"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 go directly to the model provider or local endpoint you configure, never through our servers. Multi-model workflows take a paragraph of prompt, not a pipeline: complex AI systems and 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

This README is the quick start. The detailed references are:

FEATURES.md: the complete feature inventory, checked against the source tree.
src/kiss/server/README.md: every sorcar.run() option, tools, hooks, and the Sorcar Extension Agent authoring guide.
src/kiss/agents/third_party_agents/README.md: the messaging and service agents, sign-in, and worked examples.
MODELS.md: the full per-provider model list.
kisssorcar.github.io/docs: installation, CLI, API, and messaging guides.

Table of Contents

KISS Sorcar vs Claude Code vs Cursor
Terminal-Bench 2.0
What is in the Name
Installation
Using KISS Sorcar
Messaging & Third-Party Agents
Models Supported
Contributing
License
Citation

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	709 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	✅ 44 third-party agents: 32 messaging channels (Slack, Gmail, Email (IMAP/SMTP), Phone Control, SMS, WhatsApp, Home Assistant, …) plus service agents for GitHub, Notion, Overleaf, Postgres, Brave Search, Firecrawl, and Google Workspace	⚠️ 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	"Hey Sorcar"	N/A	N/A

Terminal-Bench 2.0: KISS Sorcar vs Pi, Codex CLI, and Claude Code

The HarnessTax study (Pan, Yang, Arabzadeh, Chiang, Stoica, Zaharia; UC Berkeley and Arena Intelligence) holds the model fixed, swaps the harness between Claude Code, Codex CLI, and Pi, and finds that the harness moves cost far more than it moves what gets solved. We added KISS Sorcar to their Terminal-Bench 2.0 table: the same 30 sampled tasks, the same seven models, three attempts per task, graded by the official Terminal-Bench 2.0 verifier, with the system prompt cut to 727 words of coding rules (papers/kisssorcar/evidence/tb2_prompt.txt). Averaged over the seven models, KISS Sorcar solved 75.6% of attempts; Pi 70.0%, Codex CLI 65.7%, Claude Code 65.1%, with the best point estimate on every model.

Model	KISS Sorcar solved	$/att.	Pi solved	$/att.	Codex CLI solved	$/att.	Claude Code solved	$/att.
Claude Fable 5	80.0	1.50	71.1	1.08	72.2	0.98	75.6	1.55
Claude Opus 4.8	74.4	1.21	72.2	0.76	72.2	0.85	68.9	0.90
Claude Sonnet 4.6	76.7	2.23	65.6	0.61	63.3	0.55	62.2	0.67
Claude Haiku 4.5	51.1	0.39	47.8	0.25	31.1	0.21	41.1	0.26
GPT-5.6 Sol	85.6	0.69	83.3	0.42	78.9	0.76	71.1	1.35
GPT-5.6 Luna	78.9	0.05	76.7	0.04	72.2	0.06	70.0	0.10
Kimi K3	82.2	1.14	73.3	0.38	70.0	0.45	66.7	0.52
Mean of 7 models	75.6	1.03	70.0	0.51	65.7	0.55	65.1	0.77

Terminal-Bench 2.0, the study's 30-task sample, three attempts per task: percentage of attempts solved and cost per attempt in USD. KISS Sorcar: 630 attempts run on 25 September 2026, no turn cap, $50 budget per attempt, providers' default request parameters. The other three columns are the study's published numbers (100-turn cap, high reasoning effort, priced on a 1 September list). Bold marks the best point estimate per row.

Percentage of Terminal-Bench 2.0 attempts solved per model under KISS Sorcar, Pi, Codex CLI, and Claude Code

Thirty tasks is a small sample (the pooled 95% interval, 63.8 to 85.9, contains all three published means), so we reran Pi ourselves, paired, on Claude Fable 5 with no turn cap and the same price table. On the study's 30 tasks KISS Sorcar solved 80.0% of attempts to Pi's 68.9%, a gap of +11.1 points (95% interval +3.3 to +20.0) for five cents more per attempt; on the 57 tasks the study did not sample, 82.5% to 71.9%, a gap of +10.5 points (+2.9 to +18.7) for 63 cents more. Only Pi was rerun, on one model, and the benchmark exercises the loop, six tools, and the coding rules with the discovery procedures, memory, reviewer, and IDE features switched off. Full write-up: The Harness Tax, Audited; method and intervals: the paper, Section 5; per-attempt records: papers/kisssorcar/evidence/tb2_trials.json; the runners live in the separate benchmarkings package (a sibling checkout, or KISS_BENCHMARKINGS_DIR), which the paper scripts under papers/kisssorcar/ locate.

