Recuris

💡 Introduction

Recuris is a recursive self-improvement framework that improves a long-horizon agent by evolving its memory instead of its weights or its prompt. A frozen agent is paired with a Skill Memory M = (E, W, ρ, C). A meta-agent reads structured execution traces, locates each failure in one component of that memory, and patches only that component. A deterministic validation gate then decides, on paired held-out evidence, whether the patch survives. Recuris has the following key features:

• State-grounded memory use. Working memory drives skill invocation, so retrieval is conditioned on verified task state rather than on a chat history that grows until the state is buried.
• Targeted memory evolution. Structured trajectories (w_t, E_t, a_t, o_t) locate a failure in a specific component, instead of nudging a monolithic prompt from outcomes alone.
• Bounded by a validation gate. Candidates are admitted by paired held-out arithmetic and nothing else. No model votes on its own patch.
• Training-free and model-agnostic. The downstream agent stays frozen, and a memory evolved on one model transfers to others unchanged.

Overall, Recuris achieves higher task success, larger gains on longer horizons, and substantially fewer long-horizon failures, on both frontier and open-source agents.

🔔 News

• [2026-08] 🎉 Initial release: evaluation and evolution code, the evolved Skill Memory packages, and the frozen evaluation splits.

📊 Results

Task success (avg@4, %), each model run with the benchmark's own reference agent alone and with that same agent plus Recuris. Bold marks the better of each pair, the subscript is Δ, † marks a paired task-clustered bootstrap 95% CI excluding zero, and n/a means the model was not run on that benchmark.

Recuris improves task success in 35 of the 37 completed model and benchmark pairs, from a 3B open-source agent up to the strongest frontier models. The largest gains reach +23.3 on τ²-Retail and +16.8 on SkillFlow. Gains grow with the interaction horizon, reaching +32.2 on the longest tasks, and common long-horizon failure modes drop by up to 80%.

🛠️ Getting Started

This repository provides the code for running Recuris on τ²-Bench, SkillFlow and Terminal-Bench 2.1, the Skill Memory packages produced by the evolution loop, and the frozen evaluation splits.

📦 Install Packages

Python 3.12 and git. SkillFlow and Terminal-Bench 2.1 also need Docker with the Compose V2 plugin (docker compose version must work; harbor shells out to it for every task).

git clone https://github.com/Gen-Verse/Recuris.git recuris
cd recuris

uv sync --extra all          # or: pip install -e ".[all]"

⚙️ Setup Environment Variables

Put your endpoint in a .env file at the repository root, or export it:

OPENAI_API_KEY=...
OPENAI_BASE_URL=...

Any OpenAI-compatible endpoint works. This is needed even when the agent itself is an open-source model, because τ²-Bench scores every episode with an LLM user simulator and an LLM assertion judge, and both stay pinned to a reference model.

🚀 Quick Start

Each benchmark is run twice, once with a Skill Memory loaded and once without. The two runs differ only in the flags shown below. Both are needed, because the number that matters is the difference between them.

🔹 τ²-Bench (retail and airline)

Set up the benchmark:

bash third_party/tau2/setup.sh
uv pip install -e external/tau2-bench
recuris check-data --benchmark tau2

Install tau2 after uv sync, not before: uv sync resolves the environment to exactly what pyproject.toml declares, so running it again removes anything added with uv pip install.

Serve an open-source model locally. τ²-Bench drives the agent through tool calls, so the two tool-calling flags are required, not optional: without them vLLM rejects every request and every episode ends ungraded.

vllm serve <model-id> --port 8000 --served-mo