Update rerun requirement from 0.33 to 0.34

[dependabot]

Updates the requirements on rerun to permit the latest version.

Release notes

Sourced from rerun's releases.

0.34.0 - Viewer MCP & Learning Course

Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer.

• Python: pip install -U rerun-sdk
• Rust: cargo add rerun and cargo install rerun-cli --locked
• Online demo: https://rerun.io/viewer
• C++ FetchContent: https://github.com/rerun-io/rerun/releases/download/0.34.0/rerun_cpp_sdk.zip

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🧳 Migration guide: https://rerun.io/docs/reference/migration/migration-0-34

✨ Overview & highlights

Viewer MCP

We've added a MCP that allows an llm agent to see and interact with the Viewer! You could ask your agent to

• verify its work looks as expected in the Viewer.
• debug a bug when something doesn't show up right.
• explore a recording or dataset to search for specific patterns.

The agent has full control over the Viewer, meaning it can see and click any widget.

Here's an example where Claude Sonnet was asked to create a fancy particle animation of the Rerun logo and verify its work using the mcp in the open Viewer (sped up by a lot, except when showing the end result):

mcp_demo.mp4

Once it wrote the script, it logged the recording to the Viewer, and then iterated until the result looked as requested. It adjusted the camera position, improved the particle rendering by looking at different frames in the animation, and then debugged why the fade out animation was still showing particles on the last frame.

/goal Create a new rerun python example in this folder that uses reruns 2D shapes to recreate the rerun logo (rerun-wordmark-black.svg). There should be a nice fade-in animation in the beginning, 10 frames duration. Then pause a bit with the full rerun logo visible and then the shapes should explosively fade away with a 20 frame animation before the recording ends.

You may only stop once the recreated logo in the viewer looks close to the provided svg (black text, white background). Use the mcp to verify in the open viewer, don't ever kill it. Once done, launch an opus agent and ask it to judge how closely it looks to the original image. Keep going until it's convinced that it looks close.

See our mcp docs to get started.

Learning course

... (truncated)

Changelog

Sourced from rerun's changelog.

0.34.0 - 2026-06-30

🧳 Migration guide: https://rerun.io/docs/reference/migration/migration-0-34?speculative-link

✨ Overview & highlights

Viewer MCP

We've added a MCP that allows an llm agent to see and interact with the Viewer! You could ask your agent to

• verify its work looks as expected in the Viewer.
• debug a bug when something doesn't show up right.
• explore a recording or dataset to search for specific patterns.

The agent has full control over the Viewer, meaning it can see and click any widget.

Here's an example where Claude Sonnet was asked to create a fancy particle animation of the Rerun logo and verify its work using the mcp in the open Viewer (sped up by a lot, except when showing the end result):

mcp_demo.mp4

Once it wrote the script, it logged the recording to the Viewer, and then iterated until the result looked as requested. It adjusted the camera position, improved the particle rendering by looking at different frames in the animation, and then debugged why the fade out animation was still showing particles on the last frame.

/goal Create a new rerun python example in this folder that uses reruns 2D shapes to recreate the rerun logo (rerun-wordmark-black.svg). There should be a nice fade-in animation in the beginning, 10 frames duration. Then pause a bit with the full rerun logo visible and then the shapes should explosively fade away with a 20 frame animation before the recording ends.

You may only stop once the recreated logo in the viewer looks close to the provided svg (black text, white background). Use the mcp to verify in the open viewer, don't ever kill it. Once done, launch an opus agent and ask it to judge how closely it looks to the original image. Keep going until it's convinced that it looks close.

See our mcp docs to get started.

Learning course

https://rerun.io/learn is a great way to learn how the Rerun data model covers the full physical AI experiment loop. It is a short, hands-on course for robotics ML engineers who want the full robot learning data loop in one place:

raw data -> RRD -> derived layers -> dataset queries -> training -> evaluation

Rerun agent skills

We added new skills to the Rerun repo to make it easier to investigate existing robotics data with Rerun.

... (truncated)

Commits

• 02fc797 Bump versions to 0.34.0
• ba610f0 Update release date
• 72ced7c Bump versions to 0.34.0-rc.4
• 17a7ad2 Bump quiver to 0.4.0
• 6aa7c08 Bump versions to 0.34.0-rc.3
• 0c7df83 Rollback mimalloc version instead of dropping to v2
• 77fe877 Bump versions to 0.34.0-rc.2
• 9dd1fe8 Pass gzip: false
• cc1c7e9 Bump versions to 0.34.0-rc.1
• 528ec15 update python support table
• Additional commits viewable in compare view

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