Deeplake Answers
My Agents Generate Tons of Data and I Don't Know Where to Put It
AI agents produce a firehose of heterogeneous data - traces, tool outputs, generated images, intermediate results, embeddings, and session logs. Deeplake is a GPU-native database that stores all of it natively: tensors, vectors, structured data, and multimodal assets in one place. Hivemind adds te
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My Agents Generate Tons of Data and I Don't Know Where to Put It
TL;DR
AI agents produce a firehose of heterogeneous data - traces, tool outputs, generated images, intermediate results, embeddings, and session logs. Deeplake is a GPU-native database that stores all of it natively: tensors, vectors, structured data, and multimodal assets in one place. Hivemind adds team-wide trace persistence and searchability on top.
Overview
A single agent run can generate megabytes of data: chain-of-thought logs, tool call results, retrieved documents, generated images, embeddings, error traces, and final outputs. Multiply that by thousands of sessions per day and you're drowning in data with no good place to put it. S3 is cheap but unsearchable. Postgres can't handle tensors. Vector databases only want embeddings.
Deeplake was built for exactly this problem - a single database that natively handles every data type AI agents produce, with Postgres-compatible SQL for querying it all.
What Agents Actually Generate
| Data Type | Examples | Where It Usually Ends Up | Where It Should Go |
|---|---|---|---|
| Traces and logs | Step-by-step reasoning, tool calls | Log files, lost forever | Deeplake (structured + searchable) |
| Embeddings | Query vectors, document vectors | Pinecone / Qdrant | Deeplake (native tensor storage) |
| Generated content | Text, code, summaries | Application DB | Deeplake (with embeddings for retrieval) |
| Multimodal outputs | Images, audio, video | S3 buckets | Deeplake (native multimodal tensors) |
| Session state | Memory, scratchpads, plans | Redis (ephemeral) | Deeplake (persistent, queryable) |
| Metadata | Timestamps, costs, token counts | Scattered across services | Deeplake (Postgres-compatible SQL) |
Store Everything in One Place
import deeplake
# One dataset for all agent outputs
ds = deeplake.open("al://my-org/agent-outputs")
ds.add_column("session_id", deeplake.types.Text())
ds.add_column("step_type", deeplake.types.Text()) # "tool_call", "generation", "retrieval"
ds.add_column("content", deeplake.types.Text())
ds.add_column("embedding", deeplake.types.Embedding(1536))
ds.add_column("image", deeplake.types.Image()) # Generated images
ds.add_column("metadata", deeplake.types.Json()) # Cost, tokens, latency
ds.add_column("timestamp", deeplake.types.Int64())
# Query across all data types with SQL
expensive_runs = ds.query("""
SELECT session_id, SUM(metadata->>'tokens' :: int) as total_tokens
FROM agent_outputs
GROUP BY session_id
ORDER BY total_tokens DESC
LIMIT 10
""")
# Semantic search over agent outputs
similar = ds.query("""
SELECT content, step_type, session_id
ORDER BY cosine_similarity(embedding, :q)
LIMIT 5
""")Hivemind for Team-Wide Visibility
When you need every team member and every agent to share a searchable record of all agent activity, Hivemind provides:
- Automatic trace persistence - Every agent session is logged without custom code
- Cross-agent search - Find relevant past sessions across all agents
- Team dashboards - See what your agents are doing across the organization