Overview of the LLaMA-Omni2 Architecture

LLaMA-Omni2 encompasses models ranging from 0.5B to 14B parameters, each built atop the Qwen2.5-Instruct series. The architecture consists of:

• Speech Encoder: Utilizes Whisper-large-v3 to transform input speech into token-level acoustic representations.
• Speech Adapter: Processes encoder outputs using a downsampling layer and a feed-forward network to align with the language model’s input space.
• Core LLM: The Qwen2.5 models serve as the main reasoning engine.
• Streaming TTS Decoder: Converts LLM outputs into speech tokens using an autoregressive Transformer and then generates mel spectrograms through a causal flow matching model inspired by CosyVoice2.

A gating mechanism fuses LLM hidden states with textual embeddings before speech synthesis, enhancing contextual fidelity in the generated audio.

Streaming Generation with Read-Write Scheduling

The model adopts a read-write strategy to facilitate streaming output. Specifically, for every R tokens produced by the LLM, W speech tokens are generated. This enables synchronized textual and acoustic generation, minimizing latency without compromising fluency.

Empirical findings suggest that setting R = 3 and W = 10 provides a favorable trade-off between latency (~583 ms), alignment (ASR-WER: 3.26), and perceptual quality (UTMOS: 4.19).

Training Approach

Despite achieving competitive performance, LLaMA-Omni2 is trained on a relatively compact corpus—200K multi-turn speech-to-speech dialogue samples. These samples are synthesized from instruction-following text datasets (Alpaca, UltraChat), with diverse input voices and a consistent output voice generated using FishSpeech and CosyVoice2 models.

Training is executed in two stages:

• Stage I: Independently optimizes the speech-to-text and text-to-speech modules.
• Stage II: Fine-tunes the speech-to-speech generation path, including the gating and autoregressive decoding components.

Benchmark Results

The models are evaluated on spoken question answering and speech instruction following tasks using both speech-to-text (S2T) and speech-to-speech (S2S) modes.

The performance scales consistently with model size. Notably, LLaMA-Omni2-14B outperforms all baselines across tasks, even with substantially less training data than native SpeechLMs such as GLM-4-Voice.

Component Analyses

• Gate Fusion Module: Removing the gating mechanism increases ASR-WER and reduces speech quality, confirming its role in aligning textual and contextual signals.
• TTS Pretraining: Initializing the TTS model from Qwen2.5 and fine-tuning in a streaming setup yields the best performance. Training from scratch fails to converge effectively.
• Read/Write Strategies: Adjusting the R:W ratio impacts latency and quality. Larger W improves UTMOS but at the cost of response delay.

Additionally, the study demonstrates that multi-turn dialogue data is more effective than single-turn data in training speech interaction capabilities, and that performance plateaus around 200K samples.

Conclusion

LLaMA-Omni2 demonstrates that high-quality, low-latency spoken interaction with LLMs is feasible without the need for extensive pretraining on massive speech corpora. By combining modular architecture with autoregressive streaming synthesis, the system offers a practical pathway for real-time speech applications.

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The post LLMs Can Now Talk in Real-Time with Minimal Latency: Chinese Researchers Release LLaMA-Omni2, a Scalable Modular Speech Language Model appeared first on MarkTechPost.

In this tutorial, we’ll implement an AgentQL MCP server inside Claude Desktop, and use Claude’s built-in visualization capabilities to explore the data. Specifically, we’ll scrape an Amazon search results page for AI books, extracting details like price, rating, and number of reviews.

Step 1: Setting up dependencies

Node JS

We need npx to run the AgentQL server, which comes with Node.js.

• Download the latest version of Node.js from nodejs.org
• Run the installer.
• Leave all settings as default and complete the installation

Claude Desktop

Download Claude using https://claude.ai/download.

AgentQL API

Create your AgentQL API key at dev.agentql.com/api-keys and store it securely — you’ll need it later in this tutorial.

Step 2: Installing the packages

Once Node.js is installed, open your terminal and run the following command:

npm install -g agentql-mcp

Step 3: Configuring the MCP Server

Next, configure Claude to connect to your MCP server. Open the claude_desktop_config.json file located in the Claude installation directory using any text editor. If the file doesn’t exist, you can create it manually. Once opened, enter the following code:

{
    "mcpServers": {
      "agentql": {
        "command": "npx",
        "args": ["-y", "agentql-mcp"],
        "env": {
          "AGENTQL_API_KEY": "<YOUR_API_KEY>"
        }
      }
    }
  }

Replace <YOUR_API_KEY> with the key you generated.

Step 4: Running the server

Once the MCP configuration is complete, your server should appear in Claude. The AgentQL server includes a single powerful tool — extract_web_data — which takes a URL and a natural language description of the data structure you want to extract.

You can use any URL you want to scrape. For this tutorial, I used an Amazon search results page for AI books and asked Claude to visualize the extracted data. Claude provides an interactive terminal where it generates code to process and visualize the data — and you can edit that code as needed. Once the code was finalized, Claude presented a bar chart with interactive options to explore prices, ratings, review counts, and even a price vs. rating scatter plot, along with key summary statistics.

AgentQL can be used to scrape websites, and we can connect it with other servers like Notion or GitHub to automatically send structured data for documentation, tracking, or further automation.

This makes AgentQL a powerful tool for turning unstructured web content into actionable insights — all within a simple, natural language workflow.

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Agentic RAG: From Static Retrieval to Iterative Reasoning

At the center of this release is the evolution of RAG architectures. Traditional RAG pipelines typically involve static queries to vector stores followed by synthesis via large language models. However, this linear approach often fails in multi-perspective or multi-hop information retrieval.

Agentic RAG reframes the process by introducing autonomous retrieval agents that reason iteratively and adjust their behavior based on intermediate results. These agents improve retrieval precision and adaptability through:

• Context-Aware Query Expansion: Agents reformulate search queries dynamically based on evolving task context.
• Multi-Step Decomposition: Complex queries are broken into logical subtasks, each addressed in sequence.
• Adaptive Source Selection: Instead of querying a fixed vector store, agents select optimal sources contextually.
• Fact Verification: Dedicated evaluator agents validate retrieved content for consistency and grounding before synthesis.

