review

What is omnicache-ai?

Omnicache-ai is a Python caching library that unifies multi-layer storage for AI/agent pipelines, handling LLM responses, embeddings, retrievals, context, and semantic similarity matches. It plugs into frameworks like LangChain, LangGraph, AutoGen, CrewAI, Agno, and A2A to eliminate redundant API calls, slashing latency and token costs in agent workflows. Drop it in via adapters or middleware, and identical operations return instantly from backends like memory, disk, Redis, or vector stores.

Why is it gaining traction?

It stands out with framework-agnostic adapters that require zero code changes, plus semantic caching via cosine similarity for near-dupe queries and tag-based invalidation for safe purges. Developers love the cookbook with 40+ runnable recipes across setups, from single-process testing to distributed Redis shares. In a world of siloed github unified tools, this delivers omnicache-style consistency without custom hacks.

Who should use this?

AI engineers optimizing agent pipelines in production, especially those chaining LangChain retrievals with CrewAI crews or LangGraph checkpointers. Ideal for teams battling high LLM bills on repeated user queries, or scaling multi-agent systems like Agno risk analyzers and A2A planners across services.

Verdict

Grab it now if you're in ai/agent caching hell—install via git+https://github.com/ashishpatel26/omnicache-ai.git and test the cookbook. At 10 stars and 1.0% credibility, it's early (PyPI soon), but polished docs and MIT license make it low-risk for prototypes; watch for community growth.

(198 words)

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