Alternatives
Products that do what Lucid does
Real-time knowledge grounding for AI agents.
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Single-agent LLMs suck at long-running complex tasks. We’ve open-sourced a multi-agent orchestrator that we’ve been using to handle long-running LLM tasks. We found that single LLM agents tend to stall, loop, or generate non-compiling code, so we built a harness for agents to coordinate over shared context while work is in progress. How it works: 1. Orchestrator agent that manages task decomposition 2. Sub-agents for parallel work 3. Subscriptions to task state and progress 4. Real-time sharing of intermediate discoveries between agents We tested this on a Putnam-level math problem, but the…
Feb 2026 · github.com
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hi guys. been working on something i think is fundamentally missing in today's workflow with ai agents. vcs. i find myself struggling with questions that agents can't answer like "why did you do it?", "when did u delete this folder? why?", etc. or trying to /rewind (after a /compact...) or basically `bisect` to find when and why something was done by the agent in the current / previous session. just like git did for code, i think we are the same core capabilities with ai agents so... i developed an open source solution for that (currently supporting claude code) would love to…
May 2026 · github.com
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I built an open-source AI agent for security testing to find and fix vulnerabilities in your code. I’ve noticed how bad security vulnerabilities have gotten with everyone shipping AI code slop, so I wanted to build something that allows for vibe-coding at full speed without compromising security. Traditional security tools aren’t effective, and manual pen-testing can’t keep up with the rapidly growing AI code This tool runs your code dynamically, finds vulnerabilities, and validates them through actual exploitation. You can either run it against your codebase or enter your (or someone…
2025 · github.com
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2025 · temporal.io
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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…
Mar 2026 · github.com
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