The net result is a more intelligent RAG pipeline, capable of responding to nuanced information needs in high-stakes domains such as healthcare, legal compliance, and financial intelligence.

Rigorous Evaluation of Agent Behavior

Evaluating the performance of AI agents requires a distinct methodology from that used for static LLM outputs. Google’s framework separates agent evaluation into three primary dimensions:

1. Capability Assessment: Benchmarking the agent’s ability to follow instructions, plan, reason, and use tools. Tools like AgentBench, PlanBench, and BFCL are highlighted for this purpose.
2. Trajectory and Tool Use Analysis: Instead of focusing solely on outcomes, developers are encouraged to trace the agent’s action sequence (trajectory) and compare it to expected behavior using precision, recall, and match-based metrics.
3. Final Response Evaluation: Evaluation of the agent’s output through autoraters—LLMs acting as evaluators—and human-in-the-loop methods. This ensures that assessments include both objective metrics and human-judged qualities like helpfulness and tone.

This process enables observability across both the reasoning and execution layers of agents, which is critical for production deployments.

Scaling to Multi-Agent Architectures

As real-world systems grow in complexity, Google’s whitepaper emphasizes a shift toward multi-agent architectures, where specialized agents collaborate, communicate, and self-correct.

Key benefits include:

• Modular Reasoning: Tasks are decomposed across planner, retriever, executor, and validator agents.
• Fault Tolerance: Redundant checks and peer hand-offs increase system reliability.
• Improved Scalability: Specialized agents can be independently scaled or replaced.

Evaluation strategies adapt accordingly. Developers must track not only final task success but also coordination quality, adherence to delegated plans, and agent utilization efficiency. Trajectory analysis remains the primary lens, extended across multiple agents for system-level evaluation.

Real-World Applications: From Enterprise Automation to Automotive AI

The second half of the whitepaper focuses on real-world implementation patterns:

AgentSpace and NotebookLM Enterprise

Google’s AgentSpace is introduced as an enterprise-grade orchestration and governance platform for agent systems. It supports agent creation, deployment, and monitoring, incorporating Google Cloud’s security and IAM primitives. NotebookLM Enterprise, a research assistant framework, enables contextual summarization, multimodal interaction, and audio-based information synthesis.

Automotive AI Case Study

A highlight of the paper is a fully implemented multi-agent system within a connected vehicle context. Here, agents are designed for specialized tasks—navigation, messaging, media control, and user support—organized using design patterns such as:

• Hierarchical Orchestration: Central agent routes tasks to domain experts.
• Diamond Pattern: Responses are refined post-hoc by moderation agents.
• Peer-to-Peer Handoff: Agents detect misclassification and reroute queries autonomously.
• Collaborative Synthesis: Responses are merged across agents via a Response Mixer.
• Adaptive Looping: Agents iteratively refine results until satisfactory outputs are achieved.

This modular design allows automotive systems to balance low-latency, on-device tasks (e.g., climate control) with more resource-intensive, cloud-based reasoning (e.g., restaurant recommendations).

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Blazing Speed and Accuracy

At the heart of Parakeet TDT 0.6B’s appeal is its unmatched speed and transcription quality. The model can transcribe 60 minutes of audio in just one second, a performance that’s over 50x faster than many existing open ASR models. On Hugging Face’s Open ASR Leaderboard, Parakeet V2 achieves a 6.05% word error rate (WER)—the best-in-class among open models.

This performance represents a significant leap forward for enterprise-grade speech applications, including real-time transcription, voice-based analytics, call center intelligence, and audio content indexing.

Technical Overview

Parakeet TDT 0.6B builds on a transformer-based architecture fine-tuned with high-quality transcription data and optimized for inference on NVIDIA hardware. Here are the key highlights:

• 600M parameter encoder-decoder model
• Quantized and fused kernels for maximum inference efficiency
• Optimized for TDT (Transducer Decoder Transformer) architecture
• Supports accurate timestamp formatting, numerical formatting, and punctuation restoration
• Pioneers song-to-lyrics transcription, a rare capability in ASR models

The model’s high-speed inference is powered by NVIDIA’s TensorRT and FP8 quantization, enabling it to reach a real-time factor of RTF = 3386, meaning it processes audio 3386 times faster than real-time.

Benchmark Leadership

On the Hugging Face Open ASR Leaderboard—a standardized benchmark for evaluating speech models across public datasets—Parakeet TDT 0.6B leads with the lowest WER recorded among open-source models. This positions it well above comparable models like Whisper from OpenAI and other community-driven efforts.

This performance makes Parakeet V2 not only a leader in quality but also in deployment readiness for latency-sensitive applications.

Beyond Conventional Transcription

Parakeet is not just about speed and word error rate. NVIDIA has embedded unique capabilities into the model:

• Song-to-lyrics transcription: Unlocks transcription for sung content, expanding use cases into music indexing and media platforms.
• Numerical and timestamp formatting: Improves readability and usability in structured contexts like meeting notes, legal transcripts, and health records.
• Punctuation restoration: Enhances natural readability for downstream NLP applications.

These features elevate the quality of transcripts and reduce the burden on post-processing or human editing, especially in enterprise-grade deployments.

Strategic Implications

The release of Parakeet TDT 0.6B represents another step in NVIDIA’s strategic investment in AI infrastructure and open ecosystem leadership. With strong momentum in foundational models (e.g., Nemotron for language and BioNeMo for protein design), NVIDIA is positioning itself as a full-stack AI company—from GPUs to state-of-the-art models.

For the AI developer community, this open release could become the new foundation for building speech interfaces in everything from smart devices and virtual assistants to multimodal AI agents.

Getting Started

Parakeet TDT 0.6B is available now on Hugging Face, complete with model weights, tokenizer, and inference scripts. It runs optimally on NVIDIA GPUs with TensorRT, but support is also available for CPU environments with reduced throughput.

