Skip to main content
AgentX is a governance platform for AI agents. It records what your agents actually do in production, judges that behavior continuously, scores changes against test datasets before they ship, and turns everything it learns into concrete improvements - without touching your agent’s own code or prompts.
  • Framework-agnostic: LangChain/LangGraph, OpenAI Agents SDK, CrewAI, AutoGen, Google ADK, LlamaIndex, Anthropic/OpenAI clients, Databricks/MLflow, Moveworks, plain Python, or any OpenTelemetry-instrumented app.
  • Online and offline evaluation: judges score live traffic as it arrives (traces, whole conversations, tool behavior), and dataset runs score candidate changes before release.
  • Trajectory-aware: evaluation sees the path the agent took - which tools, in what order, with what failures - not just the final answer.
  • Self-hosted: one local binary or container, SQLite or Postgres, bring your own LLM keys. Your traces never leave your infrastructure.
PLACEHOLDER: circular diagram with four nodes - Trace (agent icon, arrows from LangChain/OpenAI/OTel logos), Monitor (judge/magnifier icon producing Signals), Evaluate (dataset + checkmark icon), Improve (wrench icon feeding back into the agent). Arrows flow clockwise: Trace to Monitor to Evaluate to Improve and back to the agent.

The governance loop: production traffic in, judged findings out, improvements validated before they ship.

​
Choose your path

Quickstart

Self-host the engine and send your first trace in five minutes

Trace your agent

Decorators, context managers, sessions, and per-framework integrations

Evaluate offline

Dataset runs on demand: LLM-as-judge scoring, similarity metrics, trajectory matching

Evaluate online

Continuous scoring of live traffic: patterns, online evaluators, deduped signals

Gate releases in CI

Fail the pipeline when quality drops below your bar

Improve the agent

Prompt and tool registries with evidence-backed, validated proposals

​
How it fits together

Four capabilities share one SDK, one trace store, and one dataset model. Each works alone; together they form a loop. AgentX sits outside your agent in every case: tracing wraps your code with a decorator, context manager, or framework callback; evaluation calls your agent function and scores what comes back. There is no prompt injection and no change to your agent’s logic.

​
Deployment model

The self-hosted stack (AgentX-trace-eval, Apache-2.0) is a single engine binary plus the real AgentX dashboard, running against local SQLite or your Postgres. You bring provider keys (OpenAI/Anthropic/Gemini) only for the LLM-judge features - trace ingest and rule-based detection run with no keys at all.
All documentation in the Trace, Online Evaluation, Offline Evaluation, and Improve tabs applies to a self-hosted instance. Start at Self-Host Overview.