Friday

Self-hosted persistent cognitive memory layer for AI coding agents.

Persists architecture decisions, schemas, and constraints across sessions via the Model Context Protocol (MCP).

The Problem

Architecture

Dual-Cortex

Cognitive Core 2.0

Quickstart

Python SDK

MCP Setup

API Reference

Friday Neural Studio — Interactive Knowledge Graph

_{Friday Neural Studio (Holosphere 4.0) — Real-time WebGL synthetic cortex visualizer rendering galactic lobe constellations, dynamic synaptic energy heatmaps, and autonomous dream cycle consolidation.}

The Problem: Session Amnesia

Modern AI coding agents (Cursor, Claude Code, Antigravity, VS Code) excel at isolated code generation. However, in continuous engineering workflows, developers encounter a structural limitation: Session Amnesia.

Current workarounds fall into three deeply flawed patterns:

                  ┌─────────────────────────────────────────────────────────┐
                  │          WHY STANDARD APPROACHES BREAK DOWN             │
                  └─────────────────────────────────────────────────────────┘

   1. Context Windows (RAM)           2. Static Rules Files             3. Standard Vector RAG
  ┌─────────────────────────┐       ┌─────────────────────────┐       ┌─────────────────────────┐
  │ • Ephemeral volatile    │       │ • Linear token tax      │       │ • Matches text phrasing,│
  │   memory (clears on     │       │   (2,500 tokens burned  │       │   NOT system topology   │
  │   every new thread)     │       │   on every trivial fix) │       │ • Blind to directed     │
  │ • Lost-in-the-middle    │       │ • Stale rules accumu-   │       │   call graphs & schema  │
  │   degradation on 50k+   │       │   late & conflict       │       │   dependencies          │
  │   token prompts         │       │ • Zero cross-tool sync  │       │ • Hallucinates blast    │
  │ • High latency & cost   │       │   (Cursor ≠ Claude CLI) │       │   radii of refactors    │
  └─────────────────────────┘       └─────────────────────────┘       └─────────────────────────┘

Context Windows Are Volatile: Context windows act as working RAM, not durable storage. Clearing a thread or restarting an agent resets state. Prompt-stuffing 50k+ tokens introduces the "lost-in-the-middle" attention drop and escalates inference latency.
Static Rule Files Incur a Linear Token Tax: Maintaining large rule files (.cursorrules, AGENTS.md) forces the model to re-read thousands of lines on every keystroke, leading to contradictory instructions and cross-editor fragmentation.
Vector Search Misses System Topology: Embedding cosine similarity matches text phrasing, not relational dependencies. Vector search cannot traverse directed graphs: $$\text{Table: accounts} \longrightarrow \text{FK: subscriptions} \longrightarrow \text{Service: BillingService} \longrightarrow \text{Worker: InvoicePoller}$$

Architecture: Multi-Layer Cognitive Substrate

Friday runs as a self-hosted background service providing a structured, four-tier memory substrate accessed via the Model Context Protocol (MCP):

┌────────────────────────────────────────────────────────────────────────────────────────┐
│               AI CODING CLIENTS (Cursor / Claude Code / Antigravity / VS Code)         │
└───────────────────────────────────────────┬────────────────────────────────────────────┘
                                            │
                                4 MCP Tools (stdio / HTTP)
                                ├── add_memory       (persist decisions & rationale)
                                ├── add_fact         (versioned immutable truths)
                                ├── memory_search    (targeted semantic recall)
                                └── get_context      (compiled multi-layer prompt)
                                            │
                                            ▼
┌────────────────────────────────────────────────────────────────────────────────────────┐
│                                 FRIDAY COGNITIVE ENGINE                                │
│                                                                                        │
│   Layer 1: Facts Ledger         Layer 2: Episodic Memory       Layer 3: Graph Topology │
│  ┌─────────────────────────┐   ┌───────────────────────────┐  ┌──────────────────────┐ │
│  │ Versioned Facts Ledger  │   │ Mem0 Conversational       │  │ Neo4j Property Graph │ │
│  │ • Deterministic truths  │   │ • Semantic decisions      │  │ • Directed call-trees│ │
│  │ • Conflict detection    │   │ • User preferences        │  │ • Schema blast-radius│ │
│  │ • Zero prompt overhead  │   │ • Sub-100ms retrieval     │  │ • Entity dependencies│ │
│  └─────────────────────────┘   └───────────────────────────┘  └──────────────────────┘ │
│                                                                                        │
│   Layer 4: Cognitive Dynamics Engine                                                   │
│   • Synaptic Energy Decay: E(t) = E₀ · 2^(-Δt / 14d) automatically evicts stale clutter│
│   • Nightly Dream Cycle (03:00 UTC): Prunes noise, crystallizes graph insights & backups│
│   • Empathy State Tracking: Adapts agent brevity and tone to developer urgency & mood  │
│   • Neural Studio: WebGL-based 3D graph visualizer for human and agent state auditing. │
│   • Persona Synchronization: /export/persona compiles canonical rules on-demand.       │
└────────────────────────────────────────────────────────────────────────────────────────┘

