MCP on SoloSoft

DeepSeek Harness: The Open-Source Agent Framework Where Everything Is a Plugin

Wed, 12 Aug 2026 00:00:00 +0000

DeepSeek Harness: The Open-Source Agent Framework Where Everything Is a Plugin

The formula is everywhere now: Model + Harness = Agent.

The model reasons. The harness handles everything else — tool execution, memory, context control, sandboxing, orchestration. And on August 2026, DeepSeek shipped what might be the most important open-source reference implementation of that formula yet.

DeepSeek Harness (dsh) is the Chinese lab’s open-source agent framework: 25.9k stars, MIT license, 12,000+ commits, and an architecture built on one radical idea — everything is a plugin.

“Models, tools, skills, sessions, sandboxes, filesystems, loops, orchestration, and UI are ALL implemented as plugins, and can be mixed, matched, replaced, and extended.”

Agent Sandbox: All-in-One Sandbox for AI Agents with Browser, Shell, and VSCode

Fri, 01 May 2026 00:00:00 +0000

AI agents need environments to execute in – places to run code, browse the web, edit files, and interact with tools. Building these environments from scratch for each agent platform is tedious and error-prone. Agent Sandbox solves this by providing a complete, pre-configured Docker sandbox that combines a browser, shell, file system, MCP server, and VSCode Server in a single containerized workspace.

Developed by agent-infra, Agent Sandbox is designed as the execution environment for AI agents that need to perform real-world tasks. Instead of cobbling together separate tools for browser automation, code execution, and file management, developers get a unified sandbox with all of these capabilities pre-integrated and ready to use.

AgentScope: Alibaba's Open-Source Multi-Agent Framework for Transparent AI Agents

Fri, 01 May 2026 00:00:00 +0000

Building production-grade multi-agent systems is notoriously complex. Coordinating communication between agents, managing distributed deployments, integrating with external tools, and ensuring observability are challenges that most frameworks tackle only partially. AgentScope, developed by Alibaba’s Tongyi Lab, addresses these challenges with a comprehensive framework designed for real-world, scalable multi-agent applications.

AgentScope distinguishes itself through its focus on transparency and controllability. Every agent’s decision-making process is observable, every message can be inspected, and the entire system can be configured through declarative specifications rather than imperative code. This makes it suitable for enterprise applications where auditability and reliability are paramount.

The framework supports both the Model Context Protocol (MCP) and Google’s Agent-to-Agent (A2A) protocol, enabling interoperability with a wide ecosystem of tools and agent platforms. Combined with its distributed communication system (MsgHub), AgentScope can orchestrate agent swarms that span multiple servers and geographic regions.

Cline: Open-Source Autonomous AI Coding Agent for VS Code

Fri, 01 May 2026 00:00:00 +0000

VS Code has become the world’s most popular code editor, and its extension ecosystem has spawned countless productivity tools. But Cline represents something fundamentally different from the autocomplete suggestions and code snippets that most AI extensions offer. It is an autonomous AI coding agent that operates within VS Code, capable of understanding your entire project, planning multi-step implementations, and executing them with your supervision.

Developed by the cline organization, Cline has rapidly gained popularity among developers who want more than inline suggestions. It can read files across your project, create new ones, run terminal commands, launch a headless browser, and interact with external tools through the Model Context Protocol. Every action requires human approval, keeping the developer firmly in control while the AI handles the heavy lifting of implementation.

FastAPI MCP: Expose FastAPI Endpoints as MCP Tools

Fri, 01 May 2026 00:00:00 +0000

If you have a FastAPI application, you have a potential goldmine of tools for AI agents. FastAPI MCP, created by tadata-org, automatically converts your existing FastAPI endpoints into MCP-compatible tools that AI assistants can discover and invoke, with zero code changes to your application.

The tool works by introspecting your FastAPI route definitions, extracting parameter schemas, descriptions, and authentication requirements, and generating MCP tool definitions on the fly. Every endpoint with a description tag becomes an MCP tool. The integration is automatic and bidirectional–changes to your API are immediately reflected in the available tools.

