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

# Exporting Data

> Export transcripts and agent run metadata for local analysis

A common task is downloading transcripts alongside their agent run metadata for local analysis. This page shows how to export using DQL.

## Exporting with DQL

### Export all transcripts and metadata

```python theme={null}
from docent import Docent
import json

client = Docent()
collection_id = "your-collection-id"

# Download all transcripts joined with agent run metadata
result = client.execute_dql(
    collection_id,
    """SELECT
  t.id AS transcript_id,
  t.name AS transcript_name,
  t.messages,
  t.metadata_json AS transcript_metadata,
  ar.id AS agent_run_id,
  ar.name AS agent_run_name,
  ar.metadata_json AS agent_run_metadata
FROM transcripts t
JOIN agent_runs ar ON ar.id = t.agent_run_id"""
)

rows = client.dql_result_to_dicts(result)

# Parse the messages JSON from each transcript
for row in rows:
    row["messages"] = json.loads(row["messages"]) if isinstance(row["messages"], str) else row["messages"]

print(f"Downloaded {len(rows)} transcripts")
print(f"First transcript has {len(rows[0]['messages'])} messages")
```

### Paginating large collections

DQL caps results at 10,000 rows. If your collection has more transcripts, use `LIMIT` and
`OFFSET` to paginate:

```python theme={null}
page_size = 1000
offset = 0
all_rows = []

while True:
    result = client.execute_dql(
        collection_id,
        f"""SELECT
  t.id AS transcript_id,
  t.name AS transcript_name,
  t.messages,
  ar.id AS agent_run_id,
  ar.name AS agent_run_name,
  ar.metadata_json AS agent_run_metadata
FROM transcripts t
JOIN agent_runs ar ON ar.id = t.agent_run_id
ORDER BY t.id
LIMIT {page_size} OFFSET {offset}"""
    )
    rows = client.dql_result_to_dicts(result)
    if not rows:
        break
    all_rows.extend(rows)
    offset += page_size

print(f"Downloaded {len(all_rows)} total transcripts")
```

### Filtering by metadata

You can narrow the export to specific runs using metadata filters:

```python theme={null}
result = client.execute_dql(
    collection_id,
    """SELECT
  t.id AS transcript_id,
  t.messages,
  ar.name AS agent_run_name,
  ar.metadata_json->>'model' AS model
FROM transcripts t
JOIN agent_runs ar ON ar.id = t.agent_run_id
WHERE ar.metadata_json->>'environment' = 'prod'"""
)
```

## See also

* [Structured queries (DQL)](/analysis/dql) — when to reach for DQL and how the Docent Agent uses it
* [DQL schema reference](/sdk/dql/schema) — full column schema
* [execute\_dql reference](/sdk/dql/execute) — operators, syntax, and SDK mechanics