Tropir - The AI that builds better AIs
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Tropir: The AI That Debugs and Improves Other AIs

In a world racing to integrate Large Language Models (LLMs) into everything—from copilots to search assistants to autonomous agents—one foundational problem remains: pipelines break. And when they do, the debugging process is painfully manual, slow, and often incomplete. That’s where Tropir enters the scene.

Founded in 2024 and backed by Y Combinator’s Spring 2025 batch, Tropir is not just another observability tool. It’s the first AI that builds better AIs—a meta-level assistant that can trace errors in your AI system, propose fixes, run the revised pipeline, and evaluate the outcomes. Tropir doesn’t stop until the issue is resolved.

With roots in elite institutions like Princeton and Georgia Tech and a technical foundation grounded in real-world experience at Philips, Duckie AI, and advanced NLP labs, the founding team—Aarush Kukreja and Ayush Karupakula—is building what may soon become indispensable infrastructure for every AI development team.

How Does Tropir Actually Work?

At the core of Tropir’s innovation is its autonomous debugging engine. It breaks down the process into a self-contained loop:

Trace → Fix → Rerun → Evaluate → Repeat

Most observability platforms stop at detection. Tropir takes things several steps further:

  • Trace: Tropir doesn’t just log issues. It dives deep into the flow of logic across prompts, retrieval systems, tool integrations, and LLM output to pinpoint why something went wrong.
  • Fix: Once the root cause is located, Tropir suggests a specific patch—no full code rewrite necessary.
  • Rerun: It implements the fix dynamically and executes the pipeline again.
  • Evaluate: Tropir then runs built-in evaluations (or custom ones) to check if the problem has been resolved.

This end-to-end loop allows Tropir to iterate until the output quality reaches your defined standard, without any need for manual intervention.

What Makes Tropir Different from Other Tools?

While the market is saturated with tools offering observability, metrics, and debugging dashboards, Tropir is fundamentally different. It doesn’t just observe—it acts.

Here’s what sets it apart:

Traceback Engine

Tropir’s secret weapon is its root-cause tracing algorithm. Whether the problem stems from hallucinated LLM output, irrelevant document retrievals, or broken API/tool calls, Tropir knows where to look.

Fix + Rerun Without Code Rewrites

Traditional fixes often require developer input to alter logic or prompts manually. Tropir introduces “smart patches” on the fly, allowing it to rerun the system with its own modifications, saving engineers hours of work.

Built-in Evals (and Customizable)

The platform comes preloaded with evaluation sets for relevance, grounding, and structural coherence. But it also allows teams to inject their own evaluation sets and scoring logic, making it customizable to the specific goals of the application.

Conversational Interface

You can talk to Tropir like you would a colleague. Ask:
“Why wasn’t @last_log grounded in retrieved context? Use @faithfulness_eval.”
Or go blunt: “This output sucks. Use the full eval set to fix it.”
Tropir handles the rest.

Plug-and-Play Integration

Getting started is frictionless. Two lines of code, and Tropir is live in your system. It starts learning immediately, tracing issues as they arise and suggesting real-time fixes.

Who Is Tropir For?

Tropir is already being adopted by teams building advanced AI agents, RAG pipelines, and LLM-based copilots. These are the types of systems where debugging is especially painful, and small improvements can lead to significant leaps in performance and reliability.

It’s perfect for:

  • Startups building AI-powered products that can’t afford lengthy downtime or debugging delays.
  • Research labs and academic groups working on prompt engineering and novel agent architectures.
  • Enterprise AI teams deploying LLMs in production workflows where uptime and performance are non-negotiable.

As generative AI becomes more embedded in real-world applications, the need for self-repairing, self-evaluating infrastructure like Tropir will only grow.

What Problems Does Tropir Solve in Today’s AI Pipelines?

LLM systems fail frequently—and often unpredictably. Common issues include:

  • Hallucinated outputs: Responses not grounded in any retrieved or factual source.
  • Broken tool calls: Malformed function invocations, timeouts, or API mismatches.
  • Irrelevant context retrieval: Systems pulling the wrong data from vector databases or knowledge bases.
  • Prompt misfires: Poorly constructed prompts leading to unintended or ambiguous output.

Tropir doesn’t just point these out. It tackles them, one by one, using its self-improving loop.

Imagine deploying an agent that uses tools, retrievals, and multiple prompt chains. Instead of poring over logs for hours to track a bug, Tropir can identify that your third prompt’s structure caused the LLM to misinterpret the intent, patch it, rerun the sequence, and evaluate the result. You get improved performance, faster cycles, and fewer headaches.

Why Is Tropir a Glimpse Into the Future of AI Infrastructure?

Tropir represents a shift from human-centered debugging to agent-centered self-repair. This mirrors broader trends in AI development where automation isn't just the end-user function—it's embedded deep within the development cycle itself.

As Aarush and Ayush put it:
“You build the flow. We make it unbreakable.”

This vision aligns with the future of modular AI systems: LLMs + tools + memory + retrieval + user interaction. The more modular the system, the more points of failure, and the more essential Tropir becomes as a stabilizing backbone.

What’s Next for Tropir?

Currently based in San Francisco and working with early adopters across the YC ecosystem, Tropir is poised to become a staple of serious AI teams. As it evolves, we can expect:

  • Deeper integrations with orchestration frameworks like LangChain, LlamaIndex, and Agentic design patterns.
  • Support for more complex evaluation pipelines, especially in regulated industries like finance or healthcare.
  • Autonomous optimization loops that not only fix bugs but also enhance performance through prompt or model tuning.
  • Insights dashboards that turn Tropir into a proactive AI coach, offering strategic recommendations on pipeline architecture.

How Can You Try Tropir?

Tropir is currently onboarding teams building complex AI systems. Integration is minimal—just two lines of code—and you can begin diagnosing and fixing errors automatically.

If you're working on agents, copilots, or RAG pipelines and struggling with system stability or performance debugging, Tropir might be the missing piece in your stack.

Final Thought

As LLM systems scale in complexity and ambition, tools that think like engineers will become invaluable. Tropir is that tool: a debugging assistant, a performance optimizer, and ultimately, a meta-AI building the next generation of more reliable, more intelligent systems.