io.github.Dakera-AI/dakera-mcp
Agent memory engine — 86 MCP tools, self-hosted, single Rust binary
Open source Open in the app JSON README (API)
About
Agent memory engine — 86 MCP tools, self-hosted, single Rust binary
Details
- Kind
- MCP servers
- Topic
- AI, RAG & memory
- Publisher
- dakera-ai
- Origin
- official
- Category
- ferramentas
- Transport
- local
- Version
- 0.10.12
- Stars
- 8
- Open pull requests
- 1
- Last push
- 2026-09-03T07:48:28Z
- Repository state
- ativo
- Language
- Rust
- License
- NOASSERTION
- Added
- 2026-08-29 03:01:49
- Updated
- 2026-09-03 08:00:27
- Origin id
io.github.Dakera-AI/dakera-mcp
README
# ⚡ dakera-mcp
[](https://github.com/Dakera-AI/dakera-mcp/actions/workflows/ci.yml) [](https://crates.io/crates/dakera-mcp) [](https://www.npmjs.com/package/@dakera-ai/dakera-mcp) [](https://crates.io/crates/dakera-mcp) [](LICENSE) [](https://dakera.ai/benchmark) [](https://glama.ai/mcp/servers/Dakera-AI/dakera-mcp) [](https://dakera.ai/docs) [](https://dakera.ai) [](https://dakera.ai/playground)
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](https://dakera.ai) — the memory engine for AI agents.
> The Dakera memory engine scores **88.2% Recall@20 on LoCoMo** (1,540 questions · LLM-judge scored) — [benchmark details](https://dakera.ai/benchmark)
---
## 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):
```json
{"profile": "power"}
```
**2. Environment variable** (applies to all requests):
```bash
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:
```bash
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):
```bash
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](https://github.com/Dakera-AI/dakera-deploy)
---
## Install
### npm / npx (Node.js 18+)
```bash
# Global install
npm install -g @dakera-ai/dakera-mcp
# Or run directly without installing
npx @dakera-ai/dakera-mcp
```
### Homebrew (macOS / Linux)
```bash
brew install dakera-ai/tap/dakera-mcp
```
### Cargo
```bash
cargo install dakera-mcp
```
### Docker
```bash
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](https://github.com/Dakera-AI/dakera-mcp/releases).
| 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):
```json
{
"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):
```json
{
"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](https://dakera.ai) for hosted instance
→ Self-host with [dakera-deploy](https://github.com/dakera-ai/dakera-deploy)
## Documentation
→ [Full docs](https://dakera.ai/docs)
→ [MCP reference](https://dakera.ai/docs/mcp)
## Related
| Repo | What it is |
|---|---|
| [dakera-py](https://github.com/dakera-ai/dakera-py) | Python SDK |
| [dakera-js](https://github.com/dakera-ai/dakera-js) | TypeScript SDK |
| [dakera-cli](https://github.com/dakera-ai/dakera-cli) | CLI |
| [dakera-deploy](https://github.com/dakera-ai/dakera-deploy) | Self-host Dakera |
---
**[dakera.ai](https://dakera.ai)** · [Documentation](https://dakera.ai/docs) · [Request Early Access](https://dakera.ai#cta)
<sub>Part of the Dakera AI open-core ecosystem. Built with Rust. Self-hosted. Zero dependencies.</sub>