Swival

Swival

A coding agent for any model. Documentation

Swival is a CLI coding agent built to be practical, reliable, and easy to use. It works with frontier models, but its main goal is to be as reliable as possible with smaller models, including local ones. It is designed from the ground up to handle tight context windows and limited resources without falling apart.

It connects to LM Studio, llama.cpp, HuggingFace Inference API, OpenRouter, Google Gemini, Gemini Enterprise Agent Platform (formerly Vertex AI), ChatGPT Plus/Pro, AWS Bedrock, Apple Foundation Models (experimental), any OpenAI-compatible server (ollama, mlx_lm.server, vLLM, etc.), or any external command (codex exec, custom wrappers, etc.), sends your task, and runs an autonomous tool loop until it produces an answer. With LM Studio and llama.cpp it auto-discovers your loaded model, so there's nothing to configure. Pure Python, no framework.

Quickstart

Pick the provider that matches how you want to run models:

| Provider | Auth | Required flags | First command | | ---------------- | ---------------------------------------------------- | ------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------- | | LM Studio | none | none | swival "Refactor src/api.py" | | llama.cpp | none | --provider llamacpp | swival --provider llamacpp "Refactor src/api.py" | | HuggingFace | HF_TOKEN or --api-key | --provider huggingface --model ORG/MODEL | swival --provider huggingface --model zai-org/GLM-5.2 "task" | | OpenRouter | OPENROUTER_API_KEY or --api-key | --provider openrouter --model MODEL | swival --provider openrouter --model z-ai/glm-5.2 "task" | | Google Gemini | GEMINI_API_KEY, OPENAI_API_KEY, or --api-key | --provider google --model MODEL | swival --provider google --model gemini-2.5-flash "task" | | GEAP (Vertex AI) | Google Cloud ADC or GOOGLE_APPLICATION_CREDENTIALS | --provider geap --gcp-project ID --location REGION --model MODEL | swival --provider geap --gcp-project my-proj --location us-central1 --model gemini-3.1-pro "task" | | ChatGPT Plus/Pro | browser auth on first run or CHATGPT_API_KEY | --provider chatgpt --model MODEL | swival --provider chatgpt --model gpt-5.5 "task" | | Generic | optional OPENAI_API_KEY | --provider generic --base-url URL --model MODEL | swival --provider generic --base-url http://127.0.0.1:8080 --model my-model "task" | | Apple FM (exp.) | none (local server) | --provider applefm | swival --provider applefm "task" | | AWS Bedrock | AWS credential chain (AWS_PROFILE, env vars, IAM) | --provider bedrock --model MODEL | swival --provider bedrock --model global.anthropic.claude-opus-4-6-v1 "task" | | Command | none | --provider command --model "COMMAND" | swival --provider command --model "codex exec --full-auto" "task" |

Run swival --help for the grouped CLI reference and copy-paste examples.

LM Studio

1. Install LM Studio and load a model with tool-calling

support. Recommended first model: qwen3-coder-next (great quality/speed tradeoff on local hardware). Crank the context size as high as your hardware allows.

1. Start the LM Studio server.
2. Install Swival (requires Python 3.13+):

uv tool install swival

On macOS you can also use Homebrew. Trust the tap first, then install:

brew trust swival/tap
brew install swival/tap/swival

1. Run:

swival "Refactor the error handling in src/api.py"

That's it. Swival finds the model, connects, and goes to work.

llama.cpp

1. Start llama-server with a model (use --fit on to auto-size context to

available memory): ``sh llama-server --reasoning auto --fit on \ -hf unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_XL ``

1. Install Swival:

``sh uv tool install swival ``

1. Run (model is auto-discovered from the server):

``sh swival --provider llamacpp "Refactor the error handling in src/api.py" ``

The default base URL is http://127.0.0.1:8080. Override with --base-url.

HuggingFace

export HF_TOKEN=hf_...
uv tool install swival
swival "Refactor the error handling in src/api.py" \
    --provider huggingface --model zai-org/GLM-5.2

You can also point it at a dedicated endpoint with --base-url and --api-key.

