fathom-rules 0.3.1

Deterministic reasoning runtime for AI agents, built on CLIPS via clipspy

Fathom

A modern Python-first expert system runtime built on CLIPS. Define rules in YAML. Evaluate in microseconds. Zero hallucinations.

Current version: 0.3.1

License: MIT

Language: Python 3.14+ (primary), Go and TypeScript SDKs in progress

Package Manager: uv

Maintained by: Kraken Networks

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Why Fathom?

Every AI agent framework lets agents decide what to do by guessing. For most tasks, that's fine.

For some tasks, guessing is unacceptable:

• Policy enforcement — "Is this agent allowed to do this?" can't be a maybe.
• Data routing — "Which databases should this query hit?" can't hallucinate a source.
• Compliance — "Did this fleet operate within NIST 800-53 controls?" needs a provable answer.
• Classification — "What clearance level does this data require?" is not a prompt engineering problem.

Fathom provides deterministic, explainable, auditable reasoning using CLIPS — a battle-tested expert system — wrapped in a modern Python library with YAML-first rule authoring.

Install

uv add fathom-rules

Quick Start

from fathom import Engine

engine = Engine()
engine.load_templates("templates/")
engine.load_rules("rules/")

engine.assert_fact("agent", {
    "id": "agent-alpha",
    "clearance": "secret",
    "purpose": "threat-analysis",
    "session_id": "sess-001",
})

engine.assert_fact("data_request", {
    "agent_id": "agent-alpha",
    "target": "hr_records",
    "classification": "top-secret",
    "action": "read",
})

result = engine.evaluate()
print(result.decision)       # "deny"
print(result.reason)         # "Agent clearance 'secret' insufficient for 'top-secret' data"
print(result.duration_us)    # 47

See the Getting Started guide for a full walkthrough.

What Ships Today

Phase 1–3 of the roadmap are complete; Phase 4 is in progress. See design.md for the full roadmap with status.

Core runtime (Python)

• YAML compiler for templates, rules, modules, and functions
• Forward-chaining evaluation with rule + module traces
• Working memory persistence across evaluations within a session
• Classification-aware operators (below, meets_or_exceeds, dominates, compartments)
• Temporal operators (count_exceeds, rate_exceeds, changed_within, last_n, distinct_count, sequence_detected)
• Rule-assertion actions (then.assert + bind) and user-defined Python functions (Engine.register_function)
• Structured JSON audit log with append-only sinks
• Ed25519 attestation service for signed evaluation results
• Fleet reasoning with Redis and Postgres backends for shared working memory

Integrations

• FastAPI REST server with bearer-token auth and rule-path jailing
• gRPC server with bearer-token auth (see protos/fathom.proto)
• MCP tool server (FathomMCPServer) for agent discovery
• LangChain adapter callback handler
• CLI — fathom validate, fathom test, fathom bench, fathom info, fathom repl
• Docker sidecar (Debian slim + uv)
• Prometheus metrics export (/metrics endpoint)

Rule packs

• fathom-owasp-agentic — OWASP Agentic Top 10 mitigations
• fathom-nist-800-53 — Access control, audit, information flow
• fathom-hipaa — PHI handling, minimum necessary, breach triggers
• fathom-cmmc — CMMC Level 2+ controls

SDKs (in progress)

• fathom-go — hand-written REST client (packages/fathom-go/); gRPC regeneration blocked on a go_package path fix
• fathom-ts — @fathom-rules/sdk v0.1.0 (packages/fathom-ts/); OpenAPI-generated client pending
• fathom-editor — React visual rule editor (packages/fathom-editor/); stub

Core Primitives

Primitive	Purpose	CLIPS Construct
Templates	Define fact schemas with typed slots	deftemplate
Facts	Typed instances asserted into working memory	working memory
Rules	Pattern-matching logic with conditions and actions	defrule
Modules	Namespace rules with controlled execution order	defmodule
Functions	Reusable logic for conditions and actions	deffunction

Key Differentiator: Working Memory

Unlike stateless policy engines (OPA, Cedar), Fathom maintains working memory across evaluations within a session:

• Cumulative reasoning — "This agent accessed PII from 3 sources — deny the 4th."
• Temporal patterns — "Denial rate spiked 400% in 10 minutes — escalate."
• Cross-fact inference — "Agent A passed data to Agent B, who is requesting external access — violation."

Integration Shapes

As a library

from fathom import Engine
engine = Engine.from_rules("rules/")
result = engine.evaluate()

As a REST sidecar

docker run -p 8080:8080 -v ./rules:/rules kraken/fathom:latest
curl -H "Authorization: Bearer $TOKEN" -X POST localhost:8080/v1/evaluate \
  -d '{"facts": [...], "ruleset": "access-control"}'

As a gRPC sidecar

# protos/fathom.proto — regenerate Go/TS clients from the proto
grpcurl -H "authorization: Bearer $TOKEN" \
  -d '{"facts": [...]}' localhost:50051 fathom.v1.Fathom/Evaluate

As an MCP tool

from fathom.integrations.mcp_server import FathomMCPServer
server = FathomMCPServer(engine)
server.serve()

Documentation

Docs live under docs/ and build with MkDocs Material (Diátaxis information architecture).

Entry points:

• Getting Started
• Tutorials
• How-to Guides
• Concepts
• Reference

Performance Targets

Operation	Target
Single rule evaluation	< 100µs
100-rule evaluation	< 500µs
Fact assertion	< 10µs
YAML compilation	< 50ms

Related Projects

• Bosun — Agent governance built on Fathom (fleet analysis, compliance attestation)
• Nautilus — Intelligent data broker built on Fathom (multi-source routing, classification-aware scoping)

Development

git clone https://github.com/KrakenNet/fathom.git
cd fathom
uv sync
uv run pytest           # 1361 tests
uv run mkdocs serve     # docs preview

Run the live REST server locally:

uv run uvicorn fathom.integrations.rest:app --reload

See CHANGELOG.md for release notes.

License

MIT — see LICENSE for details.

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Maintained by Kraken Networks · krakennetworks.com · krakn.ai