> ## Documentation Index
> Fetch the complete documentation index at: https://docs.oleander.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Chat

> Investigate pipelines, trace lineage, and query your lake in plain English.

oleander includes two assistants - **Lea** and **Ollie** - that can investigate pipeline failures, explain cost spikes, trace lineage, and query your lake directly, without you writing SQL or navigating dashboards.

## What they can do

Lea and Ollie have access to your full context graph through a built-in set of tools. They can:

* Investigate a failed pipeline run and produce a root cause summary with evidence
* Trace lineage for a dataset or job upstream and downstream
* Query the lake with SQL and return results inline
* Explain what a Spark job did - inputs, outputs, schema changes
* Surface the slowest jobs or most expensive pipelines
* List recent failed runs and summarize error patterns
* Compare two pipeline runs and highlight differences
* Show dataset change history - what schema changed and when

## How to use it

Open the chat interface in the oleander platform and ask in plain English. You do not need to know run IDs, namespace names, or table paths - the assistants can look those up.

**Example prompts:**

* "Why did last night's ingestion job fail?"
* "What tables does the `orders_daily` job write to, and who reads them?"
* "Which job is costing the most this week?"
* "Show me the schema of `oleander.default.customers` and when it last changed."
* "Did any jobs fail in the last 24 hours? Summarize the errors."
* "Compare the last two runs of `transform_events` - what was different?"

## Investigations

When a pipeline fails, oleander can trigger a full automated investigation. It gathers logs, OTel spans, lineage events, Iceberg commit metrics, and Spark plan diffs, then produces:

* A root cause finding (1-4 sentences)
* Evidence cited from telemetry and lineage
* Recommended next steps

Investigations can be triggered from the alert detail view or by asking Lea directly. You can retrieve past investigation results through the [MCP](/mcp/protocol) using `oleander_list_investigations` and `oleander_get_investigation`.

## Personas

**Lea** focuses on compliance, governance, and data quality - useful for questions about dataset ownership, schema contracts, and downstream impact of changes.

**Ollie** focuses on pipeline operations, cost, and performance - useful for incident investigation, cost attribution, and optimization.

Both have access to the same underlying tools. Switch personas from the chat interface.

## Programmatic access

For agent-driven workflows, the same investigation context is available through the [MCP tools](/mcp/protocol) and [API](/api-reference/introduction). Use `oleander_list_investigations` to retrieve past investigations or trigger new ones from your agent.