MCP Client2026
Compresses any context sent to LLMs (tool outputs, DB reads, RAG results, files, logs) to cut tokens by ~70–95% while preserving reversible originals; runs as a proxy or Python/TypeScript SDK with integrations for common agent frameworks.
AI Agent2026
Provides a framework to build, evaluate, and run AI SRE agents that investigate and remediate production incidents on your infrastructure. Includes a CLI, synthetic + end-to-end benchmark suites, and 40+ connectors for observability, infra, and LLM providers so teams can train agents and run investigations locally or in cloud.
AI Agent2026
CLI for creating, running, and managing coding agents across local hosts, containers, and cloud sandboxes. Uses SSH, git, and tmux; supports snapshots, push/pull, auto-shutdown for cost control, and provider-agnostic workflows for developer-centric agent orchestration.
AI Infra2026
Filters and compresses CLI command outputs before they reach an LLM, typically reducing token consumption by 60–90%. Single Rust binary with zero runtime dependencies, supports 100+ dev commands and integrates via a Bash hook into common AI coding tools.
AI Infra2026
Provides a REST server that lets AI agents browse sites while avoiding common bot-detection by running Camoufox (a Firefox fork with C++-level fingerprint spoofing). Returns compact accessibility snapshots, stable element refs, session isolation, proxy/geoIP support, and agent-friendly endpoints (click, type, snapshot, transcripts).
AI Agent2026
Models an AI agent's context as a file system, unifying memory, resources, and skills instead of flat vector RAG. Uses L0/L1/L2 tiered loading to cut tokens, directory-recursive plus semantic retrieval, and visualized retrieval traces for debugging.
AI Infra2026
Self-hosted coding assistant that runs frozen local LLMs with constraint-driven planning, energy-based verification, and self-verified repair to produce verified code. Emphasizes offline inference (no cloud), Docker/bare-metal deployment, and requires a 16GB+ GPU.
AI Infra2026
Runs untrusted code (LLM outputs, plugins, and third‑party tools) inside cross‑platform, policy‑driven sandboxes. Provides a unified JSON schema and a TypeScript SDK that sit on multiple containment backends (process sandboxes, LXC/Bubblewrap, microVMs). Early preview with known permissive profiles — not yet a security boundary.
MCP Server2026
Local integration layer that lets AI agents discover and securely call OpenAPI, MCP, GraphQL, or custom JavaScript functions. Centralizes a shared tool catalog, auth, and policy surface across multiple agents, with a local web UI and CLI for runtime control.
AI Infra2026
A fast, local document parser that extracts spatial text with bounding boxes from PDFs and other formats. Bundles Tesseract OCR and supports HTTP OCR servers, multi-language bindings (Rust, Node, Python, WASM) and screenshot generation; best for lightweight local pipelines but less suited to very complex or heavily scanned documents.
AI Infra2026
Acts as an OpenAI‑compatible local and cloud gateway that routes requests across 100+ LLM providers with smart routing, load balancing, retries and fallbacks. Adds policies, rate limits, semantic caching and observability for reliable, cost‑aware inference in Docker, Electron or npm installs.
AI Infra2026
Provides a reliability layer for self-hosted LLM tool-calling and multi-step agent workflows. Adds guardrails — rescue parsing, response validation, retry nudges, and a synthetic respond tool — and ships a Drop-in OpenAI-compatible proxy plus a WorkflowRunner for structured loops.