Results for “structure”

Authenticate, initialize, and use the stateful Gemini Interactions API for multi-turn conversations, streaming, structured output, and function calling on the Gemini Enterprise Agent Platform.

Build applications with Gemini API hosted models, including Gemini and Gemma 4, using multimodal content, function calling, structured outputs, and current SDKs for Python, JavaScript, Go, and Java.

Guides users through creating effective Agent Skills for Cursor, covering skill structure, best practices, and SKILL.md format.

Build automated evaluation pipelines for Python LLM applications using real LLM calls and structured test datasets.

Provides guidelines for building with Botpress's Agent Development Kit (ADK), covering primitives, project structure, CLI usage, and documentation lookups.

Provides guidelines for building with Botpress's Agent Development Kit (ADK), covering project structure, primitives, CLI commands, and documentation lookups.

Analyzes Claude Code features to uncover advanced usage patterns, best practices, and real-world applications, producing a structured guide.

Guides building production-grade AI features with LLM integration patterns, RAG architecture, prompt engineering, and cost optimization.

Generate, predict, and embed protein sequences and structures using ESM3, ESMC, and ESMFold2 with local or cloud inference.

Compiles research inputs—PDFs, code, logs, notes—into structured Agent-Native Research Artifacts with cognitive and physical layers.

Generates a complete Kotlin MCP server project with Gradle structure, dependencies, and implementation using the official kotlin-sdk library.

Rates an agent's own output on five axes — accuracy, completeness, clarity, actionability, conciseness — producing a structured scorecard with evidence and improvement suggestions.

Generate a complete Go MCP server project with proper structure, dependencies, and implementation using the official github.com/modelcontextprotocol/go-sdk.

Orchestrates building a new feature end-to-end by delegating research, planning, TDD implementation, review, and gated commit to specialized agents.

Provides structured guidance for building production-grade AI agent systems through context engineering, covering fundamentals, architectural patterns, operational excellence, and evaluation.

Drafts structured goal-loop prompts for long-running agent work with verifiable stop conditions, validation commands, and documentation requirements.

Guides project-level decisions for LLM-powered systems: task-model fit, pipeline architecture, token and cost estimation, and agent-assisted iteration.

Orchestrate multi-agent coding tasks via Claude DevFleet: plan projects, dispatch parallel agents in isolated worktrees, monitor progress, and read structured reports.

Automate OpenAI API operations: generate text and multimodal responses with structured output, create embeddings, generate images, and list models via the Composio MCP integration.

Run computational biology tools for protein structure prediction, design, docking, and molecular dynamics on managed cloud GPUs via REST API or MCP server.

Runs a structured multi-agent deliberation: four specialist agents write independent position papers, two cross-examiners debate them, and the orchestrator synthesizes a final recommendation with implementation plan and risk register.

Control LLM output with regex and grammars to guarantee valid JSON, XML, or code generation, enforce structured formats, and build multi-step workflows using Microsoft Research's Guidance framework.

Debug LLM applications using the Phoenix CLI: fetch traces, analyze errors, structure trace review with open and axial coding, inspect datasets, review experiments, and query the GraphQL API.

Call the Gemini API for text generation, chat, multimodal understanding, image/video/audio generation, streaming, function calling, structured output, and managed agents using the Interactions API in Python and TypeScript.

Writes Python and TypeScript code that calls the Gemini Interactions API for text generation, chat, multimodal understanding, image generation, streaming, research, function calling, and structured output, including migration from the legacy generateContent API.

Routes a research topic through a structured two-phase workflow: generate an extensible outline, then fan out parallel web-search agents to investigate each item into validated JSON, producing a complete markdown report with table of contents.

Provides 25 paid HTTP endpoints on Base mainnet that bill agents per call in USDC for web search, AI analysis, crypto and stock data, SEC filings, company intelligence, news, sentiment scoring, and a macro dashboard, returning structured JSON.