Whether you’re building transcription services, annotating massive audio datasets, or integrating voice into your product, Parakeet TDT 0.6B offers a compelling open-source alternative to commercial APIs.

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The document reads less like promotional material and more like an operational guidebook—emphasizing systematic evaluation, infrastructure readiness, and domain-specific integration.

1. Establish a Rigorous Evaluation Process

The first recommendation is to initiate AI adoption through well-defined evaluations (“evals”) that benchmark model performance against targeted use cases. Morgan Stanley applied this approach by assessing language translation, summarization, and knowledge retrieval in financial advisory contexts. The outcome was measurable: improved document access, reduced search latency, and broader AI adoption among advisors.

Evals not only validate models for deployment but also help refine workflows with empirical feedback loops, enhancing both safety and model alignment.

2. Integrate AI at the Product Layer

Rather than treating AI as an auxiliary function, the report stresses embedding it directly into user-facing experiences. For instance, Indeed utilized GPT-4o mini to personalize job matching, supplementing recommendations with contextual “why” statements. This increased user engagement and hiring success rates while maintaining cost-efficiency through fine-tuned, token-optimized models.

The key takeaway: model performance alone is insufficient—impact scales when AI is embedded into product logic and tailored to domain-specific needs.

3. Invest Early to Capture Compounding Returns

Klarna’s early investment in AI yielded substantial gains in operational efficiency. A GPT-powered assistant now handles two-thirds of support chats, reducing resolution times from 11 minutes to 2. The company also reports that 90% of employees are using AI in their workflows, a level of adoption that enables rapid iteration and organizational learning.

This illustrates how early engagement not only improves tooling but accelerates institutional adaptation and compound value capture.

4. Leverage Fine-Tuning for Contextual Precision

Generic models can deliver strong baselines, but domain adaptation often requires customization. Lowe’s achieved notable improvements in product search relevance by fine-tuning GPT models on their internal product data. The result: a 20% increase in tagging accuracy and a 60% improvement in error detection.

OpenAI highlights this approach as a low-latency pathway to achieve brand consistency, domain fluency, and efficiency across content generation and search tasks.

5. Empower Internal Experts, Not Just Technologists

BBVA exemplifies a decentralized AI adoption model by enabling non-technical employees to build custom GPT-based tools. In just five months, over 2,900 internal GPTs were created, addressing legal, compliance, and customer service needs without requiring engineering support.

This bottom-up strategy empowers subject-matter experts to iterate directly on their workflows, yielding more relevant solutions and reducing development cycles.

6. Streamline Developer Workflows with Dedicated Platforms

Engineering bandwidth remains a bottleneck in many organizations. Mercado Libre addressed this by building Verdi, a platform powered by GPT-4o mini, enabling 17,000 developers to prototype and deploy AI applications using natural language interfaces. The system integrates guardrails, APIs, and reusable components—allowing faster, standardized development.

The platform now supports high-value functions such as fraud detection, multilingual translation, and automated content tagging, demonstrating how internal infrastructure can accelerate AI velocity.

7. Automate Deliberately and Systematically

OpenAI emphasizes setting clear automation targets. Internally, they developed an automation platform that integrates with tools like Gmail to draft support responses and trigger actions. This system now handles hundreds of thousands of tasks monthly, reducing manual workload and enhancing responsiveness.

Their broader vision includes Operator, a browser-agent capable of autonomously interacting with web-based interfaces to complete multi-step processes—signaling a move toward agent-based, API-free automation.

Final Observations

The report concludes with a central theme: effective AI adoption requires iterative deployment, cross-functional alignment, and a willingness to refine strategies through experimentation. While the examples are enterprise-scale, the core principles—starting with evals, integrating deeply, and customizing with context—are broadly applicable.

Security and data governance are also addressed explicitly. OpenAI reiterates that enterprise data is not used for training, offers SOC 2 and CSA STAR compliance, and provides granular access control for regulated environments.

In an increasingly AI-driven landscape, OpenAI’s guide serves as both a mirror and a map—reflecting current best practices and helping enterprises chart a more structured, sustainable path forward.

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!pip install huggingface_hub
from huggingface_hub import notebook_login


notebook_login()

We install the huggingface_hub library and then import and invoke the notebook_login() function, which prompts you to authenticate your notebook session with your Hugging Face account, allowing you to seamlessly access and manage models, datasets, and other hub resources.

!pip uninstall -y torchvision


!pip install --upgrade torch torchvision --index-url https://download.pytorch.org/whl/cu118


!pip install --upgrade diffusers transformers accelerate safetensors gradio pillow

We first force-uninstalls any existing torchvision to clear potential conflicts, then reinstalls torch and torchvision from the CUDA 11.8–compatible PyTorch wheels, and finally upgrades key libraries, diffusers, transformers, accelerate, safetensors, gradio, and pillow, to ensure you have the latest versions for building and running GPU-accelerated generative pipelines and web demos.

import torch
from diffusers import StableDiffusionPipeline, StableDiffusion3Pipeline
import gradio as gr


device = "cuda" if torch.cuda.is_available() else "cpu"

We import PyTorch alongside both the Stable Diffusion v1 and v3 pipelines from the Diffusers library, as well as Gradio for building interactive demos. It then checks for CUDA availability and sets the device variable to “cuda” if a GPU is present; otherwise, it falls back to “cpu”, ensuring your models run on the optimal hardware.

pipe1 = StableDiffusionPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5",
    torch_dtype=torch.float16,
    safety_checker=None
).to(device)
pipe1.enable_attention_slicing()

We load the Stable Diffusion v1.5 model in half-precision (float16) without the built-in safety checker, transfers it to your selected device (GPU, if available), and then enables attention slicing to reduce peak VRAM usage during image generation.

pipe2 = StableDiffusionPipeline.from_pretrained(
    "stabilityai/stable-diffusion-2-base",
    torch_dtype=torch.float16,
    safety_checker=None
).to(device)
pipe2.enable_attention_slicing()