OpenRouter

export OPENROUTER_API_KEY=sk_or_...
uv tool install swival
swival "Refactor the error handling in src/api.py" \
    --provider openrouter --model z-ai/glm-5.2

Google Gemini

export GEMINI_API_KEY=...
uv tool install swival
swival "Refactor the error handling in src/api.py" \
    --provider google --model gemini-2.5-flash

GEAP (Gemini Enterprise Agent Platform / Vertex AI)

For enterprise Google Cloud setups, use the geap provider. It routes through Vertex AI using Application Default Credentials, so there is no API key to manage. --provider vertexai is accepted as an alias.

gcloud auth application-default login
uv tool install swival
swival "Refactor the error handling in src/api.py" \
    --provider geap \
    --gcp-project my-gcp-project \
    --location us-central1 \
    --model gemini-3.1-pro

Service accounts work too: set GOOGLE_APPLICATION_CREDENTIALS to the JSON key path instead of running gcloud auth.

ChatGPT Plus/Pro

Use OpenAI models through your existing ChatGPT Plus or Pro subscription -- no API key needed.

uv tool install swival
swival "Refactor the error handling in src/api.py" \
    --provider chatgpt --model gpt-5.5

On first use, a device code and URL are printed to your terminal. Open the URL, enter the code, and authorize with your ChatGPT account. Tokens are cached locally for subsequent runs.

Generic (OpenAI-compatible)

swival "Refactor the error handling in src/api.py" \
    --provider generic \
    --base-url http://127.0.0.1:8080 \
    --model my-model

Works with ollama, mlx_lm.server, vLLM, DeepSeek API, and anything else that speaks the OpenAI chat completions protocol. No API key required for local servers.

Interactive sessions

swival

The REPL carries conversation history across questions, which makes it good for exploratory work and longer tasks.

Task Input From Stdin

If you omit the positional task and pipe stdin, Swival reads the task from stdin.

swival -q `
in the REPL and Swival keeps the agent on task across turns. It's a structured
spin on the Ralph-style "keep prompting until it's done" loop. The agent
doesn't get to declare victory and walk away after one turn: the original
objective is fed back to the model after every answer, and the loop only ends
when the agent itself signals the goal is complete after a real evidence-based
audit, declares a blocker, or hits the optional token budget. This makes it
practical to point Swival at ambitious, long-running tasks like refactors,
audits, or end-to-end fixes, and let it grind for hours without giving up
halfway. Pause, resume, replace, or clear the goal at any time. See
[Goals](docs.md/goal.md) for details.

**Run on a timer.** `/loop 5m check PR status and summarize` schedules a
plain prompt to re-run every five minutes. In the REPL it registers the
schedule, runs the first iteration immediately, then returns the prompt
to you. Subsequent iterations fire between your commands in their own
snapshot, so they do not pollute the live conversation. In one-shot mode
the same command blocks and streams each iteration's answer to stdout,
which makes it the recommended way to run Swival as a long-lived poller
under `systemd`, `tmux`, or `nohup`. The interval is forgiving: `1h30m`,
`5 minutes`, `every hour`, `half an hour`, and `1 minute and 30 seconds`
all work. `/loops` lists active schedules and `/unloop ` cancels
them.

**A2A server mode.** Run `swival --serve` and your agent becomes an A2A
endpoint that other agents can call over HTTP. Multi-turn context, streaming,
rate limiting, and bearer auth are built in.

**ACP for editors.** Run `swival --acp` and Swival speaks the Agent Client
Protocol on stdio, so editors that act as ACP clients (Zed, the
`agent-client-protocol.nvim` plugin, and similar) can drive a Swival agent the
same way they drive Claude Code, Gemini CLI, or codex. See
[ACP](docs.md/acp.md) for editor setup.

**Built-in security audit.** Run `/audit` and Swival scans your codebase for
provable security bugs. Findings go through a multi-phase pipeline (triage,
deep review, verification in isolated worktrees) so the final report tends to
contain real bugs with patches rather than speculative findings. Supports
incremental resume, targeted regeneration of individual findings, and a
calibration mode for measuring triage recall. See [Security
Audit](docs.md/audit.md) for the full flag reference.

**Skills, MetaSKILLs, MCP, ACP, and A2A.** Extend the agent with SKILL.md-based
skills for reusable workflows, write dynamic skill programs in a safe Python
subset with [MetaSKILLs](docs.md/metaskills.md) (optional extra, install with
`uv tool install 'swival[metaskills]'`), connect to external tools via
the Model Context Protocol, plug into editors via the Agent Client Protocol,
and talk to remote agents via the Agent-to-Agent (A2A) protocol.

