Kastor is "Terraform for AI agents." Agents today are defined imperatively inside frameworks (LangGraph, CrewAI) or clicked together in platform UIs (OpenAI Assistants, Bedrock Agents) — there is no vendor-neutral, versionable, reviewable source of truth. Kastor provides one: a typed, declarative spec (.agent
, .tool
, .prompt
files in HCL) and a Go toolchain with two paths — kastor build
generates runnable projects for target frameworks, and kastor plan
/ kastor apply
reconcile agents as long-lived resources on hosted platforms, with state, diffs, and drift detection.
The full design lives in SPEC.md.
Homebrew:
brew tap weirdGuy/tap && brew install kastor
Install script (verifies the release checksum, installs to /usr/local/bin
or ~/.local/bin
, never sudo):
curl -fsSL https://raw.githubusercontent.com/weirdGuy/kastor/main/scripts/install.sh | sh
With Go 1.26+:
go install github.com/weirdGuy/kastor/cmd/kastor@latest
Or download an archive for your platform from the releases page, verify it against checksums.txt
, and put the kastor
binary on your PATH.
Prerequisites: Go 1.26+, Python 3.11+, an OpenAI API key, and a Tavily API key (the example's search tool runs against Tavily's hosted MCP server).
Compile the spec to a LangGraph project:
go build ./cmd/kastor
./kastor validate examples/weather/
./kastor build examples/weather/
kastor build
writes the generated project to examples/weather/gen/langgraph
(the target's declared output
). Generated output is not committed: it is reproducible from the spec, and codegen determinism is enforced by tests.
Set up the generated project:
cd examples/weather/gen/langgraph
python3 -m venv .venv
. .venv/bin/activate
pip install -r requirements.txt
The example's web_search
tool is pinned to an MCP server and tool by its spec URI, mcp://search-server/tavily_search
. How to reach that server is deployment configuration, not spec: create mcp_servers.json
in the working directory (or point the KASTOR_MCP_CONFIG
env var at a file elsewhere). For Tavily's hosted server:
{
"search-server": {
"transport": "streamable_http",
"url": "https://mcp.tavily.com/mcp/?tavilyApiKey=tvly-YOUR-KEY"
}
}
The URL embeds your API key, which is why mcp_servers.json
is gitignored — treat it as a secret, never commit it. Also note the spec URI's last path segment (tavily_search
) must name a tool the server actually advertises, or calls fail with "does not expose tool".
Export the model credential (the example's model "fast"
block uses provider openai
):
export OPENAI_API_KEY=sk-...
Run the agent:
python3 main.py weather --inputs '{"location": "Lisbon", "date": "tomorrow"}'
It prints the agent's declared output contract as JSON:
{
"weather": "..."
}
The generated README.md
inside gen/langgraph
owns the run-the-project side in full: every agent's inputs and outputs, tool bindings, and MCP configuration.
One v0 caveat (SPEC.md §3.2/§4): agent.weather
's optional forecast_context
input references agent.forecast
's output. That reference is validated at compile time and orders the dependency graph, but generated code does not run the upstream agent for you — if you want the context, run forecast
yourself and pass its summary via --inputs
.
go build ./... # build everything
go test ./... # run all tests
SPEC.md is the source of truth for design decisions; CLAUDE.md documents the day-to-day conventions.