by campfirein
Cipher empowers agents with persistent memory using vector databases and embeddings for seamless context retention and t
A memory framework for AI coding agents that retains context across conversations and sessions. Lets you preserve coding knowledge and share it with your team in real-time.
Cipher is a community-built MCP server published by campfirein that provides AI assistants with tools and capabilities via the Model Context Protocol. Cipher empowers agents with persistent memory using vector databases and embeddings for seamless context retention and t It is categorized under ai ml, developer tools.
You can install Cipher in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
NOASSERTION
Cipher is released under the NOASSERTION license.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
Useful MCP listing: Cipher is the kind of server we cite when onboarding engineers to host + tool permissions.
We wired Cipher into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Cipher reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
We evaluated Cipher against two servers with overlapping tools; this profile had the clearer scope statement.
I recommend Cipher for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
According to our notes, Cipher benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
We wired Cipher into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Strong directory entry: Cipher surfaces stars and publisher context so we could sanity-check maintenance before adopting.
We evaluated Cipher against two servers with overlapping tools; this profile had the clearer scope statement.
Cipher is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
showing 1-10 of 60
Byterover Cipher is an opensource memory layer specifically designed for coding agents. Compatible with Cursor, Codex, Claude Code, Windsurf, Cline, Claude Desktop, Gemini CLI, AWS's Kiro, VS Code, Roo Code, Trae, Amp Code and Warp through MCP, and coding agents, such as Kimi K2. (see more on examples)
Built by Byterover team
Key Features:
# Install globally
npm install -g @byterover/cipher
# Or install locally in your project
npm install @byterover/cipher
# Clone and setup
git clone https://github.com/campfirein/cipher.git
cd cipher
# Configure environment
cp .env.example .env
# Edit .env with your API keys
# Start with Docker
docker-compose up --build -d
# Test
curl http://localhost:3000/health
</details>💡 Note: Docker builds automatically skip the UI build step to avoid ARM64 compatibility issues with lightningcss. The UI is not included in the Docker image by default.
To include the UI in the Docker build, use:
docker build --build-arg BUILD_UI=true .
pnpm i && pnpm run build && npm link
# Interactive mode
cipher
# One-shot command
cipher "Add this to memory as common causes of 'CORS error' in local dev with Vite + Express."
# API server mode
cipher --mode api
# MCP server mode
cipher --mode mcp
# Web UI mode
cipher --mode ui
</details>⚠️ Note: When running MCP mode in terminal/shell, export all environment variables as Cipher won't read from
.envfile.💡 Tip: CLI mode automatically continues or creates the "default" session. Use
/session new <session-name>to start a fresh session.
The Cipher Web UI provides an intuitive interface for interacting with memory-powered AI agents, featuring session management, tool integration, and real-time chat capabilities.
Cipher supports multiple configuration options for different deployment scenarios. The main configuration file is located at memAgent/cipher.yml.
# LLM Configuration
llm:
provider: openai # openai, anthropic, openrouter, ollama, qwen
model: gpt-4-turbo
apiKey: $OPENAI_API_KEY
# System Prompt
systemPrompt: 'You are a helpful AI assistant with memory capabilities.'
# MCP Servers (optional)
mcpServers:
filesystem:
type: stdio
command: npx
args: ['-y', '@modelcontextprotocol/server-filesystem', '.']
📖 See Configuration Guide for complete details.
Create a .env file in your project root with these essential variables:
# ====================
# API Keys (At least one required)
# ====================
OPENAI_API_KEY=sk-your-openai-api-key
ANTHROPIC_API_KEY=sk-ant-your-anthropic-key
GEMINI_API_KEY=your-gemini-api-key
QWEN_API_KEY=your-qwen-api-key
# ====================
# Vector Store (Optional - defaults to in-memory)
# ====================
VECTOR_STORE_TYPE=qdrant # qdrant, milvus, or in-memory
VECTOR_STORE_URL=https://your-cluster.qdrant.io
VECTOR_STORE_API_KEY=your-qdrant-api-key
# ====================
# Chat History (Optional - defaults to SQLite)
# ====================
CIPHER_PG_URL=postgresql://user:pass@localhost:5432/cipher_db
# ====================
# Workspace Memory (Optional)
# ====================
USE_WORKSPACE_MEMORY=true
WORKSPACE_VECTOR_STORE_COLLECTION=workspace_memory
# ====================
# AWS Bedrock (Optional)
# ====================
AWS_ACCESS_KEY_ID=your-aws-access-key
AWS_SECRET_ACCESS_KEY=your-aws-secret-key
AWS_DEFAULT_REGION=us-east-1
# ====================
# Advanced Options (Optional)
# ====================
# Logging and debugging
CIPHER_LOG_LEVEL=info # error, warn, info, debug, silly
REDACT_SECRETS=true
# Vector store configuration
VECTOR_STORE_DIMENSION=1536
VECTOR_STORE_DISTANCE=Cosine # Cosine, Euclidean, Dot, Manhattan
VECTOR_STORE_MAX_VECTORS=10000
# Memory search configuration
SEARCH_MEMORY_TYPE=knowledge # knowledge, reflection, both (default: knowledge)
DISABLE_REFLECTION_MEMORY=true # default: true
</details>💡 Tip: Copy
.env.exampleto.envand fill in your values:cp .env.example .env
Cipher can run as an MCP (Model Context Protocol) server, allowing integration with MCP-compatible clients like Codex, Claude Desktop, Cursor, Windsurf, and other AI coding assistants.
To install cipher for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @campfirein/cipher --client claude
To use Cipher as an MCP server in your MCP client configuration:
{
"mcpServers": {
"cipher": {
"type": "stdio",
"command": "cipher",
"args": ["--mode", "mcp"],
"env": {
"MCP_SERVER_MODE": "aggregator",
"OPENAI_API_KEY": "your_openai_api_key",
"ANTHROPIC_API_KEY": "your_anthropic_api_key"
}
}
}
}
📖 See MCP Integration Guide for complete MCP setup and advanced features.
👉 Built‑in tools overview — expand the dropdown below to scan everything at a glance. For full details, see docs/builtin-tools.md 📘.
cipher_extract_and_operate_memory: Extracts knowledge and applies ADD/UPDATE/DELETE in one stepcipher_memory_search: Semantic search over stored knowledgecipher_store_reasoning_memory: Store high-quality reasoning tracescipher_extract_reasoning_steps (internal): Extract structured reasoning stepscipher_evaluate_reasoning (internal): Evaluate reasoning quality and suggest improvementscipher_search_reasoning_patterns: Search reflection memory for patternscipher_workspace_search: Search team/project workspace memorycipher_workspace_store: Background capture of team/project signalscipher_add_node, cipher_update_node, cipher_delete_node, cipher_add_edgecipher_search_graph, cipher_enhanced_search, cipher_get_neighborscipher_extract_entities, cipher_query_graph, cipher_relationship_managercipher_bash: Execute bash commands (one-off or persistent)Watch our comprehensive tutorial on how to integrate Cipher with Claude Code through MCP for enhanced coding assistance with persistent memory:
Click the image above to watch the tutorial on YouTube.
For detailed configuration instructions, see the CLI Coding Agents guide.
| Topic | Description |
|---|---|
| Configuration | Complete configuration guide including agent setup, embeddings, and vector stores |
| LLM Providers | Detailed setup for OpenAI, Anthropic, AWS, Azure, Qwen, Ollama, LM Studio |
| Embedding Configuration | Embedding providers, fallback logic, and troubleshooting |
| Vector Stores | Qdrant, Milvus, In-Memory vector database configurations |
| [Chat History](./ |
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.