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

The installer targets macOS and Linux on x86_64, aarch64, and arm64. It installs or checks the tools KISS Sorcar needs, builds and installs the VS Code extension, waits for the daemon the extension starts, trusts the daemon's local certificate authority in your browsers (kiss-web --trust-ca), and opens the web app at https://127.0.0.1:PORT; on a remote machine it prints the cloudflared URL to open on your own device instead.

When a new release is available, the update toast in the chat panel offers Update and Update when idle; the daemon installs an idle update as soon as no task is running, and VS Code reloads its window on its own once the new extension is in place. While the installer runs (from the Update button, the web app, or ./install.sh in a terminal), VS Code shows its current step ([4/5] Building VS Code extension...) as a non-blocking progress notification, read from ~/.kiss/.install-progress and closed when the installer exits. If the update fails, run the installation command again. It will not delete your history. After an install or update the chat panel shows the tips from src/kiss/TIPS.md once per version. Branding (src/kiss/agents/vscode/media/brand.json and a git-ignored .brand/ overlay for white-label builds) and the installer's steps are described in FEATURES.md.

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. The Custom Models section of the Settings panel registers your own models (a local vLLM/Ollama endpoint, or a provider model not in the bundled catalog); entries are stored in ~/.kiss/MY_MODELS.json and appear in the model picker alongside the bundled catalog.

The picker also lists two bundled router agents under the Router group: autorouter splits a task into units and dispatches each to the cheapest model tier that passes its acceptance check, and bestrouter runs every task on claude-fable-5-1 and has gpt-6-astra review the result read-only. They are Sorcar Extension Agents (see below), not models.

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, budget, and work-dir 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 completion from a persistent index of your working directory and home directory.
Per-task git worktree isolation with auto-commit and merge on success (a bundled merge agent resolves conflicts), or an interactive merge/discard prompt; toggle both in the Settings panel.
A pre-run task classifier that skips the worktree for tasks that write no files and gives simple tasks a lite system prompt; optionally backed by the ~typesafe/jev-latest decisions model through OpenRouter.
A model picker, per-task budget caps, chat history with tags and per-chat summaries, an agent dashboard, a Working directory panel, and inline rendering of tool-generated images.
Image and PDF attachments via the picker, paste, or drag-and-drop.
Persistent agent memory (on by default): Markdown pages under ~/.kiss/memories with a vector index, plus a per-repository memory for tasks run inside a git checkout. Toggle it in Settings or with KISS_USE_MEMORY=0.
Wake-word voice chat ("Hey 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; wrap the message in <task>…</task> to queue it as the next task instead.
Tab mirroring: every VS Code window and web client shows the same tabs, whatever folder each has open (the daemon owns the one canonical tab list), and a sub-agent opens a nested tab of its own that closes when it finishes.
A Browser tab that streams a real browser running next to the daemon to every surface; an agent's show_browser() moves the page it is browsing into that tab when a login, CAPTCHA, or live demo needs you.
Scheduled automations: ask in plain language ("every weekday at 9am, summarize my unread Slack messages") and the built-in cron agent (kiss-cron from the shell) creates, lists, pauses, resumes, or removes the schedule. Schedules are kept in Pacific time, and a job can deliver its result to a messaging channel.

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 browser tab is titled KISS Sorcar: <machine name> so several servers are easy to tell apart. Its desktop mode adds a Task Info sidebar with live token, cost, and step metrics and an agent-written progress report for the running task, plus Schedule, Apps (channel sign-in state; click one to connect it), and Spend panels, and an activity bar with an Explorer (workspace file tree with Ctrl/Cmd-click, Shift-click, and Ctrl/Cmd+A multi-select) and a Source Control view (changes grouped per worktree, with foldable sections, and a commit graph whose file rows open a side-by-side diff). File links open in content tabs with a Monaco editor, Markdown/HTML preview, and a PDF viewer. Sections 15 to 19 of FEATURES.md describe each of these in full.

