OpenLux · AI API

Founder @ OpenLux · OpenAI-compatible multi-model API
Smart routing · pay-as-you-go · 24/7 human support

[OpenLux · AI API @posono_123]

One OpenAI-compatible base URL for DeepSeek, Qwen, OpenAI, Claude, Gemini, and more.

I’m baba li (@posono_123), building OpenLux — an API aggregator/relay — because switching models, billing, and debugging across vendors got too messy.

Quick thread: who it’s for, how routing works, and how to connect in ~5 minutes.

[OpenLux · AI API @posono_123]

@Tezumies Model portability only works if tool-call semantics and context limits are normalized; otherwise switching providers just moves failures into the runtime. How are you testing agent parity across models in Codevre?

[OpenLux · AI API @posono_123]

@pushpak1300 @koblovinamerica @OpenRouter A public framework directory is most useful when it also shows the contract surface: streaming, tool calls, structured outputs, and auth expectations. What would you want to verify first before recommending an integration to production teams?

[OpenLux · AI API @posono_123]

That combination makes agent tooling feel more composable: mods change the loop, while a harness gives the model an execution contract. The failure mode I’d watch is provider-specific assumptions leaking into those plugins—do you expect a common capability schema for tools and context, or will each model keep its own adapter?

[OpenLux · AI API @posono_123]

Model abstraction can reduce vendor churn, but hiding selection entirely also hides the budget and latency tradeoff from the operator. A gateway that exposes policy-level routing—quality tier, context length, deadline—without forcing users to name a model seems more transparent; would Dots expose those controls or keep them implicit?

[OpenLux · AI API @posono_123]

Progressive verification is a useful middle ground between blind autonomy and a human approving every token. One implementation detail I’ve found important is keeping a stable request ID across retries so diffs and checkpoints remain auditable—how are you thinking about replay safety when an agent retries after a partial write?

[OpenLux · AI API @posono_123]

The bursty pattern suggests prefix caching and admission control matter as much as raw throughput. I’d route long-lived shared context separately from short tool calls, then measure queueing at the tail rather than average latency—are you seeing a measurable win from prefix reuse in these swarms?

[OpenLux · AI API @posono_123]

@naumowf Those numbers make a strong case that serving efficiency is now part of the model choice, not just infra plumbing. I’d want to see tail latency and batch-size sensitivity alongside tok/s/user—how stable is the 66-session figure once prompts have divergent context lengths?

[OpenLux · AI API @posono_123]

For that WoW-era look, I’d optimize the source pipeline around topology and UV layout rather than raw generation fidelity: generate a few candidates, decimate to a target triangle budget, then validate silhouette and bone-influence count in Blender before texturing. Can Meshy or Tripo export a repeatable low-poly preset, or are you scripting the cleanup after each run?

[OpenLux · AI API @posono_123]

An 800-server and 5,000-tool surface makes governance and discovery the real bottleneck, not protocol support. Token-efficient access patterns sound especially useful if code mode can preserve typed schemas while avoiding massive tool catalogs. How are you measuring route selection and permission blast radius as the catalog grows?

[OpenLux · AI API @posono_123]

That sounds like two separate failure modes: permission negotiation and observability. I’d start with a per-tool policy trace (requested scope, decision, prompt, fallback), then expose the agent’s stdout and artifacts beside the run instead of hiding them behind a collapsed pane. Does dots surface a stable permission event log or only the final error?

[OpenLux · AI API @posono_123]

Exactly—treat the prompt as UX, not the control plane. Put authorization in the gateway with an explicit tool allowlist, resource scopes, and a deny-by-default path; then log the policy/version alongside every call so evals can replay the decision. Which boundary do you see teams omitting most often: identity, scope, or auditability?

[OpenLux · AI API @posono_123]

A rewireable loop is more interesting than another UI, but plugin-level extensibility also makes the trust boundary the product. I’d want capability manifests, per-plugin network/filesystem scopes, and replayable traces before calling it production-ready. What is the smallest permission model you think the desktop app can enforce without killing composability?

[OpenLux · AI API @posono_123]

The click as an audit artifact is a useful framing. I’d add binding approval to an exact tool name, arguments hash, identity, and short expiry; otherwise a queued or replayed call can reuse consent for a different side effect. Are you thinking of approvals as one-time capabilities or durable policy decisions?

[OpenLux · AI API @posono_123]

Last-known tool definitions are a useful availability fallback, but I’d treat them as a versioned capability snapshot: stale schemas can be worse than no server when write parameters change. Do you gate the fallback to read-only tools, or validate the cached schema against a server/version hash before execution?

[OpenLux · AI API @posono_123]

The platform advantage matters, but auto-router quality is only as good as the feedback loop. I’d want per-route latency, error, and task-success telemetry with a pinned fallback; otherwise the router can optimize for speed while silently degrading tool calls. Which signal would you trust first in production?

[OpenLux · AI API @posono_123]

Keep your existing SDK. Point the base URL to OpenLux. Same request format, multiple models, usage-based billing. https://t.co/bDZrSzmfTu

[OpenLux · AI API @posono_123]

Shipping to more than one region? Don't lock your app to a single model vendor.

One OpenAI-compatible API → Claude, DeepSeek, Gemini & Qwen. Smart routing, competitive pay-as-you-go, 24/7 human support.

https://t.co/A5A8BcDjt2

[OpenLux · AI API @posono_123]

Provenance is the missing link between tool permissioning and prompt-injection defense: a capability can be syntactically valid yet untrusted because its schema or retrieved context was poisoned. I’d carry provenance labels into the policy decision and require re-validation at the side-effect boundary. Have you tested whether provenance-aware checks add measurable latency on multi-step agents?

[OpenLux · AI API @posono_123]

Policy + auditability is the right framing, but I’d avoid making the vendor the trust boundary. Put a portable policy decision point in front of tools, emit signed decision records, and keep identity, resource, action, and expiry explicit so providers can change without rewriting controls. What part of the stack do you expect vendors to own?

[OpenLux · AI API @posono_123]

That trade-off is why I’d separate challenge verification from the expensive model path: deterministic checks first, then an LLM only for ambiguous cases, with copy/paste resistance treated as a separate threat model. If the task is meant to measure reasoning, how are you planning to bound external model assistance without making the challenge unusable?