Key Capabilities

Feature	Description
Automatic conversion	No code changes needed to your FastAPI app
Schema extraction	Uses OpenAPI/Pydantic models for type-safe tool definitions
Auth support	Handles API keys, OAuth, and bearer tokens
Streaming	Supports SSE transport for real-time responses
Documentation	Endpoint descriptions become tool descriptions

Integration Architecture

flowchart LR
 A[FastAPI App] --> B[FastAPI MCP Adapter]
 B --> C[MCP Server]
 C --> D[Tool: GET /users]
 C --> E[Tool: POST /orders]
 C --> F[Tool: PUT /inventory]
 C --> G[Tool: DELETE /items]
 H[AI Agent] --> I[MCP Client]
 I --> J[JSON-RPC]
 J --> C

The adapter sits between your FastAPI application and the MCP protocol. It reads your route definitions and generates MCP tool definitions automatically. When an AI agent calls a tool, the adapter routes the request to the appropriate endpoint and returns the response.

MCP Servers: Official Model Context Protocol Server Implementations

Fri, 01 May 2026 00:00:00 +0000

AI agents are only as capable as the tools they can access. An agent that can read files, query databases, browse the web, and call APIs is dramatically more useful than one that only processes text. But every tool integration has historically been custom — built for a specific AI platform, requiring platform-specific code, authentication, and deployment patterns.

The Model Context Protocol (MCP), developed by Anthropic and released as an open standard, solves this fragmentation. It defines a universal protocol for AI applications to interact with external systems — a standard interface that any AI client can use to discover and invoke tools, access resources, and follow prompts. The official MCP servers repository provides reference implementations that demonstrate the protocol in action for common use cases.

MCP TypeScript SDK: Build Model Context Protocol Servers

Fri, 01 May 2026 00:00:00 +0000

The Model Context Protocol (MCP) is rapidly becoming the standard way to connect AI agents with external tools, APIs, and data sources. The official TypeScript SDK, maintained by the modelcontextprotocol organization, provides everything developers need to build MCP servers that expose functionality to AI assistants like Claude.

MCP creates a standardized interface between AI models and the tools they use. Instead of building custom integrations for every AI agent, you build an MCP server once, and any MCP-compatible client can discover and use your tools.

What the SDK Provides

Component	Description
Server framework	Build MCP servers with tool, resource, and prompt handlers
Client library	Connect to MCP servers from any TypeScript application
Transport layer	Built-in support for stdio and SSE (Server-Sent Events)
Schema validation	Type-safe tool definitions with Zod integration
Authentication	OAuth 2.0 and API key support for secure connections

MCP Architecture

flowchart LR
 A[AI Client<br/>Claude, etc.] --> B[MCP Protocol<br/>JSON-RPC]
 B --> C[MCP Server]
 C --> D[Tool: Calculator]
 C --> E[Tool: Database]
 C --> F[Tool: Web Search]
 C --> G[Resource: Files]
 B --> H[Transport Layer<br/>stdio / SSE]

The architecture follows a clean client-server pattern. The AI client communicates with the MCP server over JSON-RPC messages, and the server exposes tools and resources that the AI can invoke. The transport layer handles the underlying communication, whether that’s subprocess stdio or network SSE.

Open MCP Client: Self-Hosted Web-Based Client for Any MCP Server

Fri, 01 May 2026 00:00:00 +0000

The Model Context Protocol (MCP) is rapidly becoming the standard protocol for connecting AI applications to external tools and data sources, but the ecosystem has been missing a polished, open, and self-hostable client that can talk to any MCP server. Open MCP Client fills that gap. Built by CopilotKit, this open-source web application gives you a ChatGPT-like interface for chatting with any MCP server, with a LangGraph-powered agent managing the orchestration under the hood.

What makes Open MCP Client particularly compelling is its self-hosted nature. Instead of relying on a hosted platform with opaque data handling, you run the entire stack on your own infrastructure. This means your conversation history, tool configurations, and any data flowing through MCP tools never leave your control – a critical advantage for developers working with proprietary codebases, sensitive documents, or internal APIs.

