A managed context engineering platform that provides RAG, Text2SQL, and hybrid search to ground AI agents in real-world data with high efficiency.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install memic
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install memic using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Memic Context Engineering SDK?
Memic SDK is a managed context engineering platform designed to solve the challenges of injecting relevant data into LLM prompts. Instead of overwhelming models with raw documents, Memic handles the entire document ingestion pipeline—including chunking, embedding, and vector storage—to provide a single search API for relevant snippets. This approach is essential for developers building Openclaw Skills that require high accuracy and low token usage.
By supporting both unstructured document search (RAG) and structured database queries (Text2SQL), Memic serves as a unified interface for grounding AI agents. It ensures multi-tenant isolation and provides granular metadata filters, making it a robust choice for production-grade AI applications and sophisticated Openclaw Skills. The platform effectively bridges the gap between raw data sources and LLM reasoning capabilities.
Memic Context Engineering SDK Use Cases
- Implementing Retrieval-Augmented Generation (RAG) for AI assistants and chatbots.
- Converting natural language questions into SQL queries via Text2SQL for relational databases.
- Reducing LLM token costs by replacing full document context with targeted, ranked search results.
- Building multi-tenant AI applications with strict data isolation per environment.
- Creating advanced Openclaw Skills that interact seamlessly with local or cloud-hosted knowledge bases.
How Memic Context Engineering SDK Works
- Ingest documents like PDFs, DOCX, and PPTX or connect structured databases such as PostgreSQL and MySQL via the Memic dashboard or SDK.
- Memic automatically handles the backend pipeline of chunking, embedding, and indexing data for semantic search.
- The AI agent sends a natural language query to the Memic search API to retrieve grounded context.
- Memic routes the query to the appropriate source—documents, databases, or both—using intelligent hybrid search logic.
- The SDK returns ranked context chunks or SQL result sets complete with source attribution and relevance scores.
- The developer injects these targeted results into the LLM prompt to generate accurate, cost-effective responses for Openclaw Skills.
Memic Context Engineering SDK Setup
Install the SDK using pip:
pip install memic
Configure your environment variable with your API key obtained from the Memic dashboard:
export MEMIC_API_KEY=mk_your_key_here
Verify your installation with a simple Python script:
from memic import Memic
client = Memic()
print(f"Project ID: {client.project_id}")
Memic Context Engineering SDK Data Schema & Taxonomy
Memic organizes data into a hierarchy of Organizations, Projects, and Environments, which are automatically resolved by your API key.
| Entity | Description |
|---|---|
| File | Represents an uploaded document with properties like id, status, and total_chunks. |
| Search Result | Contains the specific content snippet, relevance score, file name, and page number. |
| Metadata | Custom key-value pairs and system tags such as reference_id or category for filtering. |
| Routing | Technical metadata explaining if a query used semantic, structured, or hybrid logic. |
Memic Context Engineering SDK Advanced Features
- Hybrid search auto-routing that intelligently switches between vector stores and relational databases based on query intent.
- Granular metadata filtering for precise scoping by reference ID, page range, or custom document categories.
- Context compaction capabilities to summarize and index long agent session logs for better long-term memory retrieval.
- Multi-tenant isolation ensuring high security and data privacy across different API keys and projects.
- Future-proof MCP server support for direct integration into Openclaw Skills and other modern model context protocols.
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