"Everything should be made as simple as possible, but not simpler." — Albert Einstein
KISS Sorcar
Open-source general-purpose AI agent for long-horizon tasks and AI discovery
KISS Sorcar is a free, simple, local-first, bring-your-own-key AI agent framework. It runs as a VS Code extension and a browser/mobile web app, both served by a local daemon, and offers a Python client API for scripting tasks. Your prompts and code are sent directly to the model provider or local endpoint you configure — not through our servers. It supports multi-model workflows just via prompts. All agents run as daemons. Complex AI systems/techniques can be replaced with a paragraph of prompt in KISS Sorcar.
curl -fsSL https://raw.githubusercontent.com/ksenxx/kiss_ai/main/scripts/install.sh | bash
Table of Contents
KISS Sorcar vs Claude Code vs Cursor
| Capability | KISS Sorcar | Claude Code | Cursor |
|---|---|---|---|
| Interfaces | VS Code extension + web/mobile app + Python API | CLI + mobile app | Custom VS Code |
| AI Discovery | ✅ simply via prompt | ❌ | ❌ |
| GEPA Prompt Optimization | ✅ simply via prompt | ❌ | ❌ |
| Multiple models from multiple vendors in the same task | ✅ Mix OpenAI, Anthropic, Gemini, Together, Z.AI, Moonshot AI, OpenRouter, Claude Code CLI, and Codex CLI | ❌ Anthropic Claude models only | ❌ One model per task |
| Primary focus | ✅ Quality — rigorous review, end-to-end tests | Speed and developer ergonomics | Speed |
| Core Agents # LoC | ~3000 | Unknown | Unknown |
| Models in bundled catalog | 640 across 9 provider categories | Claude family only | Subset chosen by Cursor |
| Bring your own API key / endpoint | ✅ Yes — keys stay on your machine | ✅ Anthropic key | ⚠️ Routed through Cursor backend |
| Open source | ✅ Apache-2.0 | ❌ Proprietary | ❌ Proprietary |
| Price | Free framework; pay only your chosen model provider | Subscription / API usage | Subscription |
| Run on top of Claude Code / Codex CLI | ✅ cc/* and codex/* namespaces |
N/A | ❌ |
| Messaging and communication channels | ✅ 32 third-party channel agents, including Slack, Gmail, Email (IMAP/SMTP), Phone Control, SMS, WhatsApp, and Home Assistant | ⚠️ Slack, mobile Remote Control, and research-preview channels for Telegram, Discord, and iMessage; no documented built-in Gmail, WhatsApp, phone-call, or SMS channel | ⚠️ Slack and Microsoft Teams Cloud Agent integrations; no documented built-in Gmail, WhatsApp, phone-call, or SMS channel |
| Scheduled automations | ✅ natural-language cron agent | ❌ | ❌ |
| Wake word for voice interaction | Sorcar | N/A | N/A |
What is in the Name
KISS Agent Framework is a deliberately small agent runtime organized around the KISS principle ("Keep it Simple, Stupid"). The name “Sorcar” pays homage to P. C. Sorcar, the legendary Bengali magician, evoking the idea of an agent that performs feats that appear magical yet are grounded in disciplined engineering. Note: Sorcar also means government in Bengali.
Installation
Full install from source
curl -fsSL https://raw.githubusercontent.com/ksenxx/kiss_ai/main/scripts/install.sh | bash
If the Update button in the settings UI fails, run the full installation command again. It will not delete your history.
The installer targets macOS and Linux on x86_64, aarch64, and arm64. It installs or checks the tools needed to run KISS Sorcar and build/install the VS Code extension.
Python package install
If you only want the Python package (the kiss-web daemon, the Python client API, and the messaging-agent entry points):
pipx install kiss-agent-framework
# or
uv tool install kiss-agent-framework
KISS Sorcar requires Python 3.13+.
Configure model access
Provide at least one model backend. You can use environment variables such as:
export ANTHROPIC_API_KEY=...
export OPENAI_API_KEY=...
export ZAI_API_KEY=...
export MOONSHOT_API_KEY=...
export TOGETHER_API_KEY=...
export OPENROUTER_API_KEY=...
export GEMINI_API_KEY=...
You can also set API keys, a custom model endpoint, and custom HTTP headers in the Settings panel of the VS Code extension or web app.
