sibyl-core 1.0.0

Core library for Sibyl - domain models, graph operations, and knowledge retrieval

sibyl-core

Core library for Sibyl. Domain models, graph operations, retrieval algorithms, the AI substrate, and tool implementations. Shared foundation for the API server and CLI.

Quick Reference

# Install
uv add sibyl-core

# Development
moon run core:lint        # Ruff check
moon run core:typecheck   # ty
moon run core:test        # Pytest

What's Here

• models/: Domain entities (Task, Project, Epic, Source, reflection, synthesis)
• backends/surreal/: SurrealDB driver, schema, and per-table operations
• retrieval/: Native context-pack retrieval, compatibility search, fusion, dedup
• ai/: Native LLM substrate, model registry, providers, validation
• embeddings/: Embedding provider clients
• services/: Memory loop, reflection, synthesis, autonomy, source adapters, and the EntityManager / RelationshipManager graph managers
• tools/: MCP tool implementations
• tasks/: Workflow engine and dependency resolution
• migrate/: Migration archive merge and rewrite logic
• auth/: JWT primitives and password hashing

Structure

src/sibyl_core/
├── models/
│   ├── entities.py       # Entity, EntityType, base classes
│   ├── tasks.py          # Task, Project, Epic, Milestone
│   ├── sources.py        # Source, Document
│   ├── context.py        # Context-pack models
│   ├── reflection.py     # Reflection candidate models
│   ├── synthesis.py      # Synthesis plan and artifact models
│   └── responses.py      # API response models
├── backends/surreal/     # Driver, schema, table operations
├── retrieval/            # Native context retrieval, fusion (RRF), dedup
├── ai/
│   ├── registry.py       # Curated LLM/embedding model registry
│   ├── providers.py      # PydanticAI provider model factory
│   ├── clients.py        # Scoped agent caching
│   └── llm/              # Extractor, Generator, config sources
├── embeddings/           # Embedding provider clients
├── services/
│   ├── graph.py          # EntityManager, RelationshipManager
│   ├── graph_client.py   # SurrealGraphClient driver wrapper
│   └── ...               # Memory loop, reflection, synthesis, source adapters
├── tools/                # MCP tool implementations
└── tasks/                # Workflow state machine, dependency resolution

Usage

Models

from sibyl_core.models import (
    Entity, EntityType, Task, TaskStatus, Project, Epic,
)

task = Task(
    name="Implement OAuth",
    content="Add Google and GitHub OAuth",
    project_id="proj_abc",
    status=TaskStatus.TODO,
)

Graph Client

from sibyl_core.services import get_graph_client
from sibyl_core.services.graph import EntityManager

client = await get_graph_client(group_id=str(org_id))
manager = EntityManager(client, group_id=str(org_id))

# CRUD
await manager.create(entity)
# Retrieval uses search or list_by_type rather than direct ID lookup
results = await manager.search(query="authentication patterns", limit=20)

Write Concurrency

The SurrealDB driver serializes WebSocket operations per client, and org-scoped graph access should use a per-org client (get_graph_client(group_id=...) returns one scoped to the org namespace).

# Native write path, no LLM extraction
await manager.create_direct(entity)

# Compatibility path with LLM-backed extraction
await manager.create(entity)

Task Workflow

from sibyl_core.tasks import TaskManager

manager = TaskManager(entity_manager, relationship_manager)
await manager.create_task_with_knowledge_links(task)
await manager.find_similar_tasks(task)
await manager.estimate_task_effort(task)

AI Substrate

from pydantic import BaseModel

from sibyl_core.ai import Extractor, Generator, LLMSurface


class ExtractedFact(BaseModel):
    name: str
    summary: str


extractor = Extractor(ExtractedFact, surface=LLMSurface.CRAWLER)
fact = await extractor.extract("Extract one fact from this document chunk.")

generator = Generator(surface=LLMSurface.SYNTHESIS)
draft = await generator.generate("Summarize this context pack.", max_tokens=512)

The substrate uses PydanticAI under sibyl_core.ai, with provider API keys passed through provider objects rather than mutating os.environ. Extractor[T] handles structured output and classified LLM errors. Generator handles text generation and streaming. Surface-specific config is resolved through an LLMConfigSource so the API can supply database-backed settings while core stays pure.

