- 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
Memory Systems
OpenLore
Persistent architectural memory for AI coding agents using queryable codebase knowledge graphs and MCP tools.
0.2K Stars
MIT License
0.0K Forks
Open sourceMCP
About
OpenLore overview
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.
Architecture memory
OpenLore focuses on persistent architectural context for coding agents.
Architecture decisions and code relationships are often the context agents need most.Queryable codebase graph
The project describes codebases as queryable knowledge graphs with static analysis.
Graph structure can expose relationships that flat notes or chat summaries miss.MCP tool surface
OpenLore includes graph-native MCP tools for agent access.
MCP makes codebase memory easier to connect to multiple agent hosts. Use cases
When to use OpenLore
Repository orientation
Help agents understand architecture and code relationships before editing.
Living specs
Use living specs and drift detection to keep project memory aligned with code.
MCP codebase context
Expose structured repository context to agent environments through MCP.
Compare
How it compares
When to choose OpenLore
Compare it with nearby memory systems by looking at hosting model, integration surface, license, and whether the official docs show the workflow you need.
FAQ
Questions
Is OpenLore open source?
Yes. The GitHub repository is listed under the MIT license.
What kind of memory does OpenLore provide?
It focuses on architectural and codebase memory for coding agents, including queryable code relationships and MCP tools.
Tags
Capabilities
Decision brief
JSON
Should you use OpenLore?
- Small scripts where repository orientation is trivial
- Teams that only need document RAG rather than codebase structure
- Verified 2026-06-10
- License: MIT
- Repo: clay-good/OpenLore
- Open-source signal
Deployment
cloud
Permission surface
shell/files, memory, external services
Decision signals
MCP
Agent packet
Structured decision data for OpenLore
This packet is the compact machine-readable view agents should use before following source links or taking action.
Capabilities
memory, context retrieval, state, mcp
Constraints
open source, mcp compatible
Deployment
cloud
Permission surface
shell/files, memory, external services
Recommended workflows
Coding agent workflow, Memory or RAG workflow
Overview
What OpenLore does
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 to evaluate it
Start by indexing a repository, inspect the generated knowledge graph and specs, then connect the memory surface to an agent workflow.
Facts
Known metadata and operating surface
These fields are separated from editorial interpretation so agents can reason over facts and missing checks.
Resource type memory system
Category Memory Systems
Maturity active
Difficulty Unknown
License MIT
Pricing open source
Verified 2026-06-10
Source confidence medium
Risk level elevated
Fit matrix
Where OpenLore fits in an agent stack
strong
Coding agent workflow
OpenLore has multiple signals for coding agent workflow, including matching tags, capabilities, category, or positioning.
- Run a small repository change and inspect the diff, tests, and rollback path.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
strong
Memory or RAG workflow
OpenLore has multiple signals for memory or rag workflow, including matching tags, capabilities, category, or positioning.
- Create, update, retrieve, correct, and delete memory or retrieval objects with real data.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Connector or protocol layer
OpenLore has at least one signal for connector or protocol layer, but should be checked against a real task before adoption.
- Connect one low-risk service, then inspect schemas, auth scope, errors, and logs.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Evaluation and observability
OpenLore has at least one signal for evaluation and observability, but should be checked against a real task before adoption.
- Add one repeatable test case and confirm results can run again in review or CI.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
weak
Browser automation
OpenLore is not primarily positioned for browser automation in the current metadata.
- Run one non-sensitive website task and inspect clicks, waits, retries, and changed URLs.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
weak
Local or private AI stack
OpenLore is not primarily positioned for local or private ai stack in the current metadata.
- Verify hardware requirements, data path, storage, and whether all calls stay in your environment.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
Inputs and outputs
What an agent should inspect
Likely inputs
- Repositories, files, issues, terminal output, and test results
- Documents, user facts, entities, context, or retrieval queries
- Tool schemas, API requests, service resources, and auth scopes
- Official setup instructions and a small real workflow
Likely outputs
- Diffs, commits, explanations, test results, or review notes
- Retrieved context, memory updates, graph relations, or citations
- Scores, traces, regression results, dashboards, or failure cases
- A decision on whether this resource fits the target workflow
Evidence
Sources, claims, and missing checks
Claims are marked separately from source links so future crawlers and reviewers can update them without rewriting the page.
GitHub github
Repository source for code, license, issues, releases, and implementation details.
Homepage npmOfficial or project-controlled source for this resource profile.
OpenLore is listed as open source.
License metadata: MITOpenLore has a recorded GitHub repository: clay-good/OpenLore.
Resource facts and GitHub source link.OpenLore supports these recorded deployment modes: cloud.
OpenAgent decision signal metadata.OpenLore is tagged with memory, context retrieval, state, mcp capabilities.
OpenAgent capability taxonomy.- Dedicated docs link is missing.
- Repository freshness has not been recorded.
Next action
How to start evaluating OpenLore
Inspect repository
Check license, recent activity, issues, examples, and security-sensitive code paths.
Open sourceOpen Homepage
Start from the official source before adopting third-party instructions.
Open source Compare
Alternatives and nearby resources
Use related resources to compare category fit, license, deployment model, and first-workflow behavior.
FAQ
Common questions about OpenLore
Is OpenLore open source?
Yes. The GitHub repository is listed under the MIT license.
What kind of memory does OpenLore provide?
It focuses on architectural and codebase memory for coding agents, including queryable code relationships and MCP tools.