We load the Stable Diffusion v2 “base” model in 16-bit precision without the default safety filter, transfers it to your chosen device, and activates attention slicing to optimize memory usage during inference.

pipe3 = StableDiffusion3Pipeline.from_pretrained(
    "stabilityai/stable-diffusion-3-medium-diffusers",
    torch_dtype=torch.float16,
    safety_checker=None
).to(device)
pipe3.enable_attention_slicing()

We pull in Stability AI’s Stable Diffusion 3 “medium” checkpoint in 16-bit precision (skipping the built-in safety checker), transfers it to your selected device, and enables attention slicing to reduce GPU memory usage during generation.

def generate(prompt, steps, scale):
    img1 = pipe1(prompt, num_inference_steps=steps, guidance_scale=scale).images[0]
    img2 = pipe2(prompt, num_inference_steps=steps, guidance_scale=scale).images[0]
    img3 = pipe3(prompt, num_inference_steps=steps, guidance_scale=scale).images[0]
    return img1, img2, img3

Now, this function runs the same text prompt through all three loaded pipelines (pipe1, pipe2, pipe3) using the specified inference steps and guidance scale, then returns the first image from each, making it perfect for comparing outputs across Stable Diffusion v1.5, v2-base, and v3-medium.

def choose(selection):
    return f" You selected: **{selection}**"


with gr.Blocks() as demo:
    gr.Markdown("## AI Social-Post Generator with 3 Models")
    with gr.Row():
        prompt = gr.Textbox(label="Prompt", placeholder="A vibrant beach sunset…")
        steps  = gr.Slider( 1, 100, value=50, step=1,     label="Inference Steps")
        scale  = gr.Slider( 1.0, 20.0, value=7.5, step=0.1, label="Guidance Scale")
    btn = gr.Button("Generate Images")
    with gr.Row():
        out1 = gr.Image(label="Model 1: SD v1.5")
        out2 = gr.Image(label="Model 2: SD v2-base")
        out3 = gr.Image(label="Model 3: SD v3-medium")
    sel = gr.Radio(
        ["Model 1: SD v1.5","Model 2: SD v2-base","Model 3: SD v3-medium"],
        label="Select your favorite"
    )
    txt = gr.Markdown()


    btn.click(fn=generate, inputs=[prompt, steps, scale], outputs=[out1, out2, out3])
    sel.change(fn=choose, inputs=sel, outputs=txt)


demo.launch(share=True)

Finally, this Gradio app builds a three-column UI where you can enter a text prompt, adjust inference steps and guidance scale, then generate and display images from SD v1.5, v2-base, and v3-medium side by side. It also features a radio selector, allowing you to select your preferred model output, and displays a simple confirmation message when a choice is made.

In conclusion, by integrating Stability AI’s state-of-the-art diffusion architectures into an easy-to-use Gradio app, you’ve seen how effortlessly you can prototype, compare, and deploy stunning visuals that resonate on today’s platforms. From A/B-testing creative directions to automating campaign assets at scale, Stability AI provides the performance, flexibility, and vibrant community support to transform your content pipeline.

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The post A Coding Guide to Compare Three Stability AI Diffusion Models (v1.5, v2-Base & SD3-Medium) Diffusion Capabilities Side-by-Side in Google Colab Using Gradio appeared first on MarkTechPost.

Furthermore, existing surveys tend to address only specific subtopics within the broader memory landscape, such as long-context handling, long-term memory, personalization, or knowledge editing. These fragmented approaches often miss essential operations like indexing and fail to offer comprehensive overviews of memory dynamics. Additionally, most prior work does not establish a clear research scope or provide structured benchmarks and tool coverage, limiting their practical value for guiding future advancements in memory for AI systems. 

Researchers from the Chinese University, the University of Edinburgh, HKUST, and the Poisson Lab at Huawei UK R&D Ltd. present a detailed survey on memory in AI systems. They classify memory into parametric, contextual-structured, and contextual-unstructured types, distinguishing between short-term and long-term memory inspired by cognitive psychology. Six fundamental operations—consolidation, updating, indexing, forgetting, retrieval, and compression—are defined and mapped to key research areas, including long-term memory, long-context modeling, parametric modification, and multi-source integration. Based on an analysis of over 30,000 papers using the Relative Citation Index, the survey also outlines tools, benchmarks, and future directions. 

The researchers first develop a three‐part taxonomy of AI memory—parametric (model weights), contextual‐structured (e.g., indexed dialogue histories), and contextual‐unstructured (raw text or embeddings)—and distinguish short‐ versus long‐term spans. They then define six core memory operations: consolidation (storing new information), updating (modifying existing entries), indexing (organizing for fast access), forgetting (removing stale data), retrieval (fetching relevant content), and compression (distilling memories). To ground this framework, they mined over 30,000 top‐tier AI papers (2022–2025), ranked them by Relative Citation Index, and clustered high‐impact works into four themes—long‐term memory, long‐context modeling, parametric editing, and multi‐source integration—thereby mapping each operation and memory type to active research areas and highlighting key benchmarks and tools. 

The study describes a layered ecosystem of memory-centric AI systems that support long-term context management, user modeling, knowledge retention, and adaptive behavior. This ecosystem is structured across four tiers: foundational components (such as vector stores, large language models like Llama and GPT-4, and retrieval mechanisms like FAISS and BM25), frameworks for memory operations (e.g., LangChain and LlamaIndex), memory layer systems for orchestration and persistence (such as Memary and Memobase), and end-user-facing products (including Me. bot and ChatGPT). These tools provide infrastructure for memory integration, enabling capabilities like grounding, similarity search, long-context understanding, and personalized AI interactions.

The survey also discusses open challenges and future research directions in AI memory. It highlights the importance of spatio-temporal memory, which balances historical context with real-time updates for adaptive reasoning. Key challenges include parametric memory retrieval, lifelong learning, and efficient knowledge management across memory types. Additionally, the paper draws inspiration from biological memory models, emphasizing dual-memory architectures and hierarchical memory structures. Future work should focus on unifying memory representations, supporting multi-agent memory systems, and addressing security concerns, particularly memory safety and malicious attacks in machine learning techniques. 

