🧠 CogniLayer v4

Stop re-explaining your codebase to AI.

Infinite speed memory · Code graph · 200K+ tokens saved

Without CogniLayer, your AI agent starts every session blind. It re-reads files, re-discovers architecture, re-learns decisions you explained last week. On a 50-file project, that's 80-100K tokens burned before real work begins.

With CogniLayer, it already knows. Three things your agent doesn't have today:

🔗 Persistent knowledge across agents - facts, decisions, error fixes, gotchas survive across sessions, crashes, and agents. Start in Claude Code, continue in Codex CLI - zero context loss

🔍 Code intelligence - who calls what, what depends on what, what breaks if you rename a function. Tree-sitter AST parsing across 10+ languages, not grep

🤖 Subagent context compression - research subagents write findings to DB instead of dumping 40K+ tokens into parent context. Parent gets a 500-token summary + on-demand memory_search retrieval

⚡ 80-200K+ tokens saved per session - semantic search replaces file reads, subagent findings go to DB instead of context. Longer sessions with subagents save more

See the Difference

Without CogniLayer

You: "Fix the login bug"

Claude: Let me read the project structure...
        Let me read src/auth/login.ts...
        Let me read src/auth/middleware.ts...
        Let me read src/config/database.ts...
        Let me understand your auth flow...
        (8 files read, 45K tokens burned, 2 minutes spent on orientation)

Claude: "Ok, I see the issue..."

With CogniLayer

You: "Fix the login bug"

Claude: [memory_search → "login auth flow"] → 3 facts loaded (200 tokens)
        [code_context → "handleLogin"] → caller/callee map in 0.2s
        Already knows: Express + Passport, JWT in httpOnly cookies,
        last login bug was a race condition in session refresh (fixed 2 weeks ago)

Claude: "This looks like the same pattern as the session refresh issue
         from March 1st. The fix is..."

That's not a small improvement. That's the difference between an agent that guesses and one that knows.

Real-World Examples

Debugging: "Why is checkout failing?"

Without CogniLayer, Claude reads 15 files to understand your e-commerce flow. With it:

memory_search("checkout payment flow")
→ fact: "Stripe webhook hits /api/webhooks/stripe, validates signature
   with STRIPE_WEBHOOK_SECRET, then calls processOrder()"
→ gotcha: "Stripe sends webhooks with 5s timeout - processOrder must
   complete within 5s or webhook retries cause duplicate orders"
→ error_fix: "Fixed duplicate orders on 2026-02-20 by adding
   idempotency key check in processOrder()"

code_impact("processOrder")
→ depth 1: createOrderRecord, sendConfirmationEmail, updateInventory
→ depth 2: InventoryService.reserve, EmailQueue.push
→ "Changing processOrder will affect 6 functions across 4 files"

Claude already knows the architecture, the past bugs, and what will break if it touches the wrong thing. Instead of 15 file reads (~60K tokens), it uses 3 targeted queries (~800 tokens).

Code Intelligence: "What happens if I change processOrder?"

Without CogniLayer, Claude greps for the function name and hopes for the best. With it:

code_context("processOrder")
→ definition: src/services/order.ts:42
→ incoming (who calls it): StripeWebhookHandler.handle, OrderController.retry,
   AdminPanel.reprocessOrder
→ outgoing (what it calls): createOrderRecord, sendConfirmationEmail,
   updateInventory, PaymentLog.write

code_impact("processOrder")
→ depth 1 (WILL BREAK):  StripeWebhookHandler, OrderController, AdminPanel
→ depth 2 (LIKELY AFFECTED): WebhookRouter, RetryQueue, AdminRoutes
→ depth 3 (NEED TESTING): 3 test files, 1 integration test
→ "Changing processOrder will affect 9 symbols across 7 files"

Before touching a single line, Claude knows the full blast radius - which files will break, which need testing, and which callers depend on the current behavior. No more surprise failures after a refactor.

