Stirling-PDF vs Tigris
Side-by-side trajectory, velocity, and editorial themes.
Stirling PDF widens distribution while it iterates on file-management ergonomics.
Stirling PDF is in a steady V2-maturing rhythm. 2.9.0 introduced server-side file sharing and alpha group signing (visual and certificate-based). 2.10.0 broadened distribution with AppImage, RPM, Homebrew, AUR, Scoop, and winget support and a new pixel-compare mode. 2.10.1 unified the Mac installer for x86 and arm. 2.11.0 ships a redesigned file-management UI as a preview, directly answering the recurring 'forced file management' feedback since the V2 launch.
The project is balancing breadth — file sharing, group signing alpha, more package formats — against UX refinement around how users discover and operate on files. Group signing in particular reads as a deliberate enterprise-feature land grab from an open-source angle, putting pressure on the lower end of the Adobe Acrobat market. The desktop story has moved from optional login to no required login at all, which suggests the team is taking the local-first install seriously.
Expect the file-management UI preview to stabilize quickly given how loud the prior feedback was, group signing to graduate out of alpha within a release or two, and continued packaging work to cover more Linux distributions and a wider self-host surface.
Tigris turns its object store into agent infrastructure with Agent Kit, agent-shell, and durable global streams.
Tigris's release stream is a sustained product-marketing push around AI-agent storage primitives. Agent Kit landed as a TypeScript SDK exposing bucket forks, workspaces, checkpoints, and event coordination. agent-shell put a virtual bash environment with persistent storage in front of those primitives. Durable global streams via S2 Lite extended the object store into a streaming substrate suitable for per-agent reasoning traces. Around the launches, case studies and tutorials (Basic Memory, the $10 self-updating knowledge base) make the pitch concrete.
Tigris is staking a position that the right substrate for AI agents is not a database, vector store, or queue — it is a globally-distributed, fork-able object store. Each blog and SDK in this batch reinforces that thesis from a different angle: storage as message queue, fork-per-agent sandboxing, storage-protected agent containment, streams for reasoning traces. The competitive map being drawn includes R2, S3 Express, Backblaze, and the agent-runtime vendors (Modal, E2B), not other databases.
Expect a managed Vector or Lance-index surface on top of buckets to compete more directly with Turbopuffer and Pinecone, and a Python counterpart to the @tigrisdata/agent-shell TypeScript runtime to widen the agent-developer surface area.
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