Deborah

Deborah is a human-readable process language for governed agentic work in human systems.

Renamed from Cairn (v0.9). The package cairn was split into Deborah (this repo — the process language) and Huldah (human-systems analysis: human factors, UI evidence, layout load, live observation). The document format keeps the Cairn name — your .cairn.md files and ```cairn fences are unchanged. See MIGRATING.md.

It gives humans and AI systems a shared way to describe, plan, interpret, and review complex work across technical, psychological, organisational, and sociological dimensions. It bridges pseudocode-style clarity with modern agentic realities: iteration, recursion, non-determinism, sync/async, queuing, outcome review, error handling, and human context.

The specification lives in SPEC.md (v0.9).

Install: pip install deborah — import deborah. (The old cairn-lang distribution now installs a compatibility shim that re-exports from here.)

Optional extras:

• pip install 'deborah[render]' — YAML stylesheets
• pip install 'deborah[web]' — deborah-serve interactive composer
• pip install 'deborah[export]' — HTML / DOCX / PDF export (python-docx + fpdf2)

What it looks like

A small slice, in the readable Narrative style:

PROCESS — Answer a question from local memory.
  1. Gather context with read-only tools (search, then compile the surrounding nodes).
  2. The model writes the answer using only what was gathered — no invented sources.
  3. Save the exchange so the next turn can resume.

The same step in the precise Formal style (same backbone, with tags + traceability):

2. Generate the answer from gathered_context.  [LLM, STOCHASTIC, SYNC] [SATISFIES: R1]
   CONSTRAINTS: answer only from retrieved context; do not invent sources.

Full worked descriptions and categorized example suites are in examples/. The example library includes real systems plus suites for corporate lifecycle, AI-native organisational change, psychological and sociological work interfaces, technical/agentic workflows, occupational health, governance/risk/compliance, and OKF-style human-systems mappings.

Rendering & export

Cairn can be turned into audience-friendly views:

deborah-render my-process.cairn.md --profile narrative_steps
# Domain examples:
#   --profile therapeutic     (psychological / regulation + feedback focus)
#   --profile change_leader   (organisational change + coalition/alignment focus)
#   --profile human_demand    (human load, support, trust + simulation findings)
#   --profile human_factors   (cognitive/social/org/incentive risks + mitigations)
deborah-render my-process.cairn.md -f html -o view.html
deborah-render my-process.cairn.md -f pdf -o plan.pdf   # requires [export]

The human-systems analysis CLIs (huldah-human-factors, huldah-agent-harness-plan, huldah-recommend-interface-changes, huldah-generate-report, the huldah-ui-* family) moved to Huldah — pip install huldah. They read the same .cairn.md documents this package defines.

Or programmatically:

from deborah.render import render_plan, export_view

view = render_plan(text, profile="operator")
pdf = export_view(view, "pdf")

from deborah import analyze_human_factors, build_agent_harness_plan
report = analyze_human_factors(text)  # pure Python; no LLM service required
plan = build_agent_harness_plan(process_path="my-process.cairn.md", ui_evidence_path="ui-evidence.json")

from deborah import CommandLLMProvider, HoglahLLMProvider, interpret_human_factors
provider = CommandLLMProvider("my-llm-wrapper --model local")
interpretation = interpret_human_factors(text, provider)

queued = HoglahLLMProvider(model="gemma3:1b")
queued_interpretation = interpret_human_factors(text, queued)