Refactoring: "Rename UserService to AccountService"

code_search("UserService")
→ class UserService in src/services/user.ts (line 14)
→ 12 references across 8 files

code_impact("UserService")
→ depth 1: AuthController, ProfileController, AdminPanel (WILL BREAK)
→ depth 2: LoginRoute, RegisterRoute, middleware/auth (LIKELY AFFECTED)
→ depth 3: 4 test files (NEED UPDATING)

memory_search("UserService")
→ decision: "UserService handles both auth and profile - planned split
   into AuthService + ProfileService (decided 2026-02-15, not yet done)"

Claude doesn't just find-and-replace. It knows there's a planned split and can suggest doing both changes at once - saving you a future refactoring session.

New session after a crash: "What was I working on?"

[SessionStart hook fires automatically]
→ bridge loaded: "Progress: Migrated 3/5 API endpoints to v2 format.
   Done: /users, /products, /orders. Open: /payments, /shipping.
   Blocker: /payments needs Stripe SDK v12 upgrade first."

memory_search("stripe sdk upgrade")
→ gotcha: "Stripe SDK v12 changed webhook signature verification -
   verify() is now async, breaks all sync handlers"

Zero re-explanation. Claude picks up exactly where it left off, including the blocker you hadn't mentioned yet.

Subagent research: "What MCP frameworks exist?"

Without CogniLayer, the subagent returns a 40K-token dump into parent context:

Parent (200K context):
  → spawn subagent: "Research community MCP servers"
  ← subagent returns: 40K tokens about 15 projects
  → all 40K crammed into parent context
  → remaining: 160K → next subagent → 120K → next → 80K...

With CogniLayer's Subagent Memory Protocol:

Parent (200K context):
  → spawn subagent: "Research MCP servers, save to memory"
  ← subagent writes details to DB, returns: "Saved 3 facts,
     search 'MCP server ecosystem'. Summary: Python dominates,
     FastMCP most popular, 3 architectural patterns."
  → parent context: ~500 tokens
  → need details? memory_search("MCP server ecosystem") → targeted pull

40K tokens compressed to 500. The findings persist in DB across sessions - not just for this conversation, but forever.

Killer Features

How It Works

You start a session
    ↓
SessionStart hook fires → injects project DNA, last session bridge, crash recovery
    ↓
You work normally - Claude saves facts, decisions, gotchas automatically via MCP tools
    ↓
You ask about code → code_context / code_impact answer in milliseconds from AST index
    ↓
Session ends (or crashes)
    ↓
Next session starts with full context - no re-reading, no re-explaining

Zero effort after install. No commands to learn, no workflow changes. CogniLayer runs in the background via hooks and MCP tools. Claude knows how to use it automatically.

Quick Start

1. Install (30 seconds)

git clone https://github.com/LakyFx/CogniLayer.git
cd CogniLayer
python install.py

That's it. Next time you start Claude Code, CogniLayer is active.

2. Optional: Turbocharge search

# AI-powered vector search (recommended - finds facts even with different wording)
pip install fastembed sqlite-vec

3. Optional: Add Codex CLI support

python install.py --codex    # Codex CLI only
python install.py --both     # Claude Code + Codex CLI

4. Verify

python ~/.cognilayer/mcp-server/server.py --test
# → "OK: All 18 tools registered."

Troubleshooting

MCP server not connecting? Run the diagnostic tool:

python diagnose.py          # Check everything
python diagnose.py --fix    # Check + auto-fix missing dependencies

Requirements

• Python 3.11+
• Claude Code and/or Codex CLI
• pip packages: mcp, pyyaml, textual (installed automatically), fastembed, sqlite-vec (optional), tree-sitter-language-pack (optional, for code intelligence)

Slash Commands (Claude Code only)

Once installed, use these in Claude Code:

Codex CLI users: Slash commands are not available in Codex. Instead, CogniLayer uses AGENTS.md instructions + MCP tools directly. See Codex CLI Integration below.

TUI Dashboard

A visual memory browser right in your terminal. 8 tabs, keyboard navigation, works on Windows, Mac, and Linux.

cognilayer                    # All projects
cognilayer --project my-app   # Specific project
cognilayer --demo             # Demo mode with sample data (try it!)

Overview - stats at a glance

Facts - searchable, filterable, color-coded by heat

Heatmap - see which knowledge is hot, warm, or cold

Clusters - related facts organized into groups

Timeline - full session history with outcomes

Screenshots show demo mode (cognilayer --demo) with sample data.

