Cruxible Core

Deterministic decision engine with DAG-based receipts. Build entity graphs, query with MCP, get auditable proof.

Define entity graphs, queries, and constraints in YAML. Run them locally from CLI or MCP, and get receipts proving exactly why each result was returned.

┌──────────────────────────────────────────────────────────────┐
│  AI Agent (Claude Code, Cursor, Codex, ...)                  │
│  Writes configs, orchestrates workflows                      │
└──────────────────────┬───────────────────────────────────────┘
                       │ calls
┌──────────────────────▼───────────────────────────────────────┐
│  MCP Tools                                                   │
│  init · validate · ingest · query · feedback · evaluate ...  │
└──────────────────────┬───────────────────────────────────────┘
                       │ executes
┌──────────────────────▼───────────────────────────────────────┐
│  Cruxible Core                                               │
│  Deterministic. No LLM. No opinions. No API keys.            │
│  Config → Graph → Query → Receipt → Feedback                 │
└──────────────────────────────────────────────────────────────┘

Quick Example

1. Define a domain in YAML:

entity_types:
  Drug:
    properties:
      drug_id: { type: string, primary_key: true }
      name:    { type: string }
  Enzyme:
    properties:
      enzyme_id: { type: string, primary_key: true }
      name:      { type: string }

relationships:
  - name: same_class
    from: Drug
    to: Drug
  - name: metabolized_by
    from: Drug
    to: Enzyme

named_queries:
  suggest_alternative:
    entry_point: Drug
    returns: Drug
    traversal:
      - relationship: same_class
        direction: both
      - relationship: metabolized_by
        direction: outgoing

2. Load data and run a deterministic query:

"Suggest an alternative to simvastatin"

3. Get a receipt — structured proof of every answer:

Raw receipt DAG rendered for readability:

Receipt RCP-17b864830ada

Query: suggest_alternative for simvastatin

Step 1: Entry point lookup
  simvastatin -> found in graph

Step 2: Traverse same_class (both directions)
  Found 6 statins in the same therapeutic class:
  n3  atorvastatin   n4  rosuvastatin   n5  lovastatin
  n6  pravastatin    n7  fluvastatin    n8  pitavastatin

Step 3: Traverse metabolized_by (outgoing) for each alternative
  n9   atorvastatin -> CYP3A4   (CYP450 dataset)
  n10  rosuvastatin -> CYP2C9   (CYP450 dataset, human approved)
  n11  rosuvastatin -> CYP2C19  (CYP450 dataset)
  n12  lovastatin -> CYP2C19    (CYP450 dataset)
  n13  lovastatin -> CYP3A4     (CYP450 dataset)
  n14  pravastatin -> CYP3A4    (CYP450 dataset)
  n15  fluvastatin -> CYP2C9    (CYP450 dataset)
  n16  fluvastatin -> CYP2D6    (CYP450 dataset)
  n17  pitavastatin -> CYP2C9   (CYP450 dataset)

Results: atorvastatin, rosuvastatin, lovastatin, pravastatin, fluvastatin, pitavastatin
Duration: 0.41ms | 2 traversal steps

Get Started

pip install "cruxible-core[mcp]"

Or use uv tool install "cruxible-core[mcp]" if you prefer uv.

Add the MCP server to your AI agent:

Claude Code / Cursor (project .mcp.json or ~/.claude.json / .cursor/mcp.json):

{
  "mcpServers": {
    "cruxible": {
      "command": "cruxible-mcp",
      "env": {
        "CRUXIBLE_MODE": "admin"
      }
    }
  }
}

Codex (~/.codex/config.toml):

[mcp_servers.cruxible]
command = "cruxible-mcp"

[mcp_servers.cruxible.env]
CRUXIBLE

... [View full README on GitHub](https://github.com/cruxible-ai/cruxible-core#readme)