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FastAPI-MCP: Native FastAPI Extension

FastAPI-MCP is an open-source library that integrates directly with Python’s FastAPI framework. All existing REST routes become MCP tools by instantiating a single class and mounting it on your FastAPI app. Input and output schemas defined via Pydantic models carry over automatically, and the tool descriptions derive from your route documentation. Authentication and dependency injection behave exactly as in normal FastAPI endpoints, ensuring that any security or validation logic you already have remains effective.

Under the hood, FastAPI-MCP hooks into the ASGI application and routes MCP protocol calls to the appropriate FastAPI handlers in-process. This avoids extra HTTP overhead and keeps performance high. Developers install it via pip, add a minimal snippet such as:

from fastapi import FastAPI
from fastapi_mcp import FastApiMCP

app = FastAPI()
mcp = FastApiMCP(app)
mcp.mount(path="/mcp")

The resulting MCP server can run on the same Uvicorn process or separately. Because it is fully open-source under the MIT license, teams can audit, extend, or customize it as needed.

RapidMCP: Zero-Code REST-to-MCP Conversion Service

RapidMCP provides a hosted, no-code pathway to transform existing REST APIs, particularly those with OpenAPI specifications, into MCP servers without changing backend code. After registering an account, a developer points RapidMCP at their API’s base URL or uploads an OpenAPI document. RapidMCP then spins up an MCP server in the cloud that proxies tool calls back to the original API.

Each route becomes an MCP tool whose arguments and return types reflect the API’s parameters and responses. Because RapidMCP sits in front of your service, it can supply usage analytics, live tracing of AI calls, and built-in rate limiting. The platform also plans self-hosting options for enterprises that require on-premises deployments. Teams who prefer a managed experience can go from API to AI-agent compatibility in under an hour, at the expense of trusting a third-party proxy.

MCPify: No-Code MCP Server Builder with AI Assistant

MCPify is a fully managed, no-code environment where users describe desired functionality in natural language, such as “fetch current weather for a given city”, and an AI assistant generates and hosts the corresponding MCP tools. The service hides all code generation, infrastructure provisioning, and deployment details. Users interact via a chat or form interface, review automatically generated tool descriptions, and deploy with a click.

Because MCPify leverages large language models to assemble integrations on the fly, it excels at rapid prototyping and empowers non-developers to craft AI-accessible services. It supports common third-party APIs, offers one-click sharing of created servers with other platform users, and automatically handles protocol details such as streaming responses and authentication. The trade-off is less direct control over the code and reliance on a closed-source hosted platform.

Speakeasy: OpenAPI-Driven SDK and MCP Server Generator

Speakeasy is known for generating strongly typed client SDKs from OpenAPI specifications, and it extends this capability to MCP by producing a fully functional TypeScript MCP server alongside each SDK. After supplying an OpenAPI 3.x spec to Speakeasy’s code generator, teams receive:

• A typed client library for calling the API
• Documentation derived directly from the spec
• A standalone MCP server implementation in TypeScript

The generated server wraps each API endpoint as an MCP tool, preserving descriptions and models. Developers can run the server via a provided CLI or compile it to a standalone binary. Because the output is actual code, teams have full visibility and can customize behavior, add composite tools, enforce scopes or permissions, and integrate custom middleware. This approach is ideal for organizations with mature OpenAPI workflows that want to offer AI-ready access in a controlled, maintainable way.

Higress MCP Marketplace: Open-Source API Gateway at Scale

Higress is an open-source API gateway built atop Envoy and Istio, extended to support the MCP protocol. Its conversion tool takes an OpenAPI spec and generates a declarative YAML configuration that the gateway uses to host an MCP server. Each API operation becomes a tool with templates for HTTP requests and response formatting, all defined in configuration rather than code. Higress powers a public “MCP Marketplace” where multiple APIs are published as MCP servers, enabling AI clients to discover and consume them centrally. Enterprises can self-host the same infrastructure to expose hundreds of internal services via MCP. The gateway handles protocol version upgrades, rate limiting, authentication, and observability. It is particularly well suited for large-scale or multi-API environments, turning API-MCP conversions into a configuration-driven process that integrates seamlessly with infrastructure-as-code pipelines.

Django-MCP: Plugin for Django REST Framework

Django-MCP is an open-source plugin that brings MCP support to the Django REST Framework (DRF). By applying a mixin to your view sets or registering an MCP router, it automatically exposes DRF endpoints as MCP tools. It introspects serializers to derive input schemas and uses your existing authentication backends to secure tool invocations. Underneath, MCP calls are translated into normal DRF viewset actions, preserving pagination, filtering, and validation logic.

Installation requires adding the package to your requirements, including the Django-MCP application, and configuring a route:

from django.urls import path
from django_mcp.router import MCPRouter

router = MCPRouter()
router.register_viewset('mcp', MyModelViewSet)

urlpatterns = [
    path('api/', include(router.urls)),
]

This approach allows teams already invested in Django to add AI-agent compatibility without duplicating code. It also supports custom tool annotations via decorators for fine-tuned naming or documentation.

