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klanex
Reliability layer for AI agent tool calls
LLM agents are good at deciding which tool to call and bad at handling it when the call fails. klanex sits between your agent and the real API. It validates payloads against your JSON Schema, absorbs 429s and outages with retries, backoff and circuit breakers, keeps credentials KMS-sealed, and reports back over a signed webhook with a full audit trail. Failures return an llm_hint your agent can paste straight into its context and self-correct from. Free sandbox key, no signup, no card.
Hi Product Hunt 👋
When building agents the same thing kept breaking. Not the reasoning, the execution. The model picks the right tool, then sends a payload with a hallucinated field, or hits a 429, or catches a five minute outage at the vendor. The agent has no principled way to react, so it retries a 400 forever, or re-prompts itself on a 503 and mangles a payload that was already correct.
klanex is the layer I wanted. You POST a tool-use intent and get an execution ID in milliseconds. It validates the payload against your JSON Schema before anything leaves the building, then owns the retries, backoff, circuit breakers and idempotency, keeps your API keys KMS-sealed and out of the model's context, and reports the outcome over a signed webhook with an audit trail you can replay byte for byte.
The part I most want feedback on is error design. Failures come back classified, and the ones a model can actually fix carry an llm_hint written to be pasted straight into its context. Transient and auth failures explicitly tell the model not to touch the arguments, which is what stops a good payload getting wrecked on retry.
You can try it right now without an account:
curl -sX POST https://api.sandbox.klanexai.com...
That mints a disposable sandbox key. 100 executions, no signup, no card. The error taxonomy is open source and stands alone too, Apache-2.0 and zero deps: github.com/chrassy/agenterr
How are you handling failed tool calls today? I'm especially curious where a four-way split (retry / re-prompt / escalate / human) breaks down for your tools.
About klanex on Product Hunt
“Reliability layer for AI agent tool calls”
klanex was submitted on Product Hunt and earned 4 upvotes and 1 comments, placing #112 on the daily leaderboard. LLM agents are good at deciding which tool to call and bad at handling it when the call fails. klanex sits between your agent and the real API. It validates payloads against your JSON Schema, absorbs 429s and outages with retries, backoff and circuit breakers, keeps credentials KMS-sealed, and reports back over a signed webhook with a full audit trail. Failures return an llm_hint your agent can paste straight into its context and self-correct from. Free sandbox key, no signup, no card.
On the analytics side, klanex competes within API, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how klanex performed against the three products that launched closest to it on the same day.
Who hunted klanex?
klanex was hunted by aljaz klanecek. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of klanex including community comment highlights and product details, visit the product overview.
aljaz klanecek