Persistent memory for the AI you build with
_{✦ Yours alone · In your projects · In the systems you share ✦}

Why Sibyl? • Quickstart • Memory Loop • CLI • MCP • FAQ

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🔮 Why Sibyl

Sibyl is persistent memory for the coding agents that live in your terminal, the copilots you talk to all day, and the shared systems you build with others. One self-hostable knowledge graph, scoped by context. Yours to run, yours to keep.

Most AI sessions start cold. Coding agents forget yesterday's decisions. Voice assistants don't know your preferences. Personal copilots lose the thread between conversations. Notes drift, tasks scatter, useful context evaporates.

A durable knowledge graph holds what matters: personal context, project work, shared spaces, source documents, decisions, agent state. Each lives in its own scope and gets tied to the others when context calls for it. A built-in memory loop (recall → act → remember → reflect) keeps hard-won context close at hand for humans and AI alike. Source-grounded synthesis turns what you already know into verified documents.

The CLI is the interaction surface. Any agent that runs shell commands (Claude Code, Codex, Cursor, Aider, Cline, your own scripts) can recall context, capture learnings, and run task workflows the same way you do. MCP is available for clients that prefer it; the depth lives in the CLI.

Every session adds up instead of starting over.

✦ What You Get

Capability	What It Means
🔮 Compounding Context	Every session adds to the graph instead of starting over. The longer you use it, the sharper it gets
🪄 The Memory Loop	recall → act → remember → reflect runs through the CLI, MCP, and hooks. Agents wake with context and leave it behind
🎯 Semantic Search	Find knowledge by meaning. "Authentication patterns" surfaces OAuth notes even when "OAuth" isn't in the text
🦋 Task Workflow	Plan with epics and tasks, then track execution across sessions and teammates in one place
🧪 Source-Grounded Synthesis	Draft verified documents from your own memory with citation, freshness, and gap checks
🌊 Source Ingestion	Crawl documentation sites and import sources (mailboxes, archives) into the same graph
💎 Scoped Multi-Tenancy	Isolated graphs per organization with role-based access. Personal, project, and team scopes stay separate by design

Dashboard	Projects
Knowledge Graph	Task Workflow

⚡ Quickstart

Homebrew

brew install hyperb1iss/tap/sibyl
sibyl init --local
sibyl serve

Homebrew installs both sibyl and sibyld. The local context runs an embedded SurrealDB store under ~/.sibyl/data/surreal; use sibyl start/sibyl stop for a background daemon or sibyl service install to write a launchd/systemd user service.

Python Tools

uv tool install sibyl-dev
sibyl init --remote https://sibyl.example.com
sibyl login

For a Python-only local install, add the daemon package:

uv tool install sibyld
sibyl init --local
sibyl serve

Docker Self-Host

sibyl docker init       # Generate ~/.sibyl/docker/.env + compose
sibyl docker up         # Start API, web, and SurrealDB
sibyl docker logs       # Follow logs
sibyl docker down       # Stop services
sibyl docker upgrade    # Pull and recreate

First Five Minutes

Everything below runs against your local Sibyl stack. MCP wiring is optional.

# Capture a learning the moment you find it
sibyl remember "Stale auth token bug" \
  "Redis TTL mismatch dropped the cached token early" --kind error_pattern

# Pull it back as working context for your next session
sibyl recall "auth token bug" --intent debug

# Or search semantically across the whole graph
sibyl search "stale auth token redis ttl"

# Package wake-up context for the next coding session
sibyl session bundle

Development Setup

# One-line setup (installs proto, moon, toolchain, dependencies)
./setup-dev.sh

# Or manually:
curl -fsSL https://moonrepo.dev/install/proto.sh | bash
proto use                  # Installs node, pnpm, python, uv
proto install moon
uv sync && pnpm install

# Configure
cp .env.example .env
# Set SIBYL_JWT_SECRET (auto-generated in dev) and at least one LLM provider key.
# Embeddings use SIBYL_OPENAI_API_KEY or SIBYL_GEMINI_API_KEY.

