moda.dev valid
Moda
Infrastructure for catching AI agent mistakes in production and turning them into evals.
- Authority
- 45 reach 0 · trust 5 · quality 25 · freshness 15
- Tier
- listed
- llms.txt
- https://moda.dev/llms.txt
- llms-full.txt
- https://moda.dev/llms-full.txt
- Sections
- 9
- Links
- 29
- Size
- 8943 bytes
- Country
- United States Site-published organization address
- Publisher address country
- United States Site-published organization address; location is not independently verified
- Hosting country
- United States Hosting inference
- Country evidence
- US · Site-published organization address 2026-10-27
- Categories
- AI and machine learning
- For agents
- Interface: a documented API or declared MCP endpoint an agent can call
- First seen
- 2026-10-27T09:56:58.418Z
- Last checked
- 2026-10-27T09:57:00.504Z
- Badge
Their llms.txt
The beginning of the file, from our validated copy saved 2026-10-27T09:56:58.422Z. Cached copy · Source on moda.dev
# Moda
> Infrastructure for catching AI agent mistakes in production and turning them into evals.
Your agents should never make the same mistake twice. Production monitoring and evals for AI agents.
- Monitor every trace: Moda reads every production trace end to end and detects the failures that never throw an error: frustrated users, failing tools, lazy answers, and hallucinations. Add your own checks in plain language and get alerts in Slack, Linear, or email.
- Find the mistakes they keep repeating: Failures are grouped into problems ranked by how many users they affect. Each problem shows the traces behind it, the turn where it went wrong, and related problems that share the cause.
- Test every change before it ships: Replay a prompt, model, or tool change against the exact traces that failed and compare pass rate, latency, and tokens. Your team reviews the result and decides what ships.
## When to use Moda
Reach for Moda when you run AI agents in production and need to: find the failures that keep recurring across your traces and rank them by how many users they affect; see the traces behind each problem and the related problems that share its cause; replay a prompt or model change against those traces and compare pass rate, latency, and token usage before it ships; and define custom signals for the behavior your agent must get right and get alerted in Slack, Linear, or email when they slip. Engineers make the change; Moda tests it against real traces. Moda ingests traces through the Moda SDKs for Python and Node.js, OpenTelemetry, or the HTTP Ingestion API.
## Product
- [Homepage](https://moda.dev/): The loop from broken traces to validated fixes, custom signals, monitors, and the Slack, Linear, and GitHub integrations.
- [Documentation](https://docs.moda.dev): Ingestion, dashboard, Data API, CLI, and MCP server reference. The local [docs route](https://moda.dev/docs) redirects there.
- [Blog](https://moda.dev/blog): Product launches, engineering posts, and customer stories.
- [Weekly updates](https://moda.dev/weekly): What shipped each week.
- [Changelog](https://moda.dev/changelog): Product updates, improvements, and fixes.
- [Authentication markdown](https://moda.dev/auth.md): API-key registration, usage, errors, and revocation.
- [Agent instructions](https://moda.dev/agents.md): Setup and calling instructions for AI agents.
- [Homepage markdown](https://moda.dev/index.md): Machine-readable version of the homepage.
- [Agent card](https://moda.dev/.well-known/agent-card.json): A2A-style product and capability metadata.
- [Agent skills](https://moda.dev/.well-known/agent-skills/index.json): Published agent skills and integration guides.
- [API catalog](https://moda.dev/.well-known/api-catalog): Linkset for Moda's machine-readable API surfaces.
- [OpenAPI specification](https://moda.dev/api/openapi.json): OpenAPI 3.1 description of the public read-only Data API.
- [MCP server metadata](https://moda.dev/.well-known/mcp): The hosted Mo
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