letta-fleet-management: Workflow Automation for AI Agents | OpenAgentSkill

Overview

Manage Letta AI agent fleets declaratively with kubectl-style CLI. Use when creating, updating, or managing multiple Letta agents with shared configurations, memory blocks, tools, folders, canary deployments, multi-tenancy, and bulk operations.

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lettactl

kubectl-style CLI for managing Letta AI agent fleets declaratively.

When to Use

Deploying multiple agents with shared configurations
Managing agent memory blocks, tools, and folders
Applying templates to existing agents
Running canary deployments before promoting to production
Multi-tenant agent management (B2B / B2B2C)
Bulk messaging across agent fleets
Importing/exporting agents between environments
Analyzing agent memory health (self-diagnosis)
Calibrating agents with first-message boot sequences
Programmatic fleet management via SDK

Core Workflow

Define agents in fleet.yaml
Apply with lettactl apply -f fleet.yaml
Verify with lettactl get agents and lettactl describe agent <name>

Fleet YAML Structure

shared_blocks:
  - name: company-context
    description: Shared company knowledge
    limit: 5000
    from_file: ./context/company.md

shared_folders:
  - name: brand_docs
    files:
      - "docs/*.md"

mcp_servers:
  - name: firecrawl
    type: sse
    server_url: "https://sse.firecrawl.dev"
    auth_header: "Authorization"
    auth_token: "Bearer ${FIRECRAWL_API_KEY}"

agents:
  - name: support-agent
    description: Customer support assistant
    tags:
      - "tenant:acme-corp"
      - "role:support"
    system_prompt:
      from_file: ./prompts/support.md
    llm_config:
      model: google_ai/gemini-2.5-pro
      context_window: 128000
    reasoning: true
    first_message: "Initialize and confirm readiness."
    memory_blocks:
      - name: persona
        description: Agent personality
        limit: 2000
        value: "You are a helpful support agent."
        agent_owned: true
    archives:
      - name: knowledge_base
        description: Long-term knowledge storage
    shared_blocks:
      - company-context
    shared_folders:
      - brand_docs
    tools:
      - send_email
      - search_docs
      - "tools/*"
    mcp_tools:
      - server: firecrawl
        tools: ["scrape", "crawl"]

See reference/fleet-config.md for full schema.

CLI Commands

Apply Configuration

lettactl apply -f fleet.yaml                    # Create/update agents
lettactl apply -f fleet.yaml --dry-run          # Preview changes
lettactl apply -f fleet.yaml --match "*-prod"   # Template mode
lettactl apply -f fleet.yaml --canary           # Deploy canary copies
lettactl apply -f fleet.yaml --promote          # Promote canary to production
lettactl apply -f fleet.yaml --recalibrate      # Re-send calibration messages

Inspect Resources

lettactl get agents                        # List all agents
lettactl get agents -o wide                # With details
lettactl get agents --tags "tenant:acme"   # Filter by tags
lettactl get blocks --shared               # Shared blocks only
lettactl get tools --orphaned              # Unused tools
lettactl describe agent <name>             # Full agent details

Messaging

lettactl send <agent> "Hello"              # Send message
lettactl send <agent> "Hi" --stream        # Stream response
lettactl send --all "support-*" "Update"   # Bulk send by pattern
lettactl send --tags "role:support" "Hi"   # Bulk send by tags
lettactl messages list <agent>             # View history
lettactl messages reset <agent>            # Clear history
lettactl messages compact <agent>          # Summarize history

Import / Export

lettactl export agent <name> -f yaml       # Export single agent
lettactl export agents --all               # Export entire fleet
lettactl import agent-export.yaml          # Import agent

Fleet Reporting

lettactl report memory                     # Memory usage report
lettactl report memory --analyze           # LLM-powered deep analysis

See reference/cli-commands.md for all options.

Canary Deployments

Test changes on isolated copies before promoting to production:

lettactl apply -f fleet.yaml --canary           # Create CANARY-* copies
lettactl send CANARY-support-agent "test msg"   # Test the canary
lettactl apply -f fleet.yaml --promote          # Promote to production
lettactl apply -f fleet.yaml --cleanup          # Remove canary agents

See reference/canary-deployments.md.

Multi-Tenancy

Tag agents for B2B and B2B2C filtering:

agents:
  - name: acme-support
    tags:
      - "tenant:acme-corp"
      - "role:support"
      - "env:production"

lettactl get agents --tags "tenant:acme-corp"
lettactl send --tags "tenant:acme-corp,role:support" "Policy update"

See reference/multi-tenancy.md.

Self-Diagnosis

Analyze agent memory health fleet-wide:

lettactl report memory                  # Usage stats for all agents
lettactl report memory --analyze        # LLM-powered analysis per agent

Reports fill percentages, stale data, redundancy, missing knowledge, and split recommendations. See reference/self-diagnosis.md.

Agent Calibration

Prime agents on creation with a boot message:

agents:
  - name: support-agent
    first_message: "Review your persona and confirm you understand your role."

Recalibrate existing agents after updates:

lettactl apply -f fleet.yaml --recalibrate
lettactl apply -f fleet.yaml --recalibrate --recalibrate-tags "role:support"

See reference/agent-calibration.md.

Template Mode

Apply configuration to existing agents matching a pattern:

lettactl apply -f template.yaml --match "*-draper"

Uses three-way merge: preserves user-added resources while updating managed ones. See reference/template-mode.md.

