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Cognitive architecture MCP server with memory, learning, and contextual understanding
Cognitive architecture MCP server with memory, learning, and contextual understanding
Valid MCP server (3 strong, 1 medium validity signals). 3 known CVEs in dependencies (0 critical, 3 high severity) Package registry verified. Imported from the Official MCP Registry.
6 files analyzed · 4 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
This plugin requests these system permissions. Most are normal for its category.
Set these up before or after installing:
Environment variable: BRAIN_USE_SQLITE
Environment variable: BRAIN_LOG_LEVEL
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-photoxpedia-bolor-brain-mcp": {
"env": {
"BRAIN_LOG_LEVEL": "your-brain-log-level-here",
"BRAIN_USE_SQLITE": "your-brain-use-sqlite-here"
},
"args": [
"-y",
"bolor-brain-mcp"
],
"command": "npx"
}
}
}From the project's GitHub README.
Pure intelligence for Claude Code. Reasoning, memory, and learning -- nothing else.
Bolor Brain is an MCP server that gives Claude Code a brain: structured reasoning, persistent memory, and learning from experience.
User --> Claude Code (Gateway + Executor)
|
+----+----+
Bolor Brain NSAF
(MCP) (MCP)
THINK EVOLVE
Bolor Brain does NOT execute anything. No file ops, no scheduling, no autonomous loop. Claude Code already does all of that. Bolor Brain only thinks.
git clone https://github.com/photoxpedia/bolor-brain-mcp.git
cd bolor-brain-mcp
pip install -e .
Add to ~/.claude/mcp-config.json:
{
"mcpServers": {
"bolor-brain": {
"command": "python",
"args": ["-m", "mcp_server"],
"cwd": "/path/to/bolor-brain-mcp"
}
}
}
/reason Why is Python popular for data science?
/debug API returns 500 errors under load
/decide PostgreSQL or MongoDB for our app?
/learn-from We fixed the memory leak by increasing connection pool
| Tool | What It Does |
|---|---|
reason_hybrid | Auto-selects best reasoning approach for any query |
reason_symbolic | Forward/backward chaining with facts and rules |
reason_knowledge_graph | Graph traversal, path finding, relationship exploration |
reason_case_based | Find similar past problems and their solutions |
reason_hypothesis | Generate and test hypotheses from observations |
reason_analogical | Cross-domain pattern transfer (atom ~ solar system) |
| Tool | What It Does |
|---|---|
remember | Store a case, fact, node, or edge |
recall | Retrieve matching cases or facts |
learn | Store problem/solution/outcome (shortcut for remember) |
forget | Delete a case or fact by ID |
| Tool | What It Does |
|---|---|
brain_stats | Cases, facts, nodes, edges count |
| Skill | When To Use |
|---|---|
/reason | Deep analysis of any complex problem |
/debug | Systematic bug hunting with hypothesis testing |
/decide | Evidence-based technical decisions |
/learn-from | Store experiences for future use |
/nsaf | NSAF evolution integration (requires NSAF MCP) |
/orchestrate | Meta-orchestration combining Bolor Brain + NSAF |
Brain state persists to ~/.bolor-brain/ as JSON:
~/.bolor-brain/
cases.json # Problem -> solution -> outcome
facts.json # Symbolic reasoning facts
knowledge.json # Knowledge graph (nodes + edges)
Knowledge compounds over time. Solve a bug once, recall the solution instantly next time.
Add NSAF to get evolution capabilities:
{
"mcpServers": {
"bolor-brain": {
"command": "python",
"args": ["-m", "mcp_server"],
"cwd": "/path/to/bolor-brain-mcp"
},
"nsaf": {
"command": "python3",
"args": ["nsaf_mcp_server.py"],
"cwd": "/path/to/nsaf",
"env": { "PYTHONPATH": "/path/to/nsaf" }
}
}
}
Together: Bolor Brain reasons about WHAT to do. NSAF evolves HOW to do it better. Claude Code executes.
See skills/nsaf.md and skills/orchestrate.md for combined workflows.
pytest tests/ -v
# 376 tests
mcp_server.py # MCP server (11 tools)
persistence.py # JSON persistence to ~/.bolor-brain/
modules/
config.py # Configuration
reasoning_engines/
symbolic_reasoner.py # Forward/backward chaining
knowledge_graph.py # Graph-based knowledge
case_based_reasoner.py # 4R cycle (retrieve, reuse, revise, retain)
hypothesis_engine.py # Hypothesis generation and testing
analogical_reasoner.py # Cross-domain pattern transfer
hybrid_reasoner.py # Orchestrates all 5 engines
skills/ # Claude Code skills
reason.md, debug.md, decide.md, learn-from.md, nsaf.md, orchestrate.md
tests/ # 376 tests
AGENT_GUARDRAILS.md # Production safety guidelines
Bolorerdene Bundgaa
MIT -- see LICENSE
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