fleet-memory/memora-docs/docs/sdks/cli.md

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CLI Reference

The Memora CLI provides command-line access to memory operations and agent management.

Installation

Pre-built Binaries

Download from the releases page:

# macOS (Apple Silicon)
curl -L https://github.com/memora/memora/releases/latest/download/memora-macos-arm64 -o memora
chmod +x memora
sudo mv memora /usr/local/bin/

Build from Source

cd memora-cli-rust
cargo build --release
cp target/release/memora /usr/local/bin/

Configuration

Set environment variables or use command flags:

export MEMORA_API_URL=http://localhost:8080
export MEMORA_AGENT_ID=my-agent

Commands

Memory Operations

put

Store a memory:

memora put <agent_id> "Alice works at Google as a software engineer"

# With context
memora put <agent_id> "Bob loves hiking" --context "hobby discussion"

# With event date
memora put <agent_id> "Meeting with Carol" --date "2024-01-15"

put-files

Store file contents as memories:

# Store a single file
memora put-files <agent_id> notes.txt

# Store multiple files
memora put-files <agent_id> file1.txt file2.md file3.json

# With context
memora put-files <agent_id> meeting-notes.txt --context "team meeting"

search

Search memories:

memora search <agent_id> "What does Alice do?"

# With options
memora search <agent_id> "hiking recommendations" --budget 100 --top-k 5

# Verbose output
memora search <agent_id> "query" -v

think

Generate a response using memories and opinions:

memora think <agent_id> "What do you know about Alice?"

# Verbose mode shows reasoning
memora think <agent_id> "Should I recommend Python or Java?" -v

Agent Management

agents

List all agents:

memora agents

Output:

Available agents:
  - alice-agent
  - bob-agent
  - tech-advisor

profile

View agent profile:

memora profile <agent_id>

Output:

Agent: my-agent

Personality:
  Openness:          0.80
  Conscientiousness: 0.60
  Extraversion:      0.50
  Agreeableness:     0.70
  Neuroticism:       0.30
  Bias Strength:     0.70

Background:
  I am a helpful AI assistant interested in technology.

set-personality

Update personality traits:

memora set-personality <agent_id> \
  --openness 0.8 \
  --conscientiousness 0.6 \
  --extraversion 0.5 \
  --agreeableness 0.7 \
  --neuroticism 0.3 \
  --bias-strength 0.7

background

Add or merge background:

# Set/merge background
memora background <agent_id> "I have expertise in distributed systems"

MCP Server

Start the MCP server:

memora mcp-server

# With custom configuration
MEMORA_API_URL=http://api.example.com memora mcp-server

Output Formats

Pretty (Default)

Human-readable formatted output:

memora search <agent_id> "query"

JSON

Machine-readable JSON output:

memora search <agent_id> "query" -o json

YAML

YAML formatted output:

memora search <agent_id> "query" -o yaml

Verbose Mode

Add -v or --verbose for detailed output:

memora search <agent_id> "query" -v

Shows:

• Request payload
• Response details
• Timing information

Global Options

Flag	Description
-v, --verbose	Verbose output
-o, --output <format>	Output format: pretty, json, yaml
--api-url <url>	Override API URL
--help	Show help
--version	Show version

Examples

Full Workflow

# Create an agent
curl -X PUT http://localhost:8080/api/agents/demo-agent \
  -H "Content-Type: application/json" \
  -d '{"background": "Demo agent"}'

# Store memories
memora put demo-agent "Alice works at Google"
memora put demo-agent "Bob is a data scientist"
memora put demo-agent "Alice and Bob are colleagues"

# Search
memora search demo-agent "Who works with Alice?"

# Think (with opinions)
memora think demo-agent "What do you know about the team?"

# Update personality
memora set-personality demo-agent \
  --openness 0.9 \
  --conscientiousness 0.7 \
  --extraversion 0.6 \
  --agreeableness 0.8 \
  --neuroticism 0.2 \
  --bias-strength 0.6

# Check profile
memora profile demo-agent