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Sibyl daemon - knowledge graph and task workflow server

Project description

Sibyl API Server

sibyld is the FastAPI + FastMCP server behind Sibyl's knowledge graph, agent memory loop, task workflow, search, synthesis, and real-time updates.

Quick Reference

# Install the embedded daemon without the web UI
curl -fsSL https://raw.githubusercontent.com/hyperb1iss/sibyl/main/install.sh | sh -s -- --daemon

# Start server from the monorepo
moon run api:serve        # or: uv run sibyld serve

# Start worker (Redis coordination only)
moon run api:worker       # or: uv run sibyld worker

# Quality checks
moon run api:test         # Run tests
moon run api:lint         # Lint
moon run api:typecheck    # Type check

What's Here

  • MCP Server: eleven tools for search, context packs, exploration, capture, memory, synthesis, and management
  • REST API: 26 routers covering entities, tasks, projects, memory, synthesis, sources, auth, settings, and admin
  • Auth System: JWT sessions, GitHub OAuth, API keys with scopes, MCP OAuth clients, RBAC
  • Background Jobs: in-process local runtime or Redis-backed arq workers, including the nightly reflection dream-cycle
  • WebSocket: Real-time updates for entities and tasks

Architecture

Sibyl API (port 3334)
├── /api/*              → FastAPI REST endpoints
├── /api/openapi.json   → OpenAPI schema
├── /mcp                → MCP server (streamable-http, 11 tools)
├── /api/ws             → WebSocket for real-time updates
└── Lifespan            → Background jobs + coordination broker

Key Directories

Directory Purpose
api/routes/ REST endpoints (26 routers: tasks, entities, memory, synthesis, crawler, auth, admin, and more)
ai/ DB-backed LLM settings, model validation routes, runtime invalidation
auth/ JWT, sessions, API keys, RBAC, MCP OAuth clients
persistence/ SurrealDB-native runtimes for auth, content, graph, and backups
crawler/ Documentation crawl and ingestion pipeline
ingestion/ Source import pipeline (mailbox and other adapters)
jobs/ Background jobs (reflection dream-cycle, crawl, backups)
coordination/ Local and Redis brokers for jobs, locks, and pub/sub
email/ Transactional email delivery
generator/ Synthetic test-data generation

Configuration

Required:

SIBYL_JWT_SECRET=...              # Auth (auto-generated in dev)
SIBYL_ANTHROPIC_API_KEY=...       # Required when LLM provider=anthropic
# SIBYL_OPENAI_API_KEY=sk-...     # Required when LLM provider=openai
# SIBYL_GEMINI_API_KEY=...        # Required when LLM provider=gemini

# Embeddings: choose OpenAI or Gemini
SIBYL_EMBEDDING_PROVIDER=openai   # openai | gemini
SIBYL_OPENAI_API_KEY=sk-...       # Required when embedding provider=openai
# SIBYL_GEMINI_API_KEY=...        # Required when embedding provider=gemini

Optional:

SIBYL_STORE=surreal                   # default; legacy is migration/source-side only
SIBYL_COORDINATION_BACKEND=auto       # auto | local | redis
SIBYL_SURREAL_URL=ws://127.0.0.1:8000/rpc
SIBYL_SURREAL_USERNAME=root
SIBYL_SURREAL_PASSWORD=root
SIBYL_REDIS_HOST=127.0.0.1            # only needed for Redis coordination
SIBYL_REDIS_PORT=6381
SIBYL_LLM_PROVIDER=anthropic          # anthropic | openai
SIBYL_LLM_MODEL=claude-haiku-4-5
SIBYL_LLM_CRAWLER_MODEL=claude-haiku-4-5
SIBYL_LLM_SYNTHESIS_MODEL=claude-sonnet-4-6
SIBYL_LLM_TEMPERATURE=0
SIBYL_LLM_TIMEOUT_SECONDS=60
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

PostgreSQL settings are only for historical archive rehearsal commands that explicitly restore a retained postgres.sql payload against an operator-managed database. They are not part of default Surreal runtime startup.

Gemini keys can also be supplied through GEMINI_API_KEY or GOOGLE_API_KEY. Changing embedding provider, model, or dimensions changes vector spaces; re-crawl sources and rebuild graph indexes before mixing old and new search results.

LLM settings are instance-wide. Environment variables win over database settings field by field; env-backed fields return 409 LOCKED_BY_ENV on write. Database settings are managed under:

GET  /api/settings/ai/llm
PUT  /api/settings/ai/llm/{surface}
POST /api/settings/ai/llm/{surface}/test
POST /api/settings/ai/keys/{provider}/test
POST /api/settings/ai/models/{model_alias}/test
GET  /api/settings/ai/registry?kind=llm

Crawler extraction and synthesis generation call sibyl_core.ai rather than provider SDKs directly. Custom model IDs are accepted as database settings with an unverified_model warning; validate them with the model test endpoint before using them in production flows.

CLI Commands

sibyld serve              # Start the HTTP server
sibyld serve -t stdio     # Start a stdio server (MCP subprocess mode)
sibyld worker             # Start the job worker (local mode exits cleanly)
sibyld up                 # Start data services + API
sibyld down               # Stop all services
sibyld db backup          # Back up the graph database
sibyld migrate import ... # Import a migration archive
sibyld generate realistic # Generate sample data

Runtime Modes

For single-machine Surreal development, run sibyld serve or sibyld up with SIBYL_STORE=surreal. The default coordination_backend=auto resolves to local, so background jobs, pending state, locks, pub/sub, and schedules all stay in-process with no Redis requirement.

Redis remains available for distributed or multi-process dev. Set SIBYL_COORDINATION_BACKEND=redis when you want the arq worker model, then run sibyld worker or moon run api:worker separately.

Key Patterns

Multi-tenancy: Every operation requires org context.

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

Write concurrency: the SurrealDB driver serializes WebSocket operations per client. Clone graph drivers per organization rather than sharing one driver across org scopes.

Request context: Auth middleware injects user and org.

from sibyl.auth.dependencies import get_current_user, get_current_organization

Dependencies

Depends on sibyl-core for models, graph client, AI substrate, and tool implementations.

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