Dakera

Máy chủ MCP tự lưu trữ dựa trên Rust dành cho bộ nhớ tác nhân AI — bộ nhớ bền vững, có thể truy vấn với tìm kiếm lai, đồ thị tri thức, nhúng tích hợp sẵn và 14 công cụ cốt lõi (có thể mở rộng lên 86+ với phân cấp dựa trên hồ sơ).

GitHub

Tài liệu

⚡ dakera-mcp

MCP server for Dakera AI. Gives any MCP-compatible AI agent persistent, queryable memory — with smart token management built in.

Works with Claude, Claude Code, and any MCP-compatible framework.

Part of Dakera AI — the memory engine for AI agents.

The Dakera memory engine scores 88.2% on LoCoMo (1,540 questions, standard eval) — benchmark details

Architecture: 14 core tools + on-demand discovery

Starting every agent session with 60+ tool schemas wastes ~15K tokens before you write a single message. dakera-mcp solves this with hybrid tool exposure:

14 tools loaded by default — the 12 highest-frequency memory operations + 2 meta-discovery tools
On-demand expansion — use dakera_discover_tools and dakera_load_tools to fetch additional tool schemas only when you need them

Default tool set (core profile)

Tool	Purpose
`dakera_store`	Store a memory with importance, tags, and type
`dakera_recall`	Semantic recall by query text
`dakera_search`	Advanced memory search with tag/type filters
`dakera_session_start`	Start a session to group related memories
`dakera_session_end`	End a session with optional summary
`dakera_batch_recall`	Bulk filter-based recall (by tags, importance, time)
`dakera_forget`	Delete specific memories by ID
`dakera_hybrid_search`	Combined vector + BM25 search
`dakera_fulltext_search`	BM25 full-text search
`dakera_knowledge_graph`	Build a knowledge graph from a seed memory
`dakera_extract`	Extract entities and structure from free-form text
`dakera_batch_forget`	Bulk delete by tags, type, or time range
`dakera_discover_tools`	Search the full tool catalog by keyword or tier
`dakera_load_tools`	Load full schemas for specific tools on demand

Profiles & token cost

Profile	Tools	~Tokens	How to enable
core	14	~2,964	Default — always loaded
admin	32	~5,975	`DAKERA_MCP_PROFILE=admin`
power	69	~13,205	`DAKERA_MCP_PROFILE=power`
all	87	~16,212	`DAKERA_MCP_PROFILE=all`

Accessing additional tools

# In your agent: discover what's available
dakera_discover_tools(tier="power")
→ returns names + descriptions, no schemas loaded

# Load schemas for the tools you want
dakera_load_tools(tools=["dakera_consolidate", "dakera_agent_stats"])
→ returns full inputSchema for each tool

Profile selection

The profile controls which tools appear in tools/list. Three ways to set it:

1. Per-request (in tools/list params):

{"profile": "power"}

2. Environment variable (applies to all requests):

DAKERA_MCP_PROFILE=power

3. Default: core (14 tools, ~2,964 tokens)

Run Dakera

The MCP server connects to a Dakera memory server. You need one running first:

docker run -d \
  --name dakera \
  -p 3300:3000 \
  -e DAKERA_ROOT_API_KEY=dk-mykey \
  ghcr.io/dakera-ai/dakera:latest

For persistent storage (recommended):

curl -sSfL https://raw.githubusercontent.com/Dakera-AI/dakera-deploy/main/docker-compose.yml \
  -o docker-compose.yml
DAKERA_API_KEY=dk-mykey docker compose up -d

curl http://localhost:3000/health  # → {"status":"ok"}

Full deployment guide (Docker Compose, Kubernetes, Helm): dakera-deploy

Install

npm / npx (Node.js 18+)

# Global install
npm install -g @dakera-ai/dakera-mcp

# Or run directly without installing
npx @dakera-ai/dakera-mcp

Homebrew (macOS / Linux)

brew install dakera-ai/tap/dakera-mcp

Cargo

cargo install dakera-mcp

Docker

docker pull ghcr.io/dakera-ai/dakera-mcp:latest

Binary download

Pre-built binaries for macOS, Linux, and Windows are available on the releases page.

Platform	File
macOS (Apple Silicon)	`dakera-mcp-aarch64-apple-darwin.tar.gz`
macOS (Intel)	`dakera-mcp-x86_64-apple-darwin.tar.gz`
Linux x64	`dakera-mcp-x86_64-unknown-linux-musl.tar.gz`
Linux arm64	`dakera-mcp-aarch64-unknown-linux-musl.tar.gz`
Windows x64	`dakera-mcp-x86_64-pc-windows-msvc.zip`

Connect

Add to .mcp.json (Claude Code) or claude_desktop_config.json (Claude Desktop):

{
  "mcpServers": {
    "dakera": {
      "command": "dakera-mcp",
      "env": {
        "DAKERA_API_URL": "http://localhost:3300",
        "DAKERA_API_KEY": "your-key"
      }
    }
  }
}

To start with the power profile (exposes 68 tools):

{
  "mcpServers": {
    "dakera": {
      "command": "dakera-mcp",
      "env": {
        "DAKERA_API_URL": "http://localhost:3300",
        "DAKERA_API_KEY": "your-key",
        "DAKERA_MCP_PROFILE": "power"
      }
    }
  }
}

Why This Exists

AI agents forget everything when the session ends. Dakera fixes that. This MCP server gives your agent a persistent memory layer with zero infrastructure overhead — point it at a Dakera instance and it works.

The 14-tool default keeps your context window lean. The meta-tools let you expand on demand when you need advanced operations like bulk vector upsert, knowledge graph traversal, or memory federation.

→ dakera.ai for hosted instance
→ Self-host with dakera-deploy

Documentation

→ Full docs
→ MCP reference

Repo	What it is
dakera-py	Python SDK
dakera-js	TypeScript SDK
dakera-cli	CLI
dakera-deploy	Self-host Dakera

dakera.ai · Documentation · Request Early Access

_{Part of the Dakera AI open-source ecosystem. Built with Rust. Self-hosted. Zero dependencies.}