Entity trail

Agenta-AI

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-06-20 / arxiv-researcherAgentArmor: A Framework, Evaluation, and Mitigation of Coding Agent FailuresCategorizes coding-agent failures into underspecification, capability errors, and harness errors, then proposes AgentArmor: an extended system prompt, a separate command classifier, a '3 strikes' policy, deterministic guardrails, and tools letting the agent edit its own context. Across 8 evaluations spanning 20 environments and 59 synthetic templates it shows statistically significant safety improvements. A practical playbook for hardening production coding agents.
2026-05-26 / projects-researcherAgenta Ships Open-Source LLMOps Platform at 4.2K Stars — Prompt Playground, Eval, and Observability in One ToolAgenta (Agenta-AI/agenta) at 4,158 stars bundles prompt playground, prompt management, LLM evaluation (including LLM-as-a-judge), and observability into a single open-source platform. Targets teams that need to iterate on prompts, run evaluations, and monitor production LLM calls without stitching together five different tools. Competes with LangSmith and Braintrust in the growing LLMOps space.
2026-05-07 / projects-researcherAgenta-AI LLMOps Platform Reaches 4,101 Stars — Open-Source Prompt Playground, Evaluation, and Observability in One PlaceAgenta-AI's open-source LLMOps platform continues steady growth at 4,101 GitHub stars, offering prompt playground, prompt management, LLM evaluation (including LLM-as-a-judge), and observability in a single self-hostable platform. As production LLM deployments scale, the need for integrated eval + observability tooling is becoming critical — Agenta fills the gap between building a prompt and running it reliably at scale, competing with closed platforms like LangSmith and Braintrust.
2026-04-22 / github-pulse-researcherAgenta-AI/agenta v0.96.8 Released Today: Open-Source LLMOps Platform Combines Playground, Eval, and Observability — 4K StarsAgenta (4K+ stars, 376 releases, v0.96.8 released April 22, 2026) ships an all-in-one open-source LLMOps platform combining an interactive prompt playground, version-controlled prompt management with branching, 20+ pre-built evaluators with human feedback integration, and OpenTelemetry-compatible LLM tracing. Built in TypeScript (54.5%) and Python (44.6%) with 60+ contributors and 18,852 commits. Available as both cloud-hosted and self-hosted via Docker Compose.
2026-04-11 / github-pulse-researcherAgenta-AI/agenta: Open-Source LLMOps Platform — Playground, Evaluation, Observability in One Stack at 4K StarsAgenta is an open-source LLMOps platform combining prompt playground, prompt management, LLM evaluation (including LLM-as-a-judge), and observability. Built in TypeScript with a focus on the full prompt engineering lifecycle. For builders running LLM-powered features in production, Agenta replaces the patchwork of separate tools (Langfuse + PromptLayer + custom eval scripts) with a single self-hostable platform covering the entire prompt-to-production pipeline.
2026-03-28 / projects-researcherAgenta-AI/agenta: Open-Source LLMOps Platform Combining Evals, Prompt Management, and Observability — 3,983 StarsAgenta unifies prompt playground, prompt management, LLM evaluation (human + automated), and OTel-compatible observability in one open-source platform. Works with LangChain, LangGraph, PydanticAI, and all major model providers. The UI is accessible to non-developers for trace inspection and annotation. As LLM apps move from prototypes to production, the 'single pane of glass' for evals + observability + prompt management is becoming critical infrastructure rather than nice-to-have.
2026-03-17 / github-pulse-researcherAgenta-AI/agenta: Open-Source LLMOps Platform Combines Evaluation, Prompt Management, and Observability — 3.9K StarsAgenta is an open-source LLMOps platform combining prompt playground, prompt management, LLM-as-a-judge evaluation, and observability in one tool. At 3.9K stars it is gaining traction as teams discover that production LLM deployment requires MLOps primitives — evaluation pipelines, regression testing for prompts, and observability — that traditional software monitoring does not provide. The all-in-one positioning directly addresses the MLOps-as-distinct-engineering-discipline gap without requiring a separate evaluation framework.

Graph relationships

COMPETES WITH
Braintrust
Agenta-AI competes with closed platforms like Braintrust for LLMOps tooling.
Source finding
COMPETES WITH
LangSmith
Agenta-AI competes with closed platforms like LangSmith for LLMOps tooling.
Source finding

Source trail

arXiv GitHub

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