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Cartha — Agent Operations Platform
Ops layer for AI agent fleets: traces, memory, hard budgets
Most teams get AI agent traces. Almost nobody gets governance. Cartha is the ops layer for agent fleets: full run timelines, scoped memory enforced on the server (user/agent/team/org), hard spend budgets that fail closed, attenuated multi-agent delegation, policies, and human-in-the-loop. Instrument in a few lines (Python SDK): @trace, @tool, wrap_openai, then see agents, costs, and memory in one dashboard. Not another chatbot builder. You bring the agents; we keep them accountable.
Hey Product Hunt 👋 We’re the team behind Cartha.
What inspired us
We were debugging an AI agent in production and couldn’t answer the only question that mattered: what did it know when it made that call—and was it even allowed to know that?
Traces told us the path. They didn’t tell us memory scope, spend limits, or who could share what across agents.
What we built
Cartha is an ops / governance layer for AI agent fleets:
• Full run timelines (tools + LLM steps)
• Scoped memory enforced on the server (user / agent / team / org)—not “trust the prompt”
• Hard budgets that stop work when spend hits the ceiling (not soft alerts)
• Multi-agent nest + attenuated delegation (parent can only hand down weaker authority)
• Policies + human-in-the-loop escalations
• Dashboard for fleets, costs, and memory
How you use it
pip install cartha-sdk → set CARTHA_API_KEY → cartha.init() → @cartha.trace / @cartha.tool / wrap_openai().
You keep your agents. We make them operable in production.
What’s different vs “LLM observability”
Observability is necessary. We care about the next layer: enforcement and accountability when agents share memory, call tools, and burn budget.
We’re live at https://cartha.in, How to Use: https://cartha.in/how-to-use
We’re early and shipping with design partners. Brutal feedback welcome:
1) What would make you install this this week?
2) What do you already use for agent traces/memory/cost?
3) What’s a hard no for putting agent memory in a SaaS?
Happy launch day, ask us anything 🚀
The hard spend budgets that fail closed are a great touch. One thing that would make this even more useful for us is a way to set per-tenant or per-project policy profiles, so a staging environment can run loose while production stays locked down without duplicating config.
One thing I'd love to see is a way to replay a specific agent run step-by-step with the ability to fork it from any intermediate decision point. That would make debugging weird multi-agent handoffs so much easier, especially when something goes sideways three layers deep and you want to test a different policy branch without rerunning the whole thing.
About Cartha — Agent Operations Platform on Product Hunt
“Ops layer for AI agent fleets: traces, memory, hard budgets”
Cartha — Agent Operations Platform was submitted on Product Hunt and earned 6 upvotes and 3 comments, placing #147 on the daily leaderboard. Most teams get AI agent traces. Almost nobody gets governance. Cartha is the ops layer for agent fleets: full run timelines, scoped memory enforced on the server (user/agent/team/org), hard spend budgets that fail closed, attenuated multi-agent delegation, policies, and human-in-the-loop. Instrument in a few lines (Python SDK): @trace, @tool, wrap_openai, then see agents, costs, and memory in one dashboard. Not another chatbot builder. You bring the agents; we keep them accountable.
Cartha — Agent Operations Platform was featured in Developer Tools (516.2k followers), Artificial Intelligence (474.1k followers) and Tech (628.3k followers) on Product Hunt. Together, these topics include over 351k products, making this a competitive space to launch in.
Who hunted Cartha — Agent Operations Platform?
Cartha — Agent Operations Platform was hunted by Pranav Dhaygude. 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.
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Pranav Dhaygude