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krafton-ai

KIRA

KIRA.

ApplicationsProductivityPython
Open on GitHub
Momentum
+0stars in 24 hours0.0%
930
Stars
105
Forks
+3
This week
7
Contributors
Created 2025-12-03 · Updated 2026-10-14 · #12570 today
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batteryho-kraftonseungjin-kraftonseungjin-developer

krafton-ai/KIRA is ranked 12,570 with 930 stars.

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Key parameters and specifications
Key parameters and specifications
ParameterValue
LanguagePython
CategoryApplications
SubcategoryProductivity
Stars930
Forks105
Contributors7
Created date2025-12-03
Updated date2026-10-14
Stars in 1 day0
Stars in 7 days3
Rank12,570
Previous rank6,198
Rank change-6,372
7-day stars rank7,149
Previous 7-day stars rank7,175
7-day stars rank change26
Total stars rank11,774
Previous total stars rank11,754
Total stars rank change-20
CountrySouth Korea
README summary

A smarter agent harness for Terminal-Bench , built on Terminus 2 Simple fixes, significant gains. Terminus-KIRA is an agent harness for Terminal-Bench, built on top of Terminus 2. Calls directly with , bypassing the base class to access native tool calling 2. On context window overflow, automatically summarizes conversation history and retries 4. NOTE] **TerminalBench Submission Correspondence** The agent used for the official TerminalBench submission was logged under the internal path . 1** | Migrated from In-Context Learning (ICL) to

**native tool calling** via LLM parameter. Fork of Terminus 2 with ICL-based JSON response parsing and full response format instructions in the system prompt.

Frequently asked questions

How many stars and forks are recorded?

krafton-ai/KIRA has 930 stars and 105 forks.

What is the repository rank?

krafton-ai/KIRA is ranked 12,570.

What language and category are recorded?

krafton-ai/KIRA has 930 stars, uses Python and belongs to Applications.

Which repository is closest in rank?

krafton-ai/KIRA is compared with THUDM/AutoWebGLM, ranked 12,567 with 930 stars.

README
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|_   _| __| _ \  \/  |_ _| \| | | | / __|___| |/ /_ _| _ \  /_\
  | | | _||   / |\/| || || .` | |_| \__ \___| ' < | ||   / / _ \
  |_| |___|_|_\_|  |_|___|_|\_|\___/|___/   |_|\_\___|_|_\/_/ \_\

A smarter agent harness for Terminal-Bench, built on Terminus 2

Simple fixes, significant gains.

What is Terminus-KIRA?

Terminus-KIRA is an agent harness for Terminal-Bench, built on top of Terminus 2. It boosts frontier model performance on Terminal-Bench through a set of minimal but effective harness-level improvements — native tool calling, multimodal support, execution optimization, and smarter completion verification.


Key Features

  • Native Tool Calling — Replaces ICL JSON/XML parsing with the LLM tools parameter for structured, reliable outputs
  • Image Analysis (Multimodal) — image_read tool for base64-encoded image analysis directly from the terminal
  • Marker-based Polling — Early command completion detection using echo markers, cutting unnecessary wait time
  • Smart Completion Verification — Double-confirmation checklist covering requirements, robustness, and multi-perspective QA (test engineer, QA engineer, user)
  • Prompt Caching — Anthropic ephemeral caching on recent messages to reduce latency and cost

Architecture

Terminus-KIRA extends Terminus 2 by replacing its ICL (In-Context Learning) response parsing with native LLM tool calling.

Tool definitions passed via the tools parameter:

Tool Purpose
execute_commands Run shell commands with analysis and plan
task_complete Signal task completion (triggers double-confirmation)
image_read Analyze image files via base64 multimodal input

How it works:

  1. Calls litellm.acompletion directly with tools=TOOLS, bypassing the base Chat class to access native tool calling
  2. The model returns structured tool calls instead of free-form text — no regex/JSON parsing needed
  3. On context window overflow, automatically summarizes conversation history and retries
  4. Marker-based polling appends echo '__CMDEND____' after each command; if the marker appears before the requested duration, execution moves on immediately

Evolution

Key milestones from development history:

# Milestone Description
1 Genesis Copy of Terminus 2 as starting point
2 Native Tool Use Replaced ICL JSON/XML parsing with LLM tools parameter
3 Output Limiting 30 KB cap on terminal output to prevent context bloat
4 Autonomy & Constraints Prompt engineering for agent autonomy and environment constraints
5 Completion Confirmation Include original instruction in completion check
6 Multimodal image_read tool for visual analysis of terminal screenshots
7 Completion Checklist Multi-perspective QA checklist (test engineer, QA, user)
8 Execution Optimization Marker-based polling and block timeout protection
9 Temperature Fix Set temperature to 1 when using reasoning effort

Usage

uv run harbor run \
    --dataset terminal-bench-sample@2.0 \
    --n-tasks 1 \
    --agent-import-path "terminus_kira.terminus_kira:TerminusKira" \
    --model anthropic/claude-opus-4-6 \
    --env docker \
    -n 1

For more details, visit our blog post.


Project Structure

├── terminus_kira/
│   ├── __init__.py
│   └── terminus_kira.py        # Main agent (native tool calling)
├── prompt-templates/
│   └── terminus-kira.txt        # System prompt
├── run-scripts/
│   ├── run_docker.sh            # Local Docker execution
│   ├── run_daytona.sh           # Daytona cloud execution
│   └── run_runloop.sh           # Runloop cloud execution
├── anthropic_caching.py         # Prompt caching utility
└── pyproject.toml

Citing Us

If you found Terminus-KIRA useful, please cite us as:

@misc{terminuskira2026,
      title={Terminus-KIRA: Boosting Frontier Model Performance on Terminal-Bench with Minimal Harness },
      author={{KRAFTON AI} and {Ludo Robotics}},
      year={2026},
      url={https://github.com/krafton-ai/kira},
}

[!NOTE] TerminalBench Submission Correspondence

The agent used for the official TerminalBench submission was logged under the internal path kraftonai-terminus/terminal_bench/agents/terminus_3/terminus_3_3.py. This corresponds to terminus_kira/terminus_kira.py in this repository — the file name and path were cleaned up during open-source preparation.


Changelog

Version Description
v1.1 Migrated from In-Context Learning (ICL) to native tool calling via LLM tools parameter. Removed verbose JSON/XML response format instructions from system prompt — the model now receives structured tool definitions directly, resulting in a significantly shorter prompt and more reliable outputs.
v1.0 Initial release. Fork of Terminus 2 with ICL-based JSON response parsing and full response format instructions in the system prompt.

KRAFTON AI & Ludo Robotics

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