KISS Sorcar: A Stupidly-Simple General-Purpose and Software Engineering AI Assistant
- URL: http://arxiv.org/abs/2604.23822v1
- Date: Sun, 26 Apr 2026 17:59:06 GMT
- Title: KISS Sorcar: A Stupidly-Simple General-Purpose and Software Engineering AI Assistant
- Abstract summary: KISS Sorcar is a general-purpose assistant and integrated development environment built on top of the KISS Agent Framework.<n> KISS Sorcar is a free, open-source Visual Studio Code extension that runs locally and effectively for long-horizon tasks.<n>The entire system was built using itself in 4.5 months, providing a continuous stress test in which any agent-introduced bug immediately impairs its own ability to work.
- Score: 8.273738980475393
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large language models can generate code and call tools with remarkable fluency, yet deploying them as practical software engineering assistants still expose stubborn gaps: finite context windows, single mistakes that derail entire sessions, agents that get stuck in dead ends, AI slop, and generated changes that are difficult to review or revert. We present KISS Sorcar, a general-purpose assistant and integrated development environment (IDE) built on top of the KISS Agent Framework, a stupidly-simple AI agent framework of roughly 1,850 lines of code. The framework addresses these gaps using a robust system prompt and through a five-layer agent hierarchy in which each layer adds exactly one concern: budget-tracked ReAct execution, automatic continuation across sub-sessions via summarization, coding, and browser tools with parallel sub-agents, persistent multi-turn chat with history recall, and git worktree isolation so every task runs on its own branch. To assess the power of the KISS agent framework, we implemented KISS Sorcar as a free, open-source Visual Studio Code extension that runs locally and effectively for long-horizon tasks, and supports browser automation, multimodal input, and Docker containers. In this research, we deliberately prioritize output quality over latency: giving a frontier model adequate time to validate its own output -- running linters, type checkers, and tests -- dramatically reduces the low-quality code that plagues faster but less thorough agents. The entire system was built using itself in 4.5 months, providing a continuous stress test in which any agent-introduced bug immediately impairs its own ability to work. On Terminal Bench 2.0, KISS Sorcar achieves a 62.2% overall pass rate with Claude Opus 4.6, comparing favorably to Claude Code (58%) and Cursor Composer 2 (61.7).
Related papers
- MTAC-IFBench: Benchmarking Instruction-Following in Multi-Turn Agentic Coding [83.0805548642958]
MTAC-IFBench is a comprehensive benchmark for instruction-following in software development.<n>It features multi-turn progressive software development instructions with diverse constraints spanning 6 primary and 18 secondary categories.<n>MTAC-IFBench identifies significant deficiencies in existing code agents in multi-turn instruction-following, with their performance degrading rapidly as the interaction session grows longer.
arXiv Detail & Related papers (2026-09-14T03:57:37Z) - Persistent Recursive Worlds Enable Autonomous Software Evolution [9.245787953561281]
EvoX Genesis makes software project persistent while allowing local agents to remain finite-lived.<n>We evaluate this organization across formation, continuation and redevelopment.<n>Results show that long-horizon software development can be organized around a persistent project rather than a persistent agent.
arXiv Detail & Related papers (2026-08-11T04:04:00Z) - Agentic Coding in the Wild: Characterizing GitHub Copilot Traces at Production Scale [6.287658270510339]
AI coding agents like GitHub Copilot, Claude Code, and Codex interleave multi-step LLM inference with tool execution.<n>We present the first production-scale characterization of this workload using sampled GitHub Copilot traces from June 2026.
arXiv Detail & Related papers (2026-07-30T20:51:51Z) - Multi-Agent Computer Use [72.79887808312706]
We argue that we should move towards evaluating and building multi-agent computer use (MACU) systems.<n>We propose a general multi-agent setup in which a manager model decomposes computer use tasks as a directed acyclic graph (DAG)<n>At each iteration, the manager dispatches parallel CUA subagents to carry out nodes on the ready frontier of the DAG.
arXiv Detail & Related papers (2026-06-01T01:29:36Z) - WildClawBench: A Benchmark for Real-World, Long-Horizon Agent Evaluation [88.10947115397971]
This work presents WildClawBench, a native-runtime benchmark of 60 human-authored, bilingual, multimodal tasks spanning six thematic categories.<n>Each task averages roughly 8 minutes of wall-clock time and over 20 tool calls.<n> Grading is hybrid, combining deterministic rule-based checks, environment-state auditing of side effects, and an LLM/VLM judge for semantic verification.
