AI tool comparison
Agent Vault vs Kelet
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Developer Tools
Agent Vault
Network-layer credential injection — agents never see your secrets
75%
Panel ship
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Community
Paid
Entry
Agent Vault is an open-source credential broker from Infisical that solves one of the nastiest unsolved problems in AI agent security: AI agents are non-deterministic and vulnerable to prompt injection attacks that could trick them into leaking secrets. The solution is elegant — Agent Vault never gives credentials to the agent at all. Instead, it acts as an HTTPS proxy, intercepting the agent's outbound API calls and injecting credentials at the network layer. The flow is simple: give the agent a scoped session token and set HTTPS_PROXY to Agent Vault's local server. The agent calls APIs normally; Agent Vault transparently swaps in the real credentials before the request leaves the machine. The agent literally cannot leak what it never had. AES-256-GCM encryption with optional Argon2id password wrapping protects the vault, and all proxied requests are logged (method, host, latency) without recording sensitive bodies. Works out of the box with Claude Code, Cursor, Codex, custom Python/TypeScript agents, and any HTTP-speaking process. Infisical is a credible backer — they already run one of the most popular open-source secrets managers. This is MIT-licensed with enterprise features planned. For teams deploying agents in sandboxed environments, this is the missing security primitive.
Developer Tools
Kelet
Reads your LLM traces, finds failure patterns, and hands you the prompt fix
75%
Panel ship
—
Community
Free
Entry
Kelet is a root-cause analysis agent for LLM applications that goes beyond trace visualization. Where most observability tools stop at showing you what happened, Kelet automatically reads your traces, cross-references failure patterns across thousands of sessions — thumbs-down ratings, abandoned conversations, LLM-judge flags — generates root cause hypotheses, and produces targeted prompt patches to address them. The workflow is: connect your traces (LangSmith, Langfuse, or direct API), let Kelet ingest your failure signals, and receive a prioritized list of failure clusters with explanations and draft prompt fixes. SOC 2 Type II certified, read-only access to traces — nothing is mutated. The indie team positions it as the missing "closing of the loop" in LLM observability: most teams can detect failures but have no systematic path from detection to fix. The HN thread surfaced a real pain point: teams know their chatbot is failing somewhere, but diagnosing which prompts, tools, or routing decisions are responsible requires manual trace archaeology. Kelet automates that archaeology and produces actionable output, not just dashboards.
Reviewer scorecard
“The network-layer injection approach is architecturally correct and I'm annoyed I didn't think of it first. This should be standard infrastructure for any team giving agents real API access. The fact that Infisical is behind it gives me confidence it won't be abandoned after a week.”
“The loop has been open for too long — collect traces, stare at them, guess at fixes, repeat. Kelet closes it. Read-only access is the right trust model for early adoption. If it actually surfaces actionable prompt patches instead of generic insights, this becomes a staple of any serious LLM app development workflow.”
“The proxy-based approach introduces a local MITM that itself becomes a high-value attack target. If Agent Vault is compromised, every credential it holds is exposed simultaneously. The API is explicitly unstable ('subject to change') — wait for a stable release before baking this into CI/CD pipelines.”
“Automated prompt patches from an LLM analyzing other LLM failures is a confidence game — how do you know the fix didn't introduce a new failure mode? Without a rigorous eval harness baked into the loop, you're swapping one unknown for another. The SOC 2 cert is good but the methodology needs more transparency.”
“Prompt injection is going to be the SQL injection of the agent era. Tooling that bakes in zero-knowledge credential handling at the infrastructure level — rather than bolting it on in prompts — is exactly the architecture shift the industry needs. Expect this pattern to become a compliance requirement.”
“LLM apps are entering the maintenance and reliability phase — the 'build it and see' era is over. Systematic failure analysis with auto-generated remediation is the natural next layer of the stack. Kelet is early, but the category is real and it will be important infrastructure within 18 months.”
“For creators running agents that touch their Shopify store, social APIs, or payment processors, this is genuinely peace of mind. I don't want to think about whether my coding agent just got manipulated into printing my Stripe key. Agent Vault makes that a non-problem.”
“If you've shipped a chatbot or AI writing tool and are drowning in 'the bot said something weird' support tickets, Kelet is the triage system you didn't know you needed. Finding which prompt variant is responsible for the weirdness has historically been a manual nightmare.”
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