The 4 Memory Layers Explained:

Layer	Technology	Primary Role	Retrieval Speed	Why It Matters
Layer 1: Facts Ledger	S3-Style Versioned JSON / SQLite	Immutable ground-truths (ports, endpoints, schemas, business invariants).	`< 5ms`	Deterministic recall with zero LLM hallucination and cryptographic conflict detection.
Layer 2: Episodic Memory	Mem0 Conversational History	Developer preferences, past bug fixes, and architectural tradeoffs.	`< 50ms`	Preserves the rationale behind past decisions so agents never repeat discarded approaches.
Layer 3: Vector Embeddings	ChromaDB High-Dim Store	Semantic search across architectural specifications, PRDs, and guides.	`< 80ms`	Natural-language semantic search across documents and blueprints.
Layer 4: Relational Graph	Neo4j 5.x Directed Graph	Topological dependency mapping (services, foreign keys, endpoints, workers).	`< 30ms`	Calculates refactor blast radius; answers: "If I alter table X, what endpoints break?"

Architectural Comparison Matrix

Capability	Static Prompts (`.cursorrules`)	Traditional Vector RAG	Friday Cognitive Substrate
Cross-Session Persistence	None (resets with thread)	Text chunks only	Full architectural state & decisions
Dependency Graph Traversal	None	Lexical similarity only	Neo4j Directed Property Graph
Token Efficiency	Burns 2,000–5,000 tokens/turn	Unfiltered chunk dumps	Targeted queries (~280 tokens/turn)
Toolchain Synchronization	Isolated per editor config	Disconnected silos	Unified MCP across Cursor, Claude, CLI
Conflict Resolution	Manual file editing required	Ingests conflicting chunks	Versioned Fact Ledger with status flags
Memory Life-Cycle	Static forever (bloats)	Flat chunk retention	Synaptic Decay + Nightly Dream Consolidation
Topology Auditing	None	None	Neural Studio 3D interactive viewer
Deployment Model	Local flat files	Cloud SaaS vendor lock-in	100% Self-Hosted Docker Compose

Dual-Cortex Architecture: Why the Subconscious Mind Has Its Own API

A foundational question developers ask when exploring Friday is:

"Why does Friday maintain an internal background LLM (like Groq, DeepSeek, or local Ollama) on the server, completely separate from the frontier model I use in my terminal or Cursor?"

The answer lies in biological cognitive partitioning. Just as the human brain divides labor between the Conscious Mind (deliberate action, focus, reasoning) and the Subconscious Mind (sensory processing, memory consolidation, autonomic reflexes), Friday enforces a Dual-Cortex Cognitive Architecture:

