Trace Replay and Network Simulation Framework
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Updated
Apr 14, 2021 - C
Trace Replay and Network Simulation Framework
Local-first, IM-native AI coworker runtime with async role agents, trace replay, evidence, and agentic evaluation.
🔬This program benchmarks the I/O performance per container. You can see the implementation of this program on our website.
Generate reproducible deep-learning workload traces by mapping production GPU-cluster traces to profiled executable workloads.
Real-world evaluation framework for AI coding agents — measures safety, containment, cost, and autonomy beyond just correctness.
Deterministic intermediate representation for AI agents — compile, verify, execute, and replay structured intent.
morph-replay-runner is a command-line interface (CLI) tool designed to execute TRACE-REPLAY-KIT bundles with branch-N parallelism on Morph Cloud. This tool streamlines the process of running replay tasks, ensuring efficient and scalable execution with comprehensive evidence collection and CERT-V1 compliance.
Check AI agent projects before they go live: local reports, trace replay, and failure-to-eval for MCP/RAG/LLM apps.
Agent engineering studio with deterministic tool orchestration, policy guardrails, trace replay, and scenario evaluations.
Record, replay, evaluate, and regression-test AI agents with deterministic traces, calibrated LLM-as-a-judge evals, OpenTelemetry observability, and CI quality gates.
A reference implementation for tracing, replaying, evaluating, and governing agent runs across trust boundaries.
Turn failed agent traces into replayable regression cases.
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