GraphQL-MCP: Converting GraphQL Endpoints to MCP

GraphQL-MCP is a community-driven library that wraps a GraphQL server and exposes its queries and mutations as individual MCP tools. It parses the GraphQL schema to generate tool manifests, mapping each operation to a tool name and input type. When an AI agent invokes a tool, GraphQL-MCP constructs and executes the corresponding GraphQL query or mutation, then returns the results in a standardized JSON format expected by MCP clients. This solution is valuable for organizations using GraphQL who want to leverage AI agents without settling on a REST convention or writing bespoke GraphQL calls. It supports features like batching, authentication via existing GraphQL context mechanisms, and schema stitching to combine GraphQL services under one MCP server.

gRPC-MCP: Bridging gRPC Services for AI Agents

gRPC-MCP focuses on exposing high-performance gRPC services to AI agents through MCP. It uses protocol buffers’ service definitions to generate an MCP server that accepts JSON-RPC-style calls, internally marshals them to gRPC requests, and streams responses. Developers include a small adapter in their gRPC server code:

import "google.golang.org/grpc"
import "grpc-mcp-adapter"

func main() {
  srv := grpc.NewServer()
  myService.RegisterMyServiceServer(srv, &MyServiceImpl{})
  mcpAdapter := mcp.NewAdapter(srv)
  http.Handle("/mcp", mcpAdapter.Handler())
  log.Fatal(http.ListenAndServe(":8080", nil))
}

This makes it easy to bring low-latency, strongly typed services into the MCP ecosystem, opening the door for AI agents to call business-critical gRPC methods directly.

Choosing the Right Tool

Selecting among these eight solutions depends on several factors:

• Preferred development workflow: FastAPI-MCP and Django-MCP for code-first integration, Speakeasy for spec-driven code generation, GraphQL-MCP or gRPC-MCP for non-REST paradigms.
• Control versus convenience: Libraries like FastAPI-MCP, Django-MCP, and Speakeasy give full code control, while hosted platforms like RapidMCP and MCPify trade off some control for speed and ease.
• Scale and governance: Higress shines when converting and managing large numbers of APIs in a unified gateway, with built-in routing, security, and protocol upgrades.
• Rapid prototyping: MCPify’s AI assistant allows non-developers to spin up MCP servers instantly, which is ideal for experimentation and internal automation.

All these tools adhere to the evolving MCP specification, ensuring interoperability among AI agents and services. By choosing the right converter, API providers can accelerate the adoption of AI-driven workflows and empower agents to orchestrate real-world capabilities safely and efficiently.

The post 8 Comprehensive Open-Source and Hosted Solutions to Seamlessly Convert Any API into AI-Ready MCP Servers appeared first on MarkTechPost.

Linear complexity language models have emerged as alternatives to transformer-based architectures that suffer from quadratic computational demands when processing long sequences. The RWKV model series combines transformer parallelizability during training with RNN-like recurrent state representation. RWKV has evolved through multiple iterations, from the foundational RWKV-4 to RWKV-5 to RWKV-6 to RWKV-7. Hybrid language models, including Jamba, Zamba, and MiniMax, enhance hybrid designs uniquely. Further, Native Sparse Attention organizes tokens into temporal blocks with three distinct attention paths: compressed coarse-grained tokens, selectively retained fine-grained tokens, and sliding windows for local contextual information. Other attention includes SeerAttention and Block Attention (MoBA).

Researchers from Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, Hohai University, Nanjing, Shenzhen University, and Qinghai University, Xining, have proposed a novel hybrid architecture called RWKV-X that combines RWKV’s efficiency for short-range modeling with a sparse attention mechanism designed to capture long-range context. Unlike previous hybrid approaches, RWKV-X achieves linear-time complexity during training and constant-time complexity during inference decoding. It shows near-perfect accuracy on the 64K passkey retrieval benchmark when pretrained on 64K-token sequences continuously. The model consistently outperforms previous RWKV-7 models on long-context benchmarks while maintaining strong performance on short-context tasks.

RWKV-X is a hybrid architecture that integrates RWKV-7 blocks with sparse attention blocks. Rather than training from scratch, RWKV-X builds upon existing models using an interleaved block expansion approach and zero-initialization mechanism inspired by LLaMA Pro. The training follows a two-stage process:

• First, the model trains on short 1024-token contexts from the MiniPile dataset while freezing all parameters except the newly added blocks. 
• The second stage involves long-context continual pretraining using the ProLong-64K dataset and a context length of 64K tokens, processing approximately 1 billion tokens total. During this phase, all parameters are unfrozen and jointly optimized. The training employs Long-context Cross-Entropy (LongCE) loss, which dynamically weights tokens based on their importance.

The Short-context evaluation reveals that RWKV-X maintains competitive performance across standard benchmarks. The smaller RWKV-X (0.22B) achieves an average score of 51.0, comparable to RWKV-7’s 51.8. At a larger scale, RWKV-X (3.6B) reaches 71.9, closely matching RWKV-7 (2.9B, 72.8) and Qwen2.5-3B (71.4), while surpassing LLaMA3.2-3B (69.7). These results confirm RWKV-X’s effectiveness as a general-purpose LLM backbone without sacrificing performance on shorter contexts. Moreover, efficiency analysis demonstrates RWKV-X’s superior scaling characteristics for long sequences. At 128K tokens, RWKV-X achieves a 1.37 times speedup over Flash-Attention v3, with this advantage expanding as context length increases.

In this paper, researchers introduced RWKV-X, which emerges as a hybrid language model that successfully combines RWKV’s efficiency for modeling short-range dependencies with a novel sparse attention mechanism designed specifically for long-range context modeling. While RWKV-X demonstrates strong performance and efficiency in long-context language modeling, several limitations remain. First, its sparse attention mechanism, which relies on top-k chunk selection, employs a heuristic approach that may overlook semantically relevant dependencies. Second, the current implementation shows sparse attention decoding running slower than vanilla RWKV, indicating that further engineering efforts are needed to optimize performance.

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The post RWKV-X Combines Sparse Attention and Recurrent Memory to Enable Efficient 1M-Token Decoding with Linear Complexity appeared first on MarkTechPost.

Model Context Protocol (MCP): An Overview  

The Model Context Protocol (MCP) was introduced to standardize how AI agents discover and invoke external tools and data sources. MCP is an open protocol that defines a common JSON-RPC-based API layer between LLM hosts and servers. In effect, MCP acts like a “USB-C port for AI applications”, a universal interface that any model can use to access tools. MCP enables secure, two-way connections between an organization’s data sources and AI-powered tools, replacing the piecemeal connectors of the past. Crucially, MCP decouples the model from the tools. Instead of writing model-specific prompts or hard-coding function calls, an agent simply connects to one or more MCP servers, each of which exposes data or capabilities in a standardized way. The agent (or host) retrieves a list of available tools, including their names, descriptions, and input/output schemas, from the server. The model can then invoke any tool by name. This standardization and reuse are a core advantage over prior approaches.