OpenClaw: Open-Source AI Agent Platform

Fri, 01 May 2026 00:00:00 +0000

The AI agent ecosystem is fragmented. Every agent builder has its own tool format, deployment model, and skill definition. OpenClaw aims to unify this landscape with an open-source platform that supports building, deploying, and sharing AI agents with a skill marketplace and native MCP support.

OpenClaw provides a complete environment for agent development. Developers can create agents using a visual builder or code, equip them with tools from a community marketplace, deploy them to various targets, and orchestrate multi-agent workflows. The platform is designed to be self-hosted, giving organizations full control over their agent infrastructure.

Platform Components

Component	Description
Agent builder	Visual and code-based agent construction
Skill marketplace	Community-contributed tools and capabilities
MCP runtime	Native support for Model Context Protocol servers
Multi-agent orchestrator	Coordinate multiple agents for complex tasks
Deployment manager	One-click deploy to cloud or on-premises

Agent Architecture

flowchart LR
 A[User Query] --> B[Agent Orchestrator]
 B --> C[Agent A<br/>Research]
 B --> D[Agent B<br/>Analysis]
 B --> E[Agent C<br/>Generation]
 C --> F[MCP Tools]
 D --> F
 E --> F
 F --> G[External APIs]
 F --> H[Databases]
 F --> I[File System]
 C --> J[Skill: Web Search]
 D --> K[Skill: Data Viz]
 E --> L[Skill: Markdown]

Agents in OpenClaw are modular. Each agent has a specific role and set of skills. The orchestrator routes tasks to the appropriate agent, and agents invoke MCP tools as needed. Skills from the marketplace plug into this architecture seamlessly.

OpenManus: Open-Source Framework for Building General AI Agents with 55K Stars

Fri, 01 May 2026 00:00:00 +0000

The open-source AI agent landscape has a new leader. OpenManus, developed by FoundationAgents (the same team behind MetaGPT), has rapidly grown to over 55,000 GitHub stars by offering something the community desperately wanted: a flexible, modular, and genuinely open framework for building general-purpose AI agents.

OpenManus fills a gap that emerged when commercial AI agent products like Anthropic’s Claude Code and OpenAI’s Codex CLI gained traction but remained proprietary. The community wanted an open alternative – a framework they could inspect, modify, extend, and self-host. OpenManus delivered.

At its core, OpenManus provides a Python-based platform where AI agents can browse the web, execute code, manipulate files, call APIs, and collaborate with other agents. Its architecture is designed to be model-agnostic, tool-extensible, and deployment-flexible – running on everything from a laptop to a production server.

Pixelle-MCP: Open-Source Multimodal AIGC Solution Bridging ComfyUI and LLMs via MCP

Fri, 01 May 2026 00:00:00 +0000

The Model Context Protocol (MCP) is reshaping how AI applications communicate, but most MCP tools remain narrowly focused on text and data queries. Pixelle-MCP shatters that limitation by turning ComfyUI – the most popular visual workflow engine for AI-generated content – into a full multimodal MCP server. Developed by Alibaba’s AIDC-AI team, this open-source solution lets any MCP-compatible client invoke complex AIGC pipelines for images, sound, video, and text using natural language.

The core insight behind Pixelle-MCP is elegant: instead of building multimodal generation capabilities from scratch, it repurposes ComfyUI’s vast ecosystem of community-built workflows as MCP-callable tools. Anyone who has designed a ComfyUI pipeline for stable diffusion, audio generation, or video synthesis can now expose that workflow to any LLM client as a simple API, with zero additional code.

Roo Code: Open-Source AI Coding Agent with Multiple Expert Modes

Fri, 01 May 2026 00:00:00 +0000

The landscape of AI coding assistants has grown crowded, but few tools have captured developer attention as rapidly as Roo Code. With over 23,000 GitHub stars and a rapidly growing community, Roo Code has distinguished itself through a design philosophy that treats AI as a multi-modal collaborator rather than a single-purpose autocomplete.