**Small enough to read and hack.** A compact Python codebase with no framework
underneath. If something doesn't work the way you want, change it.

**CLI-native.** stdout is exclusively the final answer. All diagnostics go to
stderr. Pipe Swival's output straight into another command or a file.

**Extensible with [custom commands](docs.md/custom-commands.md).** Drop scripts
or prompt templates into `~/.config/swival/commands/` and invoke them with `!name`
in the REPL. The [swival-commands](https://github.com/Swival/swival-commands)
community repo has ready-made commands like a security auditor and a PR reviewer.

## Documentation

Full documentation is available at [swival.dev](https://swival.dev/).

- [Getting Started](docs.md/getting-started.md) -- installation, first run, what
  happens under the hood
- [Usage](docs.md/usage.md) -- one-shot mode, REPL mode, CLI flags, piping,
  exit codes
- [Tools](docs.md/tools.md) -- what the agent can do: file ops, search, editing,
  web fetching, thinking, task tracking, command execution
- [Safety and Sandboxing](docs.md/safety-and-sandboxing.md) -- path resolution,
  symlink protection, filesystem access modes, command execution modes
- [Security Audit](docs.md/audit.md) -- multi-phase `/audit` scan with triage,
  deep review, verification in isolated worktrees, and patch generation
- [Skills](docs.md/skills.md) -- creating and using SKILL.md-based agent skills
- [Metaskills](docs.md/metaskills.md) -- portable dynamic skill workflow
  specification for users and agent implementers
- [Customization](docs.md/customization.md) -- config files, project instructions,
  system prompt overrides, tuning parameters
- [Context Management](docs.md/context-management.md) -- compaction, snapshots,
  knowledge survival, and how Swival handles tight context windows
- [Providers](docs.md/providers.md) -- LM Studio, HuggingFace, OpenRouter,
  Google Gemini, Gemini Enterprise Agent Platform (Vertex AI), ChatGPT Plus/Pro,
  AWS Bedrock, generic OpenAI-compatible server, and command (external program)
  configuration
- [MCP](docs.md/mcp.md) -- connecting external tool servers via the Model Context
  Protocol
- [A2A](docs.md/a2a.md) -- connecting to remote agents via the Agent-to-Agent
  protocol
- [ACP](docs.md/acp.md) -- driving Swival from ACP-aware editors like Zed and
  agent-client-protocol.nvim via the Agent Client Protocol
- [Reports](docs.md/reports.md) -- JSON reports for benchmarking and evaluation
- [Web Browsing](docs.md/web-browsing.md) -- Chrome DevTools MCP, Lightpanda
  MCP, and agent-browser for web interaction
- [Reviews](docs.md/reviews.md) -- external reviewer scripts for automated QA
  and LLM-as-judge evaluation
- [Secret Encryption](docs.md/secrets.md) -- transparent encryption of
  credentials before they reach the LLM provider
- [Outbound LLM Filter](docs.md/llm-filter.md) -- user-defined scripts to
  redact or block outbound LLM requests
- [Lifecycle Hooks](docs.md/lifecycle-hooks.md) -- startup/exit hooks for
  syncing state to remote storage
- [Custom Commands](docs.md/custom-commands.md) -- REPL custom command setup
  and execution (see also the [community commands repo](https://github.com/Swival/swival-commands))
- [Command Middleware](docs.md/command-middleware.md) -- pre-execution command
  rewriting and policy enforcement (RTK integration)
- [Python API](docs.md/python-api.md) -- library API for embedding Swival in
  Python applications
- [Not Just for Frontier Models](docs.md/open-models.md) -- why Swival is
  built to work well with small and open models too
- [Using Swival with AgentFS](docs.md/agentfs.md) -- copy-on-write filesystem
  sandboxing for safe agent runs
- [Using Swival with nono](docs.md/nono.md) -- OS-enforced sandboxing with
  network controls and atomic rollback

## Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [Swival](https://github.com/Swival)
- **Source:** [Swival/swival](https://github.com/Swival/swival)
- **License:** MIT
- **Homepage:** https://swival.dev

Install and usage instructions live in the source repository linked above.