@omarsar0 writes on X that the fastest path to genuinely understanding agent harnesses is to build one from scratch in TypeScript or Python, starting with a minimal ReAct implementation prompted from Google's original paper, targeting three clean components—an LLM inference module (multi-model, OpenRouter-backed, with separable system prompt), an MCP tools module for interoperability, and a simple agent loop that ties them together—then logging every input/output at each boundary and iterating against a small set of diverse test tasks so each change is inspectable. The punchline: skip the framework first, because only once you've felt the loop, the tokens, and the tool calls in your own code do the "next steps"—memory, skills, subagents—stop being black boxes you configure and become modules you actually know how to tune.
- LLM module: wraps inference across multiple frontier models via OpenRouter; system prompt either embedded or isolated for context-engineering experiments
- Tools module: implement as MCP (Model Context Protocol) tools for cross-harness interoperability, or as bespoke functions if experienced
- Agent loop: ReAct pattern (alternating reasoning traces and action calls) encapsulating both LLM and tools; exit conditions handled via system-prompt instructions (non-deterministic), code-level checks (deterministic), or both
- Logging strategy: capture loop in/out, every LLM call in/out, and every tool-call in/out; run a fixed diverse task suite after each modification
- Scaling path: keep architecture modular so memory, skills, and subagent orchestration can be bolted on once the core loop is understood
- Shortcut alternatives (if not building from scratch): Pi SDK or LangChain harness tooling
- LLM module: wraps inference across multiple frontier models via OpenRouter; system prompt either embedded or isolated for context-engineering experiments
- Tools module: implement as MCP (Model Context Protocol) tools for cross-harness interoperability, or as bespoke functions if experienced
- Agent loop: ReAct pattern (alternating reasoning traces and action calls) encapsulating both LLM and tools; exit conditions handled via system-prompt instructions (non-deterministic), code-level checks (deterministic), or both
- Logging strategy: capture loop in/out, every LLM call in/out, and every tool-call in/out; run a fixed diverse task suite after each modification
- Scaling path: keep architecture modular so memory, skills, and subagent orchestration can be bolted on once the core loop is understood
- Shortcut alternatives (if not building from scratch): Pi SDK or LangChain harness tooling
This article compares Model Context Protocol (MCP), Function Calling, and OpenAPI Tools for integrating tools and resources with language models, outlining their strengths, limits, security considerations, and ideal use cases.
This article discusses Model Context Protocol (MCP), an open standard designed to connect AI agents with tools and data. It details the key components of MCP, its benefits (improved interoperability, future-proofing, and modularity), and its adoption in open-source agent frameworks like LangChain, CrewAI, and AutoGen. It also includes case studies of MCP implementation at Block and in developer tools.
This blog post explains that Large Language Models (LLMs) don't need to understand the Model Context Protocol (MCP) to utilize tools. MCP standardizes tool calling, simplifying agent development for developers while the LLM simply generates tool call suggestions based on provided definitions. The article details tool calling, MCP's function, and how it relates to context engineering.
The official Python SDK for Model Context Protocol servers and clients. It allows building MCP clients, servers, and provides tools for interacting with LLMs in a standardized way.
An MCP server that gives language models temporal awareness and time calculation abilities. Teaching AI the significance of the passage of time through collaborative tool development.
The Universal Tool Calling Protocol (UTCP) is an open standard that describes how to call existing tools directly, eliminating the need for wrappers. It focuses on direct communication with tool endpoints (HTTP, gRPC, WebSocket, CLI, etc.) to reduce latency and maintain existing security and billing systems.
LLM 0.26 introduces tool support, allowing LLMs to access and utilize Python functions as tools. The article details how to install, configure, and use these tools with various LLMs like OpenAI, Anthropic, Gemini, and Ollama models, including examples with plugins and ad-hoc functions. It also discusses the implications for building 'agents' and future development plans.
This article details the Model Context Protocol (MCP), a new approach to integrating Large Language Models (LLMs) like Azure OpenAI with tools. MCP focuses on structured data exchange to improve reliability, observability, and functionality, moving beyond simple text-in, text-out interactions. It aims to standardize how LLMs interact with tools, enhancing their ability to utilize those tools effectively.
