Claude’s tool use API lets you give the model a set of callable functions — web search, database queries, code execution, API calls — and have it decide when to call them, with what arguments, and how to use the results to complete a task. It is the foundational mechanism for building agents with Anthropic’s … Read more
Agno (formerly Phidata) is an agent framework designed for building multi-modal, multi-agent applications with a strong emphasis on built-in memory, knowledge, and tool integrations. It positions itself as a full-stack agent platform rather than a minimal orchestration layer — providing pre-built integrations for databases, vector stores, file systems, and external APIs alongside the agent loop … Read more
A coding agent and a standard LLM used for coding are not the same thing, even when the underlying model is identical. The distinction is architectural: a standard LLM generates code in a single prompt-response cycle and stops. A coding agent operates in a loop — it writes code, executes it, reads the output, decides … Read more
LangMem is LangChain’s dedicated memory library for AI agents, released in early 2025. It provides a structured system for managing the four types of agent memory — in-context working memory, episodic memory of past interactions, semantic memory of facts about the user and domain, and procedural memory of learned workflows — with explicit extraction, storage, … Read more
Pydantic AI is an agent framework built by the team behind Pydantic, the Python data validation library used by FastAPI and most of the modern Python ecosystem. Released in late 2024, it takes a different design philosophy from LangGraph, CrewAI, and AutoGen: it prioritises type safety, structured outputs, and native integration with Python’s type system … Read more
Evaluating AI agents is harder than evaluating standard language models, and teams that do not invest in it early pay for it later through production failures they cannot diagnose. The challenge is that agent quality is multidimensional: an agent can produce a correct final answer via a broken reasoning path, or produce a wrong answer … Read more
Memory is what separates a useful AI agent from an expensive autocomplete tool. Without memory, an agent forgets every conversation the moment it ends, cannot learn from past interactions, and cannot build understanding of a user or task across sessions. With the right memory architecture, an agent accumulates context that makes it progressively more useful … Read more
Adding web browsing to an AI agent lets it retrieve current information, navigate websites, fill forms, and extract data from live pages — capabilities that static language model knowledge cannot provide. A browsing-capable agent can check today’s news, look up a product price, read a documentation page, or scrape a table of data in real … Read more
Qwen QwQ-32B is Alibaba’s open-weights reasoning model — a 32-billion-parameter model that matches or exceeds the performance of much larger models on mathematics, coding, and logical reasoning benchmarks by using extended chain-of-thought reasoning at inference time. Running it locally with Ollama takes about five minutes and gives you a capable reasoning model that runs entirely … Read more
DeepSeek-R2 is the successor to DeepSeek R1, released in mid-2026, and it maintains the trend R1 established: open-weights reasoning models that match or exceed proprietary frontier performance at dramatically lower training cost. R2 represents a meaningful step beyond R1 on most benchmarks, and its release continued the pattern of the open-weights ecosystem closing the gap … Read more