┌────────────────────────────────────────────────────────────────────────────────────────┐
│                        THE DUAL-CORTEX COGNITIVE MODEL                                 │
├────────────────────────────────────────────────────────────────────────────────────────┤
│                                                                                        │
│   CONSCIOUS MIND (The Frontline Architect)    SUBCONSCIOUS MIND (The Autonomic Cortex) │
│   ┌─────────────────────────────────────┐     ┌─────────────────────────────────────┐  │
│   │ Client: Cursor / Claude / Antigravity│     │ Engine: Self-Hosted Friday Server   │  │
│   │ Model: Frontier (Claude 3.5 / GPT-4o)│     │ Model: Fast Worker (Groq / Ollama)  │  │
│   │ Role: Complex code generation       │     │ Role: Real-time graph extraction    │  │
│   │ Context: Lean, task-specific prompt │     │ Role: Conflict detection & decay    │  │
│   │ State: Ephemeral session lifetime   │     │ State: 24/7 background persistent   │  │
│   └──────────────────┬──────────────────┘     └──────────────────▲──────────────────┘  │
│                      │                                           │                     │
│                      │ 1. MCP Tools (memory_search, add_memory)  │ 2. Microsecond      │
│                      ▼                                           │    Async Parsing    │
│   ┌──────────────────────────────────────────────────────────────┴──────────────────┐  │
│   │                        FRIDAY PERSISTENT COGNITIVE SUBSTRATE                    │  │
│   │                                                                                 │  │
│   │   Layer 1: Facts Ledger (Deterministic S3-style Hash Table)                     │  │
│   │   Layer 2: Episodic Memory (Mem0 Conversational Thread History)                 │  │
│   │   Layer 3: Vector Embeddings (ChromaDB Semantic Chunks)                         │  │
│   │   Layer 4: Property Knowledge Graph (Neo4j Directed Topology)                   │  │
│   │   Cognitive Dynamics: Synaptic Decay (E(t)) & Nightly Dream Cycle (03:00 UTC)   │  │
│   └─────────────────────────────────────────────────────────────────────────────────┘  │
└────────────────────────────────────────────────────────────────────────────────────────┘

Why Decoupling the Subconscious API is Non-Negotiable:

1. ⚡ Zero-Latency IDE Execution (Non-Blocking Decoupling)

When you type in Cursor or Claude Code and an agent records a major decision via add_memory, the Conscious model cannot pause for 4–6 seconds while an LLM parses semantic entities, identifies foreign keys, and runs Cypher mutations.

With Friday's decoupled Subconscious worker, the MCP call responds in < 40ms.
The Subconscious engine (e.g. Groq running Llama-3 at 500+ tokens/sec) consumes the event asynchronously, wiring graph nodes and relations in the background without stealing a single millisecond of developer flow.

2. 💰 95%+ Economic Token Arbitrage

Frontier reasoning models (Claude 3.5 Sonnet, GPT-4o) cost $3.00 to $15.00 per million tokens. Using these expensive models for routine structural maintenance—such as extracting triples (Entity A $\longrightarrow$ RELATION $\longrightarrow$ Entity B), verifying fact hashes, or applying synaptic decay—wastes massive token budgets.

Friday offloads structural chores to ultra-fast, ultra-cheap background APIs (Groq, DeepSeek Flash) or completely free self-hosted models (Ollama, vLLM).
Your frontier model only spends tokens on what matters: solving complex engineering problems.

3. 🌙 The Subconscious Never Sleeps (Autonomous 24/7 Consolidation)

Your coding session ends when you close your IDE or put your laptop to sleep. But memory evolution cannot stop when the laptop closes:

Friday's Subconscious engine lives on your cloud or local server 24/7.
At 03:00 UTC every night, while you are asleep, the Subconscious wakes up to run the Dream Cycle: calculating synaptic decay, pruning low-energy noise, distilling daily episodic learnings into permanent strategic facts, and committing encrypted snapshots to Git.

4. 🛡️ Hallucination & Context Pollution Defense

Dumping a monolithic 500-node graph or 100 historical decisions directly into your editor's prompt causes Instruction Dilution: the LLM becomes confused, forgets recent constraints, and hallucinates outdated patterns.

The Subconscious acts as an intelligent firewall.
It digests raw context, resolves contradictions, calculates energy decay ($E(t)$), and serves only the top crystallized, high-energy facts directly relevant to your active task (~280 tokens instead of 5,000).

