Millions of documents
A question as simple as “which services are failing?” touches millions of log lines and spans.
Coralogix Headless Usage.
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How customers use Coralogix from the terminal and from coding agents, through the Coralogix CLI and the Coralogix MCP server. Every figure is live.
Tooling
The coding agents people use to drive Coralogix from the CLI.
User behaviour
What people do once they work headlessly: what they ask Coralogix for, and what they build.
What coding agents ask Coralogix for - Last 7 Days
Log and span queriesMetrics queriesConfiguration
Learn more: The Coralogix CLI
Why agents work well on Coralogix
Agents use Coralogix differently from people. They ask more questions, read every answer into a limited context window, and come back to the same data again and again. Five things make that work.
Server-side aggregation
An agent that pulls raw logs into its context fills it within a few thousand lines, and pays in tokens for every one. Coralogix turns that around: a DataPrime query filters, groups and aggregates millions of documents where they live, and sends back only the rows that answer the question.
The agent asks which services are failing and gets a ranked list, not a log dump. Its context window stays free for reasoning.
Documents, query, insight
One DataPrime query
DataPrime filters, groups and aggregates them in the customer’s own storage, in seconds.
A few rows back
The agent reads the handful of rows that answer the question, not the million that don’t.
Embedded skills
Installing the Coralogix CLI adds more than 20 skills to a coding agent: short, tested guides for querying logs, spans and metrics, writing DataPrime, building dashboards, defining alerts and SLOs, following a RUM session and cutting costs.
The agent loads the skill it needs when it needs it. Instead of guessing at syntax it explores: it finds the right fields, runs the right query and follows the answer to the next question.
Adaptive formatting with TOON
The Coralogix CLI writes its answers in three formats: text for people at a terminal, JSON for scripts, and TOON for agents. One flag, -o toon, or a profile default, and every answer comes back sized for a context window.
TOON, Token-Oriented Object Notation, states each field name once and then lists the values row by row, where JSON repeats every key on every row. The data is identical; the agent reads a fraction of it. The more rows an answer has, the bigger the saving.
JSONevery key on every row
[{"service":"checkout","errors":412,"p95_ms":830}, {"service":"payments","errors":96,"p95_ms":410}, {"service":"search","errors":12,"p95_ms":220}, {"service":"auth","errors":3,"p95_ms":95}, {"service":"inventory","errors":0,"p95_ms":180}]
242 characters
TOONfield names once, then the values
services[5]{service,errors,p95_ms}: checkout,412,830 payments,96,410 search,12,220 auth,3,95 inventory,0,180
118 characters, 51% fewer
Unlimited data access, without rehydration
Agents ask follow-up questions, and the follow-up is often about last month rather than the last hour. Where older data sits in cold storage, that means a rehydration job first: restore the data, wait, then query.
Coralogix gives you unlimited access to your data, queried directly in your own cloud object storage. There is no restore step and no second hot copy to pay for, so an agent gets an answer about last quarter as readily as one about the last five minutes.
No charge per query
Agents are curious. A single investigation can run dozens of queries, and a team of agents runs thousands a day.
Coralogix does not charge per query or per byte scanned, so nobody has to ration an agent’s curiosity, or decide in advance which questions are worth the bill.
See the architecture for yourself
Server-side aggregation, querying in place and flat pricing all come from the same design: Streama, and the telemetry lake beneath it. Walk through the model that handles billions of daily queries across hundreds of petabytes.
Take the Streama Tour