io.github.Lore-Context/lore-context-mcp

Governed AI-agent memory, Evidence Ledger traces, evals, and portable context tools.

2 0 tools GitHub

No known CVEs

No license

Actively maintained

Last commit 2d ago

Works with most clients

Transport: stdio

0 tools

Grade F

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Finance

Step 1

Install in your client

Config is the same across clients — only the file and path differ.

Supported in Claude Desktopstdio · Node 18+

Paste into ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "io-github-lore-context-lore-context-mcp": {
      "command": "<see-readme>",
      "args": []
    }
  }
}

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Read me

What io.github.Lore-Context/lore-context-mcp does

The control plane for AI-agent memory, eval, and governance.

Test This Server

No automated test available for this server. Check the GitHub README for setup instructions.

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Score breakdown

36/100across 5 weighted dimensions

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Security

OSV.dev

No known CVEs.

No package registry to scan.

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Frequently Asked Questions

Is io.github.Lore-Context/lore-context-mcp safe to use?: io.github.Lore-Context/lore-context-mcp has no known CVEs as of the latest MCPpedia security scan. It does not require authentication, so any local process can connect — keep this in mind in shared environments.
How do I install io.github.Lore-Context/lore-context-mcp?: io.github.Lore-Context/lore-context-mcp can be installed by cloning its GitHub repository and following the setup instructions in the README.
What AI clients work with io.github.Lore-Context/lore-context-mcp?: io.github.Lore-Context/lore-context-mcp is compatible with claude-desktop, cursor, claude-code. It uses stdio transport.
Is io.github.Lore-Context/lore-context-mcp actively maintained?: io.github.Lore-Context/lore-context-mcp is actively maintained — last commit was 2 days ago. It has 2 GitHub stars.

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Community

Discussion

Start a conversation

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Has anyone used this with Cursor?How do you handle auth?Any alternatives?

What is Lore Context

Lore Context is an open-core control plane for AI-agent memory: it composes context across memory, search, and tool traces; evaluates retrieval quality on your own datasets; routes governance review for sensitive content; and exports memory as a portable interchange format you can move between backends.

It does not try to be another memory database. The unique value is what sits on top of memory:

Context Query — single endpoint composes memory + web + repo + tool traces, returns a graded context block with provenance.

Memory Eval — runs Recall@K, Precision@K, MRR, stale-hit-rate, p95 latency on datasets you own; persists runs and diffs them for regression detection.

Governance Review — six-state lifecycle (candidate / active / flagged / redacted / superseded / deleted), risk-tag scanning, poisoning heuristics, immutable audit log.

MIF-like Portability — JSON + Markdown export/import preserving provenance / validity / confidence / source_refs / supersedes / contradicts. Works as a migration format between memory backends.

Multi-Agent Adapter — first-class agentmemory integration with version probe + degraded-mode fallback; clean adapter contract for additional runtimes.

When to use it

Use Lore Context when...	Use a memory database (agentmemory, Mem0, Supermemory) when...
You need to prove what your agent remembered, why, and whether it was used	You just need raw memory storage
You run multiple agents (Claude Code, Cursor, Qwen, Hermes, Dify) and want shared trustable context	You're building a single agent and OK with a vendor-locked memory tier
You require local or private deployment for compliance	You prefer a hosted SaaS
You need eval on your own datasets, not vendor benchmarks	Vendor benchmarks are sufficient signal
You want to migrate memory between systems	You don't plan to ever switch backends

Use Lore Context when...

Use a memory database (agentmemory, Mem0, Supermemory) when...

You need to prove what your agent remembered, why, and whether it was used

You just need raw memory storage

You run multiple agents (Claude Code, Cursor, Qwen, Hermes, Dify) and want shared trustable context

You're building a single agent and OK with a vendor-locked memory tier

You require local or private deployment for compliance

You prefer a hosted SaaS

You need eval on your own datasets, not vendor benchmarks

Vendor benchmarks are sufficient signal

You want to migrate memory between systems

You don't plan to ever switch backends

Quick Start

# 1. Clone + install git clone https://github.com/Lore-Context/lore-context.git cd lore-context && pnpm install # 2. Run the quickstart helper and inspect the activation report pnpm quickstart -- --dry-run --activation-report # 3. Generate a real API key (do not use placeholders in any environm ... [View full README on GitHub](https://github.com/lore-context/lore-context#readme)

Frequently Asked Questions

Is io.github.Lore-Context/lore-context-mcp safe to use?

io.github.Lore-Context/lore-context-mcp has no known CVEs as of the latest MCPpedia security scan. It does not require authentication, so any local process can connect — keep this in mind in shared environments.

How do I install io.github.Lore-Context/lore-context-mcp?

io.github.Lore-Context/lore-context-mcp can be installed by cloning its GitHub repository and following the setup instructions in the README.

What AI clients work with io.github.Lore-Context/lore-context-mcp?

io.github.Lore-Context/lore-context-mcp is compatible with claude-desktop, cursor, claude-code. It uses stdio transport.

Is io.github.Lore-Context/lore-context-mcp actively maintained?

io.github.Lore-Context/lore-context-mcp is actively maintained — last commit was 2 days ago. It has 2 GitHub stars.

What is Lore Context

It does not try to be another memory database. The unique value is what sits on top of memory:

Context Query — single endpoint composes memory + web + repo + tool traces, returns a graded context block with provenance.

Memory Eval — runs Recall@K, Precision@K, MRR, stale-hit-rate, p95 latency on datasets you own; persists runs and diffs them for regression detection.

Governance Review — six-state lifecycle (candidate / active / flagged / redacted / superseded / deleted), risk-tag scanning, poisoning heuristics, immutable audit log.

Multi-Agent Adapter — first-class agentmemory integration with version probe + degraded-mode fallback; clean adapter contract for additional runtimes.

When to use it

Use Lore Context when...	Use a memory database (agentmemory, Mem0, Supermemory) when...
You need to prove what your agent remembered, why, and whether it was used	You just need raw memory storage
You run multiple agents (Claude Code, Cursor, Qwen, Hermes, Dify) and want shared trustable context	You're building a single agent and OK with a vendor-locked memory tier
You require local or private deployment for compliance	You prefer a hosted SaaS
You need eval on your own datasets, not vendor benchmarks	Vendor benchmarks are sufficient signal
You want to migrate memory between systems	You don't plan to ever switch backends

Use Lore Context when...

Use a memory database (agentmemory, Mem0, Supermemory) when...

You need to prove what your agent remembered, why, and whether it was used

You just need raw memory storage

You run multiple agents (Claude Code, Cursor, Qwen, Hermes, Dify) and want shared trustable context

You're building a single agent and OK with a vendor-locked memory tier

You require local or private deployment for compliance

You prefer a hosted SaaS

You need eval on your own datasets, not vendor benchmarks

Vendor benchmarks are sufficient signal

You want to migrate memory between systems

You don't plan to ever switch backends

Quick Start