The `kiss-web` daemon

The kiss-web daemon hosts the agents, chat sessions, and the web app, and services every client command over WSS (port 8787 by default). Browsers sign in with the password from Settings; same-machine clients (the VS Code extension, the Python client, cron jobs, and channel agents) present a per-start token the daemon writes to ~/.kiss/sorcar-local.json (KISS_SORCAR_LOCAL overrides the path). 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

# Trust the daemon's TLS certificate in this user's browsers and exit.
kiss-web --trust-ca

The web app is always served over HTTPS. The Local and LAN URLs use a certificate issued by a machine-local certificate authority kept in ~/.kiss/tls/; kiss-web --trust-ca installs it in the browsers on the daemon's machine, and a phone or tablet on the same network installs it from https://<lan-ip>:PORT/ca.crt (compare the SHA-256 fingerprint the command prints). The CA key never leaves ~/.kiss/tls/, and the server certificate is re-issued automatically when it expires or the LAN address changes.

Python client API

Any Python process can run a task on the daemon with kiss.server.sorcar.run and block until it finishes (up to timeout, one hour by default):

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, chat_id, use_worktree, auto_commit, max_budget, model_config, use_web_tools, use_memory, tool_profile, docker_image, timeout, and more) plus options that customize the agent itself: tools (path of a Python file whose get_tools() returns extra tool functions, imported and run in the daemon process), system_prompt, append_to_system_prompt, append_to_prompt, append_basic_tools=False (restrict the agent to finish plus your tools), and extension_agent_path (run a Sorcar Extension Agent). Every option is documented in src/kiss/server/README.md.

Sorcar Extension Agents (SEAs)

A Sorcar Extension Agent (SEA) is a plain Python file, <name>/<name>_sea.py, whose path you pass as extension_agent_path to sorcar.run(). The daemon imports it on every run and calls its top-level functions named after run()'s parameters (prompt(), model(), max_budget(), tools(), system_prompt(), ...) to compute the run's parameters; parameters without a getter keep whatever the caller passed. Every SEA also defines description(), one sentence that /<name> help prints. Two hook getters, llm_call_hook() and tool_call_hook(), return functions that run before every model call and every tool call (a tool hook returning anything but "OK" suppresses the call and hands its string to the model). One file is a complete custom agent:

# weather/weather_sea.py — a minimal SEA
import requests


def description() -> str:
    return "Reports the current weather in San Francisco from wttr.in."

def prompt() -> str:
    return "Look up the current weather in San Francisco and report it."

def max_budget() -> float:
    return 0.50

def if_append_basic_tools() -> bool:
    return False  # restrict the agent to finish + our tools

def 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 tools() -> list:
    """Return the tools the agent may call."""
    return [get_weather]

from kiss.server import sorcar

result = sorcar.run("placeholder", extension_agent_path="weather/weather_sea.py")

Slash commands. Every SEA folder in a scanned folder is also a chat command: /xxx some text runs xxx/xxx_sea.py as a sub-agent with "some text" as the task. The channel agents are registered this way (/slack, /gmail, ...), and so are the 17 bundled SEAs in src/kiss/agents/seas/: /ask (answer a question about the current task from its persisted events), /sh (run a shell command), /dummy (a plain sub-agent), /coding (the unattended benchmark harness), /rsi7d (seven-day recursive self-improvement of the bundled SEAs from their logged runs), /merge (resolve a conflicted git merge), /task_update (progress report on a task), /autorouter and /bestrouter (the model routers above), /skillopt (optimize a skill or prompt constant against an evaluation set), /write_paper, /review_paper, and /revise_and_review_paper (write, review, and iterate on a research paper), /git_extract_knowledge (build and maintain a repository's durable memory), /write (prose for a general audience), and /remember and /forget (add or remove a standing instruction in ~/.kiss/SORCAR.md). List your own SEA folders, one per line, in ~/.kiss/SEAS.md; they are picked up within two seconds. The getter semantics, type checking, hooks, and dispatch flow are in src/kiss/server/README.md and docs/sea-commands.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. Remote servers that follow the MCP authorization spec (Notion, Linear, Asana, or any URL) are signed into from the chat with the connect_mcp_server tool; tokens are stored under ~/.kiss/mcp_auth/. A curated catalog of privacy-first MCP connectors ships in connectors/.
"Tricks" (inject-instruction snippets) come from your ~/.kiss/MY_INJECTION.md and the bundled src/kiss/INJECTIONS.md; the Inject panel in the chat lists, inserts, edits, and deletes them.

