sibyl

mcp
Guvenlik Denetimi
Gecti
Health Gecti
  • License — License: AGPL-3.0
  • Description — Repository has a description
  • Active repo — Last push 0 days ago
  • Community trust — 23 GitHub stars
Code Gecti
  • Code scan — Scanned 12 files during light audit, no dangerous patterns found
Permissions Gecti
  • Permissions — No dangerous permissions requested
Purpose
This tool provides a collective intelligence runtime for AI agents. It combines a knowledge graph, task orchestration, and persistent memory to help coding sessions retain context across multiple interactions.

Security Assessment
Overall risk: Medium. The MCP server is designed to ingest documents, interact with databases (such as PostgreSQL, FalkorDB, and Neo4j), and build a persistent knowledge graph, which inherently requires accessing and storing potentially sensitive user data. The automated code scan of 12 files found no dangerous patterns, no hardcoded secrets, and no dangerous permission requests. However, because the tool processes external inputs and manages persistent storage, proper data sanitization and secure database configurations should be verified by the developer during implementation.

Quality Assessment
The project demonstrates strong maintenance and community health. It is licensed under AGPL-3.0, which is excellent for open-source transparency but requires careful review if you plan to use this tool in proprietary commercial software. The repository is highly active, with its last push occurring today, and has garnered 23 GitHub stars, indicating a small but growing base of early community trust.

Verdict
Use with caution: The automated security checks are clean and the project is actively maintained, but its heavy reliance on external databases and document ingestion requires you to verify your own secure infrastructure before deploying.
SUMMARY

Collective intelligence runtime for AI agents. Knowledge graph + orchestration + persistent memory.

README.md

Sibyl

Build With Memory That Compounds
✦ Knowledge Graph + Task Workflow ✦

Graphiti FalkorDB PostgreSQL FastAPI Next.js moon

CI Status Latest Release License

Why Sibyl?QuickstartCLIWeb UIFAQ


🔮 The Vision

Persistent memory for your projects, tasks, and research. A collective intelligence that compounds
with every session and makes your graph more useful over time.

Most coding sessions start cold. No memory of what worked, what failed, or what you learned
yesterday. Notes drift. Tasks scatter. Useful context disappears.

Sibyl changes that.

A knowledge graph gives your work persistent memory. Epics and tasks structure execution. Search,
docs ingestion, and graph exploration keep hard-won context close at hand for both humans and tools.

The whole becomes greater than the sum of its parts.

✦ What You Get

Capability What It Means
🔮 Collective Intelligence Every session compounds. The graph gets smarter as your team and tools capture real work
🎯 Semantic Search Find knowledge by meaning—"authentication patterns" finds OAuth solutions even if "OAuth" isn't in the text
🧠 Persistent Memory What you learn today helps tomorrow. Patterns, decisions, and gotchas stay searchable across sessions
🦋 Task Workflow Plan with epics and tasks. Track execution across sessions and teammates in one place
📚 Doc Ingestion Crawl and index external documentation into your graph
🏢 Multi-Tenancy Isolated graphs per organization. Enterprise-ready from day one
🌐 Graph Visualization Interactive D3 visualization of your knowledge connections
Dashboard
Dashboard
Projects
Projects
Graph
Knowledge Graph
Tasks
Task Workflow

⚡ Quickstart

One-Liner Install

curl -fsSL https://raw.githubusercontent.com/hyperb1iss/sibyl/main/install.sh | sh

Installs uv (if needed), installs sibyl-dev, starts Sibyl. Done.

Manual Install (UV)

uv tool install sibyl-dev
sibyl local start

Alternative: pipx

pipx install sibyl-dev
sibyl local start

CLI Commands

sibyl local start    # Start all services
sibyl local stop     # Stop services
sibyl local status   # Show running services
sibyl local logs     # Follow logs
sibyl local reset    # Nuke and start fresh

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 apps/api/.env.example apps/api/.env
# Add SIBYL_OPENAI_API_KEY + SIBYL_JWT_SECRET

# Launch everything
moon run dev

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

Retrieval Benchmarks

# Synthetic retrieval and ranking benchmarks
moon run bench-retrieval

# Live read-only benchmark against your running Sibyl stack
moon run bench-live

bench-live exercises the real /api/search path with your CLI auth context and auto-skips when
the local stack or auth is unavailable.

