Kredisco
Summary
When output comes back wrong in a multi-agent pipeline, you guess which step caused it — because agents report their own performance, and they always say they did fine.
Kredisco scores AI agents the way a credit bureau scores borrowers: receipts are signed by the caller, not the agent, so every score is built from observed retries, failures, latency, and cost rather than self-reported claims. Wrap existing calls with a one-line tracker, name the agent once, and a file starts accumulating. The score is normalized against the median for that task class, so nine thousand tokens only means something relative to what other agents doing the same job actually consumed. The tool is early-access and cloud-only, with no self-hosted option described. Teams that need on-premise observability or audit logs they control will hit that wall immediately.
Bottom line: Pick this if you have a working multi-agent pipeline and no objective way to tell which agent is the drag — skip it if your compliance posture requires data residency or self-hosted infrastructure.
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Pros
Sign in to edit- Caller-signed receipts mean agent scores are built from what the orchestrator observed — retries, failures, latency — so an underperforming agent cannot report itself as healthy while quietly costing you money.
- Per-class median normalization, so token count or latency is evaluated against agents doing the same task rather than a global average, which means a slow agent on a hard task does not get penalized alongside a slow agent on a trivial one.
- Wraps existing call sites with a single function, so you start accumulating a track record without rewriting your pipeline or adding a new service layer.
- Provider-agnostic by design — the vendor page names LangGraph, CrewAI, and plain loops as supported targets, so the scoring layer does not require you to standardize on a specific framework.
Cons
Sign in to edit- No self-hosted option is described anywhere on the vendor page. Teams whose security or compliance posture blocks third-party telemetry have no workaround — they switch to an open-source observability stack they can run inside their own network.
- Early-access stage means the vendor is still collecting use cases rather than publishing uptime commitments or data retention guarantees. Teams building production pipelines that need contractual SLAs sign up for a tool that cannot yet offer them.
- Score history is tied to the Kredisco service, not exported to infrastructure you own. If the service changes pricing, access terms, or discontinues, the track record you built against your agents goes with it — teams that need durable, portable observability data plan for migration from the start.
About
- API Available
- Yes
- Self-Hosted
- No
- Last Updated
- 2026-09-21T16:18:58.018Z
Best For
Who it's for
- Teams running multi-agent systems
- Developers needing objective agent performance data
- Early adopters building agent infrastructure
What it does well
- Ranking agents in multi-agent pipelines by observed performance
- Identifying underperforming agents causing errors or delays
- Building track records for agents across different teams or deployments
Integrations
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Sign Up to ContributeFrequently Asked Questions
- Does Kredisco have an API?
- Yes. Kredisco exposes a developer API. See the official documentation at https://kredisco.com for details.
- When was Kredisco released?
- Kredisco was first released in 2026.
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Most multi-agent observability tools show you what happened. Kredisco tells you whether it was good relative to the agents doing the same work. The core workflow: wrap the function call you are already making with `kd.track()`, pass a validator and retry budget, and Kredisco records cost, latency, retries, and outcome tagged by task class. Receipts accumulate into a history. History gets scored against the median for that class. The result is a ranked list of agents by what they actually delivered, not what they claimed.
The differentiating design choice is that the caller signs the receipt, not the agent. This mirrors how credit bureaus work — lenders report on borrowers, borrowers do not report on themselves. An agent running in your pipeline cannot inflate its own score. That asymmetry is the product; without it, every agent in a system has an incentive to look clean.
Kredisco fits teams already running LangGraph, CrewAI, or plain loops who want a track record attached to each agent without rebuilding their call layer. The integration surface described in the docs is a Python wrapper around existing calls — no new architecture required to start collecting data. Where it breaks: there is no self-hosted option described anywhere on the vendor page, so teams with data residency requirements or internal security reviews blocking third-party telemetry have no path forward. The tool is also in early access, meaning the vendor is actively soliciting use cases and war stories — production SLA guarantees are not described.
The API accepts an `api_key` and a GitHub sign-in flow for early access. The Python client shown on the vendor page supports a `validate` lambda and a `retries` parameter per tracked call, which means pass/fail logic and retry cost both feed the score automatically from the first wrapped call.