Interactive composer: deborah-serve

What it's for

• Documenting requirements and technical specifications in design documents.
• Reverse-engineering hidden or unclear processes out of existing code, AI systems, or legacy implementations.
• Defining governed agentic processes — recursive calls, iterative refinement, dynamic LLM decisions, tool boundaries, serialized agent discussions, trace, and outcome alignment.
• Real-world use cases: recursive agentic workflows (chat interfaces, autonomous systems), low-resource queuing, semantic engines, multi-step reasoning.
• Describing work as it actually happens: technical mechanisms embedded in human contexts, including cognitive, emotional, organisational, and social dynamics.
• New constructs for human dimensions: REGULATION, APPRAISAL, FEEDBACK, MACRO (psych), COALITION, ALIGN, VISION, RESISTANCE (org), SOCIALIZE, SYMBOLIC_INTERACTION, ROLE (socio), etc.
• Multi-scale STATE (e.g. scope: org.team), enhanced EMERGENT with attrs, domain-aware validation.
• Human demand mapping: ORIENT / ACT / CLOSE demand, recovery, trust, support, AI role-play simulation findings, and cognitive-load metrics for human-facing process steps.
• Human factors semantics: a browsable lens library for plausible cognitive, psychological, social, organisational, behavioural-economic, and incentive risks, with qualitative probability/impact estimates.
• Augmentation process semantics: human-AI role complementarity, cognitive-state adaptation, interaction richness, trust calibration, and bias dynamics.

Put simply: Cairn treats software work, thinking work, and organisational work as processes embedded in human systems, not as purely mechanical flows.

Philosophy

Human-first readability

The primary goal is maximum human readability. Anyone — technical or not — should read a Cairn description and quickly understand the process without wrestling with syntax, jargon, or abstraction. We remove cognitive barriers so attention stays on what the process actually does, not on decoding notation.

This matters because agentic work is rarely just computation. It often includes judgement, uncertainty, memory, motivation, trust, conflict, change, and review. Cairn keeps those human dimensions describable without losing the governed runtime spine that lets AI systems validate and interpret plans.

Least abstract, simplest language possible

• Concrete, everyday words wherever they suffice.
• Short, direct sentences; active voice.
• Structure scaffolds without getting in the way.
• Details (constraints, context, edge cases) are optional layers consulted when needed — the main flow stays clean and punchy.

Consistency through core verbs

A small recommended lexicon (Initialize, Propose, Evaluate, Decide, Update, Execute, Iterate, Queue, Merge, Handle…) gives a consistent rhythm and "process feel" that helps readers scan, compare, and mentally simulate flows — and helps multiple people or LLMs write consistently. Verbs are not rigid rules; clarity always wins.

Balance of structure and flexibility

• Numbered steps + indentation give sequence and hierarchy.
• PLAN envelopes turn a PROCESS into a versioned live plan that can be revised when new information arrives.
• Tags ([LLM, SYNC, DYNAMIC]) add precision without cluttering prose.
• One canonical backbone is projected into audience render profiles (precise ai, readable operator, and more) — serving machines and humans alike.
• CONTEXT and CONSTRAINTS supply supporting knowledge on demand.

Human-system awareness

Cairn can describe psychological, organisational, and sociological processes alongside technical ones because governed agentic work happens inside human systems. These dimensions are not a separate ambition from the agentic use case; they are part of the operating environment that a useful agentic process must be able to notice, express, and review.

The human-factors layer is portable by design. The OKF concept bundle supplies a local semantic lookup for cognitive load, trust, social pressure, organisational change, behavioural economics, and incentive patterns. The offline analyzer can run as a normal PyPI module without a service dependency; an LLM can later be attached as an optional interpreter that reads the same report and starts a richer design conversation.

LLM integration is provider-neutral. A command provider receives JSON on stdin (task, prompt, and context) and returns either plain text or JSON with a text field. That means local llama.cpp/Ollama scripts, hosted-model CLIs, Claude/Codex wrappers, or a Hoglah queue submitter can all sit behind the same adapter. Hoglah is a natural fit for durable queued analysis jobs, retries, and audit trails, and HoglahLLMProvider is available when hoglah is installed, but Cairn does not require it.

See examples/llm_command_stub.py for the minimal provider contract.

Practical and evolving

Cairn is meant to be used "in anger" on real projects, evolving from actual needs rather than theoretical perfection.

The ultimate test: a reader thinks "I get what's happening here," not "I need to learn the notation first."

Status

Cairn carries two independent version numbers, and they are not meant to match:

The package version is the single source of truth for anything installable; it is set in pyproject.toml, recorded in CHANGELOG.md, and tagged (v0.8.2). The specification version moves only when the language changes — v0.9 added versioned live PLAN envelopes for bounded recursive revision of a PROCESS backbone.