# Install CLIs globally (editable, source changes reflect immediately)
moon run install-dev

# Launch the default local-dev stack (SurrealDB + API + web)
moon run dev

# Verify
curl http://localhost:3334/api/health

moon run dev is the single-machine flow. When SIBYL_SURREAL_URL is unset it starts local SurrealDB, points the API at ws://127.0.0.1:8000/rpc, and stores data files in .moon/cache/surreal-dev. Jobs and schedules run in-process by default (SIBYL_COORDINATION_BACKEND=local). Set SIBYL_SURREAL_URL to connect to a hosted SurrealDB endpoint, including Surreal Cloud, instead.

Ports:

Service	Port	URL
API + MCP	3334	http://localhost:3334
Web UI	3337	http://localhost:3337
SurrealDB	8000	ws://localhost:8000/rpc
Redis/Valkey	6381	optional

🪄 The Memory Loop

Sibyl is built around a durable loop that both humans and agents follow:

recall ──▶ act ──▶ remember ──▶ reflect
   ▲                                │
   └────────────────────────────────┘

1. Recall working context before you start. sibyl recall "<goal>" returns a compact context pack: active work, decisions, plans, constraints, and recent lessons, scoped to your linked project.
2. Act with that context in hand.
3. Remember durable knowledge as you learn it. sibyl remember stores decisions, plans, ideas, claims, procedures, and gotchas so the next session does not rediscover them.
4. Reflect at clean breakpoints. sibyl reflect distills raw session notes into reviewable memory candidates and can persist them into the graph.

sibyl recall "ship the context graph" --intent build
sibyl remember "Use context packs" "Group memory before dispatching agents" --kind decision
sibyl reflect "We decided X. Next we build Y." --title "Planning checkpoint" --persist

Memory is graded, auditable, and scoped. Raw captures stay verbatim, reflection candidates pass an automatic review before promotion, and a nightly dream-cycle keeps the graph consolidated. See docs/guide/capturing-knowledge.md.

The CLI

The CLI is the power-user interface. Clean output, built for scripting and durable project workflows.

uv tool install sibyl-dev    # published package
uv tool install sibyld       # direct daemon CLI package
moon run cli:install         # or install from source

Command Families

# Memory loop
sibyl recall "<goal>"                 # Compile working context
sibyl remember "Title" "Body"         # Store durable memory
sibyl reflect "<notes>" --persist     # Distill notes into candidates
sibyl capture "<quick note>"          # Fast verbatim capture
sibyl search "authentication patterns"

# Knowledge & graph
sibyl add "Redis pooling" "Pool size must be >= concurrent requests"
sibyl explore related ent_xyz         # Find connected entities
sibyl entity show <id>                # Full content by ID

# Task workflow
sibyl task list --status todo,doing
sibyl task start <task_id>
sibyl task complete <task_id> --learnings "Key insight: check TTL first"

# Synthesis, sources, projects, orgs
sibyl synthesis draft "Onboarding guide"
sibyl crawl add "https://docs.example.com" --name "Example Docs"
sibyl project link proj_xxx

Task Workflow

backlog ──▶ todo ──▶ doing ──▶ review ──▶ done ──▶ archived
                       │
                       ▼
                    blocked

Output Formats

sibyl task list           # Table output (default)
sibyl task list --json    # JSON for scripts
sibyl task list --csv     # For spreadsheets

Full command reference: docs/cli/.

🦋 Web UI

A full admin interface at http://localhost:3337:

• Dashboard: Stats overview, recent activity, quick actions
• Tasks: Kanban-style workflow with inline editing
• Graph: Interactive force-directed visualization of knowledge connections
• Search: Semantic search with filters
• Memory: The memory workspace, raw captures, source imports, and synthesis
• Sources: Configure and inspect documentation crawling
• Settings: Organizations, teams, API keys, security, LLM routing, backups

Built with: Next.js 16, React 19, React Query, Tailwind CSS 4, and the SilkCircuit design system.

