Building Agents

Compare coding agents head to head on your own tasks for pass rate, cost and time

Design agent tool sets, action spaces and observation formats for better completion

Diagnose failed agent runs with capture, diagnosis and recovery reports, not retries

Add x402 payments to agents with per-task budgets and non-custodial wallets

Score each finished task on accuracy, clarity and other axes with evidence

Cut LLM API spend with model routing by task, budget tracking, retries and caching

Build task-local agent harnesses with eval gates and reusable skill extraction

Run long-lived agents with kill switches, observability and safe rollbacks

Define capability and regression evals before coding and track pass@k reliability

Restructure prompts to raise LLM cache hit rates and cut API costs

Benchmark language models on MMLU, GSM8K and other tasks with lm-eval

Constrain LLM output with grammars to guarantee valid JSON responses

Run over 150 hosted AI image, video and language apps through inference.sh

Get structured LLM outputs validated with Pydantic models

Let an agent pay HTTP 402 APIs through the Machine Payments Protocol

Embed a natural-language copilot that operates the interface of a web app

Let an agent make payments with Stripe Link cards and approval steps

Build, test and publish n8n AI agents with tools, memory and model settings

Build LLM API apps with current model IDs, tool use, streaming and caching

Build, test and publish Dify workflows and chatflows through the difyctl CLI

Migrate prompts and API calls to the Opus 4.5 model from older model versions

Preregister a paid model eval with bars, arms and stop rules before any spend

Find and label every LLM workflow in a repo to break down spend by workflow

Review LLM cost, latency, errors and routing evidence to see where spend goes

Start, approve, cancel, promote or roll back LLM experiments with safety checks

Evaluate a token optimization candidate against a paired baseline with explicit approval

Compare and run hosted AI models from Replicate for image, audio and video pipelines

Prototype agents that read live RuView sensing events and persist their state

Send messages and manage threads on a DeerFlow platform over HTTP

Send tasks to the OmniRoute A2A server and test its JSON-RPC agent protocol

Submit and monitor batch LLM inference jobs from the OmniRoute CLI

Send chat completions and run an interactive REPL against any OmniRoute model

Review LLM token usage, call logs and per-connection spend from the OmniRoute CLI

Create LLM eval suites, run benchmarks and compare model scorecards from the CLI

List LLM models, aliases and provider variants from the OmniRoute catalog

Add, test and rotate AI provider connections and check their metrics from the CLI

Create and test LLM routing combos with fallback chains from the CLI

Call OmniRoute built-in A2A skills for routing, quotas, costs and health reports

Set spending limits and token quotas per API key and track consumption

Build LLM routing combos with 19 strategies and fallback chains

Call OpenAI-compatible chat, embeddings, image, audio and rerank endpoints

Search the OmniRoute model catalog and resolve model aliases across providers

Add, update and test AI provider connections and OAuth flows via the REST API

Query OmniRoute call logs and token usage by provider, model, status and cost

Check AGNTCY SLIM and CASA integration status and transport settings

Format chat prompts for local model templates and route queries to matching patterns

Compare cost per million tokens for local model servers against hosted APIs

Create and adapt learning agents built on Dynamic Agentic Architecture

Send one-shot chat completions to DeepSeek through its OpenAI-compatible endpoint