mcpField
B
bolor-brain-mcp

bolor-brain-mcp

Local
github.com/photoxpediaAI & Agents2Updated 37w ago

About bolor-brain-mcp

bolor-brain-mcp is an MCP server that adds structured reasoning, persistent memory, and learning from experience to Claude Code. It combines several reasoning engines with local JSON persistence and does not execute tasks.

Key features

  1. 01Select a reasoning approach automatically
  2. 02Use symbolic reasoning and knowledge graphs
  3. 03Reason from cases and hypotheses
  4. 04Store and retrieve cases and facts
  5. 05Delete memory entries by ID
  6. 06Persist state in JSON files

Use cases

  1. 01Analyze complex problems
  2. 02Investigate bugs through hypotheses
  3. 03Compare technical options
  4. 04Save solutions for later retrieval

MCP support

The MCP server provides Claude Code with reasoning, memory, and statistics tools; it does not perform file operations or other task execution.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"photoxpedia-bolor-brain": {
"command": "npx",
"args": [
"-y",
"bolor-brain-mcp"
],
"env": {
"BRAIN_USE_SQLITE": "<BRAIN_USE_SQLITE>",
"BRAIN_LOG_LEVEL": "<BRAIN_LOG_LEVEL>"
}
}
}
}
Run commandnpx -y bolor-brain-mcp
Environment variables to set
BRAIN_USE_SQLITEEnable SQLite storage (default: true)
BRAIN_LOG_LEVELLog level (default: INFO)

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npmbolor-brain-mcp1.1.0

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Bolor Brain MCP

Pure intelligence for Claude Code. Reasoning, memory, and learning -- nothing else.

License: MIT Python 3.11+ MCP Compatible


What Is This?

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
  • Claude Code = Gateway + tool execution + sessions + permissions
  • Bolor Brain = Reasoning engines + memory + learning + persistence
  • NSAF = Strategy evolution + self-improvement (separate MCP server)

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.


Quick Start

1. Install
git clone https://github.com/photoxpedia/bolor-brain-mcp.git
cd bolor-brain-mcp
pip install -e .
2. Configure Claude Code

Add to ~/.claude/mcp-config.json:

{
  "mcpServers": {
    "bolor-brain": {
      "command": "python",
      "args": ["-m", "mcp_server"],
      "cwd": "/path/to/bolor-brain-mcp"
    }
  }
}
3. Use
/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

MCP Tools (11)

Reasoning (6)
ToolWhat It Does
reason_hybridAuto-selects best reasoning approach for any query
reason_symbolicForward/backward chaining with facts and rules
reason_knowledge_graphGraph traversal, path finding, relationship exploration
reason_case_basedFind similar past problems and their solutions
reason_hypothesisGenerate and test hypotheses from observations
reason_analogicalCross-domain pattern transfer (atom ~ solar system)
Memory (4)
ToolWhat It Does
rememberStore a case, fact, node, or edge
recallRetrieve matching cases or facts
learnStore problem/solution/outcome (shortcut for remember)
forgetDelete a case or fact by ID
Utility (1)
ToolWhat It Does
brain_statsCases, facts, nodes, edges count

Skills (6)

SkillWhen To Use
/reasonDeep analysis of any complex problem
/debugSystematic bug hunting with hypothesis testing
/decideEvidence-based technical decisions
/learn-fromStore experiences for future use
/nsafNSAF evolution integration (requires NSAF MCP)
/orchestrateMeta-orchestration combining Bolor Brain + NSAF

Persistence

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.


With NSAF

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.


Testing

pytest tests/ -v
# 376 tests

Project Structure

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

Author

Bolorerdene Bundgaa

License

MIT -- see LICENSE

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.