VS Code Extension Installation
To install only the KISS Sorcar extension, open Visual Studio Code, search for KISS Sorcar in the extension marketplace, install it, and relaunch VS Code. Press ESC if you do not have a specific API key ready, but configure at least one model backend before running tasks.
Using KISS Sorcar
KISS Sorcar has three client interfaces, all served by one local daemon: the VS Code extension, the remote web/mobile app, and the Python client API. A fourth interface, the sorcar terminal command, runs a SorcarAgent directly in the current directory without the daemon: sorcar -t "Summarize README.md" runs an inline task, sorcar -f task.txt runs the file's content as the task (exactly one of -t/-f is required; see sorcar --help for the model and budget flags).
VS Code extension and web/mobile app
Open the KISS Sorcar sidebar in VS Code (or the remote web app in a browser) and type or speak your task. The chat interface provides:
@file/folder mentions with ranked project-file completion.- Per-task git worktree isolation — worktrees are pre-warmed in the background for fast task start, with auto-commit and merge on success, or an interactive merge/discard prompt — toggle both in the Settings panel.
- A model picker, per-task budget caps, chat history with resume (filtered to the current workspace by default), and an agent dashboard (burger menu, bottom-left).
- Wake-word voice chat ("sorcar, …") via the mic button, including steering a running agent by voice.
- Live steering: inject a message into a running agent, or switch its model mid-run.
- Tab mirroring — every VS Code window and web client opened on the same workspace shows the same tabs with the same contents; the tab bar is scoped to the client's workspace directory, and sub-agents dispatched with
run_agentopen their own tab in the calling workspace. - Scheduled automations: ask in plain language ("every weekday at 9am, summarize my unread Slack messages") and the built-in cron agent creates, lists, pauses, resumes, or removes the schedule. A job runs an unattended LLM task or a plain shell command and can deliver its result to any authenticated messaging channel (e.g.
telegram:123456,email:user@example.com).
The remote web app is the same interface served over a cloudflared tunnel: copy the URL and password from the Settings panel and open it on any device.
The kiss-web daemon
The kiss-web daemon hosts all agents, chat sessions, and the web app, and services every client command — including config reads/writes, default-model lookup, and the wake-word listener — over its socket. The VS Code extension starts it automatically; you can also manage it yourself:
# Start the daemon (serves the web app and the extension).
kiss-web
# Pin the daemon's working directory.
kiss-web --workdir "$HOME/projects/my-repo"
# Print the active remote (cloudflared) URL and exit.
kiss-web --url
Python client API
Any Python process can run a task on the daemon with kiss.server.sorcar.run and block until it finishes:
from kiss.server import sorcar
result = sorcar.run("Summarize README.md", work_dir="/path/to/repo")
print(result.text, result.success, result.cost, result.tokens, result.steps)
# Continue the same chat (the agent sees the prior task as context):
follow_up = sorcar.run("Now fix the typos you found", chat_id=result.chat_id)
run() accepts keyword options mirroring the chat interface — model, work_dir, scope_work_dir (workspace directory the task's tab is scoped to, when different from the execution work_dir), chat_id, use_worktree, auto_commit, max_budget, model_config (custom endpoint/headers), web_tools, is_parallel, timeout, stop_on_timeout (also stop the task when timeout expires; default False — the task keeps running), sock_path (daemon socket override) — plus options to customize the agent itself:
tools="/path/to/my_tools.py"— a Python file whoseget_tools()function returns the functions the daemon registers as extra agent tools. The functions are never serialized: only the path travels over the socket, and the daemon imports the file, callsget_tools(), and runs the tools in its own process.system_prompt— replace the default system prompt for the run (and its sub-agents);append_to_system_prompt/append_to_prompt— append text to the system prompt or task prompt instead of replacing them.append_basic_tools=False— restrict the agent tofinishplus yourtoolsfile, dropping the built-in toolset.extension_agent_path— run a full Sorcar Extension Agent (SEA), a Python file that computes the run's parameters and tools on the daemon; see Sorcar Extension Agents (SEAs) below.