Upgrading

The upgrade is safe and non-destructive. Your memory is never lost:

git pull
python install.py

What happens under the hood:

• Code files are replaced with the latest versions
• config.yaml is never overwritten (your settings are safe)
• memory.db is backed up automatically before any migration
• Schema migration is purely additive (new columns/tables, never deletions)
• CLAUDE.md blocks update automatically on next session start

Rollback

If something goes wrong:

# Your backup is timestamped
cp ~/.cognilayer/memory.db.backup-YYYYMMDD-HHMMSS ~/.cognilayer/memory.db
# Restore old code
git checkout <previous-commit> && python install.py

Configuration

Edit ~/.cognilayer/config.yaml:

# Language - "en" (default) or "cs" (Czech)
language: "en"

# Your projects directory
projects:
  base_path: "~/projects"

# Indexer settings
indexer:
  scan_depth: 3
  chunk_max_chars: 2000

# Search defaults
search:
  default_limit: 5
  max_limit: 10

Known Limitations

• Concurrent CLIs: Running Claude Code and Codex CLI simultaneously on the same project may cause session tracking conflicts. Use one CLI at a time per project.
• Codex file tracking: Codex CLI has no hooks, so automatic file change tracking is not available for Codex sessions.
• Code intelligence: Requires tree-sitter-language-pack (~20MB). Without it, all other 14 tools work normally.
• TUI: Requires textual package. Read-only except for resolving contradictions.

Architecture (for the curious)

Everything below is for developers who want to understand how CogniLayer works under the hood.

System Overview

Claude Code / Codex CLI Session
    │
    ├── SessionStart hook (Claude Code) / session_init tool (Codex)
    │   └── Injects Project DNA + last session bridge into CLAUDE.md
    │
    ├── MCP Server (18 tools)
    │   ├── memory_search    - Hybrid FTS5 + vector search with staleness detection
    │   ├── memory_write     - Store facts (14 types, deduplication, auto-embedding)
    │   ├── memory_delete    - Remove outdated facts by ID
    │   ├── memory_link      - Bidirectional Zettelkasten-style fact linking
    │   ├── memory_chain     - Causal chains (caused, led_to, blocked, fixed, broke)
    │   ├── file_search      - Search indexed project docs (chunked, not full files)
    │   ├── file_index       - Index project docs (README, configs, PRD) into file_chunks
    │   ├── project_context  - Get project DNA + health metrics
    │   ├── session_bridge   - Save/load session continuity summaries
    │   ├── session_init     - Initialize session for Codex CLI (replaces hooks)
    │   ├── decision_log     - Query append-only decision history
    │   ├── verify_identity  - Safety gate before deploy/SSH/push
    │   ├── identity_set     - Configure project Identity Card
    │   ├── recommend_tech   - Suggest tech stacks from similar projects
    │   ├── code_index       - Index codebase via tree-sitter AST parsing
    │   ├── code_search      - Find symbols (functions, classes, methods) by name
    │   ├── code_context     - 360° view: callers, callees, child methods
    │   └── code_impact      - Blast radius analysis (BFS traversal of references)
    │
    ├── PostToolUse hook (Claude Code only)
    │   └── Logs every file Write/Edit to changes table (<1ms overhead)
    │
    ├── PreCompact hook (Claude Code only)
    │   └── Saves comprehensive bridge before context compaction
    │
    └── SessionEnd hook / session_bridge(save)
        └── Closes session, builds emergency bridge if needed

