GRASP

GRASP is a self-improvement method that learns a small, regression-gated skill library from an agent's own failure traces. A proposed skill is kept only when it improves performance on a held-out probe set — so the library grows by keeping what demonstrably helps and discarding what doesn't.

This repository is two things:

1. A reusable method + framework (grasp/) — apply GRASP to your own agent and tasks, and benchmark your own self-improvement method against GRASP and five baselines through a small plug-in interface.
2. The full paper artifact — four benchmark families (benchmarks/) and all released results behind the paper (results/).

Install

pip install -e .          # core depends only on PyYAML

Quickstart (no Docker, no server)

Watch GRASP learn skills on a laptop in minutes, on a self-contained slice of MedAgentBench's read-only FHIR lookup tasks served by an in-process mock:

# point the 'local' backend at any OpenAI-compatible endpoint
export OPENAI_BASE_URL="http://localhost:8000/v1"
export OPENAI_API_KEY="EMPTY"
export GRASP_MODEL="your-model-name"

python -m examples.quickstart.run --agent local

It writes a val-accuracy learning curve and the learned skill library under examples/quickstart/runs/. See examples/quickstart/.

Use GRASP on your own agent

Implement a Task (how to sample, run, and score your environment) and run GRASP on it:

from grasp import run_grasp
run_grasp(MyTask(), "config.yaml", agent="local")

• Task — samples(), rollout(sample, agent), evaluate(sample, output), plus optional failure_tags / protocol_hook / updater_* hooks.
• Method — GRASP is the reference Method; subclass it to benchmark your own self-improvement method on the same tasks.

Benchmarks (the paper artifact)

Each benchmark is self-contained under benchmarks/, with its own README for environment setup (conda, Docker, data) and a run_all.sh <backend> [run_name] helper.

The paper compares GRASP against a no-skills baseline and five self-improvement methods, all implemented in each benchmark directory: grasp (GRASP, ours), memory_cycle (Sequential memory), batch_memory_cycle (Batch memory), expel_cycle (ExpeL), evo_memory_cycle (Evo-MedAgent), skillx_cycle (SkillX).

The executing agent and skill-writer use the same model; five backends are selectable at run time (gptoss, deepseek, gemini, gpt5, gpt4, or a generic local OpenAI-compatible endpoint). No secrets are stored in the repository — presets read endpoints and keys from environment variables. See each benchmark's configs/agents/README.md.

Released results

All numbers behind the paper live under results/ — per-seed validation, test, and OOD accuracies for every cell of Tables 1–5, the learned skill libraries, the frozen transfer libraries, and the run configurations. Reproduce the headline tables directly:

python results/reproduce_tables.py                 # Table 1 (all models) + Table 5
python results/reproduce_tables.py gpt-oss-120b     # one model

See results/README.md for the full directory↔cell map.

License

MIT (see LICENSE) for the GRASP core, examples, and docs. Vendored benchmark code under benchmarks/AgentBench/ and benchmarks/FHIR-AgentBench/ retains its own upstream license.

Citation

See CITATION.cff.