Sorcar Extension Agents (SEAs)
A Sorcar Extension Agent (SEA) is a plain Python file whose path you pass as extension_agent_path to sorcar.run(). The daemon imports the file on every run and calls its top-level get_X() functions to compute the run's parameters; parameters without a getter keep whatever the caller passed. One file can define the task prompt, system prompt, model, budget, tools, and safety hooks — a complete custom agent:
# weather_agent.py — a minimal SEA
import requests
def get_prompt() -> str:
return "Look up the current weather in San Francisco and report it."
def get_max_budget() -> float:
return 0.50
def get_use_worktree() -> bool:
return False # no repo changes expected
def get_if_append_basic_tools() -> bool:
return False # restrict the agent to finish + our tools
def get_system_prompt() -> str:
return ("You are a weather assistant. Use the get_weather tool "
"to look up weather, then call finish with the result.")
def get_weather(city: str) -> str:
"""Return current weather for a city from wttr.in.
Args:
city: City name to look up.
"""
resp = requests.get(f"https://wttr.in/{city}?format=3", timeout=10)
resp.raise_for_status()
return resp.text.strip()
def get_tools() -> list:
"""Return the tools the agent may call."""
return [get_weather]
from kiss.server import sorcar
result = sorcar.run(
"placeholder", # required non-blank; overridden by get_prompt()
extension_agent_path="weather_agent.py",
)
Key points:
- Overridable parameters. Every
sorcar.run()parameter excepttimeout,stop_on_timeout,sock_path,scope_work_dir,web_tools,is_parallel, andextension_agent_pathitself has a getter:get_prompt(),get_work_dir(),get_model(),get_chat_id(),get_system_prompt(),get_tools(),get_use_worktree(),get_auto_commit(),get_max_budget(),get_model_config(),get_if_append_basic_tools()(overridesappend_basic_tools),get_append_to_system_prompt(), andget_append_to_prompt().web_toolsandis_parallelalways keep the values passed torun()(their defaults when the caller passed none). - Atomic, type-checked overrides. Getters run in the daemon process and are re-imported from source on every run. Each return value is type-checked; overrides apply only after every getter succeeds, and a broken getter fails the task with a diagnostic in
TaskResult.text. - Tools, two ways.
get_tools()may return a list of callables — making the script its own tools file — or the path of a separate Python file whoseget_tools()returns the callables. Either way the tools execute in the daemon process; nothing is serialized over the socket.get_tools()overrides (does not append to) the caller'stoolsargument. - Hook getters.
get_llm_call_hook()andget_tool_call_hook()return functions with norun()equivalent (callables can't travel the wire).llm_call_hook(new_messages)runs before every LLM call and its return value replaces the outgoing messages;tool_call_hook(name, args)runs before every tool call — returning"OK"lets the tool execute, any other string suppresses the call and is given to the model as the tool's result:
# guarded_agent.py — veto dangerous shell commands
def tool_call_hook(name, args):
if name == "Bash" and "rm -rf" in str(args.get("command", "")):
return "Blocked: destructive command"
return "OK"
def get_tool_call_hook():
return tool_call_hook
The full authoring guide — every getter's semantics, error handling, chat continuation, model configuration, and a complete worked example — is in src/kiss/server/README.md.
Skills, MCP servers, and customization
- Agent Skills loaded from
~/.kiss/skills,<project>/.kiss/skills, Claude skill directories,.agents/skills, and bundled Sorcar skills. - MCP server discovery from
~/.kiss/mcp.json,<project>/.kiss/mcp.json, and<project>/.mcp.json; OAuth tokens are persisted under~/.kiss/mcp_auth/. - "Tricks" button entries read from
~/.kiss/INJECTIONS.md(one per## Tricksection), seeded on install from the bundledsrc/kiss/INJECTIONS.md. Edit the file to customise the dropdown; remove it to regenerate from the bundled defaults. - Welcome-screen sample-task chips are the concatenation of two
## Task-sectioned Markdown files: (1)~/.kiss/MY_TASK_TEMPLATES.md— your personal tasks, auto-created on first launch with the seed## Task\n\nHi!\nand never overwritten thereafter; (2) the bundledsrc/kiss/SAMPLE_TASKS.md— sample tasks shipped with the extension, read directly from the package so every upgrade delivers the latest chips. To customise your chips edit~/.kiss/MY_TASK_TEMPLATES.md; to reset it remove the file.
