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Evidence

• Show HN: Tamper evident audit logs for LangGraph/CrewAI agents — I have running into the same problems recently instrumenting my LangChain and CrewAI agent pipelines. If something goes wrong with a tool call, there was no way to way to prove what the agent did, in what order, and whether the logs have been modified. Observability platforms like LangSmith and Langfuse are great at optimizing for agent behavior, tokens and costs but they do not produce legally defensible and auditable artifacts. That's the reason why I decided to build RootSign, an SDK that instruments your CrewAI and LangGraph agents and produces cryptograhic audit logs for CrewAI and LangGraph agents. RootSign adds a cryptographic hash chain to every tool call. If any record is modified after the fact, "rootsign verify" detects it.<p>What it does: - SHA-256 hash chain across every Action record in a session - Human-in-the-loop checkpoints with Approval records for certain agent actions - PII redacted before hashing (StandardPIIConfig out of the box) - Works with LangGraph and CrewAI — AutoGen coming soon - Local first (Postgres + Timescale) — no cloud dependency<p>What it doesn't do (yet): compliance dashboard, cloud backend, policy engine, all on the roadmap.<p>Please try it out on the Github repo, contributions and feedback are always welcome.

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• Show HN: Resolve AI – Your AI Production Engineer — Hello. I am Madhu, a Software Engineer at Resolve AI. We launched our product today and we are thrilled to share it with you all and get feedback: <a href="https://resolve.ai/" rel="nofollow">https://resolve.ai/</a><p>Our team at Resolve AI comes with a wealth of experience in this space. I was an early contributor to Kubernetes at Google where I worked on Kubernetes and associated technologies for ~6 years. More recently, I was the tech lead for the Kubernetes-based compute platform at Robinhood where my teams were in a number of SEVs per year, not necessarily caused by the platform itself but still supported (pretty much the story of life for Infrastructure Engineers everywhere). Our co-founders, Spiros Xanthos and Mayank Agarwal co-created OpenTelemetry at their previous startup Omnition (acquired by Splunk). More recently, Spiros was the GM and Senior Vice President of Splunk Observability and Mayank was the lead architect for all of Splunk's observability product lines. We have all lived the problems we are trying to solve.<p>Resolve is AI for production engineers. Production systems are dynamic and complex. Addressing common production engineering concerns like incident troubleshooting, cloud operations, security, compliance and cost involves painfully piecing together information from many teams (service on-call rotations, Platform, SRE, etc) and multiple (routinely 10+) different tools (observability, CI/CD, infrastructure, paging, chat, etc). These tools were not designed to work together, pushing the complexity on humans.<p>Resolve AI is tackling this challenge by building an AI Production Engineer with the goal of automating the majority of tasks across incident management, cloud operations, security engineering, compliance, and cost management. As the first step in our ambitious journey, we are automating incident troubleshooting as it is the most direct way to prevent outages and improve reliability while relieving engineers from the most stressful part of their job. Our goal is to automate the resolution of 80%+ of alerts and incidents without human involvement.<p>Resolve AI automatically maps and keeps up-to-date a complete knowledge graph of any production environment, without needing any upfront training or user input. It builds knowledge of which tools and signals are relevant for any situation. It comes pre-built with models for various tool categories such as metrics, logs, traces, alerts, seamlessly connecting with category- and vendor-specific products like Prometheus, Splunk, GCP, AWS, Azure and others. These models automatically and continuously adapt to each customer's environment.<p>With the state-of-the-art reasoning engine that’s composed of multiple agents, Resolve AI is able to investigate novel incidents, accurately determine causality, learn and adapt as it encounters new situations and perform various complex actions.<p>Generative AI is inherently probabilistic and not always 100% accurate. Without full context, AI models may hallucinate, potentially misleading users. For an AI that takes actions, building user trust is paramount; it must present clear evidence for any decision or action. We address these challenges by building an interface that supports claims with evidence, present findings with context and allow humans to collaborate with the system so that they can guide the system when needed.<p>Our video demo is on the website. Please take a look. We really appreciate your feedback. We are also happy to hop on a call to show a demo live if you are interested (madhu@resolve.ai).

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• Show HN: I built a hypervisor and client for inference on consumer compute — I'm the founder of Scalattice, this is my second company, third total product. I'm a 2x founder building some challenging software, some easy software, and some curiosity based tools that I've just always wanted to be a part of!<p>So here is Scalattice.<p>Scalattice is an OpenAI-compatible inference API. You keep the OpenAI SDK, swap base_url + API key, and call open models (Qwen3, Llama 3.3 70B, Gemma 3, DeepSeek R1, etc.). Inference is performed by a provider node on our distributed network of consumer hosted inference machines, we run an open source agent (<a href="https://github.com/scalattice/scalattice-agent" rel="nofollow">https://github.com/scalattice/scalattice-agent</a>) which works in Rust to run the inference and get the provider paid.<p>In short, anyone can become a provider, offer up their machine with our Windows/Linux Rust agent, and earn some extra cash, or start a farm of machines to make big bucks.<p>I decided to build some innovative behavioural traits to the API for higher performance/security: 1. output vetting - Scalattice Cloud vets the response against replica responses requested on the API header N times. 2. security tiers - Using some split inference, the job is completed in chunks by multiple providers and in part by Scalattice Cloud hypervisor for added security/privacy (at an additional cost) 3. regional policy - I know how important data residency is to developer clients (from my experience with Digital ID Infrastructure) and so I built the platform to let developers specify the region for inference.<p>We pay our providers a majority share (Currently 80%) of every token spent by the developer.<p>We currently back the network with a failover of our own company machines which ensures we never drop a request.<p>If you want to give it a try in a couple of minutes, we are currently running a "Top Up $10 for UNLIMITED QWEN3" for an entire month of API calling, (terms and conditions apply).<p>If you want to give it a try in a couple of minutes: 1. Create a key at <a href="https://scalattice.cloud/developers" rel="nofollow">https://scalattice.cloud/developers</a> 2. Use the copy-paste snippet on the linked post 3. Live rates: <a href="https://scalattice.com/pricing" rel="nofollow">https://scalattice.com/pricing</a><p>Or, if you want to be a provider (We are really keen to onboard people and get them earning): 1. Create a machine at <a href="https://scalattice.cloud/providers" rel="nofollow">https://scalattice.cloud/providers</a> 2. Download our Windows app, or use our on-page instructions to curl the agent installation script for Linux. 3. Attach a provider token to the app or Linux agent from the Machine created on the website. 4. Control the machine from the website, select which models you want to offer, and the physical hardware components to use for inference and the hours of operation. 5. Earn!<p>Happy to take feedback - especially on DX, pricing clarity, and what would make you try this instead of Together / Fireworks / OpenRouter / Salad etc.<p>This is my first time posting on HN, please let me know if you think the product has potential, there are obviously trade-offs with non-datacentre inference (latency, capability of cards), but I think this could serve a decent amount of developers very well, because not everyone needs H100 cards for their inference.<p>Thanks again.

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