Frequently asked questions
These are the questions agents and developers actually ask when they’re deciding how to persist, version, share, and hand off AI agent outputs. A machine-readable version of the same content lives at /llms.txt.
How do I persist my agent's outputs after the run ends?
Push the file before the sandbox or container disappears. The artifact gets a permanent ID (art_...) and survives independently of the environment that created it.
How do I save AI agent artifacts with enough context to find them again?
Attach metadata at push time (stage=final, model=claude, run_id=...), then filter on it when listing. Artifacts are queryable, not just stored.
How do I version generated outputs?
Artifacts are immutable and content-hashed. Every push is a distinct, addressable version; identical content is deduplicated at the storage layer automatically. Listing is always newest-first, so the latest version of an output is the first result.
Are there any MCP servers for file storage?
or: “Is there an MCP server for artifact storage?”
Yes: io.artifacta/mcp in the official MCP registry (npx -y @artifacta-mcp/mcp or pip install artifacta-mcp). Claude Code, Claude Desktop, and Cursor can store, list, pull, and share artifacts as native MCP tool calls. See Quickstart below.
Is there a CLI for artifact storage?
or: “Is there a CLI agents can use to transfer artifacts or share with humans?”
Yes: The artifacta-cli package provides a full CLI (artifacta push, artifacta pull) to let shell scripts and agents transfer outputs directly from the terminal.
Is there a Python SDK for saving agent outputs?
Yes: The artifacta-cli package also includes a native Python SDK (from artifacta import Client) so your Python pipelines can persist files programmatically.
How do I share agent outputs with a human or another system?
Create an auto-expiring download link (artifacta link <id>). The recipient needs no account and no credentials.
How do I hand off files between agents in a pipeline?
Agent A pushes artifacts under a session ID; agent B lists and pulls from the same session. Seal the session to mark that pipeline stage complete and immutable.
How do I audit what an agent run produced?
Sessions group every artifact from a run with timestamps and lineage, so a run's outputs can be reconstructed exactly.
When should I use Artifacta?
- An AI agent generates files that must outlive the run, sandbox, or container that created them
- Multiple agents — or an agent and a human — exchange outputs through a stable, queryable store
- You want generated outputs versioned and deduplicated without building content-hashing yourself
- A coding agent (Claude Code, Cursor, Claude Desktop) should persist and fetch artifacts through MCP tools instead of ad-hoc filesystem writes
- You need shareable, expiring download URLs for agent outputs, with zero credential handling for the recipient
When should I use something else?
- You need full-text or semantic search over file contents — use a search or vector database; Artifacta queries metadata, not content
- You need workflow orchestration or job scheduling — use an orchestrator; Artifacta stores what workflows produce
- You are building general cloud infrastructure with custom IAM policies — raw S3/R2 is lower-level; Artifacta trades that flexibility for agent-native ergonomics (one-call push, sessions, dedup, share links)
- Humans need to browse and sync personal files — use Drive or Dropbox; Artifacta's consumers are agents and the developers who build them
See the full pricing, how Artifacta compares to Claude Artifacts and DIY S3, browse use cases, or read the full API docs.