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Alternatives

Products that do what WAIL does

AI Runtime Control & Governance Layer

  1. 1
    OpenBox171

    See, verify, and govern every agent action.

    Apr 2026

  2. 2
    Phinq82

    Stops AI agents before they break something

    24d ago · phinq.co

  3. 3
    GLM-5154

    Open-weights model for long-horizon agentic engineering

    Feb 2026

  4. 4
    Loomal92

    Identity infrastructure for AI agents

    Apr 2026

  5. 5

    An open benchmark for AI agents that test APIs

    May 2026

  6. 6

    Zero-trust security gateway for AI agents

    Jun 2026

  7. 7
    Angy88

    Multi‑agent pipelines w/ AI‑driven scheduling + safety check

    Mar 2026

  8. 8

    Build & scale AI \ agents as microservices with IAM

    Dec 2025

  9. 9

    AI multi-agent coding assistant for your terminal

    Feb 2026

  10. 10

    Memory infrastructure for AI coding agents

    Feb 2026

  11. 11

    The Confidential AI Gateway

    Dec 2025

  12. 12PS

    I didn't want to buy a standalone computer or repurpose a laptop to run constantly so I could maintain a system to sync my LLMs, so I built this. It's a simple overview of my system, laid out in a way easy to unpack and replicate for yourself. The project is meant to be configured individually, and uniquely, since one solution might not be what's best for another. If anything, maybe it gives you some ideas on how to implement things for your own project. Best wishes, Ryan.

    27d ago · pacslate.com

  13. 13LC

    Prompt instructions like 'never do X' don't hold up in production. LLMs ignore them when context gets long or users push hard. Limits sits between your agent and the real world. Every action — database writes, API calls, refunds — gets intercepted and checked against your rules before it executes. Deterministically. No LLM involved in enforcement. Three modes: Conditions: hard rules on structured data Guideance: validate LLM output before it reaches the user and give the agent chance to reason and retry Guardrails: scan for PII, toxicity, prompt injection etc One line to integrate: npm…

    Feb 2026 · limits.dev

  14. 14

    The verified execution layer for cloud infrastructure.

    19d ago · snapflow.online

  15. 15CS

    I got tired of AI agents forgetting what they were doing the moment their context window filled. The current industry solution is to write massively bloated agent harnesses full of defensive spaghetti just to stop models from drifting. The problem is treating chat history as project state. A conversation is not a ledger. Castra is a compiled Go binary that strips orchestration rights from the LLM. State lives in an encrypted, local SQLite database (castra.db). The LLM is just a stateless executor — it reads the DB, executes a highly constrained task, and the result is written back subject to…

    Apr 2026 · github.com

  16. 16
    MOTA9

    AI agents that follow your rules, not their imagination.

    25d ago · motaai.dev

  17. 17

    Give AI agents your company data. Only what they can see.

    3d ago · warehousd.com

  18. 18SN

    Hey folks, I am finally posting a Show HN post for a project I have been working on for several months now, and it's in a state where I already get a lot of value myself, so I am happy to share broadly. The pitch line is: "Skybear.NET is a managed platform automating Synthetic HTTP API testing." At the moment, the main source file format supported for your API tests are Hurl.dev files [1]. Hurl is a CLI tool wrapping `curl` and it's really awesome. At least check that out :) I am not affiliated directly with the Hurl CLI tool, and the platform I am building provides full Hurl compatibility.…

    2024 · skybear.net

  19. 19MA

    Hi HN, A couple weeks ago I shared an early version of a side project I’ve been tinkering with called Persistent Mind Model. I built it at home on an i7-10700K / 32GB RAM / RTX 3080 because I was curious whether an AI could keep a stable “mind” over time, that could "think" about it's own identity as an LLM, instead of resetting every session. After a lot more tinkering, I think the architecture is finally in a solid place. Basically, it saves everything the AI does, thoughts, decisions, updates as a chain of events in a local SQLite database. Because the “identity” is stored in…

    Nov 2025 · github.com

  20. 20PC

    Parallax is a CLI for orchestrating independent AI agent cohorts (Claude, Codex, etc.) over isolated, append-only logs or streams. Each cohort operates on its own log and does not see the intermediate reasoning of others i.e. disagreement is enforced at the infrastructure layer rather than prompted at runtime. Agents write to sequenced, durable logs and a separate moderator agent subscribes to all streams, monitors progress, issues steering instructions when necessary, and synthesizes outputs at the end. That means, coordination is just done over a log with natural language, which allows us…

    Mar 2026 · github.com

  21. 21
    Aartiq2

    For Questions That Matter

    10d ago · aartiq.ponsrischool.in

  22. 22

    Backtesting & live execution infra for AI trading agents

    9d ago · emidlabs.com

  23. 23

    Scoped, revocable credentials for AI agents — fully audited

    23d ago · fullmakt.ai

  24. 24VA

    Hi all, Gorkem here. I started VerifyWise [1] to make AI governance less painful. Today, we’re launching our open-source platform to help teams take control of their AI compliance process. VerifyWise helps organizations navigate AI governance by providing audit readiness, risk registers, model fairness checks, and compliance documentation. Those are all built into a single platform you can self-host. We’ve been quietly building VerifyWise for a while, and we’re now at a place where it’s ready for more teams to try. Since we started, we've: - Released our core platform on GitHub:…

    2025 · verifywise.ai

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