Cartha tracks agent permissions, memory, and true task cost
Cartha is a management layer for running more than a few AI agents while keeping their actions, , and costs visible. A few lines of Python or can connect it to an agent, including one built with LangGraph or a custom workflow. Each run records memory use, , AI model steps, applied rules, and spending in order.
Parent and child work remains linked as a nested rather than being flattened into one list. When two attempts at the same task produce different results, Cartha can locate the first step where their paths separated. limits access by user, agent, team, or organization, so a support agent cannot read finance data merely because both use the same service connection.
Teams can choose whether blocked access returns an empty result or a clear refusal. Costs are calculated by agent, tool call, and completed task, with retries and failed rework included in the final task total.
Key points
- A few lines of Python or can add tracking to an existing agent.
- Each run records memory, , model steps, rules, and costs in sequence.
- Two runs can be compared to find the first step where their behavior differed.
- Memory access can be enforced separately for users, agents, teams, and .
- Completed-task cost includes retries and work repeated after failures.