MCP Integration

Connect Claude Code, Cursor, or any MCP client to Sibyl:

{
  "mcpServers": {
    "sibyl": {
      "type": "http",
      "url": "http://localhost:3334/mcp",
      "headers": {
        "Authorization": "Bearer sk_your_api_key"
      }
    }
  }
}

The Tool API

Sibyl exposes eleven MCP tools, organized by what they do:

Tool	Purpose
search	Unified semantic search across graph and crawled docs
context	Compile an agent context pack for a goal (intent + depth)
explore	Navigate the graph: list, related, traverse, dependencies
add	Create knowledge: episodes, patterns, tasks, projects
remember	Capture durable memory: decisions, plans, ideas, claims
reflect	Distill raw notes into reviewable memory candidates
synthesis_plan	Plan source-grounded synthesis from authorized memory
synthesis_draft	Draft, verify, and optionally remember an artifact
synthesis_verify	Verify citation, freshness, and gap coverage
manage	State changes: task lifecycle, crawling, analysis, admin
logs	Recent server logs (requires OWNER role)

Claude Code Skills & Hooks

Sibyl ships with skills and hooks for built-in Claude Code integration.

moon run skills:install    # Install the /sibyl skill
moon run hooks:install     # Install context hooks

The /sibyl skill gives Claude Code full CLI access. Hooks inject context automatically:

Hook	Trigger	Action
SessionStart	Session begins	Prints a compact session bundle with active tasks and relevant memory
UserPromptSubmit	Every prompt	Searches the graph and injects relevant patterns as context

See skills/ and hooks/ for implementation details.

Architecture

sibyl/
├── apps/
│   ├── api/              # sibyld - FastAPI + MCP server daemon
│   ├── cli/              # sibyl  - REST client CLI
│   ├── web/              # Next.js 16 frontend
│   └── e2e/              # End-to-end tests
├── packages/python/
│   └── sibyl-core/       # Shared library (models, graph, ai, retrieval, services)
├── skills/               # Claude Code skills
├── hooks/                # Claude Code context hooks
├── charts/               # Helm chart for Kubernetes
├── infra/                # Ansible self-host + local compose
└── docs/                 # Documentation site (VitePress)

Stack:

• Backend: Python 3.13 / FastAPI / FastMCP / SurrealDB-native runtime
• Frontend: Next.js 16 / React 19 / React Query / Tailwind 4
• Storage: SurrealDB unifies graph, content, and auth. PostgreSQL is retained only for migration and archive rehearsal.
• AI routing: Anthropic, OpenAI, and Gemini swap per surface; embeddings are pluggable.
• Coordination: In-process by default; Redis/Valkey is optional for multi-process or distributed deployments.
• Build: moonrepo + uv (Python) + pnpm (TypeScript)
• Compatibility: Legacy Graphiti-shaped records are handled by Sibyl-owned Surreal projection and archive code. No supported install pulls Graphiti.

See docs/guide/why-surreal.md for the rationale and docs/guide/storage-modes.md for the mode matrix.

Authentication

JWT Sessions (Web UI)

SIBYL_JWT_SECRET=your-secret-key        # Required (auto-generated in dev)
SIBYL_ACCESS_TOKEN_EXPIRE_MINUTES=60    # Optional (default: 60)

API Keys (Programmatic Access)

sibyl auth api-key create --name "CI/CD" --scopes mcp,api:read
# Scopes: mcp, api:read, api:write

OAuth (GitHub)

SIBYL_GITHUB_CLIENT_ID=...
SIBYL_GITHUB_CLIENT_SECRET=...

MCP endpoints enforce Bearer auth when a JWT secret is set (SIBYL_MCP_AUTH_MODE=auto). See docs/api/ for the full auth reference.

