Gulama
Security-first open-source AI agent. The secure alternative
What it does
Personal AI agents handle your files, emails, credentials, and conversations. Most treat security as an afterthought. Gulama is built security-first from the ground up. 15+ security mechanisms: AES-256-GCM encryption, sandboxed execution, Ed25519-signed skills, canary tokens, cryptographic audit trail. 100+ LLM providers, 19 built-in skills, 10 channels, full MCP support, multi-agent orchestration, RAG memory, voice wake word. Free. Open source. MIT license.
Does the same job
all alternatives →- OSOpen source alternative to ChatGPT and ChatPDF-like AI tools2023 · github.com · ▲234
Hey everyone, We have been building SecureAI Tools -- an open-source application layer for ChatGPT and ChatPDF-like AI tools. It works with locally running LLMs as well as with OpenAI-compatible APIs. For local LLMs, it supports Ollama which supports all the gguf/ggml models. Currently, it has two features: Chat-with-LLM, and Chat-with-PDFs. It is optimized for self-hosting use cases and comes with basic user management features. Here are some quick demos: * Chat with documents using OpenAI's GPT3.5 model: https://www.youtube.com/watch?v=Br2D3G9O47s * Chat with documents…
- OSOpen-source playground to red-team AI agents with exploits publishedMar 2026 · github.com · ▲30
We build runtime security for AI agents. The playground started as an internal tool that we used to test our own guardrails. But we kept finding the same types of vulnerabilities because we think about attacks a certain way. At some point you need people who don't think like you. So we open-sourced it. Each challenge is a live agent with real tools and a published system prompt. Whenever a challenge is over, the full winning conversation transcript and guardrail logs get documented publicly. Building the general-purpose agent itself was probably the most fun part. Getting it to reliably use…
- IBI built a firewall for agents because prompt engineering isn't securityJan 2026 · github.com · ▲7
Hi HN, I’m the creator of Cordum. I’ve been working in DevOps and infrastructure for years (currently in the fintech/security space), and as I started playing with AI agents, I noticed a scary pattern. Most "safety" mechanisms rely on system prompts ("Please don't do X") or flimsy Python logic inside the agent itself. If we treat agents as autonomous employees, giving them root access and hoping they listen to instructions felt insane to me. I wanted a way to enforce hard constraints that the LLM cannot override, no matter how "jailbroken" it gets. So I built Cordum. It’s an open-source…
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
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