Ports:

Service Port URL
API + MCP 3334 http://localhost:3334
Web UI 3337 http://localhost:3337
FalkorDB 6380

🗂️ Core Workflow

Sibyl is strongest when it stays close to the work itself:

  1. Capture knowledge from debugging, implementation, and research
  2. Search semantically when you need the pattern again
  3. Track execution with projects, epics, and tasks
  4. Ingest docs so external references live beside internal learnings
  5. Explore the graph to see how ideas, tasks, and sources connect

The CLI

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

# Install globally
moon run cli:install

# Or install the published package directly
uv tool install sibyl-dev

Core Commands

# Search your knowledge
sibyl search "authentication patterns"
sibyl search "OAuth" --type pattern

# Add knowledge
sibyl add "Redis connection pooling" "Pool size must be >= concurrent requests to avoid blocking"

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

# Explore the graph
sibyl explore related ent_xyz    # Find connected entities
sibyl explore traverse ent_xyz   # Walk outward from an entity

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

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 D3 visualization of knowledge connections
  • Search — Semantic search with filters
  • Sources — Configure documentation crawling
  • Settings — Organizations, API keys, preferences

Built with: Next.js 16, React 19, React Query, Tailwind CSS, 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 4-Tool API

Tool Purpose Examples
search Find by meaning Patterns, tasks, docs, errors
explore Navigate structure List entities, traverse relationships
add Create knowledge Episodes, patterns, tasks
manage Lifecycle & admin Task workflow, crawling, health

Claude Code Skills & Hooks

Sibyl ships with skills and
hooks for seamless Claude Code integration.

Install:

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

/sibyl skill — Full CLI access from Claude Code:

/sibyl search "authentication patterns"
/sibyl task list --status doing
/sibyl add "OAuth insight" "Token refresh needs..."

Hooks — Automatic context injection:

Hook Trigger Action
SessionStart Session begins Shows active tasks, reminds to capture learnings
UserPromptSubmit Every prompt Searches graph, injects relevant patterns

The UserPromptSubmit hook extracts keywords from your prompt, searches Sibyl, and injects matching
patterns as context—so Claude always knows what you've learned before.

See skills/ and hooks/ for implementation details.

Architecture

sibyl/
├── apps/
│   ├── api/              # FastAPI + MCP server (sibyld)
│   ├── cli/              # REST client CLI (sibyl)
│   └── web/              # Next.js 16 frontend
├── packages/python/
│   └── sibyl-core/       # Shared library (models, graph, tools)
├── skills/               # Claude Code skills
├── charts/               # Helm charts for K8s
└── docs/                 # Documentation

Stack:

  • Backend: Python 3.13 / FastMCP / FastAPI / Graphiti / FalkorDB
  • Frontend: Next.js 16 / React 19 / React Query / Tailwind 4
  • Database: FalkorDB (graph) + PostgreSQL (relational)
  • Build: moonrepo + uv (Python) + pnpm (TypeScript)
  • Integrations: Claude Code, MCP clients, and project-local hooks

Authentication

JWT Sessions (Web UI)

SIBYL_JWT_SECRET=your-secret-key    # Required
SIBYL_JWT_EXPIRY_HOURS=24            # Optional

API Keys (Programmatic Access)

# Create via CLI
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=...

Deployment

Docker Compose (Production)

docker compose -f docker-compose.prod.yml up -d

Kubernetes (Helm)

helm install sibyl ./charts/sibyl \
  --set backend.existingSecret=sibyl-secrets \
  --set backend.database.existingSecret=sibyl-postgres

See docs/deployment/ for detailed guides:

Development

# Start everything
moon run dev

# Individual services
moon run dev-api          # API + worker
moon run dev-web          # Frontend only

# Quality checks
moon run api:test         # Run API tests
moon run api:lint         # Lint
moon run web:typecheck    # TypeScript check
moon run core:check       # Full check on core library

# Database
moon run docker-up        # Start FalkorDB + PostgreSQL
moon run docker-down      # Stop databases

Entity Types

Type What It Holds
pattern Reusable coding patterns
episode Temporal learnings, discoveries
task Work items with full workflow
project Container for related work
epic Feature-level grouping
rule Sacred constraints, invariants
source Knowledge origins (URLs, repos)
document Crawled/ingested content

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.

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?

  • OpenAI (required): For embeddings (text-embedding-3-small)
  • Anthropic (optional): For additional model-powered extraction workflows

A typical solo developer uses ~$5/month in API costs.

Can multiple people collaborate?

Yes. Organizations have isolated graphs with role-based access. Multiple users can share knowledge,
assign tasks, and collaborate on the same graph.

Is it production-ready?

Sibyl is in active development (v0.1.x). The core features work well, but expect rough edges. 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:

  • Stronger project boundaries — Finish project membership and permission flows end to end
  • Deeper graph retrieval — Better ranking, traversal, deduplication, and relationship-aware
    search
  • Brainstorming spaces — Dedicated areas for ideation before execution
  • External data connectors — Feed more docs, repos, and notes into the graph
  • Workflow polish — Sharper epic/task planning, progress views, and review loops

The graph gets smarter. The workflow gets sharper.

💜 Philosophy

Search Before Implementing

The graph knows things. Before you code:

sibyl search "what you're building"
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.

Capture What You Learn

If it took time to figure out, save it:

sibyl add "Descriptive title" "What, why, how, caveats"

Bad: "Fixed the bug" Good: "JWT refresh fails when Redis TTL expires. Root cause: token
service doesn't 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


Star on GitHub    Ko-fi

If Sibyl helps your team remember, give us a ⭐ or support the project

✦ Built with obsession by Hyperbliss Technologies

Yorumlar (0)

Sonuc bulunamadi