Deployment

Docker Compose

sibyl docker init
sibyl docker up

Kubernetes (Helm)

helm install sibyl ./charts/sibyl \
  --set backend.existingSecret=sibyl-secrets \
  --set backend.surreal.existingSecret=sibyl-surreal \
  --set ingress.enabled=true

See docs/deployment/ for detailed guides:

• Docker Compose
• Kubernetes
• Environment Variables

Knowledge Model

Sibyl models a wide range of entity types so memory stays structured, not just a pile of notes:

• Work: task, epic, project, milestone
• Knowledge: pattern, episode, procedure, rule, guide, error_pattern
• Memory: decision, plan, idea, claim, artifact, session, note
• Sources: source, document, domain, community

The full registry and how types relate is documented in docs/guide/entity-types.md.

FAQ

Who is Sibyl for?

Solo developers who want durable memory for projects and debugging. Teams who want shared knowledge that compounds. Anyone building with AI who is tired of repeating context every session.

Do I need AI agents to use Sibyl?

No. The knowledge graph and task system are the core product: documentation, task tracking, captured learnings, and semantic search over what your team already knows. AI agents make the memory loop automatic, but they are not required.

How does it compare to Mem0 / LangMem / similar?

Sibyl is self-hosted and open source. You own your data. It includes a full task workflow system, not just memory. It has a web UI for humans, not just APIs for machines. And it keeps knowledge, tasks, and docs connected in one graph instead of scattering them across tools.

What LLM APIs do I need?

• Anthropic, OpenAI, or Gemini (required): for language-model surfaces such as crawler extraction, synthesis, and reflection.
• OpenAI or Gemini (required): for embeddings and semantic search.

Providers and models are configurable globally or per surface, and the web admin settings page can save instance-wide model routing. A typical solo developer uses around $5/month in API costs.

Is it production-ready?

Sibyl is in active development (v0.10.x, heading toward 1.0). SurrealDB is the default runtime for graph, content, and auth; legacy PostgreSQL paths are retained only for migration and archive rehearsal. We use Sibyl to build Sibyl. Every feature, task, and learning you see here was tracked inside the system itself.

🎯 Roadmap

Where we're headed after v0.10.0, toward 1.0:

• Corpus Runtime: real private source corpora can be imported, searched, inspected, corrected, and synthesized without leaking scope.
• Memory Workspace OS: automatic memory decisions become visible, explainable, correctable, and undoable from one product surface.
• Surreal-only closure: Graphiti and Redis-required local assumptions leave the supported runtime once native behavior has receipts.
• 1.0 evidence freeze: release claims cite gates, artifacts, and install rehearsals.

The graph gets smarter. The workflow gets sharper. See docs/architecture/SIBYL_1_0_ROADMAP.md.

💜 Philosophy

Recall Before You Act

The graph knows things. Before you code, pull context:

sibyl recall "what you're building" --intent build
sibyl search "error you hit" --type episode

Work In Task Context

Never do significant work outside a task. Tasks provide traceability, progress tracking, and knowledge linking.

Remember What You Learn

If it took time to figure out, save it:

sibyl remember "Descriptive title" "What, why, how, caveats" --kind decision

Bad: "Fixed the bug." Good: "JWT refresh fails when Redis TTL expires. Root cause: token service does not handle WRONGTYPE. Fix: try/except with regeneration fallback."

Complete With Learnings

sibyl task complete <id> --learnings "Key insight: ..."

The graph should be smarter after every session.

Contributing

We welcome contributions. See CONTRIBUTING.md for guidelines.

# Fork, clone, then:
./setup-dev.sh
moon run dev

# Make changes, then:
moon run :check           # Lint + typecheck + test

License

AGPL-3.0. See LICENSE.

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<sub>If Sibyl helps your team remember, give us a star or support the project

✦ Built with obsession by Hyperbliss Technologies ✦</sub>