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.

Kastor is an early proof of concept.

Working today:

• parse .agent, .tool, .prompt, and kastor.hcl
• validate references and prompt variables
• build runnable LangGraph projects
• examples: weather agent, content scheduler agent

Planned for v0:

• kastor plan/apply
• local state file and drift detection
• Deploy to aws/azure platforms.

This is not another agent runtime/framework.

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:

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.