context-compiler 0.2.1

Deterministic conversational state engine for LLM applications.

Context Compiler

A deterministic directive engine that converts explicit user instructions into structured conversational state for LLM applications.

Modern language models reason well but are unreliable at maintaining consistent state across interactions.

Corrections compete with earlier statements, constraints disappear, and long conversations accumulate contradictions.

The Context Compiler introduces a deterministic state layer that governs authoritative conversational state independently of the model.

The model performs reasoning and generation while the compiler manages facts and constraints. Once accepted, directives remain authoritative until explicitly corrected or reset.

Why “Compiler”?

Context Compiler treats explicit user directives as inputs to a deterministic process.

Instead of relying on the LLM to remember constraints across a conversation, user instructions are compiled into structured state before the model runs.

The idea is similar to a traditional compiler: user directives are translated into a structured representation that the rest of the system can rely on.

Installation

• Python 3.11+
• pip install context-compiler
• Dev/test: uv sync --group dev and uv run pytest
• Examples: see examples/README.md
• Demonstrations: see demos/README.md

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10-Second Example

User sets a constraint once:

User: don't use peanuts

State becomes:

{
  "facts": {
    "focus.primary": null
  },
  "policies": {
    "prohibit": ["peanuts"]
  },
  "version": 1
}

Later in the conversation:

User: how should I make this curry?

The host supplies the authoritative state to the model so the constraint persists across turns.

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Architecture

User Input
   ↓
Context Compiler
   ↓
Decision (passthrough | update | clarify)
   ↓
Host Application
   ↓
LLM

The compiler governs authoritative conversational state and never calls the LLM. The host decides whether to call the model based on the returned Decision.

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Decision API

Each user message produces a Decision.

class Decision(TypedDict):
    kind: Literal["passthrough", "update", "clarify"]
    state: dict | None
    prompt_to_user: str | None

Meaning:

kind	host behavior
passthrough	forward user input to LLM
update	forward input with updated state
clarify	show prompt_to_user and do not call the LLM

Host Integration Example

engine = create_engine()
decision = engine.step(user_input)

if decision["kind"] == "clarify":
    show_to_user(decision["prompt_to_user"])
else:
    state = decision["state"] or engine.state
    messages = build_messages(state, user_input)
    render(call_llm(messages))

API Reference

API	Description
create_engine(...)	Create a new compiler engine, optionally with replacement initial state.
step(user_input)	Parse one user turn and return a deterministic Decision.
engine.state	Read or replace full in-memory authoritative state.
export_json()	Export current state as JSON for persistence/transport.
import_json(payload)	Load state from exported JSON payload.

---

State Model

The compiler maintains an authoritative state:

{
  "facts": {
    "focus.primary": null
  },
  "policies": {
    "prohibit": []
  },
  "version": 1
}

State Access and Persistence

Hosts may inspect or replace in-memory state (engine.state) or persist it using export_json() and import_json(). State changes occur only through directives processed by step(). Storage is managed by the host application.

Fact Schema

The current schema contains a single exclusive slot: facts["focus.primary"]. This demonstrates deterministic fact replacement and correction behavior. Richer schemas may be introduced in future releases.

State Properties

• Facts are exclusive (last write wins)
• Policies are additive
• No inference or semantic reasoning

Identical input sequences always produce identical compiler state. LLM responses may still vary unless deterministic decoding is used by the host.

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Directive Examples

Hard negative directive:

User: don't use peanuts

Result:

{
  "policies": {
    "prohibit": ["peanuts"]
  }
}

Fact configuration:

User: use vegetarian curry

State update:

facts.focus.primary = "vegetarian curry"

Correction:

User: actually vegan curry

Result:

facts.focus.primary = "vegan curry"

Ambiguous mutation:

User: no use peanuts

Compiler response:

Decision.kind = "clarify"

No state mutation occurs until confirmation.

Reset Commands

Two explicit reset commands are supported:

• reset policies clears policies.prohibit but preserves the current fact (facts["focus.primary"])
• clear state resets the full state to initial values

Example:

Before:

{
  "facts": {"focus.primary": "vegetarian curry"},
  "policies": {"prohibit": ["peanuts"]},
  "version": 1
}

After reset policies:

{
  "facts": {"focus.primary": "vegetarian curry"},
  "policies": {"prohibit": []},
  "version": 1
}

After clear state:

{
  "facts": {"focus.primary": null},
  "policies": {"prohibit": []},
  "version": 1
}

---

Examples

Integration examples are available in the examples/ directory.

See examples/README.md for walkthroughs.

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Quickstart

Run the interactive REPL:

context-compiler

Run an example:

python examples/01_persistent_guardrails.py

Run tests:

uv run pytest

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Guarantees

• State changes only through explicit user directives or confirmation.
• Identical input sequences produce identical compiler state.
• Model responses never modify compiler state.
• Ambiguous directives trigger clarification instead of changing state.

These invariants are verified through behavioral tests and Hypothesis-based property tests.

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Design Notes

More detailed design and milestone documents are available in:

• Project overview
• M1 design document

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License

Apache-2.0.