engram-cli 2.1.1

AI-powered skill & memory generator for codebases - fully local, no API keys needed

Engram CLI

AI-powered skill & memory generator for codebases. Fully local, no API keys needed.

What it does

Point Engram at any codebase and it generates structured skills (architectural knowledge) and memories (exploration sessions) using a local AI model. Zero API cost, fully air-gapped.

$ engram analyze fastapi/fastapi

╭────────────────────────────────────────╮
│ Engram v2.1.0 - Local AI Code Analyzer │
╰────────────────────────────────────────╯

Phase 1: Heuristic Analysis
  Languages: Python (89%), Markdown (6%), Shell (3%)
  Frameworks: FastAPI, Starlette, Pydantic, Uvicorn, pytest
  Patterns: REST API, Middleware, Documentation site

Phase 2: Local Model Inference (qwen2.5-coder:7b)
  [1/5] Generating architecture skill...
  [2/5] Generating patterns skill...
  [3/5] Generating testing skill...
  [4/5] Generating project overview...
  [5/5] Generating activity analysis...

╭───────── Results for fastapi/fastapi ──────────╮
│ Generated 3 skills + 2 memories                │
│ Model: qwen2.5-coder:7b | Time: 42s | Cost: $0 │
╰────────────────────────────────────────────────╯

Install

# 1. Install Ollama (one-time)
brew install ollama          # macOS
# or: curl -fsSL https://ollama.com/install.sh | sh  # Linux

# 2. Install Engram CLI
brew install pipx && pipx install engram-cli   # macOS (recommended)
# or: pip install engram-cli                   # Linux / virtualenv

The first run will automatically download the Qwen2.5-Coder 7B model (~4.5GB, one-time).

Usage

# Analyze current directory
engram analyze .

# Analyze a GitHub repo
engram analyze https://github.com/pallets/flask

# Shorthand
engram analyze pallets/flask

# Specify org name for output
engram analyze . --org mycompany/myrepo

# Use a larger model for better quality
engram analyze . --model qwen2.5-coder:14b

# Heuristic-only (no model, instant)
engram analyze . --skip-model

# JSON output for piping
engram analyze . --json-only | jq '.skills | length'

# List recommended models
engram models

# Browse analysis results in a local web viewer
engram browse

# Start the viewer server without opening browser
engram serve

# Check version
engram version

Output

By default, outputs both JSON and Markdown:

engram-output/myrepo/
├── engram-analysis.json          # Combined analysis + generated content
├── skills/
│   ├── architecture/SKILL.md     # Architecture overview
│   ├── patterns/SKILL.md         # Code patterns & conventions
│   └── testing/SKILL.md          # Testing strategy (if tests detected)
└── memories/
    └── sessions/
        ├── 2026-02-13-myrepo-overview.md    # Project deep dive
        └── 2026-02-13-myrepo-activity.md    # Recent activity analysis

How it works

Layer 1: Heuristic Analysis (instant, no model)

• Walks the file tree, counts files/extensions
• Parses package.json, Cargo.toml, go.mod, pyproject.toml for dependencies
• Detects frameworks (React, FastAPI, Tokio, etc.) from dependency lists
• Identifies test infrastructure, CI/CD, Docker, K8s configs
• Extracts git metadata (commits, contributors, dates)
• Detects architectural patterns from directory structure

Layer 2: Local Model Inference (~40s per repo)

• Feeds heuristic context into structured prompts
• Qwen2.5-Coder 7B generates natural language skills and memories
• Produces architecture overviews, pattern analysis, testing guides
• Session memories document the exploration findings

Models

Model	Size	RAM	Quality	Speed
qwen2.5-coder:7b (default)	4.5GB	8GB	Good	~30 tok/s
qwen2.5-coder:14b	8.5GB	16GB	Very Good	~18 tok/s
qwen2.5-coder:32b	18GB	24GB	Excellent	~10 tok/s
deepseek-coder-v2:16b	9GB	16GB	Very Good	~18 tok/s

Development

git clone https://github.com/engram-hq/engram-cli
cd engram-cli
python -m venv .venv && source .venv/bin/activate
pip install ".[dev]"
pytest tests/ -v

License

MIT