Roo Code integrates as a VS Code extension but goes far beyond the capabilities of typical code completion tools. It operates as a full-fledged AI agent that can read and write files, execute terminal commands, browse the web, and interact with external services through the Model Context Protocol (MCP). The defining innovation is its expert mode system: instead of one AI assistant that does everything, Roo Code provides specialized personas optimized for different tasks.

Supermemory MCP: Persistent Memory for AI Agents via MCP

Fri, 01 May 2026 00:00:00 +0000

One of the biggest limitations of current AI agents is their lack of persistent memory. Each new conversation starts from scratch, forcing users to repeat context and preferences. Supermemory MCP solves this by providing a persistent memory layer that AI agents can read from and write to across sessions, all through the Model Context Protocol.

Developed by supermemoryai, this MCP server gives AI agents the ability to remember facts about users, recall past interactions, and build a knowledge base over time. It supports structured and unstructured memory, automatic summarization, and configurable retention policies. The result is AI agents that learn and improve with every interaction.

Claude Code 2026 Complete Guide: From Basics to Multi-Agent Architecture

Fri, 27 Mar 2026 00:00:00 +0000

Claude Code is Anthropic’s command-line AI coding assistant (CLI tool) that reads and writes your entire codebase, executes shell commands, and calls external APIs directly from the terminal. In 2025/2026, it has evolved far beyond a code-completion tool into a full AI operating system capable of taking over development, marketing, and everyday automation workflows. This guide takes you from the basics to advanced multi-agent architecture.

Last updated: March 27, 2026 · Synthesized from 31 practitioner research sources

What Are the Most Important New Features in Claude Code 2026?

Direct answer: In 2025/2026, Claude Code added Dispatch remote mode, /by the way for mid-task side questions, /loop scheduled tasks, Fast Mode, and structured Automemory — upgrading the AI from a “chat tool” into an autonomous agent that can run 24/7 in the background.

The Complete Claude Code Handbook: From Installation to Multi-Agent Collaboration

Mon, 01 Jan 0001 00:00:00 +0000

When AI stops just answering questions and starts writing code, running deployments, and self-optimizing while you sleep — that is where Claude Code genuinely changes the rules of the game.

In late 2024, Anthropic released Claude Code — a tool that is not just another coding assistant. It is a local AI agent that lives in your terminal or IDE and can autonomously read and write local files, execute bash commands, control browsers, and even dispatch sub-agents to complete complex tasks in parallel. The concept of Vibe Coding took off alongside it: you no longer copy and paste AI output. Claude Code writes the code directly into your project and deploys it.

MCP on SoloSoft

DeepSeek Harness: The Open-Source Agent Framework Where Everything Is a Plugin

DeepSeek Harness: The Open-Source Agent Framework Where Everything Is a Plugin

Agent Sandbox: All-in-One Sandbox for AI Agents with Browser, Shell, and VSCode

AgentScope: Alibaba's Open-Source Multi-Agent Framework for Transparent AI Agents

Cline: Open-Source Autonomous AI Coding Agent for VS Code

FastAPI MCP: Expose FastAPI Endpoints as MCP Tools

Key Capabilities

Integration Architecture

MCP Servers: Official Model Context Protocol Server Implementations

MCP TypeScript SDK: Build Model Context Protocol Servers

What the SDK Provides

MCP Architecture

Open MCP Client: Self-Hosted Web-Based Client for Any MCP Server

OpenClaw: Open-Source AI Agent Platform

Platform Components

Agent Architecture

OpenManus: Open-Source Framework for Building General AI Agents with 55K Stars

Pixelle-MCP: Open-Source Multimodal AIGC Solution Bridging ComfyUI and LLMs via MCP

Roo Code: Open-Source AI Coding Agent with Multiple Expert Modes

Supermemory MCP: Persistent Memory for AI Agents via MCP

Claude Code 2026 Complete Guide: From Basics to Multi-Agent Architecture

Table of Contents

What Are the Most Important New Features in Claude Code 2026?

The Complete Claude Code Handbook: From Installation to Multi-Agent Collaboration