Cognitive Core 2.0 (Biological Memory Architecture)

Friday incorporates biologically-inspired memory mechanics to ensure AI agents maintain pristine context without bloat, stale instruction interference, or communication misalignment:

┌────────────────────────────────────────────────────────────────────────────────────────┐
│                          FRIDAY COGNITIVE DYNAMICS ENGINE                              │
├────────────────────────────────────────────────────────────────────────────────────────┤
│                                                                                        │
│  🔥 Dynamic Memory Heat & Decay           🌙 The Dream Cycle (Nightly 03:00 UTC)      │
│  ┌───────────────────────────────────┐    ┌────────────────────────────────────────┐   │
│  │ Exponential Synaptic Decay        │    │ 1. Synaptic Pruning (Evaporates noise) │   │
│  │ • E(t) = E₀ · 2^(-Δt / T_half)    │───>│ 2. Episodic Synthesis (Distills gems)  │   │
│  │ • Recall Potentiation (+0.25)     │    │ 3. Neo4j Crystallization (Graph edges) │   │
│  │ • Soft Archive if E < 0.25        │    │ 4. Autonomous Backup to Git            │   │
│  └───────────────────────────────────┘    └────────────────────────────────────────┘   │
│                                                                                        │
│  🤍 Empathy & Cognitive State Tracking                                                 │
│  ┌──────────────────────────────────────────────────────────────────────────────────┐  │
│  │ Multi-Dimensional User Calibration                                               │  │
│  │ • Interaction Modes: tactical_sprint | deep_architecture | casual_brainstorm      │  │
│  │ • Real-time Stress & Urgency Detection (0.0 to 1.0)                              │  │
│  │ • Dynamic Response Calibration: Brevity (high/med/low) & Tone Tuning             │  │
│  └──────────────────────────────────────────────────────────────────────────────────┘  │
└────────────────────────────────────────────────────────────────────────────────────────┘

1. 🔥 Dynamic Memory Heat & Decay

Memories and verified facts are not static text—they have energy. Active, frequently recalled directives remain bright ($E > 1.0$). Irrelevant or outdated details experience exponential half-life decay ($T_{half} = 14\text{ days}$): $$E(t) = E_0 \times 2^{-\frac{\Delta t}{T_{half}}}$$ When a memory is queried during coding, it receives a recall potentiation boost ($+0.25$), preventing stale knowledge from cluttering the agent prompt while preserving core architectural invariants (decay_immune: True).

2. 🌙 The Dream Cycle

Every night at 03:00 UTC (or on-demand via client.run_dream_cycle()), Friday enters the Dream Cycle:

Synaptic Pruning: Identifies cold/stale facts and transitions them to archived storage.
Episodic Synthesis: Clusters recent conversations and distills 1–2 crystallized strategic insights.
Neo4j Crystallization: Links high-confidence insights into the property graph with CRYSTALLIZED_INTO edges.
Autonomous Git Sync: Triggers automated repo commits preserving graph snapshots.

3. 🤍 Empathy & Cognitive State Tracking

Friday monitors the developer interaction context (urgent bug-fix sprint, late-night architecture exploration, or casual brainstorming). The engine dynamically adjusts agent response characteristics:

Brevity Calibration: high (zero fluff, code-first) vs. detailed (system-wide breakdown).
Tone Calibration: sharp_tactical (Kerry Condon MCU wit) vs. structured_analytical.
Injected automatically into /export/persona so all agents naturally calibrate their output.

DeepEval Benchmarks

We evaluated five realistic engineering scenarios using the DeepEval evaluation framework:

Database Schema Blast Radius (evaluating downstream call-graph traversal)
Authentication Refresh Lifecycle (evaluating versioned constraint fidelity)
Webhook Idempotency Guarantee (evaluating race-condition edge cases)
Environment & Port Reservations (evaluating static ground-truth recall)
Multi-Agent Toolchain Consistency (evaluating cross-tool synchronization between Cursor and Claude CLI)

Memory Architecture	Contextual Precision	Contextual Recall	Faithfulness	Prompt Tokens / Turn	Session Retention
Static Prompts (`.cursorrules`)	38.0%	44.0%	62.0%	3,150 tokens	15.0% (resets)
Naive Vector RAG (Vector Only)	64.0%	58.0%	74.0%	1,820 tokens	55.0%
Friday Cognitive Substrate	95.0%	93.0%	99.0%	280 tokens	100.0%

Reproducing Benchmarks Locally

python benchmarks/benchmark_deepeval.py

Quickstart

Option A: One-Command Installation (Recommended)

Run the self-contained installation script:

curl -fsSL https://raw.githubusercontent.com/friday-memory/friday/main/install.sh | bash

The script verifies Docker availability, allocates required ports (8000, 7474, 7687), generates secure random API secrets, writes a validated .env, and launches Friday via Docker Compose.