Package 0.8.2 — complete export support (html/docx/pdf), interactive deborah-serve, executable grammar + conformance, multiple render profiles, and real usage examples across the family stack.

A structural grammar is in GRAMMAR.md. Refined by describing real systems (Tirzah, Hoglah, Mahalath, etc.).

Repository

• SPEC.md — the specification (v0.9).
• GRAMMAR.md — structural EBNF for the skeleton.
• examples/ — real systems described in Cairn (Tirzah, Hoglah, Mahalath, Mahlah, Milcah, Mizpah); see tirzah-system.cairn.md for end-to-end composition.
• CHANGELOG.md — how the spec has evolved.
• docs/HUMAN-FACTORS-METHODOLOGY.md — how to annotate human demand, factors, qualitative risk, and LLM-assisted review.
• docs/HCI-TOUCHPOINTS.md — how consuming LLMs should analyze UI touchpoints and cognitive-aesthetic load.
• docs/FUNCTIONAL-LAYOUT-LOAD.md — how to estimate form/layout traversal load from UI geometry.
• docs/usage-modes.md — PyPI, recursive LLM, manual GitHub-link agent, embedded, and CI/review-gate usage modes.
• docs/orchestration/manual-agent-analysis.cairn.md — a Cairn-described orchestration pattern for manual agent analysis.
• docs/orchestration/agent-harness-playbook.md — concrete CLI/Python sequence for tool-assisted interactive agent use.
• docs/future-usage-logging-spec.md — future plan for real-world touchpoint logging and analysis.
• docs/augmentation-integration-notes.md — how the augmentation process research was mapped into Cairn.
• okf/ — an Open Knowledge Format knowledge bundle: Cairn's concepts and reference, as linked markdown.

Feedback & contributing

Cairn evolves from real use, so feedback is the point — especially from describing your own processes in it. That is exactly how v0.7 was shaped.

• Ambiguity, gap, or rough edge? Open a feedback issue.
• A new construct, tag, or change? Open a proposal (same chooser) — say what real process motivated it; concrete beats theoretical.
• Questions, ideas, show-and-tell? Use the Discussions tab.
• See CONTRIBUTING.md for how proposals are handled.

License

Apache License 2.0.

Conformance (cairn package)

Cairn is primarily a spec, but it also ships a tiny, dependency-free conformance surface so a runtime can validate the plans it produces instead of embedding a private dialect:

import deborah

# Runtime PLAN dict conformance (SPEC §4.5)
errors = deborah.validate_plan(plan_dict)   # [] when conformant

# Structural grammar (GRAMMAR.md EBNF + SPEC §12 well-formedness)
doc = deborah.parse_document(cairn_text_or_markdown)
errors = deborah.validate_document(doc)   # [] when well-formed
plan = deborah.document_to_plan(doc)        # first PLAN or PROCESS → plan dict

# Simplified human-readable views
view = deborah.render_plan(cairn_text_or_markdown, profile="narrative_steps")
cairn.CANONICAL_PLAN                       # an executable known-good fixture
cairn.PLAN_CONSTRUCTS                      # the allowed step constructs (SPEC §5)

CLI: deborah-validate examples/hoglah.cairn.md · deborah-render examples/hoglah.cairn.md

View composer (deborah-serve)

An interactive, local composer for building a transformation view of a process and saving the recipe as a named template:

pip install 'deborah[web]'
deborah-serve            # http://127.0.0.1:8795

Paste a Cairn process, pick a profile and options (language, format, depth, sections, layout), watch the view update live, then Save as template. A template is persisted as a stylesheet under ~/.cairn/templates/<name>.json, so it is directly reusable on the CLI: deborah-render --stylesheet ~/.cairn/templates/<name>.json input.cairn.md.

Grammar parser: docs/GRAMMAR-PARSER.md. Simplified views: docs/VIEW-GENERATOR.md.

Tirzah's recursive planner is tested against deborah.validate_plan so its output cannot drift from the grammar.

Works the same on native Linux and WSL. Cairn has no hard runtime dependency on Keturah; when Keturah is installed, deborah.manifest uses it, and otherwise Cairn provides a small compatible manifest surface.

pip install -e ".[dev]" && pytest