MCP’s open specification defines three core roles:

• Host – The LLM application or user interface (e.g., a chat UI, IDE, or agent orchestration engine) that the user interacts with. The host embeds the LLM and acts as an MCP client.
• Client – The software module within the host that implements the MCP protocol (typically via SDKs). The client handles messaging, authentication, and marshalling model prompts and responses.
• Server – A service (local or remote) that provides context and tools. Each MCP server may wrap a database, API, codebase, or other system, and it advertises its capabilities to the client.

MCP was explicitly inspired by the Language Server Protocol (LSP) used in IDEs: just as LSP standardizes how editors query language features, MCP standardizes how LLMs query contextual tools. By using a common JSON-RPC 2.0 message format, any client and server that adheres to MCP can interoperate, regardless of the programming language or LLM used.

Technical Design and Architecture of MCP  

MCP relies on JSON-RPC 2.0 to carry three types of messages, requests, responses, and notifications, allowing agents to perform both synchronous tool calls and receive asynchronous updates. In local deployments, the client often spawns a subprocess and communicates over stdin/stdout (the stdio transport). In contrast, remote servers typically use HTTP with Server-Sent Events (SSE) to stream messages in real-time. This flexible messaging layer ensures that tools can be invoked and results delivered without blocking the host application’s main workflow. 

Under the MCP specification, every server exposes three standardized entities: resources, tools, and prompts. Resources are fetchable pieces of context, such as text files, database tables, or cached documents, that the client can retrieve by ID. Tools are named functions with well-defined input and output schemas, whether that’s a search API, a calculator, or a custom data-processing routine. Prompts are optional, higher-level templates or workflows that guide the model through multi-step interactions. By providing JSON schemas for each entity, MCP enables any capable large language model (LLM) to interpret and invoke these capabilities without requiring bespoke parsing or hard-coded integrations. 

The MCP architecture cleanly separates concerns across three roles. The host embeds the LLM and orchestrates conversation flow, passing user queries into the model and handling its outputs. The client implements the MCP protocol itself, managing all message marshalling, authentication, and transport details. The server advertises available resources and tools, executes incoming requests (for example, listing tools or performing a query), and returns structured results. This modular design, encompassing AI and UI in the host, protocol logic in the client, and execution in the server, ensures that systems remain maintainable, extensible, and easy to evolve.

Interaction Model and Agent Workflows  

Using MCP in an agent follows a simple pattern of discovery and execution. When the agent connects to an MCP server, it first calls the ‘list_tools()’ method to retrieve all available tools and resources. The client then integrates these descriptions into the LLM’s context (e.g., by formatting them into the prompt). The model now knows that these tools exist and what parameters they take. When the agent decides to use a tool (often prompted by a user’s query), the LLM emits a structured call (e.g., a JSON object with ‘”call”: “tool_name”, “args”: {…}’). The host recognizes this as a tool invocation, and the client issues a corresponding ‘call_tool()’ request to the server. The server executes the tool and sends back the result. The client then feeds this result into the model’s next prompt, making it appear as additional context.

This workflow replaces brittle ad-hoc parsing. The Agents SDK will call ‘list_tools()’ on MCP servers each time the agent is run, making the LLM aware of the server’s tools. When the LLM calls a tool, the SDK calls the ‘call_tool()’ function on the server behind the scenes. This protocol transparently handles the loop of discover→prompt→tool→respond. Furthermore, MCP supports composable workflows. Servers can define multi-step prompt templates, where the output of one tool serves as the input for another, enabling the agent to execute complex sequences. Future versions of MCP and related SDKs will already be adding features such as long-running sessions, stateful interactions, and scheduled tasks.

Implementations and Ecosystem  

MCP is implementation-agnostic. The official specification is maintained on GitHub, and multiple language SDKs are available, including TypeScript, Python, Java, Kotlin, and C#. Developers can write MCP clients or servers in their preferred stack. For example, the OpenAI Agents SDK includes classes that enable easy connection to standard MCP servers from Python. InfraCloud’s tutorial demonstrates setting up a Node.js-based file-system MCP server to allow an LLM to browse local files.

A growing number of MCP servers have been published as open source. Anthropic has released connectors for many popular services, including Google Drive, Slack, GitHub, Postgres, MongoDB, and web browsing with Puppeteer, among others. Once one team builds a server for Jira or Salesforce, any compliant agent can use it without rework. On the client/host side, many agent platforms have integrated MCP support. Claude Desktop can attach to MCP servers. Google’s Agent Development Kit treats MCP servers as tool providers for Gemini models. Cloudflare’s Agents SDK added an McpAgent class so that any FogLAMP can become an MCP client with built-in auth support. Even auto-agents like Auto-GPT can plug into MCP: instead of coding a specific function for each API, the agent uses an MCP client library to call tools. This trend toward universal connectors promises a more modular autonomous agent architecture.

In practice, this ecosystem enables any given AI assistant to connect to multiple data sources simultaneously. One can imagine an agent that, in one session, uses an MCP server for corporate docs, another for CRM queries, and yet another for on-device file search. MCP even handles naming collisions gracefully: if two servers each have a tool called ‘analyze’, clients can namespace them (e.g., ‘ImageServer.analyze’ vs ‘CodeServer.analyze’) so both remain available without conflict.

