CLI Reference

npm install -g @alveare-ai/cli

alveare --version
# alveare-cli/1.4.2

alveare config set api-key alv_live_abc123...

export ALVEARE_API_KEY="alv_live_abc123..."

alveare infer -s summarise "The quarterly report shows revenue grew 23%..."

# Output:
# Revenue grew 23% YoY, driven by strong enterprise adoption...

# Classify with low temperature for consistency
alveare infer -s classify -t 0.2 "I want a refund for my order"

# Extract structured data
alveare infer -s extract "Invoice #1234, Amount: $5,000, Due: March 30"

# Read from file
alveare infer -s summarise -f report.txt -m 256

# Generate code
alveare infer -s code "Write a Python function that checks if a string is a palindrome"

# JSON output for scripting
alveare infer -s classify --json "Need to update my address"
# {"id":"inf-abc123","specialist":"classify","result":"account_update","tokens_used":24,"latency_ms":98}

alveare chat

# Alveare Chat (alveare-chat) — type /quit to exit
#
# You: What is a cognitive hive?
#
# Alveare: A cognitive hive is an architecture where multiple
# specialized AI agents share a single underlying model. Each
# specialist has its own system prompt and parameters, but they
# all run on the same GPU, reducing memory and cost.
#
# You: How does that save money?
#
# Alveare: Instead of loading 6 separate models for 6 tasks,
# a hive loads one model and uses different system prompts to
# create specialists. That's 80-90% less GPU memory.

alveare chat -t 0.9 -m 1024

alveare models

# ID                   OWNED BY
# alveare-classify      alveare
# alveare-summarise     alveare
# alveare-extract       alveare
# alveare-qa            alveare
# alveare-chat          alveare
# alveare-code          alveare

alveare usage

# Period:   2026-03-01 to 2026-03-31
# Requests: 42,370 / 100,000 (42.4%)
# Tokens:   8,420,156
#
# By specialist:
#   classify   18,200
#   summarise  15,830
#   extract     8,340

alveare health

# Status:  ok
# Version: 1.4.2
# Uptime:  30d 0h 0m

# Show all configuration
alveare config show
# api-key:  alv_live_***...abc
# base-url: https://api.alveare.ai

# Set a config value
alveare config set api-key alv_live_newkey123...
alveare config set base-url https://custom.endpoint.com

# Get a specific config value
alveare config get api-key
# alv_live_***...abc

# Pipe a file
cat report.txt | alveare infer -s summarise

# Pipe command output
git log --oneline -20 | alveare infer -s summarise "Summarise these commits"

# Extract from curl output
curl -s https://example.com/api/data | alveare infer -s extract

# Chain specialists: extract then classify
cat email.txt | alveare infer -s extract --json | jq -r .result | alveare infer -s classify

# Process multiple files in a loop
for f in docs/*.txt; do
  echo "--- $f ---"
  cat "$f" | alveare infer -s summarise -m 128
  echo
done

alveare infer -s classify --json "I love this product"
# {"id":"inf-abc123","specialist":"classify","result":"positive","tokens_used":24,"latency_ms":98}

alveare models --json
# {"object":"list","data":[{"id":"alveare-classify",...},...]}

alveare usage --json
# {"period_start":"2026-03-01T00:00:00Z","requests_used":42370,...}

# Extract just the result with jq
alveare infer -s extract --json "Jane Smith, jane@acme.com" | jq -r .result