arXiv Detail & Related papers (2026-05-11T17:49:43Z) - ClawMark: A Living-World Benchmark for Multi-Turn, Multi-Day, Multimodal Coworker Agents [77.73176261673471]
We introduce bench, a benchmark for coworker agents built around multi-turn multi-day tasks.<n>The current release contains 100 tasks across 13 professional scenarios, executed against five stateful sandboxed services.<n>The strongest model reaches 75.8 weighted score, but the best strict Task Success is only 20.0%, indicating that partial progress is common.
arXiv Detail & Related papers (2026-04-26T16:05:02Z) - Workstream: A Local-First Developer Command Center for the AI-Augmented Engineering Workflow [0.0]
We present Workstream, an open-source, local-first developer command center that aggregates pull requests, task management, calendar, AI-powered code review, historical review intelligence, repository AI-readiness scoring, and agent observability into a single interface.<n>We describe the system architecture, a novel 5-category AI readiness scoring algorithm, a review intelligence pipeline that mines historical PR reviews for team-specific patterns, and an agent observability layer implementing the Model Context Protocol (MCP), Agent-to-Agent (A2A), and Agent Observability Protocol (AOP)
arXiv Detail & Related papers (2026-04-18T16:24:29Z) - NL2Repo-Bench: Towards Long-Horizon Repository Generation Evaluation of Coding Agents [79.29376673236142]
Existing benchmarks fail to rigorously evaluate the long-horizon capabilities required to build complete software systems.<n>We present NL2Repo Bench, a benchmark explicitly designed to evaluate the long-horizon repository generation ability of coding agents.
arXiv Detail & Related papers (2025-12-14T15:12:13Z) - DeepAgent: A General Reasoning Agent with Scalable Toolsets [111.6384541877723]
DeepAgent is an end-to-end deep reasoning agent that performs autonomous thinking, tool discovery, and action execution.<n>To address the challenges of long-horizon interactions, we introduce an autonomous memory folding mechanism that compresses past interactions into structured episodic, working, and tool memories.<n>We develop an end-to-end reinforcement learning strategy, namely ToolPO, that leverages LLM-simulated APIs and applies tool-call advantage attribution to assign fine-grained credit to the tool invocation tokens.
arXiv Detail & Related papers (2025-10-24T16:24:01Z) - AgentRL: Scaling Agentic Reinforcement Learning with a Multi-Turn, Multi-Task Framework [76.96794548655292]
Large language models (LLMs) have sparked growing interest in building generalist agents that can learn through online interactions.<n>Applying reinforcement learning (RL) to train LLM agents in multi-turn, multi-task settings remains challenging due to lack of scalable infrastructure and stable training algorithms.<n>We present the AgentRL framework for scalable multi-turn, multi-task agentic RL training.
arXiv Detail & Related papers (2025-10-05T13:40:01Z) - Evaluating Software Development Agents: Patch Patterns, Code Quality, and Issue Complexity in Real-World GitHub Scenarios [13.949319911378826]
This study evaluated 4,892 patches from 10 top-ranked agents on 500 real-world GitHub issues.<n>No single agent dominated, with 170 issues unresolved, indicating room for improvement.<n>Most agents maintained code reliability and security, avoiding new bugs or vulnerabilities.<n>Some agents increased code complexity, many reduced code duplication and minimized code smells.
arXiv Detail & Related papers (2024-10-16T11:33:57Z) - CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents [49.68117560675367]
Crab is the first benchmark framework designed to support cross-environment tasks.<n>Our framework supports multiple devices and can be easily extended to any environment with a Python interface.<n>The experimental results demonstrate that the single agent with GPT-4o achieves the best completion ratio of 38.01%.
arXiv Detail & Related papers (2024-07-01T17:55:04Z) - CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges [41.038584732889895]
Large Language Models (LLMs) have shown promise in automated code generation but typically excel only in simpler tasks.
Our research pivots towards evaluating LLMs in a more realistic setting -- real-world repo-level code generation.
We present CodeAgent, a novel LLM-based agent framework that employs external tools for effective repo-level code generation.
arXiv Detail & Related papers (2024-01-14T18:12:03Z) - Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub [79.31134731122462]
We introduce OpenAct benchmark to evaluate the open-domain task-solving capability, built on human expert consultation and repositories in GitHub.<n>We present OpenAgent, a novel LLM-based agent system that can tackle evolving queries in open domains through autonomously integrating specialized tools from GitHub.
arXiv Detail & Related papers (2023-12-28T15:47:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.