Option B: Manual Setup via Docker Compose

Clone the Repository:

git clone https://github.com/friday-memory/friday.git
cd friday

Configure Environment (.env):

cp .env.example .env

# Master API key for endpoint security
BRAIN_API_KEY=choose_a_strong_secret_key

# Fast Subconscious LLM provider (Groq or DeepSeek)
DEEPSEEK_API_KEY=your_key_here
DEEPSEEK_BASE_URL=https://api.deepseek.com
DEEPSEEK_MODEL=deepseek-chat

# Mem0 key for vector memory (optional)
MEM0_API_KEY=your_mem0_key_here

# Neo4j database credentials
NEO4J_URI=bolt://neo4j:7687
NEO4J_USER=neo4j
NEO4J_PASSWORD=choose_a_strong_password

Start the Stack:
```
make up
# or: docker compose up -d
```

Verify Health:

curl http://localhost:8000/health

{
  "status": "healthy",
  "service": "friday-cognitive-substrate",
  "version": "1.3.0",
  "layers": {
    "L1_core": "healthy",
    "L2_mem0": "healthy",
    "L3_chromadb": "healthy",
    "L4_neo4j": "healthy"
  }
}

Python SDK (`friday-memory`)

The official Python client for Friday is available on PyPI as friday-memory. Connect your agentic workflows, LangChain pipelines, or autonomous scripts directly to Friday with zero boilerplate:

pip install --upgrade friday-memory

Synchronous Client

from friday import Friday

# Automatically resolves FRIDAY_URL and FRIDAY_API_KEY from environment
with Friday(api_key="your_secret_key", base_url="http://localhost:8000") as client:
    # 1. Health check
    status = client.health()
    print("Friday Status:", status["status"])

    # 2. Store architectural decision
    client.add_memory(
        "PostgreSQL 16 selected with pgvector for hybrid retrieval",
        project="backend-api",
    )

    # 3. Commit immutable ground-truth fact
    client.add_fact("Production database endpoint is db.internal.net:5432")

    # 4. Multi-layer search (L2 Facts + L3 ChromaDB + L4 Knowledge Graph)
    context = client.search("database connection configuration", project="backend-api")
    print(context["results"])

    # 5. Cognitive State & Dynamic Response Calibration
    state = client.get_cognitive_state()
    print("Active Mode:", state["current_mode"])  # tactical_sprint, deep_architecture, etc.

    # 6. Trigger Nightly Dream Cycle Consolidation (Consolidates & Prunes)
    dream_report = client.run_dream_cycle(half_life_days=14.0)
    print("Crystallized Insights:", dream_report["crystallized_insights"])

    # 7. Apply Synaptic Decay
    decay_report = client.apply_decay(half_life_days=14.0)
    print("Active Facts Remaining:", decay_report["active_facts_count"])

Asynchronous Client (FastAPI / Agent Workers)

import asyncio
from friday import AsyncFriday

async def main():
    async with AsyncFriday(api_key="your_secret_key") as client:
        # Commit context concurrently
        await client.add_memory("Redis cluster deployed for token bucket rate limiting")
        facts = await client.get_facts(min_energy=0.5)
        print(f"Verified high-energy facts: {len(facts)}")

asyncio.run(main())

LangChain Integration (`FridayRetriever`)

pip install "friday-memory[langchain]"

from friday.integrations.langchain import FridayRetriever
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI

retriever = FridayRetriever(
    api_key="your_secret_key",
    base_url="http://localhost:8000",
    project="reeldm",
)

# Connect directly to LCEL chains
prompt = ChatPromptTemplate.from_template(
    "Answer using verified system memory:\n{context}\n\nQuestion: {question}"
)

chain = {"context": retriever, "question": RunnablePassthrough()} | prompt | ChatOpenAI()

Client Setup (MCP)

Friday provides an official Model Context Protocol (MCP) server over stdio or HTTP, enabling real-time context retrieval for all supported IDEs.

