Manage Prompt Workflows Like Production Systems

Agenta helps teams operationalize prompt engineering with tooling for evaluation, feedback, deployment, and ongoing LLMOps work.

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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 (2026-07-18).

Continuity30% of headline86

Activity on 85 of 90 tracked days in the last 90 and 28 of 30 in the last 30.

Closure30% of headline71

Merged 667 of 951 PRs opened and closed 158 of 217 issues opened over the last 90 tracked days — PR flow carries 55% of this component, issue flow 45%.

Shipping20% of headline100

110 releases in the last 180 days, 43 in the last 90 and 8 in the last 30 — a steady cadence scores highest.

Liveness10% of headline100

Last push 1 days ago — freshness decays as pushes age (roughly halves every 83 days without a push).

Support burden10% of headline0

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

Adoption confidence100

Modeled — how confidently teams are adopting this repo. Stargazers

Maintenance quality82

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

Risk score29

Modeled — lower is better; adoption and continuity risk. Repository

Stays active (30d)84%

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

Stays active (90d)92%

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

Release rhythm (180d)100

Regularity of releases over the last 180 days. Releases

Maintainer bus risk (90d)9%

Modeled — lower is better; concentration of commits in few maintainers. Contributors graph

Topics

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About Agenta

Agenta is an LLMOps platform focused on prompt management, evaluation, human feedback, and deployment. Its value proposition is consolidation: instead of stitching together separate tools for prompt tracking, review, and release workflows, teams can manage more of the LLM lifecycle in one place. That makes it relevant for organizations trying to operationalize prompt engineering and introduce process around how AI behavior is tested and shipped. Agenta is less about base model training and more about the management layer that sits around production LLM applications. For teams struggling with fragmented prompt workflows, that can be appealing. Its fit is strongest where collaboration, evaluation, and deployment discipline are becoming necessary, but a company does not want to assemble a fully custom LLMOps stack from scratch. In that sense, Agenta serves as workflow infrastructure for teams moving from ad hoc prompting toward managed AI delivery.

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