npm install -g openlmlib
openlmlib setup  # Interactive wizard with React TUI

pipx install openlmlib
openlmlib setup

git clone https://github.com/Vedant9500/LMlib.git
cd LMlib
pip install -e .
openlmlib setup

# Check health
openlmlib doctor

# Add a finding
openlmlib add \
  --project myproj \
  --claim "Contextual chunking improves retrieval by 15-30%" \
  --confidence 0.85 \
  --evidence "https://arxiv.org/example" \
  --reasoning "Benchmarks show context-aware chunking outperforms fixed-size"

# Search
openlmlib query "retrieval techniques" --final-k 5

# List findings
openlmlib list --limit 20

# Interactive setup (recommended)
openlmlib setup

# Or configure specific IDEs
openlmlib mcp-config --ide vscode --ide cursor

# Codex CLI / Claude Code
openlmlib mcp-config --ide codex_cli --ide claude_code

[mcp_servers.openlmlib.env]
OPENLMLIB_MCP_PREWARM = "1"              # default: 1
OPENLMLIB_MCP_PREWARM_DELAY_SEC = "5"    # default: 5
OPENLMLIB_EMBED_PREWARM = "1"            # default: 1

openlmlib add \
  --project retrieval \
  --claim "Dynamic chunk sizing reduces hallucination by 20%" \
  --confidence 0.78 \
  --evidence "https://example.com/study" \
  --reasoning "Adaptive chunk size based on query complexity..." \
  --caveats "Requires query complexity estimation" \
  --tags retrieval,chunking,evaluation

# Semantic search with reasoning traces
openlmlib query "contextual retrieval" \
  --final-k 5 \
  --reasoning-trace

# With filters
openlmlib query "retrieval" \
  --project myproj \
  --tags retrieval \
  --confidence-min 0.8

# Create session from template
openlmlib-mcp --call create_from_template '{
  "template_id": "deep_research",
  "title": "Research on Retrieval",
  "created_by": "gpt-4"
}'

# Join session
openlmlib-mcp --call join_session '{
  "session_id": "sess_20260409_abc12345",
  "model": "claude-3",
  "role": "worker"
}'

# Send and receive messages
openlmlib-mcp --call send_message '{...}'
openlmlib-mcp --call poll_messages '{...}'

# Add artifacts (reports, analysis)
openlmlib-mcp --call save_artifact '{...}'

session_start           - Start session with context from previous sessions
session_end             - End session and auto-generate summary
log_observation         - Log tool executions for memory building
query_memory            - Adaptive memory retriever for relevant observations
search_memory           - Layer 1: Search index (~75 tokens/result)
memory_timeline         - Layer 2: Chronological context (~200 tokens/result)
get_observations        - Layer 3: Full details (~750 tokens/result)
inject_context          - Auto-inject relevant context at session start
session_recap           - Synthesized recap of recent sessions (~150-250 tokens)
topic_context           - Deep dive on specific topics (~500-800 tokens)
ingest_git_history      - Auto-ingest from git history (no manual logging!)

# Start of session - automatically loads relevant context
session_start(
    session_id="sess_20260414_001",
    query="memory retrieval optimization"
)
# Returns: Context from previous sessions with relevant observations

# During work - observations are logged automatically
log_observation(
    session_id="sess_20260414_001",
    tool_name="Edit",
    tool_input="Modified memory_retriever.py",
    tool_output="Added auto_inject_context method"
)

# End of session - auto-generates summary
session_end(session_id="sess_20260414_001")
# Creates synthesized knowledge: files touched, decisions, next steps

# Next session - continue seamlessly
session_recap(limit=3)
# Returns: Structured knowledge from last 3 sessions

OpenLMlib
├── Knowledge Base
│   ├── SQLite (metadata, full-text search)
│   ├── FAISS/Numpy (vector index)
│   └── JSON findings (portable, human-readable)
│
├── MCP Server (76 tools)
│   ├── 17 core library tools
│   ├── 11 memory tools (session lifecycle, adaptive retrieval, retroactive ingestion)
│   └── 48 collaboration tools
│
├── CLI
│   ├── Setup and configuration
│   ├── Finding management
│   └── Diagnostics (doctor command)
│
└── CollabSessions
    ├── Message bus (SQLite + JSONL)
    ├── Artifact store
    ├── Session templates
    └── Context compaction

# Setup and diagnostics
openlmlib setup          # First-run bootstrap
openlmlib doctor         # Health check
openlmlib --version      # Show version

# Knowledge base
openlmlib init           # Initialize storage
openlmlib add            # Add finding
openlmlib list           # List findings
openlmlib get            # Get finding details
openlmlib query          # Semantic retrieval
openlmlib delete         # Delete finding

# Collaboration
openlmlib-mcp            # MCP server (auto-configured)

# Backup and restore
openlmlib backup         # Create backup
openlmlib restore        # Restore from backup

# Remove package
npm uninstall -g openlmlib    # if installed via npm
pipx uninstall openlmlib      # if installed via pipx
pip uninstall openlmlib       # if installed from source

# Remove data (optional)
rm -rf ~/.openlmlib           # global install data
rm -rf data/                  # local install data

git clone https://github.com/Vedant9500/LMlib.git
cd LMlib
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install -e ".[dev,faiss]"

# Run tests
python -m unittest discover -s tests -p "test_*.py" -v

# Run MCP server manually
python -m openlmlib.mcp_server --settings ./config/settings.json