Messaging & Third-Party Agents

KISS Sorcar includes 44 third-party agents that act on messaging services, mailboxes, devices, and web services on your behalf. 32 are messaging-channel agents:

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

Ten more are service agents that give Sorcar authenticated API tools for productivity and data services:

Brave Search (kiss-brave) · Firecrawl (kiss-firecrawl) · GitHub (kiss-github) · Google Calendar (kiss-gcal) · Google Docs (kiss-gdocs) · Google Drive (kiss-gdrive) · Google Sheets (kiss-gsheets) · Notion (kiss-notion) · Overleaf (kiss-overleaf) · PostgreSQL (kiss-postgres)

In a chat task, just say what you want ("send 'running late' to Alice on WhatsApp", "list my open GitHub PRs") and Sorcar dispatches the matching agent through its run_agent tool. Each agent also has its own CLI entry point (kiss-slack, kiss-gmail, kiss-whatsapp, ...). Gateway-capable channels also work inbound: a recurring poll tick (ask for "an always-on Telegram gateway" in chat) runs each new message as a Sorcar task, with thread continuity, sender allow-lists, and an optional pairing handshake. 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).

Sign-in. Every service agent carries check_<service>_auth / authenticate_<service> tools, so a task can connect a service on the spot, and the Apps panel of the sidebar starts such a task when you click a service that is not connected. The six Google Workspace agents go through Composio (Google only lets verified OAuth apps request Workspace scopes, so no Google token is stored locally); GitHub, Microsoft Teams, Slack, and Discord use public OAuth apps with device-code or PKCE flows and no client secret. Every sign-in ends on a page only you may complete, shown in the Browser tab under the daemon; the agent never asks for your password or a 2FA code. On Linux, credentials for the 18 Muse-covered connectors are isolated by default behind a local auth daemon that swaps opaque surrogate tokens for the real ones at the network edge and applies an allow/deny/ask policy with an audit log (python -m kiss.agents.third_party_agents.muse_auth; opt out with KISS_MUSE_AUTH=0).

The complete catalog, credentials, and 26 worked examples are in src/kiss/agents/third_party_agents/README.md.

Models Supported

KISS Sorcar ships a catalog of 709 models across 9 provider categories, with built-in prices, context lengths, and capability flags (fc function calling, gen generation, emb embedding, dec typed decisions via OpenRouter's /api/alpha/decisions). The source of truth is src/kiss/core/models/MODEL_INFO.json; the per-provider counts and the full model list are in MODELS.md. Models are grouped by the provider that routes them, so the cc/* and codex/* namespaces (Claude Code CLI and Codex CLI) are categories of their own, and the open-weight openai/gpt-oss-* and google/gemma-* models count under Together AI, which serves them.

Cost and budget tracking use the catalog prices, except for openrouter/* models, where the cost OpenRouter reports for each response is billed instead, since the same model id is priced differently per upstream route. A response the adapters reject after the provider has billed it still counts towards the task's cost and budget, and the task total shown in the UI includes the task classifier's spend, every earlier session of a task continued after a crash, and the whole spend of the sub-tasks the task dispatches with run_agent and run_parallel.

Contributing

Contributions in the form of issues are welcome. KISS Sorcar should be able to help implement and review them. If you want to send a pull request (PR), please make sure that all Python and JavaScript tests pass across Mac OSX, Linux, Windows.

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}
}

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