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Measures generative AI inference performance with token-level metrics (TTFT, inter-token latency), latency, and throughput under realistic traffic patterns. Provides a multiprocess engine, real-time TUI dashboard, extensible plugins, and integrations for telemetry and result uploads, aimed at inference benchmarking and capacity planning.
Turns Chromium into a local-first AI browser with an embedded assistant that can summarise pages, extract structured data, automate web tasks, and run scheduled agents. Built as an open-source Chromium fork with 53+ built-in browser tools, 40+ app integrations, and support for BYO AI keys or fully local models (Ollama / LM Studio).
Translates full-length books, subtitles, and documents with LLMs while preserving original formatting and structure. Uses intelligent chunking to handle arbitrarily long files, supports local or cloud providers, and resumes interrupted jobs without losing progress.
Interactive terminal-native AI coding agent that generates, edits, and runs code while managing multiple model providers and optional durable execution. Privacy-first design with local model support, round-robin model distribution, and a flexible agent system for custom workflows.
Parses the local JSONL logs that coding-agent CLIs write and turns them into token and cost reports, no API keys or telemetry. Breaks spend down by day, month, session, and Claude's 5-hour billing windows across Claude Code, Codex, Gemini CLI and more.
Demonstrates orchestration of specialist customer-service agents built with the OpenAI Agents SDK, pairing a Python backend for agent logic with a Next.js UI (ChatKit) to visualize routing, guardrails, and demo flows. Useful for prototyping multi-agent customer-service workflows; uses mock flight data and requires an OpenAI API key.
Extracts structured data from unstructured text with LLMs, mapping every extraction to its exact character span in the source for visual review. Uses few-shot examples, schema enforcement, and multi-pass chunking to handle long documents.
Composes AI agent teams from a Ghost+Shell+Model formula: each Bot pairs a prompt/MCP/Skills Ghost with a Chat, ClaudeCode, or Dify shell and a model like Claude or DeepSeek. Bots form Teams that run as traceable Tasks, wired to GitHub and DingTalk.
Bundles your prompt and project files into a single context package and submits that bundle to one or multiple LLMs (GPT‑5.x, Gemini, Claude, etc.) via API or optional browser automation. Key features: multi-model runs, file-globbing and token-aware bundles, session lineage and replay, and a CLI-first workflow for code reviews, audits, and multi-model comparisons.
Shows per-provider usage meters, credit balances, and reset countdowns for AI coding providers directly in the macOS menu bar. Privacy-first design reuses existing sessions (OAuth, cookies, API keys) and includes a CLI, widgets, live status badges, and optional cost/spend charts across 50+ providers.
Provides an IntelliJ IDEA plugin that embeds both Anthropic Claude Code and OpenAI Codex into the IDE — conversation-aware code assistance, @file context, session management, agent/skills commands, and provider switching for in-IDE AI coding workflows.
Provides an in‑IDE interface for IntelliJ IDEA to interact with Anthropic Claude Code and OpenAI Codex for AI-assisted coding. Supports dual-engine switching, file-aware context, session history, agent skills, MCP extensions, and security/permission controls.