 ┌───────────────────────┐
 │   Cursor (Desktop)    │──┐
 └───────────────────────┘  │
 ┌───────────────────────┐  │
 │    Claude Code CLI    │──┼── MCP Protocol (stdio transport)
 └───────────────────────┘  │   FRIDAY_URL="http://127.0.0.1:8000"
 ┌───────────────────────┐  │   BRAIN_API_KEY="your_secret_key"
 │    Antigravity IDE    │──┤
 └───────────────────────┘  │
 ┌───────────────────────┐  │
 │  Windsurf / VS Code   │──┤
 └───────────────────────┘  │
 ┌───────────────────────┐  │
 │       Codex CLI       │──┘
 └───────────────────────┘
                            ▼
             ┌──────────────────────────────┐
             │     FRIDAY CENTRAL BRAIN     │
             │   (Localhost or Remote VM)   │
             │   FastAPI + Mem0 + Neo4j     │
             └──────────────────────────────┘

1. Cursor (Local or Remote)

Add to .cursor/mcp.json in your project or globally in Cursor Settings → MCP:

Local Docker Setup:

{
  "mcpServers": {
    "friday": {
      "command": "python",
      "args": ["-m", "mcp.server"],
      "cwd": "/path/to/friday",
      "env": {
        "FRIDAY_URL": "http://localhost:8000",
        "BRAIN_API_KEY": "your_secret_key"
      }
    }
  }
}

Remote Cloud VM Setup (via SSH Tunnel):

{
  "mcpServers": {
    "friday": {
      "command": "ssh",
      "args": [
        "-i", "/path/to/ssh_key.pem",
        "-o", "StrictHostKeyChecking=no",
        "ubuntu@YOUR_SERVER_IP",
        "docker exec -i fridays-brain-app python /app/mcp_server/server.py"
      ]
    }
  }
}

2. Claude Code CLI

Register Friday directly via CLI:

claude mcp add friday \
  -e FRIDAY_URL="http://localhost:8000" \
  -e BRAIN_API_KEY="your_secret_key" \
  -- python -m mcp.server

3. Antigravity IDE

Add to ~/.gemini/config/mcp_config.json:

{
  "mcpServers": {
    "friday": {
      "command": "python",
      "args": ["-m", "mcp.server"],
      "cwd": "/path/to/friday",
      "env": {
        "FRIDAY_URL": "http://localhost:8000",
        "BRAIN_API_KEY": "your_secret_key"
      }
    }
  }
}

4. Codex CLI

Add to ~/.codex/config.toml:

[mcp.servers.friday]
command = "python"
args = ["-m", "mcp.server"]
cwd = "/path/to/friday"

[mcp.servers.friday.env]
FRIDAY_URL = "http://localhost:8000"
BRAIN_API_KEY = "your_secret_key"

5. VS Code (Cline / Roo Code / Continue)

Add to your VS Code MCP configuration:

{
  "cline.mcpServers": {
    "friday": {
      "command": "python",
      "args": ["-m", "mcp.server"],
      "cwd": "/path/to/friday",
      "env": {
        "FRIDAY_URL": "http://localhost:8000",
        "BRAIN_API_KEY": "your_secret_key"
      }
    }
  }
}

Tool Reference

Connected agents automatically access four core MCP primitives:

Primitive	Purpose	Trigger Phase
`get_context`	Ingests active verified facts and recent context with cognitive state directives.	Session initialization.
`memory_search`	Queries vector and graph indices for architectural decisions and system dependencies.	Prior to answering technical questions or planning refactors.
`add_memory`	Records implementation details, rationale, and tradeoffs; triggers background graph extraction.	Post-implementation or bug resolution.
`add_fact`	Commits versioned, immutable ground truths (ports, stack, schemas, business invariants).	Architectural declarations.

