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Products that do what Raptor does

Hot patch, cache, protect your for LLM API. Built in Rust.

  1. 1

    Protect, cache and hot patch your LLM APIs. Built in Rust.

    Nov 2025

  2. 2LA

    I built LocalGPT over 4 nights as a Rust reimagining of the OpenClaw assistant pattern (markdown-based persistent memory, autonomous heartbeat tasks, skills system). It compiles to a single ~27MB binary — no Node.js, Docker, or Python required. Key features: - Persistent memory via markdown files (MEMORY, HEARTBEAT, SOUL markdown files) — compatible with OpenClaw's format - Full-text search (SQLite FTS5) + semantic search (local embeddings, no API key needed) - Autonomous heartbeat runner that checks tasks on a configurable interval - CLI + web interface + desktop GUI - Multi-provider:…

    Feb 2026 · github.com

  3. 3

    Spin up secure sandboxes in ~100 ms

    Nov 2025

  4. 4LO

    Hey HN, we’re Robert, Din and Temirlan from Laminar (https://www.lmnr.ai), an open-source observability and analytics platform for complex LLM apps. It’s designed to be fast, reliable, and scalable. The stack is RabbitMQ for message queues, Postgres for storage, Clickhouse for analytics, Qdrant for semantic search - all powered by Rust. How is Laminar different from the swarm of other “LLM observability” platforms? On the observability part, we’re focused on handling full execution traces, not just LLM calls. We built a Rust ingestor for OpenTelemetry (Otel) spans with GenAI…

    2024 · github.com

  5. 5

    Protect your LLM applications with a few lines of code.

    2023

  6. 6LT

    Current AI-assisted CLI tools are often part of larger systems and work better on Linux. I built llm-term to address these. It's a Rust-based tool that compiles into a single binary file. You only need to download the binary, add it to your PATH, and configure your OpenAI key to get started. While llm-term offers an option for gpt-4o, it works great with gpt-4o-mini. So it's not costly. I appreciate any feedback or suggestions.

    2024 · github.com

  7. 7AO

    Hi! I’m Nathan: an ML Engineer at Mozilla.ai: I built agent-of-empires (aoe): a CLI application to help you manage all of your running Claude Code/Opencode sessions and know when they are waiting for you. - Written in rust and relies on tmux for security and reliability - Monitors state of cli sessions to tell you when an agent is running vs idle vs waiting for your input - Manage sessions by naming them, grouping them, configuring profiles for various settings I'm passionate about getting self-hosted open-weight LLMs to be valid options to compete with proprietary closed models. One…

    Jan 2026 · github.com

  8. 8

    Connect AI agents to browser through raw CDP

    Apr 2026 · openbrowser.me

  9. 9AC

    Multi-tier exact-match cache for AI agents backed by Valkey or Redis. LLM responses, tool results, and session state behind one connection. Framework adapters for LangChain, LangGraph, and Vercel AI SDK. OpenTelemetry and Prometheus built in. No modules required - works on vanilla Valkey 7+ and Redis 6.2+. Shipped v0.1.0 yesterday, v0.2.0 today with cluster mode. Streaming support coming next. Existing options locked you into one tier (LangChain = LLM only, LangGraph = state only) or one framework. This solves both. npm:…

    Apr 2026

  10. 10LA

    G'day, HN! I'm one of the maintainers of `llm`. I've been working alongside a trusty group of contributors to bring this project to life, and we're now at a point where we're ready to share it with the world. Large language models (LLMs) are taking the computing world by storm due to their emergent abilities that allow them to perform a wide variety of tasks, including translation, summarization, code generation, and even some degree of reasoning. However, the ecosystem around LLMs is still in its infancy, and it can be difficult to get started with these models. `llm` is a one-stop shop for…

    2023 · github.com

  11. 11

    I wanted to run AI from inside the JVM. I started out with the standard REST sidecar, ripped that out to use Project Panama (Foreign Function & Memory API) in the new JDK versions to interface directly with llama.cpp. I still wasn't happy with how that functioned, so I built libargus.cc to get a clean ABI to expose a structured API up in the JVM landscape. It still uses Project Panama to interface directly with llama.cpp, whisper.cpp, and ggml compute graphs. I have zero-allocation on the hot paths, memory segments for prompts and tokens are allocated once inside confined Arenas. Raw…

