@grackle-ai/plugin-knowledge

What it does

The Grackle Knowledge plugin provides a semantic knowledge graph backed by Neo4j, enabling agents to persist and query findings across a workspace. Rather than storing isolated discoveries, agents write structured insights—architectural decisions, bugs, API patterns, dependency issues—to a Neo4j backend, where they become queryable by concept (not keyword) and automatically available to other agents in the workspace. The plugin surfaces these findings through semantic search and graph visualization within the Grackle environment.

Who it's for

Teams running multiple AI agents under Grackle who need shared context across autonomous tasks. Specifically: multi-agent orchestration teams that require persistent, queryable findings rather than ephemeral LLM sessions, and organizations building hierarchical agent workflows where discoveries made by one agent automatically inform downstream task decomposition.

Common use cases

• Share architectural decisions and design trade-offs discovered by a Code Reviewer agent with downstream implementation agents
• Accumulate API patterns learned from inspecting multiple codebases and query them semantically when tackling new integrations
• Track discovered bugs, dependencies, and technical debt across agent tasks so no finding is isolated to a single session
• Enable semantic search across findings rather than keyword-only lookups—for example, querying "async error handling patterns" after agents have explored multiple repos

Setup pitfalls

• Requires a running Neo4j instance; configure connection details via environment variables and secure authentication before spawning agents
• Three secrets detected in the codebase; audit and rotate credentials before deploying to shared environments
• Reads and writes the filesystem and makes network calls to Neo4j; run agents in sandboxed environments with explicit network policies for Neo4j access
• Pre-1.0 experimental software; validate knowledge graph queries and persistence in dev environments before relying on findings for critical decisions