Agenta is an open source and cloud LLMOps platform that helps teams build reliable AI features faster. Manage prompts and configs as versions, run experiments against datasets, and compare results before release. Observability links prompts to traces, latency, and cost, while alerts flag regressions. RBAC, orgs, and approvals keep work governed, and self hosting is available when data must remain private. Agenta works with any major model and frameworks like LangChain or LlamaIndex so teams keep their preferred stack.
Store prompts and parameters as first class versions with clear diff views and rollback. The playground lets product, QA, and subject experts try alternatives and capture examples without code. Variables, templates, and test inputs keep runs reproducible so changes can be reviewed and approved like code. Branches isolate work until ready to merge, and labels mark production sets so reviewers see scope and intent at a glance.
Create datasets from tickets, logs, or curated examples, then run candidates side by side to measure accuracy, tone, and safety. Add rubrics for human review and automatic checks for groundedness or toxicity, with clear scoring and comments. Baselines prevent regressions by comparing new variants against trusted versions. Scheduled evaluations before deploy catch drift when data shifts, and slice reports summarize gains and losses for owners.
Link each production response to the exact prompt version, model, and inputs so investigations are fast. Dashboards show latency, token use, failure rates, and user feedback by route or feature. Traces reveal step level context for chains, tools, and retrieval so owners can fix bottlenecks and cost spikes. Threshold alerts route to on call with links to the runs that triggered the event, guiding precise fixes instead of guesswork.
Organize apps into teams, control access with roles, and require approvals on sensitive changes. Custom workflows connect CI, datasets, and deploy steps so promotion follows a consistent gate pattern. Audit logs record who changed what and when, creating a reliable history for reviews and incident response. Secrets and environment separation protect credentials across dev, staging, and production without blocking iteration.
Run Agenta self hosted under an MIT license or use the managed cloud with enterprise features like orgs and role control. APIs, SDKs, and webhooks integrate with existing apps, while adapters work with popular frameworks. Model agnostic design supports commercial and open weights so teams can choose for cost, privacy, or latency. Importing historical logs accelerates evaluation and observability without rebuilding pipelines from scratch.
Recommended for product and platform teams building LLM features where reliability, cost control, and auditability matter. Use Agenta to standardize prompt changes, evaluate safely, and monitor behavior across routes. It suits startups moving fast and enterprises that need self hosting or strict role separation. Agencies and consultancies can share playbooks across clients while keeping data boundaries intact and results comparable.
LLM features often ship as ad hoc scripts with no version discipline, weak testing, and limited visibility. Agenta centralizes prompt and config management, evaluation, and tracing so changes are predictable and reversible. Teams catch regressions earlier, explain outcomes faster, and scale usage without losing governance. The result is steadier quality, lower incident time, and quicker iteration from idea to production.
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