LLMOps · Eval

A skill that spawns parallel reasoning processes under distorted cognitive frames, then scores and prunes them with a separate critic pass.

Raven wraps agents in durable memory and self-refining skills so workflows survive past the chat session.

iFixAi runs up to 32 inspections against any LLM or agent and returns a letter-grade scorecard in minutes, using a separate provider as judge so the model isn't grading its own homework.

A single Zig binary with an embedded Svelte dashboard that installs, supervises, and cross-wires local AI agents, workflow engines, and tracing tools so you don't have to juggle separate terminals.

It exists to stop your AI gateway from quietly burning through quotas, cash, and expired OAuth tokens without leaving a paper trail.

codex-keysmith exists because copying a Markdown file and editing one TOML key is simple, but existing files, active hooks, and interrupted writes are not.

Token Monitor reads local logs from two dozen AI coding tools to surface live token burn, costs, and limits in one place, synced across all your machines.

Moss exists because calling out to a remote vector database adds 200–500 ms of latency—enough to kill a real-time conversation—so it runs embedding and search inside your process instead.

A hybrid CLI tool that uses deterministic pipelines to keep LLM agents from drifting off-target during code review.

AIHelms wraps LiteLLM in a Vue management layer so finance can trace every token back to the department that spent it.

TongFlow exists because chaining text, image, video, and audio models shouldn't require writing a new Python script every time.

repowise indexes a codebase into five queryable intelligence layers—dependency graphs, git history, docs, architectural decisions, and deterministic health scores—so MCP-compatible agents can answer "why" instead of grepping for "what".

Pairs a 424-page textbook with Jupyter notebooks to teach AI agent design patterns.

It chains a dozen LLM agents into a visual workflow so your on-call engineer only has to tap 'approve' instead of SSHing in at 3 AM.

Heym exists because serious AI automation shouldn't require a sales call to access observability, evals, or multi-agent orchestration.

Most AI scientist tools are monolithic prompt pipelines; FAROS treats research automation as a composable runtime problem rather than a single-agent stack.

A cross-CLI skill that turns your static notes into a self-updating knowledge base for Claude, Codex, Gemini, and OpenCode.

Hyperresearch turns Claude Code into a tiered, multi-agent research pipeline that persists every source in a searchable, compounding vault.

ai-memory captures session context from Claude, Codex, and others into a git-backed markdown wiki so you can quit one agent and resume in another without starting from scratch.

Macro exists because context dies when your email, chat, docs, and tasks live in separate apps, so it links them into one queryable workspace with shared AI memory.