r/AIToolsPromptWorkflow

Builders-welcome post with the substance up front (disclosure: I'm the maintainer). OmniRoute is a free, MIT, self-hosted AI gateway — one OpenAI-compatible endpoint over 237 providers — built around two problems: runs dying on a provider 429, and tokens bleeding on tool/log output.

One endpoint, 237 providers — 90+ of them free. You point any tool or agent at a single OpenAI-compatible endpoint (localhost:20128/v1) and it can reach 237 LLM providers without you rewriting anything. 90+ have free tiers and 11 are free forever (no card), which aggregates to ~1.6B documented free tokens/month — and that's honest, pool-deduped math (we count each shared pool once instead of inflating it; the methodology is public in the repo). There's a one-command setup-* for 13+ coding tools (Claude Code, Codex, Cursor, Cline, Roo, Kilo, Gemini CLI…), so switching your existing setup over takes seconds.

Fallback combos — so it never stops mid-task. A "combo" is a ladder of models the router walks automatically: your subscription first, then API keys, then cheap models, then free ones. When a provider returns a 500 or you hit a rate limit, it slides to the next target in milliseconds, mid-request, and your tool never even sees the error. There are 17 routing strategies (priority, weighted, round-robin, cost-optimized, auto/coding:fast…) plus three resilience layers — a per-provider circuit breaker, a per-key cooldown, and a per-model lockout — so one dead key can't take down a whole provider.

Fusion — an ensemble mode for the hard steps. Beyond simple routing, there's a fusion strategy that fans a single prompt out to a panel of different models in parallel and then has a judge model synthesize one best answer (mixture-of-agents, built in). It's cost-aware, so easy turns stay on one fast model and it only fuses when the step is worth it.

A 10-engine compression pipeline — the part most routers don't have. Every request flows through a transparent compression pass you can toggle/stack per combo. Instead of one trick, it stacks the best of the open-source ecosystem: RTK filters command/tool output (git diffs, test logs, builds) at 60–90%, Microsoft's LLMLingua-2 does ML semantic pruning, Caveman handles prose, session-dedup strips repeats across turns. Critically, code, URLs and JSON are preserved byte-perfect, and a default-on inflation guard throws the compressed version away and sends the original if compressing would actually grow the prompt — it never makes things worse. On tool-heavy sessions that's ~89% average input-token reduction (an 8k-token git diff becomes a few hundred). Full credit to every upstream project (RTK, Caveman, LLMLingua-2, Troglodita) is in the README.

Agent-native — the agent can drive the router itself. There's a built-in MCP server (95 tools across 30 audited scopes, over stdio / SSE / streamable-HTTP), plus A2A (v0.3, JSON-RPC 2.0) support. That means an agent can query providers, switch combos, read its own remaining quota and manage memory through the gateway — not just consume tokens through it.

It's 100% local (zero telemetry, AES-256-GCM at rest), MIT-licensed, has a prompt-injection guard on every LLM route, opt-in memory, and runs on npm, Docker, desktop or your phone via Termux.

For context on whether it's worth your time: it's grown to ~9.8K GitHub stars, 1,490+ forks and 280+ contributors in ~4.5 months, with 21,000+ automated tests and 1,830+ issues closed — so it's a battle-tested project, not a brand-new experiment.

npm install -g omniroute

GitHub: https://github.com/diegosouzapw/OmniRoute · Site: https://omniroute.online

Would value a critique of the routing/compression architecture from this crowd.

Ask questions across your Markdown notes using a fully local Graph RAG engine. Built for Obsidian vaults, works with any folder of Markdown files. Extracts entity-relation triples from wikilinks & YAML frontmatter, retrieves answers via hybrid search (vector + BM25 + temporal). Multilingual. No cloud. Runs on Ollama.

https://github.com/benmaster82/Kwipu

Built a small tool web security research.

You query it in plain English and it returns actual writeups with the source URL and the exact section that matches your question. No AI summaries or made-up answers.

Right now it's focused on XSS, WAF bypasses, CSP, CORS, SSRF, request smuggling, XS-Leaks, cache poisoning, prototype pollution, JWT/auth stuff, etc. Server-side coverage is next.

I mainly built it because when I'm stuck, somebody has usually already written about a similar problem. Finding that writeup is the hard part, especially for newer techniques that general models often miss.

Would genuinely appreciate feedback on where it fails. If you try it, let me know what you searched for and whether the results were actually useful.

Heyy, I'm sharing this for people interested in Claude Governance. I have shared a link to more resources in the comment section.

You can access seedance 2.0 for free using Dola ai website, but soon you will realise, it gives you only few credits daily, for this you have to use Google skills, once you sign up, you can literally keep this running all day without any limit, I am going share tutorials link if you want see in details. https://youtu.be/KCbY-5gbbK8?si=jzQK3k5FDtKabbSH

𝐀 𝐥𝐨𝐨𝐩 𝐧𝐞𝐞𝐝𝐬 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐚 𝐩𝐫𝐨𝐦𝐩𝐭.

It needs to gather the right context, solve the actual problem, and build things the way you like.

It needs a plan it can carry across sessions, a way to check the work against your real goal, and a record of every decision it made while you were away.

That is what I am building with 𝐆𝐨𝐚𝐥𝐬. https://github.com/ShivamGupta42/goals

𝐆𝐨𝐚𝐥𝐬 is a small CLI + plugin for Claude Code and Codex. Your agent gets commands to create the goal, take the next step, check status, view a dashboard, import a proven loop, and improve the loop itself.

Everything it does is saved in 𝐟𝐢𝐥𝐞𝐬 𝐲𝐨𝐮 𝐨𝐰𝐧: the goal, the current phase, the decisions, the evidence, the failed checks, and the history.

Can we make agents run longer? We can.

The better question: can a long-running agent build something you can 𝐭𝐫𝐮𝐬𝐭, 𝐯𝐞𝐫𝐢𝐟𝐲, 𝐟𝐢𝐱, 𝐫𝐞𝐮𝐬𝐞, 𝐚𝐧𝐝 𝐜𝐨𝐧𝐭𝐢𝐧𝐮𝐞?

That is where Goals comes in:

- 𝐏𝐥𝐚𝐢𝐧-𝐄𝐧𝐠𝐥𝐢𝐬𝐡 𝐠𝐨𝐚𝐥𝐬: say what you want and Goals turns it into tracked phases.

- 𝐕𝐞𝐫𝐢𝐟𝐢𝐞𝐝 𝐬𝐭𝐞𝐩𝐬: a step is accepted only after its proof actually runs.

- 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬 𝐲𝐨𝐮 𝐜𝐚𝐧 𝐫𝐞𝐚𝐝: every choice is explained by goal, risk, and reversibility, and you can change it.

- 𝐅𝐢𝐱𝐚𝐛𝐥𝐞 𝐟𝐚𝐢𝐥𝐮𝐫𝐞𝐬: a failed check points to the next repair instead of a vague retry.

- 𝐑𝐞𝐮𝐬𝐚𝐛𝐥𝐞 𝐥𝐨𝐨𝐩𝐬: import a proven loop, fill in the gaps, and Goals validates it before you run it.

- 𝐑𝐞𝐬𝐮𝐦𝐚𝐛𝐥𝐞 𝐰𝐨𝐫𝐤: pick up after a /clear, a new session, or a switch between Claude Code and Codex.

The project is open source:
https://github.com/ShivamGupta42/goals

If you are running long loops, try Goals on one real project and tell me where it falls short. PRs and suggestions are welcome :)