Advantages of MCP Over Prior Paradigms  

MCP brings several key benefits that earlier methods lack:

• Standardized Integration: MCP provides a single protocol for all tools. Whereas each framework or model previously had its way of defining tools, MCP means that the tool servers and clients agree on JSON schemas. This eliminates the need for separate connectors per model or per agent, streamlining development and eliminating the need for custom parsing logic for each tool’s output.
• Dynamic Tool Discovery: Agents can discover tools at runtime by calling ‘list_tools()’ and dynamically learning about available capabilities. There is no need to restart or reprogram the model when a new tool is added. This flexibility stands in contrast to frameworks where available tools are hardcoded at startup.
• Interoperability and Reuse: Because MCP is model-agnostic, the same tool server can serve multiple LLM clients. With MCP, an organization can implement a single connector for a service and have it work with any compliant LLM, thereby avoiding vendor lock-in and reducing duplicate engineering efforts.
• Scalability and Maintenance: MCP dramatically reduces duplicated work. Rather than writing ten different file-search functions for ten models, developers write one MCP file-search server. Updates and bug fixes to that server benefit all agents across all models.
• Composable Ecosystem: MCP enables a marketplace of independently developed servers. Companies can publish MCP connectors for their software, allowing any AI to integrate with their data. This encourages an open ecosystem of connectors analogous to web APIs.
• Security and Control: The protocol supports clear authorization flows. MCP servers describe their tools and required scopes, and hosts must obtain user consent before exposing data. This explicit approach improves auditability and security compared to free-form prompting.

Industry Impact and Real-World Applications  

MCP adoption is growing rapidly. Major vendors and frameworks have publicly invested in MCP or related agent standards. Organizations are exploring MCP to integrate internal systems, such as CRM, knowledge bases, and analytics platforms, into AI assistants.

Concrete use cases include:

• Developer Tools: Code editors and search platforms (e.g., Zed, Replit, Sourcegraph) utilize MCP to enable assistants to query code repositories, documentation, and commit history, resulting in richer code completion and refactoring suggestions.
• Enterprise Knowledge & Chatbots: Helpdesk bots can access Zendesk or SAP data via MCP servers, answering questions about open tickets or generating reports based on real-time enterprise data, all with built-in authorization and audit trails.
• Enhanced Retrieval-Augmented Generation: RAG agents can combine embedding-based retrieval with specialized MCP tools for database queries or graph searches, thereby overcoming the limitations of LLMs in terms of factual accuracy and arithmetic.
• Proactive Assistants: Event-driven agents monitor email or task streams and autonomously schedule meetings or summarize action items by calling calendar and note-taking tools through MCP.

In each scenario, MCP enables agents to scale across diverse systems without requiring the rewriting of integration code, delivering maintainable, secure, and interoperable AI solutions.

Comparisons with Prior Paradigms  

• Versus ReAct: ReAct-style prompting embeds action instructions directly into free text, requiring developers to parse model outputs and manually handle each action. MCP provides the model with a formal interface using JSON schemas, enabling clients to manage execution seamlessly.
• Versus Toolformer: Toolformer ties tool knowledge to the model’s training data, necessitating retraining for new tools. MCP externalizes tool interfaces entirely from the model, enabling zero-shot support for any registered tool without retraining.
• Versus Framework Libraries: Libraries like LangChain simplify building agent loops but still require hardcoded connectors. MCP shifts integration logic into a reusable protocol, making agents more flexible and reducing code duplication.
• Versus Autonomous Agents: Auto-GPT agents typically bake tool wrappers and loop logic into Python scripts. By using MCP clients, such agents need no bespoke code for new services, instead relying on dynamic discovery and JSON-RPC calls.
• Versus Function-Calling APIs: While modern LLM APIs offer function-calling capabilities, they remain model-specific and are limited to single turns. MCP generalizes function calling across any client and server, with support for streaming, discovery, and multiplexed services.

MCP thus unifies and extends previous approaches, offering dynamic discovery, standardized schemas, and cross-model interoperability in a single protocol.

Limitations and Challenges  

Despite its promise, MCP is still maturing:

• Authentication and Authorization: The spec leaves auth schemes to implementations. Current solutions require layering OAuth or API keys externally, which can complicate deployments without a unified auth standard.
• Multi-step Workflows: MCP focuses on discrete tool calls. Orchestrating long-running, stateful workflows often still relies on external schedulers or prompt chaining, as the protocol lacks a built-in session concept.
• Discovery at Scale: Managing many MCP server endpoints can be burdensome in large environments. Proposed solutions include well-known URLs, service registries, and a central connector marketplace, but these are not yet standardized.
• Ecosystem Maturity: MCP is new, so not every tool or data source has an existing connector. Developers may need to build custom servers for niche systems, although the protocol’s simplicity keeps that effort relatively low.
• Development Overhead: For single, simple tool calls, the MCP setup can feel heavyweight compared to a quick, direct API call. MCP’s benefits accrue most in multi-tool, long-lived production systems rather than short experiments.

Many of these gaps are already being addressed by contributors and vendors, with plans to add standardized auth extensions, session management, and discovery infrastructure.

In conclusion, the Model Context Protocol represents a significant milestone in AI agent design, offering a unified, extensible, and interoperable approach for LLMs to access external tools and data sources. By standardizing discovery, invocation, and messaging, MCP eliminates the need for custom connectors per model or framework, enabling agents to integrate diverse services seamlessly. Early adopters across development tools, enterprise chatbots, and proactive assistants are already reaping the benefits of maintainability, scalability, and security that MCP offers. As MCP evolves, adding richer auth, session support, and registry services, it is poised to become the universal standard for AI connectivity, much like HTTP did for the web. For researchers, developers, and technology leaders alike, MCP opens the door to more powerful, flexible, and future-proof AI solutions.

Sources

• https://modelcontextprotocol.io/specification/2025-03-26   
• https://modelcontextprotocol.io/introduction 
• https://python.langchain.com/docs/concepts/tool_calling/ 
• https://www.anthropic.com/news/model-context-protocol 
• https://openai.github.io/openai-agents-python/mcp/ 
• https://arxiv.org/abs/2210.03629   
• https://arxiv.org/abs/2302.04761 
• https://arxiv.org/html/2404.04442v1

The post How the Model Context Protocol (MCP) Standardizes, Simplifies, and Future-Proofs AI Agent Tool Calling Across Models for Scalable, Secure, Interoperable Workflows Traditional Approaches to AI–Tool Integration appeared first on MarkTechPost.