{
  "schema_version": "openagent.resource.v1",
  "id": "res_openlore",
  "slug": "openlore",
  "status": "published",
  "identity": {
    "name": "OpenLore",
    "one_liner": "Persistent architectural memory for AI coding agents using queryable codebase knowledge graphs and MCP tools.",
    "short_description": "OpenLore is an open-source memory layer for AI coding agents. It turns codebases into queryable knowledge graphs with static analysis, living specs, drift detection, and MCP tools so agents can recover architectural context instead of re-discovering it every session."
  },
  "classification": {
    "resource_type": "memory_system",
    "primary_category": "memory-systems",
    "subcategories": [
      "memory",
      "context-retrieval",
      "state",
      "mcp",
      "coding-agent"
    ]
  },
  "positioning": {
    "why_it_matters": "Coding agents lose time and quality when they lack project memory. OpenLore focuses on durable architectural memory, not just chat history or vector search.",
    "best_for": [
      "Teams using coding agents on large or long-lived repositories",
      "Developers who want graph-backed project memory and architecture context",
      "Agent builders exposing codebase knowledge through MCP tools"
    ],
    "not_for": [
      "Small scripts where repository orientation is trivial",
      "Teams that only need document RAG rather than codebase structure"
    ],
    "use_cases": [
      "personal-memory"
    ],
    "target_audience": [
      "developer",
      "agent_builder"
    ],
    "maturity": "active"
  },
  "decision_signals": {
    "deployment_modes": [
      "cloud"
    ],
    "open_source": true,
    "local_first": false,
    "self_hostable": false,
    "has_api": false,
    "has_gui": false,
    "supports_mcp": true,
    "supports_docker": false
  },
  "facts": {
    "license": "MIT",
    "pricing_model": "open_source",
    "github_stars": 163,
    "github_forks": 22,
    "github_repo_full_name": "clay-good/OpenLore",
    "last_verified_at": "2026-06-10"
  },
  "capabilities": {
    "core_capabilities": [
      "memory",
      "context-retrieval",
      "state",
      "mcp"
    ],
    "interfaces": [
      "repo"
    ]
  },
  "links": {
    "primary_url": "https://github.com/clay-good/OpenLore",
    "items": [
      {
        "type": "github",
        "label": "GitHub",
        "url": "https://github.com/clay-good/OpenLore"
      },
      {
        "type": "npm",
        "label": "Homepage",
        "url": "https://www.npmjs.com/package/openlore"
      }
    ]
  },
  "media": {
    "thumbnail_url": "https://github.com/clay-good.png",
    "og_image_url": "https://github.com/clay-good.png",
    "thumbnail_brief": {
      "resource_type": "memory_system",
      "visual_motif": "layered cards, archive grid, or stacked memory tiles",
      "background_style": "minimal editorial surface with restrained open-source accent color",
      "title_overlay": "OpenLore",
      "subtitle": "Persistent architectural memory for AI coding agents using queryable codebase knowledge graphs and MCP tools.",
      "avoid": [
        "noisy poster layout",
        "large marketing slogans",
        "random gradient blobs"
      ]
    }
  },
  "tags": {
    "category": [
      "memory-system",
      "open-source"
    ],
    "capability": [
      "memory",
      "context-retrieval",
      "state",
      "mcp"
    ],
    "constraint": [
      "open-source",
      "mcp-compatible"
    ],
    "scenario": [
      "personal-memory"
    ]
  },
  "relationships": {},
  "machine_readable": {
    "canonical_url": "https://www.openagent.bot/memory-systems/openlore",
    "json_url": "https://www.openagent.bot/memory-systems/openlore.json",
    "markdown_url": "https://www.openagent.bot/memory-systems/openlore.md"
  },
  "seo": {
    "title": "OpenLore: Persistent Architectural Memory for AI Coding Agents",
    "description": "OpenLore is an open-source memory layer that turns codebases into queryable knowledge graphs for AI coding agents and MCP workflows."
  },
  "editorial": {
    "trust_note": "Verified from source links and project metadata.",
    "core_strengths": [
      {
        "title": "Architecture memory",
        "description": "OpenLore focuses on persistent architectural context for coding agents.",
        "why_it_matters": "Architecture decisions and code relationships are often the context agents need most."
      },
      {
        "title": "Queryable codebase graph",
        "description": "The project describes codebases as queryable knowledge graphs with static analysis.",
        "why_it_matters": "Graph structure can expose relationships that flat notes or chat summaries miss."
      },
      {
        "title": "MCP tool surface",
        "description": "OpenLore includes graph-native MCP tools for agent access.",
        "why_it_matters": "MCP makes codebase memory easier to connect to multiple agent hosts."
      }
    ],
    "use_case_notes": [
      {
        "title": "Repository orientation",
        "description": "Help agents understand architecture and code relationships before editing."
      },
      {
        "title": "Living specs",
        "description": "Use living specs and drift detection to keep project memory aligned with code."
      },
      {
        "title": "MCP codebase context",
        "description": "Expose structured repository context to agent environments through MCP."
      }
    ],
    "compare_notes": [
      {
        "title": "When to choose OpenLore",
        "summary": "Compare it with nearby memory systems by looking at hosting model, integration surface, license, and whether the official docs show the workflow you need."
      }
    ],
    "getting_started": [
      {
        "label": "Open the GitHub repository",
        "url": "https://github.com/clay-good/OpenLore",
        "type": "github"
      },
      {
        "label": "Open the npm package",
        "url": "https://www.npmjs.com/package/openlore",
        "type": "npm"
      }
    ],
    "seo_article": {
      "intro": "OpenLore provides persistent architectural memory for AI coding agents.",
      "what_it_is": "It turns a codebase into a queryable knowledge graph and exposes context through MCP tools.",
      "why_it_matters": "Coding agents need durable architecture context to avoid re-learning the same repository every session.",
      "how_it_works": "Start by indexing a repository, inspect the generated knowledge graph and specs, then connect the memory surface to an agent workflow.",
      "faq": [
        {
          "question": "Is OpenLore open source?",
          "answer": "Yes. The GitHub repository is listed under the MIT license."
        },
        {
          "question": "What kind of memory does OpenLore provide?",
          "answer": "It focuses on architectural and codebase memory for coding agents, including queryable code relationships and MCP tools."
        }
      ]
    }
  },
  "timestamps": {
    "created_at": "2026-06-10T00:00:00.000Z",
    "updated_at": "2026-06-10T00:00:00.000Z",
    "published_at": "2026-06-10T00:00:00.000Z"
  }
}