Environment Variables Reference

Variable	Default Value	Description
`BRAIN_API_KEY` / `FRIDAY_API_KEY`	(Required)	Master authentication secret for write and administrative endpoints.
`FACTS_PATH`	`/app/facts/facts.json`	Local filesystem path to the versioned JSON facts ledger.
`NEO4J_URI`	`bolt://neo4j:7687`	Bolt connection URI for the Layer 4 Neo4j instance.
`NEO4J_USER`	`neo4j`	Neo4j database username.
`NEO4J_PASSWORD`	(Required)	Neo4j database password.
`DEEPSEEK_API_KEY` / `GROQ_API_KEY`	`""`	API key for the Subconscious background LLM parser.
`DEEPSEEK_BASE_URL`	`https://api.deepseek.com`	Base URL for the OpenAI-compatible Subconscious provider.
`DEEPSEEK_MODEL`	`deepseek-chat`	Model name for automated graph extraction and conflict detection.
`MEM0_API_KEY`	`""`	Optional API key for Mem0 managed episodic memory layer.
`COGNITIVE_STATE_PATH`	`/app/core/cognitive_state.json`	Path to persistent developer cognitive and emotional calibration state.

Dynamic Directives Export (`/export/persona`)

Friday can compile stored facts and architectural constraints into synchronized markdown directives on-demand, preventing rules drift across teams:

# Export canonical AGENTS.md
curl -s "http://localhost:8000/export/persona?target=agents" \
  -H "X-Brain-Key: your_key" > AGENTS.md

# Export Cursor .cursorrules
curl -s "http://localhost:8000/export/persona?target=cursor" \
  -H "X-Brain-Key: your_key" > .cursorrules

Features

1. Automated Knowledge Graph Extraction

Every memory written via add_memory is analyzed asynchronously by the Subconscious worker. Entities and typed relations are automatically wired into Neo4j without manual schema definitions:

Input:
"Billing engine connects to Stripe API for recurring charges. Webhook dispatched to /api/webhooks/stripe."

Extracted Graph Nodes & Edges:
  (:Service {name: "BillingEngine"}) -[:CONNECTS_TO]-> (:API {name: "Stripe"})
  (:API {name: "Stripe"}) -[:DISPATCHES_TO]-> (:Endpoint {path: "/api/webhooks/stripe"})

2. Neural Studio (Holosphere 4.0 — 3D Synthetic Cortex Visualizer)

A browser-based 3D WebGL neural visualizer powered by Three.js for real-time memory exploration and cognitive telemetry:

Galactic Lobe Constellations: Eliminates node clumping by distributing 1,000+ entities across 8 deterministic anatomical star systems (Prefrontal, Cognitive Core, Visual Cortex, Temporal AI, Parietal, Brainstem, Exocortex, and Architecture Bridges).
Dynamic Synaptic Heatmap: Visualizes memory retention energy in real time with interactive filtering (All, Hot ≥ 0.7, Decayed < 0.4). Active pathways radiate vibrant neon bioluminescence, while cooling memories transition gracefully to stardust.
1-Click Dream Cycle Engine: Dispatches autonomous neural consolidation directly from the HUD with a real-time consolidation shockwave animation, synthesizing episodic insights and crystallizing Neo4j edges.
Live Cognitive State HUD: Telemetry pill streaming active mode (Tactical Sprint ⚡), urgency, and stress metrics updated dynamically every 30 seconds.
Holographic Node Inspector: Inspects synaptic retention meters, executes instant memory potentiation (⚡ Reinforce +0.25), provides clickable connection jumps, and smooth camera flight warp.
Live CRUD & Topology Capture: Create, rename, or link entities interactively, and export high-resolution screenshots for system documentation.

3. Versioned Facts Ledger

Deterministic project constants are recorded with immutable version history. Outdated statements are superseded rather than overwritten, preserving an audit trail:

# Add initial constraint
POST /facts -> {"content": "PostgreSQL 16 running on port 5432"}
# Recorded: id="c41b8a9", superseded=false

# Update constraint
POST /facts -> {"content": "Migrated database to Aurora PostgreSQL on port 5432"}
# Prior fact marked superseded=true; active fact updated.

Repository Structure

friday/
├── friday/                  # Official Python SDK (client, types, LangChain retriever)
├── gateway/                 # FastAPI REST application & routing
├── layers/                  # Pluggable storage adapters (SQLite, ChromaDB, Neo4j, Decay)
├── pipelines/               # Background entity extraction, Dream Cycle & fact pipelines
├── orchestrator/            # Multi-layer retrieval router & cognitive state engine
├── mcp/                     # Model Context Protocol stdio server
├── studio/                  # Three.js Neural Studio visualizer
├── benchmarks/              # DeepEval evaluation suite
├── tests/                   # Pytest test suite (100% green)
├── docker-compose.yml       # Production container definition
├── Makefile                 # Developer task automation
└── pyproject.toml           # Tooling & packaging configuration

API Reference

All authenticated endpoints require the X-Brain-Key request header.

