Knowledge Graph Memory | Knowledge Graph Tool
Structured entity-relation storage with cross-skill state sharing for agents
What Is Knowledge Graph Memory?
Knowledge Graph Memory is an advanced structured database and cross-skill state sharing framework on EasyClaw. Designed specifically for AI agents, it goes beyond flat list memories to construct a multi-dimensional, semantic Entity-Relation network (Graph database) locally on your system — allowing different AI skills to write, link, and traverse complex task dependencies, user preferences, and project parameters headlessly.
The skill is built for developers building multi-agent systems, project managers tracking complex task blocker chains, and research engineers mapping domain-specific semantic networks.
The expected outcome is a highly structured, queryable Knowledge Graph: featuring automated entity extraction, relation mapping, and full dependency-chain traversals returned instantly in your conversation.
How Knowledge Graph Memory Works
1. Initialize the local graph engine. The skill initializes a local, file-based Graph database (storing nodes, edges, and properties) in your workspace configuration directory.
2. Entity-Relation extraction. Provide your raw project updates or instructions. The skill parses the text, extracts core Entities (e.g., "Alice," "Project A," "Task 1"), and maps their semantic Relations (e.g., `Alice -> OWNER_OF -> Project A`, `Task 1 -> DEPENDS_ON -> Task 2`).
3. Cross-skill state sharing. The graph acts as a central memory vault. Different EasyClaw skills can write to and read from this same graph, ensuring your agent maintains cohesive context across different tasks.
4. Dependency chain traversal. For project tracking, the skill runs recursive graph traversals to identify blocking tasks (e.g., showing the full blocker chain for `task_002`), mapping out exactly what needs to be completed first.
5. Output generation. Graph states and query results are delivered in clean Markdown tables and visual branch trees, ready to analyze or copy.
Key Features
- Semantic Entity-Relation storage: Builds multi-dimensional graphs of nodes, edges, and properties.
- Cross-skill state sync: Share state variables and project memories across different active skills.
- Dependency chain traverser: Recursively maps out task blocker chains and prerequisites.
- Dynamic graph appender: Easily add new entities and build links to existing nodes.
- Direct JSON database writes: Saves all graph metrics and properties locally to protect security.
- Visual graph trees: Outputs structured, hierarchical text-based graph trees in chat.
What Problems Does Knowledge Graph Memory | Knowledge Graph Tool Solve?
1. Mapping out a complex task blocker chain
A project manager has a software launch with 20 dependent tasks and wants to find the critical path. They ask the skill to map the dependencies: setting `task_003` as dependent on `task_002`, which depends on `task_001`. The skill traverses the graph, returning the full, sequential blocker chain, letting the manager identify exactly which bottleneck task is freezing the launch timeline.
2. Sharing context between design and writing skills
An agency uses multiple AI skills. They set up "Project Alpha" owned by "Alice" in the graph memory. When they activate Amazon Listing Creator later, the writing skill queries the graph, automatically retrieves Alice's saved brand preferences and project parameters, and drafts the copy accordingly, preventing the user from re-explaining context.
3. Building a structured client CRM graph
A consulting agency wants to map their client relationships. They input raw meeting notes. The skill extracts entities and builds links: `ClientA -> WORKS_AT -> CompanyX`, `CompanyX -> USES_PRODUCT -> ToolY`, `ClientA -> RETENTION_RISK -> High`. The agency can query the graph to see all high-risk clients associated with specific products instantly.
4. Tracking software dependency updates
A developer is managing a modular Node.js project. They use the skill to map module dependencies. If a package experiences an error, they ask: "What does task_002 depend on?" The skill traverses the graph and flags that updating `task_002` will affect 3 other modules, de-risking system updates.
5. Automatically prompting for missing query context
A user asks a vague question: *"Show me all tasks related to this project"* without naming the project. The skill's context logic intercepts the request and prompts politely: *"To traverse the memory graph, could you please specify: What is the project ID or name?"* preventing empty database searches.
Example Workflow
A project lead needs to log a project owner and link it to a task list.
1. They open EasyClaw and activate Knowledge Graph Memory.
2. They run: *"Record: Set Alice as owner of Project A and attach Task_002 to it. Task_002 depends on Task_001."*
3. The skill extracts the entities (Alice, Project A, Task_002, Task_001), writes the nodes, and builds the semantic edges.
4. It compiles the local JSON graph and updates the database.
5. It outputs a visual text-based graph tree showing the new relations and dependencies in the chat.
Graph databases structured and linked in under 30 seconds.
Getting Started with Knowledge Graph Memory | Knowledge Graph Tool
Add this skill to your EasyClaw workspace
Describe your task in a chat message
Review the output and iterate if needed
Export or share the results directly from EasyClaw
Combine with other skills to build automated workflows
Frequently Asked Questions
What is a Knowledge Graph?
A Knowledge Graph is a structured database composed of nodes (entities like people, projects, or tasks) and edges (the semantic relationships linking them, like "OWNER_OF" or "DEPENDS_ON"). It allows for complex, multi-layered data querying.
Do I need to install Neo4j or another graph server?
No. The skill runs a lightweight, high-performance, file-based graph database engine locally inside your workspace, requiring no external database servers or complex software setups.
How does "cross-skill state sharing" work?
The graph database is saved to a centralized, shared file in your workspace. When you activate different EasyClaw skills, they can read from this central file, retrieving the context, parameters, and relationships saved by other skills.
Can the skill identify critical paths in projects?
Yes. By traversing the task dependency edges recursively, the graph engine can identify the sequence of dependent tasks that determines the minimum time needed to complete a project (the critical path).
Is my private company data secure?
Yes. In compliance with strict corporate security and data privacy standards, all graph processing, relation mapping, and JSON database writes are executed entirely locally on your machine.
What is a "detached HEAD" state?
This is a standard Git terminology. In the context of graph memory, "unconnected nodes" are orphan entities that lack relationship edges. The skill's diagnostic cleaner automatically flags or prunes these orphan nodes to keep the database fast.
Can I export the graph data?
Yes. The complete graph schema, node properties, and edge matrices are written directly as standard, structured JSON and CSV files under `public/data/memory_exports/` in your workspace.
How does the skill handle ambiguous queries?
If you request a dependency check or a project status without providing enough context, the skill's context logic will politely prompt you for the specific project name or node ID to prevent duplicate or failed queries.
Can I build graphs in languages other than English?
Yes. The entity-extraction and relation-mapping engines support multilingual parsing, allowing you to build and query semantic graphs in Chinese, English, German, French, or Spanish.
Is there an offline audit log of my graph actions?
Yes. Every successful node creation, edge connection, and traversal query is logged locally under `public/data/logs/` in your workspace, creating a clear history of your database operations.
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