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.

First time? Run swival with no arguments on a terminal: if you have no
config yet, a short setup wizard walks you through picking a provider and
writes a starter config to ~/.config/swival/config.toml.

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

sh
uv tool install --python 3.14 swival

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

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

4. Run:

sh
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

2. Install Swival:

sh
   uv tool install --python 3.14 swival

3. 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

sh
export HF_TOKEN=hf_...
uv tool install --python 3.14 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

sh
export OPENROUTER_API_KEY=sk_or_...
uv tool install --python 3.14 swival
swival "Refactor the error handling in src/api.py" \
    --provider openrouter --model z-ai/glm-5.2

Google Gemini

sh
export GEMINI_API_KEY=...
uv tool install --python 3.14 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.

sh
gcloud auth application-default login
uv tool install --python 3.14 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.

sh
uv tool install --python 3.14 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)

sh
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

sh
swival

The REPL carries conversation history across questions, which makes it good for
exploratory work and longer tasks. /model opens an interactive picker over
whatever your provider can serve right now (for HuggingFace, the models
currently backed by inference providers, with context and pricing shown), and
favorites tagged with * stay pinned across sessions for quick switching.

Task Input From Stdin

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

sh
swival -q < objective.md

cat prompts/review.md | swival --provider huggingface --model zai-org/GLM-5.2

Useful for long prompts, shell-quoting avoidance, and scripted workflows.

Updates and uninstall

sh
uv tool upgrade swival    # update (uv)
uv tool uninstall swival  # remove (uv)
brew upgrade swival       # update (Homebrew)
brew uninstall swival     # remove (Homebrew)

What makes it different

Reliable with small models. Context management is one of Swival's strengths.
It keeps things clean and focused, which is especially important when you are
working with models that have tight context windows. Graduated compaction,
persistent thinking notes, and a todo checklist all survive context resets, so
the agent doesn't lose track of multi-step plans even under pressure.

Your models, your way. Works with LM Studio, llama.cpp, HuggingFace
Inference API, OpenRouter, Google Gemini, the Gemini Enterprise Agent Platform
(formerly Vertex AI), ChatGPT Plus/Pro, AWS Bedrock, any OpenAI-compatible server,
and any external command. With LM Studio and llama.cpp,
it auto-discovers whatever model you have loaded, so there is nothing to configure. With
HuggingFace or OpenRouter, point it at any supported model. With Google Gemini,
use Gemini models through Google's native API. For Google Cloud enterprise
setups, the geap provider authenticates via Application Default Credentials
and routes through Vertex AI. With ChatGPT Plus/Pro,
authenticate through your browser and use OpenAI's models through your existing
subscription. With AWS Bedrock, pick up the AWS credential chain and call any
Bedrock-hosted model. The experimental applefm provider talks to a local Apple
Foundation Models server. With the generic provider, connect to ollama, mlx_lm.server,
vLLM, or any other compatible server. With the command provider, shell out to any
program that reads a prompt on stdin and writes a response on stdout. You pick
the model and the infrastructure.

Review loop and LLM-as-a-judge. Swival has a configurable review loop that
can run external reviewer scripts or use a built-in LLM-as-judge to
automatically evaluate and retry agent output. Good for quality assurance on
tasks that matter.

Built for benchmarking. Pass --report report.json and Swival writes a
machine-readable evaluation report with per-call LLM timing, tool
success/failure counts, context compaction events, and guardrail interventions.
Useful for comparing models, settings, skills, and MCP servers systematically
on real coding tasks.

Secrets stay on your machine. Swival transparently detects API keys and
credential tokens in LLM messages and encrypts them before they leave your
machine when you enable secret encryption with --encrypt-secrets. The LLM
never sees the real values. Decryption happens locally when the response comes
back, so tools still work normally. See [Secret Encryption](docs.md/secrets.md)
for details.

Cross-session memory. The agent remembers things across sessions. It stores
notes in a local memory file and retrieves the most relevant entries for each
new conversation using BM25 ranking, so context from past work carries forward
without bloating the prompt. Use /learn in the REPL to teach it something
on the spot.

Pick up where you left off. When a session is interrupted by Ctrl+C, max
turns, or context overflow, Swival saves its state to disk. Next time you run it
in the same directory, it picks up where it left off: what it was doing, what
it had figured out, and what was left.

Stick with a goal until it's done. Set an objective with /goal
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 --python 3.14 '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
community repo has ready-made commands like a security auditor and a PR reviewer.

Documentation

Full documentation is available at swival.dev.

• [Getting Started](docs.md/getting-started.md) -- install

…

Collapse