Build LLM Apps Around Live Data

LLM App is built for teams serving AI applications where real-time pipelines and LLM behavior need to work together.

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about 3 years
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Reliability breakdown

What drives the reliability score

Component scores measured daily from GitHub activity (2026-03-08).

Continuity30% of headline12

Activity on 0 of 39 tracked days in the last 90 and 0 of 30 in the last 30.

Closure30% of headline0

Merged 0 of 0 PRs opened and closed 0 of 0 issues opened over the last 90 tracked days β€” PR flow carries 55% of this component, issue flow 45%.

Shipping20% of headline0

0 releases in the last 180 days, 0 in the last 90 and 0 in the last 30 β€” a steady cadence scores highest.

Liveness10% of headline100

Last push 15 days ago β€” freshness decays as pushes age (roughly halves every 83 days without a push).

Support burden10% of headline100

Open-issue load isn't tracked for this repo yet.

Adoption confidence23

Modeled β€” how confidently teams are adopting this repo. Stargazers

Maintenance quality35

Modeled from PR/issue responsiveness and upkeep signals. Activity pulse

Risk score50

Modeled β€” lower is better; adoption and continuity risk. Repository

Stays active (30d)0%

Modeled chance the repo stays active over 30 days. Activity pulse

Stays active (90d)1%

Modeled chance the repo stays active over 90 days. Activity pulse

Release rhythm (180d)0

Regularity of releases over the last 180 days. Releases

Maintainer bus risk (90d)98%

Modeled β€” lower is better; concentration of commits in few maintainers. Contributors graph

Measured history

Project metrics

Weekly GitHub totals with 30, 90, and 180 day views.

Issues opened

Mar 8, 2026

Current0

Previous0

Weekly total

110
Full metrics details

GitHub API observation. Historical values render only when the source provides a real observation for that day.

46 observed daily rows. Missing days are not fabricated.

DateValue

Issues closed

Mar 8, 2026

Current0

Previous0

Weekly total

110
Full metrics details

GitHub Search issue totals queried for exact UTC days; rolling values are sums of proven daily counts.

46 observed daily rows. Missing days are not fabricated.

DateValue

Pull requests opened

Mar 8, 2026

Current0

Previous0

Weekly total

110
Full metrics details

GitHub API observation. Historical values render only when the source provides a real observation for that day.

46 observed daily rows. Missing days are not fabricated.

DateValue

Pull requests closed

Mar 8, 2026

Current0

Previous0

Weekly total

110
Full metrics details

GitHub Search pull-request totals queried for exact UTC days; rolling values are sums of proven daily counts.

46 observed daily rows. Missing days are not fabricated.

DateValue

Pull requests merged

Mar 8, 2026

Current0

Previous0

Weekly total

110
Full metrics details

GitHub API observation. Historical values render only when the source provides a real observation for that day.

46 observed daily rows. Missing days are not fabricated.

DateValue

About Llm App

LLM App is a production-oriented framework for building and serving AI applications alongside LLM-enabled real-time data pipelines. That positioning makes it distinct from prompt-only tooling: it targets systems where model behavior is part of a larger data flow that must operate continuously and serve live application needs. This is especially relevant for teams building retrieval-heavy or pipeline-driven AI products where data freshness and operational delivery matter. Its strength is in treating LLM applications as running systems rather than isolated model calls. For organizations interested in real-time AI workflows, that can be a better fit than lighter frameworks that focus mainly on prompts or single-step orchestration. LLM App is most compelling when the problem involves serving, data movement, and application structure together. It is less about local experimentation and more about production-minded AI pipeline design.

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