Messaging & Third-Party Agents
KISS Sorcar includes 32 third-party channel agents that act on messaging services, mailboxes, and devices on your behalf:
BlueBubbles · DingTalk · Discord · Email (IMAP/SMTP) · Feishu · Gmail · Google Chat · Home Assistant · iMessage · IRC · LINE · Matrix · Mattermost · Microsoft Teams · Nextcloud Talk · Nostr · ntfy · Phone Control · QQ · Signal · SimpleX · Slack · SMS · Synology Chat · Telegram · Tlon · Twitch · Webhook · WeCom · WeiXin · WhatsApp · Zalo
In a chat task, just say what you want ("send 'running late' to Alice on WhatsApp") — Sorcar dispatches the matching channel agent through its run_agent tool. Each channel also has its own CLI entry point (kiss-slack, kiss-gmail, kiss-whatsapp, …) for running channel tasks directly from the shell.
Two infrastructure agents round out the set: an A2A agent (kiss-a2a) exposing Sorcar over the agent-to-agent protocol, and an OpenAI-compatible server (kiss-oai) that serves Sorcar behind an OpenAI-style HTTP API. It also ships a Govee smart-home CLI for controlling IoT lights (on/off, brightness, color, and color temperature) via the Govee Developer API.
These agents live in src/kiss/agents/third_party_agents/.
Models Supported
KISS Sorcar ships a catalog of 640 models across 9 provider categories, with built-in prices, context lengths, and capability flags (fc function calling, gen generation, emb embedding). The source of truth is src/kiss/core/models/MODEL_INFO.json.
| Provider category | Catalog entries |
|---|---|
| OpenAI | 110 |
| Anthropic | 14 |
| Gemini / Google | 27 |
| Together AI | 91 |
| Z.AI | 8 |
| Moonshot AI | 10 |
| OpenRouter | 358 |
Claude Code CLI (cc/*) |
14 |
Codex CLI (codex/*) |
8 |
Current catalog capability totals:
- 620 generation-capable models
- 463 function-calling-capable models
- 11 embedding models
Full model list:
OpenAI (110)
computer-use-previewcomputer-use-preview-2025-03-11gpt-3.5-turbogpt-3.5-turbo-0125gpt-3.5-turbo-1106gpt-3.5-turbo-16kgpt-4gpt-4-0613gpt-4-turbogpt-4-turbo-2024-04-09gpt-4.1gpt-4.1-2025-04-14gpt-4.1-minigpt-4.1-mini-2025-04-14gpt-4.1-nanogpt-4.1-nano-2025-04-14gpt-4ogpt-4o-2024-05-13gpt-4o-2024-08-06gpt-4o-2024-11-20gpt-4o-minigpt-4o-mini-2024-07-18gpt-4o-mini-search-previewgpt-4o-mini-search-preview-2025-03-11gpt-4o-search-previewgpt-4o-search-preview-2025-03-11gpt-5gpt-5-2025-08-07gpt-5-chat-latestgpt-5-minigpt-5-mini-2025-08-07gpt-5-nanogpt-5-nano-2025-08-07gpt-5.1gpt-5.1-2025-11-13gpt-5.1-chat-latestgpt-5.2gpt-5.2-2025-12-11gpt-5.2-chat-latestgpt-5.3-chat-latestgpt-5.4gpt-5.4-2026-03-05gpt-5.4-minigpt-5.4-mini-2026-03-17gpt-5.4-nanogpt-5.4-nano-2026-03-17gpt-5.5gpt-5.5-2026-04-23gpt-5.5-2026-04-23-highgpt-5.5-2026-04-23-lowgpt-5.5-2026-04-23-mediumgpt-5.5-2026-04-23-xhighgpt-5.5-highgpt-5.5-lowgpt-5.5-mediumgpt-5.5-xhighgpt-5.6-lunagpt-5.6-luna-highgpt-5.6-luna-lowgpt-5.6-luna-mediumgpt-5.6-luna-xhighgpt-5.6-solgpt-5.6-sol-highgpt-5.6-sol-lowgpt-5.6-sol-mediumgpt-5.6-sol-xhighgpt-5.6-terragpt-5.6-terra-highgpt-5.6-terra-lowgpt-5.6-terra-mediumgpt-5.6-terra-xhighgpt-audiogpt-audio-1.5gpt-audio-2