Building Agents

Guarantee valid JSON, XML or code from models with Pydantic-typed structured generation

Trace, evaluate and monitor LLM apps with the open-source Phoenix observability tool

Add Olakai monitoring to an existing LLM app with custom KPIs and validation

Start a new AI agent project with Olakai monitoring, SDK setup and KPI tracking

Build production Tavily integrations for web search, extraction and crawling

Run untrusted Python or shell code safely in an isolated Cube Sandbox

Query several LLM models in parallel and compare their answers side by side

Design RAG pipelines with chunking, embeddings, hybrid search, reranking and evals

Trace and evaluate LLM apps with the Phoenix observability platform

Understand Phoenix datasets, splits and how they feed evaluators and experiments

Write code or LLM-as-judge evaluators that score agent and pipeline outputs

Run and compare dataset experiments to check whether a prompt change helped

Find failure patterns in LLM agent traces with open coding and annotations

Write efficient GraphQL queries against the Phoenix observability API

Choose the right inference backend to deploy or serve a MiniCPM5 model

Run MiniCPM5 models on desktop and Android with Google LiteRT-LM

Run MiniCPM5 GGUF models on CPU or consumer GPUs with llama.cpp

Chat with MiniCPM5 in LM Studio using GGUF or Apple MLX and a local server

Run MiniCPM5 natively on Apple Silicon Macs with the MLX framework

Run MiniCPM5 on a laptop with Ollama using the released GGUF build

Serve MiniCPM5 over SGLang with prefix caching and tool-call parsing

Generate text with MiniCPM5 in a Python script using Hugging Face Transformers

Serve MiniCPM5 as a high-throughput OpenAI-compatible API with vLLM

Serve MiniCPM5-2B with vLLM-Ascend on Huawei Ascend NPU hardware

Get and configure free API keys for LLM providers with free tiers

Recover from failed tool calls with a decision tree instead of blind retries

Self-check output quality over time to catch degradation before review does

Generate valid Omnigent agent directories from patterns and templates

Detect Python agent frameworks from imports and map them to executor types

Look up Omnigent config format, executor types and skill structure conventions

Write an AI-SPEC.md design contract for a phase that builds an AI system

Audit an AI phase's evaluation coverage and write a remediation plan

Investigate AgentCore agent sessions in CloudWatch Logs and trace slow tool calls

Split extracted text into overlapping chunks sized for LLM context windows

Analyze an agent's runtime history and apply protocol-limited self-improvements

Diagnose and repair tool calls that fail, loop or use the wrong arguments

Add the Honcho SDK to Python or TypeScript apps with peers, sessions and representations

Route LLM requests across providers with failover and cost tracking through Vercel

Store every LLM generation with unique IDs, addressable URLs and cost tracking

Build chat, tool calling, streaming and structured output with the Vercel AI SDK

Build durable AI agents with sessions, tools and skills in the eve framework

Diagnose failed OpenAI API requests from auth, quota, rate limit and network errors

Build OpenAI-backed apps, scripts and generators with API key setup handled

Plan real-time AI coaching, compliance and routing on Twilio contact centers

Connect third-party LLM agent runtimes to Twilio voice, messaging and memory

Plan an AI conversational agent on Twilio and pick the right architecture

Give AI agents retrieval over your documents, FAQs and product catalogs with Twilio

Build real-time AI voice agents with speech recognition, TTS and WebSocket streaming

Search a configured knowledge base and cite document passages in answers