Skip to main content
Glama
dokiromboide

reflex-brain

by dokiromboide
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Reflex Brain

Continual learning brain for AI agents — lessons, skills, and memories with evidence-based refinement, rollback, and GraphRAG retrieval.

License: MIT Python Status

Overview

Reflex Brain is an agent-agnostic, continual learning memory system that gives AI agents the ability to:

  • Learn continuously from interactions — extract lessons, skills, and memories with evidence

  • Refine knowledge through evidence-based updates (/refine equivalent) with full audit trail

  • Rollback safely — snapshot-based versioning with one-click revert

  • Retrieve intelligently — GraphRAG + continual memory hybrid queries with quality filtering

  • Run anywhere — MCP server for Hermes/Claude Code/Codex, or as standalone library

Related MCP server: Consciousness MCP Server

Architecture

┌─────────────────────────────────────────────────────────────┐
│                      AGENT LAYER                            │
│  Hermes │ Claude Code │ Codex │ OpenInterpreter │ Custom   │
└────────────────────────────┬────────────────────────────────┘
                             │ MCP / Python API
                             ▼
┌─────────────────────────────────────────────────────────────┐
│                    REFLEX BRAIN CORE                        │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐          │
│  │  Lessons    │  │  Skills     │  │  Memories   │          │
│  │  (versioned)│  │  (executable)│  │  (episodic) │          │
│  └──────┬──────┘  └──────┬──────┘  └──────┬──────┘          │
│         │                │                │                  │
│         └────────────────┼────────────────┘                  │
│                          ▼                                   │
│              ┌──────────────────────┐                        │
│              │  Refinement Engine   │  ← Evidence-based      │
│              │  (propose/validate/  │     updates + rollback │
│              │   apply/rollback)    │                        │
│              └──────────┬───────────┘                        │
│                         │                                    │
│              ┌──────────▼───────────┐                        │
│              │  Snapshot Store      │                        │
│              │  (full checkpoints)  │                        │
│              └──────────────────────┘                        │
└─────────────────────────────────────────────────────────────┘

Key Features

Feature

Description

Lessons

Versioned knowledge units with evidence, confidence scores, and semantic clusters

Skills

Executable capabilities (Python/JS) with interfaces, tests, and versioning

Memories

Episodic memories with importance decay and temporal context

Refinement Engine

Proposes, validates, and applies updates based on evidence weight

Snapshots

Full state checkpoints for safe rollback

Hybrid Querier

Combines GraphRAG (conversations) + Continual (lessons/skills/memories)

Quality Filtering

Penalizes tool outputs, boosts substantial content, prioritizes high-value types

MCP Server

Thin wrapper exposing reflex_* tools over stdio

Daemon

Background processor for passive capture + continuous embedding

Installation

# From PyPI (when published)
pip install reflex-brain

# From source
git clone https://github.com/jesuscaicedo800/reflex-brain.git
cd reflex-brain
pip install -e ".[dev]"

Quickstart

As MCP Server (Hermes, Claude Code, Codex)

# config.yaml
mcp_servers:
  reflex-brain:
    command: "python"
    args: ["-m", "continual_brain.mcp.server"]
    env:
      HF_HUB_OFFLINE: "1"
      REFLEX_DB_PATH: "~/reflex-brain/continual.db"
      REFLEX_FAISS_PATH: "~/reflex-brain/"

Restart your agent — tools available: reflex_query, reflex_propose_lesson, reflex_apply_refinement, reflex_rollback, reflex_snapshot.

As Python Library

from continual_brain import ReflexBrain

brain = ReflexBrain(db_path="continual.db")

# Query (GraphRAG + Continual hybrid)
results = brain.query("DIAN facturación electrónica", top_k=5)

# Propose a lesson from recent session
proposal = brain.propose_lesson("DIAN compliance", session_id="sess_123")

# Apply with evidence threshold
brain.apply_refinement(proposal, auto_apply=True)

# Rollback if needed
brain.rollback(refinement_id="ref_abc")

Run Daemon (Background Processing)

# Foreground
reflex-brain daemon --poll-interval 3 --batch-size 50

# Background service
reflex-brain daemon --daemonize

Configuration

Env Var

Default

Description

REFLEX_DB_PATH

./continual.db

SQLite database path

REFLEX_FAISS_PATH

./

FAISS index directory

HF_HUB_OFFLINE

0

Set 1 to disable HF Hub requests

REFLEX_EMBED_MODEL

all-MiniLM-L6-v2

Sentence transformer model

Development

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Lint
ruff check .

# Type check
mypy continual_brain

# Pre-commit
pre-commit install

Project Structure

reflex-brain/
├── continual_brain/
│   ├── core/           # Models, Store, RefinementEngine, Evidence
│   ├── query/          # BrainQuerier, ContinualQuerier, HybridQuerier
│   ├── daemon/         # Processor, Extractor, Embedder
│   ├── mcp/            # Thin MCP server wrapper
│   └── cli/            # CLI entry points
├── tests/
│   ├── unit/           # Unit tests
│   └── integration/    # Integration tests
└── pyproject.toml

License

MIT © Jesus Caicedo

Related

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Maintenance

ActivitySlowing
ResponsivenessUnresponsive

Resources

Related MCP Connectors

Related MCP Servers