Record LLM and API interactions. Replay them offline with zero network calls. Reproduce any agent run instantly.
Grounded in research: Based on the paper "Deterministic Replay for AI Agent Systems" (arXiv:2607.16200),
agreplintroduces formal replay invariants and noise-aware differential analysis to make stochastic agent runs fully reproducible.
AI agents that couple LLMs with external tools and APIs are inherently non-deterministic. Debugging a failing run by re-executing your script is slow, expensive, and often impossible to reproduce due to LLM sampling variance, external API state mutation, and environmental noise.
Traditional tracing tools (LangSmith, W&B) let you observe bugs after they happen. agrepl lets you reproduce them with mathematical determinism.
agrepl is a deterministic execution layer for AI agents. It intercepts external interactions at the transport layer, serialises them into structured execution traces, and serves them back with 100% fidelity (
- Deterministic Invariant: Replay is a canonical key lookup, not a re-execution. Zero network calls, zero token consumption.
- Zero-Instrumentation: Transport-level MITM proxy. Works out of the box with Python, Node, Go, curl, or any CLI without code modifications or SDKs.
- Noise-Aware Diffing: Distinguishes semantic agent drift from non-semantic infrastructure noise (CDN headers, dynamic timestamps).
- Shareable Ground Truth:
sharean execution trace →pullon another machine → Replay the exact bug locally.
curl -sSL https://raw.githubusercontent.com/taiwrash/agrepl/main/scripts/install.sh | bashRun your agent through agrepl. It captures all HTTP(S) and LLM interactions.
agrepl record -- python agent.pyRe-run instantly. agrepl finds the original command and re-executes it in a deterministic sandbox.
agrepl run run-010The Edge: Replay works without an internet connection. No new API calls are made. Zero latency. Zero cost.
Share your run with your team for collaborative debugging.
agrepl auth login
agrepl share run-001agrepl will generate a unique ID. Your teammates can then use agrepl pull [id] to reproduce the exact failure on their machines.
- Debug failing workflows: Replay the exact session where your agent drifted.
- CI/CD Testing: Run integration tests with guaranteed, deterministic outputs.
- Save Credits: Iterate on tool logic or response handling without hitting paid APIs.
- Bug Reports: Attach a
run.jsonto a ticket so teammates can reproduce the bug locally.
Logging tells you what happened. Replay lets you re-live it.
Traditional tracing tools show you a post-mortem. agrepl provides a live, interactive environment where your code thinks it's talking to the real world, but it's actually talking to a deterministic local cache. Empirical evaluation across five benchmark agent workloads (
See how we compare to LangSmith, VCR.py, and more: agrepl vs. The Orbit
- Basic CLI (record, replay, list, diff)
- Local JSON storage
- MITM Proxy for HTTP/HTTPS interception
- Automatic Root CA trust injection
- Structural JSON matching for robust replays
- Binary data support (for gRPC and images)
- gRPC / HTTP/2 optimized matching
- Official SDK Wrappers (Python, Node.js)
- Fuzzy/AI-based request matching
- Enhanced Remote Storage (Push/Pull)
- CI/CD integration for regression testing
- Token cost estimation & analytics
agrepl is developed alongside academic research on deterministic execution systems for stochastic AI agents. If you use agrepl in your research, agent evaluations, or benchmark suites, please cite our paper:
@article{mudasiru2026deterministic,
title={Deterministic Replay for AI Agent Systems},
author={Mudasiru, Rasheed},
journal={arXiv preprint arXiv:2607.16200},
year={2026},
eprint={2607.16200},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2607.16200},
doi={10.48550/arXiv.2607.16200}
}The full paper is available on arXiv: arXiv:2607.16200 [cs.AI].
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