    Jul 2026 · github.com

  12. 12AT

    I recently built a small open-source tool to benchmark different LLM API endpoints — including OpenAI, Claude, and self-hosted models (like llama.cpp). It runs a configurable number of test requests and reports two key metrics: • First-token latency (ms): How long it takes for the first token to appear • Output speed (tokens/sec): Overall output fluency Demo: https://llmapitest.com/ Code: https://github.com/qjr87/llm-api-test The goal is to provide a simple, visual, and reproducible way to evaluate performance across different LLM providers, including…

    2025 · llmapitest.com

  13. 13TO

    Hi HN! We're Gabriel & Viraj, and we're excited to open source TensorZero. To be a little cheeky, TensorZero is an open-source platform that helps LLM applications graduate from API wrappers into defensible AI products. 1. Integrate our model gateway 2. Send metrics or feedback 3. Unlock compounding improvements in quality, cost, and latency It enables a data & learning flywheel for LLMs by unifying: • Inference: one API for all LLMs, with <1ms P99 overhead • Observability: inference & feedback → your database • Optimization: better prompts, models, inference strategies • Experimentation:…

    2024 · github.com

  14. 14

    Cuts your LLM API costs by 40-70%. One line of code.

    May 2026 · semanticguard.dev

  15. 15
    GitHub5

    Transparent semantic cache for LLM API calls on Redis VS

    Jun 2026 · github.com

  16. 16

    The smart, Rust-built LLM cache and agent memory layer

    Jun 2026 · crowkis.com

  17. 17CR

    Clawbernetes turns OpenClaw into an AI-native infrastructure manager. Instead of YAML, Helm charts, and kubectl — you have a conversation. "Deploy Llama 70B on the node with the most VRAM" → agent selects the best node, pulls the image, starts the container with GPU passthrough, sets up health monitoring. "Why is inference slow?" → checks GPU temps, VRAM, CPU load. "GPU 0 at 89°C — thermal throttling. Want me to reduce batch size?" 23 crates, 74K lines of Rust, 1,866 tests, zero unsafe in core. Supports CUDA, Metal, ROCm, Vulkan, and CPU SIMD. Components: - clawnode: node agent with 80+…

    Feb 2026 · github.com

  18. 18AR
  19. 19

    We have built Tarit as a hypervisor built from ground up for running AI agent and RL environments. It is based on rust-vmm and can be used as a replacement for firecracker. Firecracker was built to serve a different need of primarily serverless compute and hence does not have primitives like live snapshots without pausing the VM operations. We also provide a basic orchestrator that handles placement of the microVMs, creating clusters with HA, maintaining a warm pool of VMs, and takes care of setting up networking and monitoring. Our benchmarks on a metal instance shows an acquire VM from…

    Jul 2026 · github.com

  20. 20AU

    Agentpanel is an observability platform for optimizing the control flow, performance, token usage, and correctness of LLM&#x2F;AI agents! Built-in @rustlang, the first release of Agent Panel currently features an AI gateway that provides seamless access to 100+ LLMs across 20+ platforms, including OpenAI GPT-4o, Gemini 1.5 Pro latest, AnthropicAI Claude 3.5, MistralAI, Cohere, Groq,Perplexity AI, and more.

    2024 · github.com

  21. 21

    One api. 2200+ LLMs. Persistent AI memory.

    Oct 2025

  22. 22IM
  23. 23

    Save 89% of wasted tokens on your AI coding agent

    Mar 2026 · rtk-ai.app

  24. 24CM

    Hey HN, I've been building AutoAgents, an AI agent framework in Rust. Today I'm sharing a feature I haven't seen done well elsewhere: composable middleware layers for LLM inference pipelines. The problem Every agent framework lets you swap LLM providers. Almost none of them give you a structured way to enforce safety, caching, or data sanitization in the inference path itself. You end up with guardrails as application-level if-statements, caching bolted on as a separate service, and PII handling as a "we'll add it later" TODO that never ships. This gets worse with local models. Cloud APIs…

    Mar 2026 · github.com

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