File Structure

~/.cognilayer/
├── memory.db              # SQLite (WAL mode, FTS5, 17 tables)
├── config.yaml            # Configuration (never overwritten by installer)
├── active_session.json    # Current session state (runtime)
├── mcp-server/
│   ├── server.py          # MCP entry point (18 tools)
│   ├── db.py              # Shared DB helper (WAL, busy_timeout, lazy vec loading)
│   ├── i18n.py            # Translations (EN + CS)
│   ├── init_db.py         # Schema creation + migration
│   ├── embedder.py        # fastembed wrapper (BAAI/bge-small-en-v1.5, 384-dim)
│   ├── register_codex.py  # Codex CLI config.toml registration
│   ├── indexer/           # File scanning and chunking
│   ├── search/            # FTS5 + vector hybrid search
│   ├── code/              # Code Intelligence (tree-sitter parsers, indexer, resolver)
│   └── tools/             # 18 MCP tool implementations
├── hooks/
│   ├── on_session_start.py    # Project detection, DNA injection, crash recovery
│   ├── on_session_end.py      # Session close, emergency bridge, episode building
│   ├── on_file_change.py      # PostToolUse file change logger + context monitoring
│   ├── on_pre_compact.py      # PreCompact bridge preservation
│   ├── generate_agents_md.py  # Codex AGENTS.md generator
│   └── register.py            # Claude Code settings.json registration
├── tui/                       # TUI Dashboard (Textual)
│   ├── app.py                 # Main application (8 tabs, keyboard nav)
│   ├── data.py                # Read-only SQLite data access layer
│   ├── styles.tcss            # CSS stylesheet
│   ├── screens/               # 8 tab screen modules
│   └── widgets/               # Heat cell, stats card widgets
└── logs/
    └── cognilayer.log

Database Schema (17 tables)

Hybrid Search

Two search engines combined for maximum recall:

1. FTS5 - SQLite fulltext search for exact keyword matching
2. Vector embeddings - fastembed (BAAI/bge-small-en-v1.5, 384-dim, CPU-only ONNX) with sqlite-vec for cosine similarity
3. Hybrid ranker - 40% FTS5 + 60% vector similarity, with heat score boosting

Vector search is optional - FTS5 works standalone without any extra dependencies.

Heat Decay

Facts have a "temperature" that models relevance over time:

Decay rates vary by fact type - error_fix and gotcha facts decay slower (they stay relevant longer) than task facts. Each search hit boosts a fact's heat score.

Code Intelligence

Powered by tree-sitter AST parsing with language-pack support for 10+ languages:

Indexing runs with a configurable time budget (default 30s). Partial results are usable immediately. Unresolved references are re-resolved on the next incremental run.

Subagent Memory Protocol

When Claude spawns research subagents, the raw findings can be 40K+ tokens. Without the protocol, all of that goes into the parent's context window. The Subagent Memory Protocol uses the CogniLayer database as a side channel:

Subagent                              Parent
   │                                     │
   ├── research (WebSearch, Read...)     │
   ├── synthesize findings               │
   ├── memory_write(consolidated facts)  │  ← data goes to DB, not context
   └── return: 500-token summary ────────┤  ← only summary enters context
                                         │
                        memory_search() ──┤  ← parent pulls details on demand

Key design decisions:

• Synthesis over granularity - subagents group related findings into cohesive facts, not one-per-discovery
• Task-specific tags - each subagent gets a unique tag (e.g. tags="subagent,auth-review") for filtering via memory_search(tags="auth-review")
• Keywords inside facts - each fact ends with Search: keyword1, keyword2 so retrieval works even after context compaction
• Write-last pattern - all memory_write calls happen as the last step before return, saving subagent turns and tokens
• Foreground-first - subagents launch as foreground (reliable MCP access), user can Ctrl+B to background
• Graceful fallback - if MCP tools are unavailable, findings go directly in return text

The protocol is injected into CLAUDE.md automatically and requires no user configuration.

Codex CLI Integration

Codex CLI has no hook system, so CogniLayer adapts:

Same memory database shared between both CLIs.

Codex onboarding workflow

AGENTS.md includes a FIRST RUN section that instructs Codex to:

1. session_init() - register project, load DNA + bridge
2. file_index() - index documentation files so file_search works
3. code_index() - index source code so code_search/context/impact work
4. Read key files and save findings via memory_write() (the AI does intelligent analysis)

This matches what /onboard does in Claude Code, but through AGENTS.md instructions instead of a slash command.

Project Identity Card

Deployment safety system that prevents "oops, wrong server" incidents:

• Safety locking - locked fields require explicit update + audit log entry
• Hash verification - SHA-256 detects tampering of safety-critical fields
• Required field checks - verify_identity blocks deploy if critical fields are missing
• Audit trail - every safety field change is logged with timestamp and reason

Contributing

Contributions are welcome! Please open an issue first to discuss what you'd like to change.

License

Elastic License 2.0 - Free to use, modify, and distribute. You may not provide it as a managed/hosted service.