025-08-28gpt-audio-minigpt-audio-mini-2025-10-06gpt-audio-mini-2025-12-15gpt-image-1gpt-image-1-minigpt-image-1.5gpt-image-2gpt-image-2-2026-04-21o1o1-2024-12-17o3o3-2025-04-16o3-deep-researcho3-deep-research-2025-06-26o3-minio3-mini-2025-01-31o4-minio4-mini-2025-04-16o4-mini-deep-researcho4-mini-deep-research-2025-06-26openai/gpt-oss-120bopenai/gpt-oss-120b-highopenai/gpt-oss-120b-lowopenai/gpt-oss-120b-mediumopenai/gpt-oss-20bopenai/gpt-oss-20b-highopenai/gpt-oss-20b-lowopenai/gpt-oss-20b-mediumtext-embedding-3-largetext-embedding-3-smalltext-embedding-ada-002
Anthropic (14)
claude-fable-5claude-fable-5-1claude-haiku-4-5claude-haiku-4-5-20251001claude-opus-4-5claude-opus-4-5-20251101claude-opus-4-6claude-opus-4-7claude-opus-4-8claude-opus-5claude-sonnet-4-5claude-sonnet-4-5-20250929claude-sonnet-4-6claude-sonnet-5
Gemini / Google (27)
gemini-2.5-flashgemini-2.5-flash-imagegemini-2.5-flash-litegemini-2.5-progemini-3-flash-previewgemini-3-pro-imagegemini-3.1-flash-imagegemini-3.1-flash-litegemini-3.1-flash-lite-imagegemini-3.1-flash-lite-previewgemini-3.1-flash-tts-previewgemini-3.1-pro-previewgemini-3.5-flashgemini-3.5-flash-litegemini-3.5-transcribegemini-3.5-transcribe-livegemini-3.6-flashgemini-3.7-flashgemini-3.8-flashgemini-embedding-001gemini-embedding-2gemini-embedding-2-previewgemini-omni-1.1-flashgemini-omni-flash-previewgoogle/gemma-2-27b-itgoogle/gemma-3n-E4B-itgoogle/gemma-4-31B-it
Together AI (91)
BAAI/bge-base-en-v1.5Qwen/QwQ-32BQwen/Qwen2-1.5B-InstructQwen/Qwen2-VL-72B-InstructQwen/Qwen2.5-14B-InstructQwen/Qwen2.5-72B-InstructQwen/Qwen2.5-72B-Instruct-TurboQwen/Qwen2.5-7B-Instruct-TurboQwen/Qwen2.5-Coder-32B-InstructQwen/Qwen2.5-VL-72B-InstructQwen/Qwen3-235B-A22B-Instruct-2507-tputQwen/Qwen3-235B-A22B-Thinking-2507Qwen/Qwen3-Coder-480B-A35B-Instruct-FP8Qwen/Qwen3-Coder-Next-FP8Qwen/Qwen3-Next-80B-A3B-InstructQwen/Qwen3-Next-80B-A3B-ThinkingQwen/Qwen3-VL-32B-InstructQwen/Qwen3-VL-8B-InstructQwen/Qwen3.5-397B-A17BQwen/Qwen3.5-9BQwen/Qwen3.6-PlusQwen/Qwen3.7-MaxQwen/Qwen3.7-PlusQwen/Qwen3.8-2.4T-A95BQwen/Qwen3.8-Flasharcee-ai/trinity-minideepcogito/cogito-v1-preview-llama-70Bdeepcogito/cogito-v1-preview-llama-70B-Turbodeepcogito/cogito-v1-preview-llama-8Bdeepcogito/cogito-v1-preview-qwen-14Bdeepcogito/cogito-v1-preview-qwen-32Bdeepcogito/cogito-v2-1-671bdeepseek-ai/DeepSeek-R1deepseek-ai/DeepSeek-R1-0528deepseek-ai/DeepSeek-R1-0528-tputdeepseek-ai/DeepSeek-R1-Distill-Llama-70Bdeepseek-ai/DeepSeek-R1-Distill-Qwen-1.5Bdeepseek-ai/DeepSeek-R1-Distill-Qwen-14Bdeepseek-ai/DeepSeek-V3-0324deepseek-ai/DeepSeek-V3.1deepseek-ai/DeepSeek-V4-Flash-0731deepseek-ai/DeepSeek-V4-Prodeepseek-ai/DeepSeek-V4-Pro-0813deepseek-ai/deepseek-coder-33b-instructessentialai/rnj-1-instructintfloat/multilingual-e5-large-instructmeta-llama/Llama-3-70b-chat-hfmeta-llama/Llama-3-8b-chat-hfmeta-llama/Llama-3.1-405B-Instructmeta-llama/Llama-3.2-1B-Instructmeta-llama/Llama-3.2-3B-Instruct-Turbometa-llama/Llama-3.3-70B-Instruct-Turbometa-llama/Llama-3.3-70B-Instruct-Turbo-testmeta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8meta-llama/Llama-4-