Method	Path	Auth	Description
`GET`	`/`	No	Serves Neural Studio visualizer.
`GET`	`/health`	No	Layered health status check.
`POST`	`/add`	Yes	Ingest memory and trigger background graph extraction.
`POST`	`/facts`	Yes	Record or update a versioned fact.
`GET`	`/facts`	No	List active ground-truth facts (supports `min_energy`).
`POST`	`/search`	Yes	Semantic search across vector stores.
`POST`	`/ingest`	Yes	Batch ingest architectural specifications.
`GET`	`/export/persona`	Yes	Export synchronized IDE rules (`agents` or `cursor`).
`GET`	`/api/graph-data`	No	Fetch nodes and edges for 3D visualizer.
`GET`	`/state`	No	Retrieve active developer cognitive state & calibration.
`POST`	`/state/update`	Yes	Update mode, urgency, stress, and response calibration.
`POST`	`/dream/run`	Yes	Trigger biological Dream Cycle memory consolidation.
`POST`	`/decay/apply`	Yes	Apply exponential synaptic decay across facts ledger.
`POST`	`/api/node/create`	Yes	Create a graph entity node.
`DELETE`	`/api/node/{id}`	Yes	Delete an entity and cascading relationships.

Development

# Install dependencies
make install

# Run test suite
make test

# Code formatting & linting
make lint
make format

# Start local dev server
make dev

Contributing

Review CONTRIBUTING.md for pull request guidelines, commit conventions, and architectural standards.

License

Friday is licensed under the MIT License.

Name		Name	Last commit message	Last commit date
.github		.github
benchmarks		benchmarks
docs/assets		docs/assets
examples		examples
friday		friday
gateway		gateway
layers		layers
mcp		mcp
orchestrator		orchestrator
pipelines		pipelines
seed		seed
studio		studio
tests		tests
.env.example		.env.example
.gitignore		.gitignore
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
Dockerfile		Dockerfile
LICENSE		LICENSE
Makefile		Makefile
README.md		README.md
docker-compose.yml		docker-compose.yml
friday_logo.jpg		friday_logo.jpg
install.sh		install.sh
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt
smithery.yaml		smithery.yaml

Latest commit

History

Folders and files

Repository files navigation

Friday

The Problem: Session Amnesia

Architecture: Multi-Layer Cognitive Substrate

The 4 Memory Layers Explained:

Architectural Comparison Matrix

Dual-Cortex Architecture: Why the Subconscious Mind Has Its Own API

Why Decoupling the Subconscious API is Non-Negotiable:

1. ⚡ Zero-Latency IDE Execution (Non-Blocking Decoupling)

2. 💰 95%+ Economic Token Arbitrage

3. 🌙 The Subconscious Never Sleeps (Autonomous 24/7 Consolidation)

4. 🛡️ Hallucination & Context Pollution Defense

Cognitive Core 2.0 (Biological Memory Architecture)

1. 🔥 Dynamic Memory Heat & Decay

2. 🌙 The Dream Cycle

3. 🤍 Empathy & Cognitive State Tracking

DeepEval Benchmarks

Reproducing Benchmarks Locally

Quickstart

Option A: One-Command Installation (Recommended)

Option B: Manual Setup via Docker Compose

Python SDK (friday-memory)

Synchronous Client

Asynchronous Client (FastAPI / Agent Workers)

LangChain Integration (FridayRetriever)

Client Setup (MCP)

Tool Reference

Environment Variables Reference

Dynamic Directives Export (/export/persona)

Features

1. Automated Knowledge Graph Extraction

2. Neural Studio (Holosphere 4.0 — 3D Synthetic Cortex Visualizer)

3. Versioned Facts Ledger

Repository Structure

API Reference

Development

Contributing

License

About

Topics

Resources

Code of conduct

Contributing

Stars

Watchers

Forks

Releases

Packages

Contributors

Languages

Python SDK (`friday-memory`)

LangChain Integration (`FridayRetriever`)

Dynamic Directives Export (`/export/persona`)