Entity Types

Sibyl models 33 entity types so memory stays structured. The registry lives in models/entities.py and covers, among others:

• Work: task, epic, project, milestone, team
• Knowledge: pattern, episode, procedure, rule, guide, template, error_pattern, tool, language, topic
• Memory: decision, plan, idea, claim, artifact, session, note, preference
• People & places: person, place, event
• Sources: source, document, domain, community, knowledge_source, config_file, slash_command

Relationship Types

from sibyl_core.models import RelationshipType

# Knowledge
RelationshipType.APPLIES_TO, REQUIRES, CONFLICTS_WITH, SUPERSEDES

# Task
RelationshipType.BELONGS_TO, DEPENDS_ON, BLOCKS, REFERENCES

Configuration

SIBYL_LLM_PROVIDER=anthropic          # anthropic | openai
SIBYL_LLM_MODEL=claude-haiku-4-5
SIBYL_LLM_TEMPERATURE=0
SIBYL_LLM_MAX_TOKENS=2048
SIBYL_LLM_TIMEOUT_SECONDS=60

# Surface-specific values override shared LLM values.
SIBYL_LLM_CRAWLER_PROVIDER=gemini
SIBYL_LLM_CRAWLER_MODEL=gemini-3-1-flash-lite
SIBYL_LLM_SYNTHESIS_PROVIDER=anthropic
SIBYL_LLM_SYNTHESIS_MODEL=claude-sonnet-4-6

SIBYL_ANTHROPIC_API_KEY=...           # LLM provider key
SIBYL_OPENAI_API_KEY=sk-...           # LLM or embedding provider key
SIBYL_GEMINI_API_KEY=...              # LLM or embedding provider key

SIBYL_EMBEDDING_PROVIDER=openai       # openai | gemini
SIBYL_EMBEDDING_MODEL=text-embedding-3-small
SIBYL_EMBEDDING_DIMENSIONS=1536
SIBYL_GRAPH_EMBEDDING_PROVIDER=openai
SIBYL_GRAPH_EMBEDDING_MODEL=text-embedding-3-small
SIBYL_GRAPH_EMBEDDING_DIMENSIONS=1024

LLM settings are instance-wide. Environment variables win over database settings and mark individual fields as locked.

Gemini keys can also come from GEMINI_API_KEY or GOOGLE_API_KEY. Changing embedding provider, model, or dimensions requires re-embedding existing graph and document vectors before comparing old and new search results.

To add a first-class LLM provider, add a provider factory branch in sibyl_core.ai.providers, add registry entries in sibyl_core.ai.registry, extend LLMProviderName and the API DTOs, and add a live probe to scripts/llm/verify_registry.py.

Key Patterns

Multi-tenancy: Every operation requires org context.

manager = EntityManager(client, group_id=str(org.id))

Node shapes: Native retrieval queries direct Surreal records. Archive compatibility keeps old Episodic/Entity records readable without Graphiti.

SELECT * FROM entity WHERE entity_type = $type;

Creation paths: direct native writes first, LLM-backed extraction when explicitly needed.

await manager.create_direct(entity)  # Native write path, no LLM
await manager.create(entity)         # Compatibility extraction path

Legacy Compatibility

Legacy Graphiti-shaped records remain readable through Sibyl-owned Surreal projection and archive code. The package no longer exposes a Graphiti compatibility extra or installs the Graphiti Core package.

Testing

# With mock LLM (fast, deterministic)
SIBYL_MOCK_LLM=true uv run pytest tests/

# Live model tests (costs money)
uv run pytest tests/live --live-models

# Retrieval benchmark suite
moon run core:bench-retrieval

# Live read-only search benchmark against a running stack
moon run core:bench-live

# Live context-pack smoke benchmark
moon run core:bench-context

core:bench-live probes the real /api/search path with CLI auth. core:bench-context probes /api/context/pack. Both benchmarks are read-only. Saved reports can be compared with uv run python benchmarks/compare_eval_reports.py <baseline.json> <candidate.json>.