Scout-17B-16E-Instructmeta-llama/Meta-Llama-3-70B-Instruct-Turbometa-llama/Meta-Llama-3-8B-Instructmeta-llama/Meta-Llama-3-8B-Instruct-Litemeta-llama/Meta-Llama-3.1-70B-Instruct-Referencemeta-llama/Meta-Llama-3.1-70B-Instruct-Turbometa-llama/Meta-Llama-3.1-8B-Instruct-Referencemeta-llama/Meta-Llama-3.1-8B-Instruct-Turbomistralai/Ministral-3-14B-Instruct-2512mistralai/Mistral-7B-Instruct-v0.1mistralai/Mistral-7B-Instruct-v0.2mistralai/Mistral-7B-Instruct-v0.3mistralai/Mistral-Small-24B-Instruct-2501mistralai/Mixtral-8x7B-Instruct-v0.1moonshotai/Kimi-K2-Instructmoonshotai/Kimi-K2-Instruct-0905moonshotai/Kimi-K2-Thinkingmoonshotai/Kimi-K2.5moonshotai/Kimi-K2.6moonshotai/Kimi-K2.7-Codemoonshotai/Kimi-K3moonshotai/Kimi-K3-highmoonshotai/Kimi-K3-lowmoonshotai/Kimi-K3-maxnvidia/Llama-3.1-Nemotron-70B-Instruct-HFnvidia/NVIDIA-Nemotron-Nano-9B-v2nvidia/nemotron-3-ultra-550b-a55bzai-org/GLM-4.5-Air-FP8zai-org/GLM-4.6zai-org/GLM-4.7zai-org/GLM-5zai-org/GLM-5.1zai-org/GLM-5.2zai-org/GLM-5.2-highzai-org/GLM-5.2-maxzai-org/GLM-5.3zai-org/GLM-5.3-Flash
Z.AI (8)
glm-4-32b-0414-128kglm-4.5glm-4.5-airglm-4.5-airxglm-4.5-flashglm-4.5-xglm-4.6glm-4.7
Moonshot AI (10)
kimi-k2.5kimi-k2.6kimi-k2.7-codekimi-k3kimi-k3-highkimi-k3-lowkimi-k3-maxmoonshot-v1-128kmoonshot-v1-32kmoonshot-v1-8k
OpenRouter (358)
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Claude Code CLI (cc/*) (14)
cc/claude-fable-5cc/claude-fable-5-1cc/claude-haiku-4-5-20251001cc/claude-opus-4-5-20251101cc/claude-opus-4-6cc/claude-opus-4-7cc/claude-opus-4-8cc/claude-opus-5cc/claude-sonnet-4-5-20250929cc/claude-sonnet-4-6cc/claude-sonnet-5cc/haikucc/opuscc/sonnet
Codex CLI (codex/*) (8)
codex/codex-auto-reviewcodex/defaultcodex/gpt-5.4codex/gpt-5.4-minicodex/gpt-5.5codex/gpt-5.6-lunacodex/gpt-5.6-solcodex/gpt-5.6-terra
Contributing
Contributions in the form of issues are welcome. KISS Sorcar should be able to help implement and review them.
License
Apache-2.0. See LICENSE.
Citation
If you use KISS Sorcar in your research, please cite:
@misc{sen2026kisssorcar,
title = {KISS Sorcar: A Stupidly-Simple General-Purpose and Software Engineering AI Assistant},
author = {Sen, Koushik},
year = {2026},
eprint = {2604.23822},
archivePrefix = {arXiv},
primaryClass = {cs.SE},
url = {https://arxiv.org/abs/2604.23822}
}
1 maintainer
Release files for kiss-agent-framework 2026.9.11
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| kiss_agent_framework-2026.9.11.tar.gz | 22.6 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| kiss_agent_framework-2026.9.11-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 30.9 MB
Release files / kiss_agent_framework-2026.9.11.tar.gz
| Download URL | kiss_agent_framework-2026.9.11.tar.gz |
|---|---|
| Size | 22.6 MB |
| Tags | Source |
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Release files / kiss_agent_framework-2026.9.11-py3-none-any.whl
| Download URL | kiss_agent_framework-2026.9.11-py3-none-any.whl |
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| Size | 8.3 MB |
| Tags | Python 3 |
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