SDK Usage

import { LettaCtl } from 'lettactl';

const ctl = new LettaCtl({ lettaBaseUrl: 'http://localhost:8283' });

// Deploy from YAML
await ctl.deployFromYaml('./fleet.yaml');

// Programmatic fleet config
const config = ctl.createFleetConfig()
  .addSharedBlock({ name: 'kb', description: 'Knowledge', limit: 5000, from_file: 'kb.md' })
  .addAgent({
    name: 'support-agent',
    description: 'Support AI',
    system_prompt: { from_file: 'prompts/support.md' },
    llm_config: { model: 'google_ai/gemini-2.5-pro', context_window: 32000 },
    shared_blocks: ['kb'],
    tags: ['team:support'],
  })
  .build();

await ctl.deployFleet(config);

// Send message with callbacks
await ctl.sendMessage('agent-id', 'Hello', {
  onComplete: (run) => console.log('Done:', run.id),
});

// Template mode
await ctl.deployFromYaml('./template.yaml', { match: '*-prod' });

See reference/sdk-usage.md for full API.

View source ↗

File metadata

name: letta-fleet-management
description: Manage Letta AI agent fleets declaratively with kubectl-style CLI. Use when creating, updating, or managing multiple Letta agents with shared configurations, memory blocks, tools, folders, canary deployments, multi-tenancy, and bulk operations.
license: MIT

View original text

---
name: letta-fleet-management
description: Manage Letta AI agent fleets declaratively with kubectl-style CLI. Use when creating, updating, or managing multiple Letta agents with shared configurations, memory blocks, tools, folders, canary deployments, multi-tenancy, and bulk operations.
license: MIT
---

# lettactl

kubectl-style CLI for managing Letta AI agent fleets declaratively.

## When to Use

- Deploying multiple agents with shared configurations
- Managing agent memory blocks, tools, and folders
- Applying templates to existing agents
- Running canary deployments before promoting to production
- Multi-tenant agent management (B2B / B2B2C)
- Bulk messaging across agent fleets
- Importing/exporting agents between environments
- Analyzing agent memory health (self-diagnosis)
- Calibrating agents with first-message boot sequences
- Programmatic fleet management via SDK

## Core Workflow

1. Define agents in `fleet.yaml`
2. Apply with `lettactl apply -f fleet.yaml`
3. Verify with `lettactl get agents` and `lettactl describe agent <name>`

## Fleet YAML Structure

```yaml
shared_blocks:
  - name: company-context
    description: Shared company knowledge
    limit: 5000
    from_file: ./context/company.md

shared_folders:
  - name: brand_docs
    files:
      - "docs/*.md"

mcp_servers:
  - name: firecrawl
    type: sse
    server_url: "https://sse.firecrawl.dev"
    auth_header: "Authorization"
    auth_token: "Bearer ${FIRECRAWL_API_KEY}"

agents:
  - name: support-agent
    description: Customer support assistant
    tags:
      - "tenant:acme-corp"
      - "role:support"
    system_prompt:
      from_file: ./prompts/support.md
    llm_config:
      model: google_ai/gemini-2.5-pro
      context_window: 128000
    reasoning: true
    first_message: "Initialize and confirm readiness."
    memory_blocks:
      - name: persona
        description: Agent personality
        limit: 2000
        value: "You are a helpful support agent."
        agent_owned: true
    archives:
      - name: knowledge_base
        description: Long-term knowledge storage
    shared_blocks:
      - company-context
    shared_folders:
      - brand_docs
    tools:
      - send_email
      - search_docs
      - "tools/*"
    mcp_tools:
      - server: firecrawl
        tools: ["scrape", "crawl"]
```

See `reference/fleet-config.md` for full schema.

## CLI Commands

### Apply Configuration
```bash
lettactl apply -f fleet.yaml                    # Create/update agents
lettactl apply -f fleet.yaml --dry-run          # Preview changes
lettactl apply -f fleet.yaml --match "*-prod"   # Template mode
lettactl apply -f fleet.yaml --canary           # Deploy canary copies
lettactl apply -f fleet.yaml --promote          # Promote canary to production
lettactl apply -f fleet.yaml --recalibrate      # Re-send calibration messages
```

### Inspect Resources
```bash
lettactl get agents                        # List all agents
lettactl get agents -o wide                # With details
lettactl get agents --tags "tenant:acme"   # Filter by tags
lettactl get blocks --shared               # Shared blocks only
lettactl get tools --orphaned              # Unused tools
lettactl describe agent <name>             # Full agent details
```

### Messaging
```bash
lettactl send <agent> "Hello"              # Send message
lettactl send <agent> "Hi" --stream        # Stream response
lettactl send --all "support-*" "Update"   # Bulk send by pattern
lettactl send --tags "role:support" "Hi"   # Bulk send by tags
lettactl messages list <agent>             # View history
lettactl messages reset <agent>            # Clear history
lettactl messages compact <agent>          # Summarize history
```

### Import / Export
```bash
lettactl export agent <name> -f yaml       # Export single agent
lettactl export agents --all               # Export entire fleet
lettactl import agent-export.yaml          # Import agent
```

### Fleet Reporting
```bash
lettactl report memory                     # Memory usage report
lettactl report memory --analyze           # LLM-powered deep analysis
```

See `reference/cli-commands.md` for all options.

## Canary Deployments

Test changes on isolated copies before promoting to production:

```bash
lettactl apply -f fleet.yaml --canary           # Create CANARY-* copies
lettactl send CANARY-support-agent "test msg"   # Test the canary
lettactl apply -f fleet.yaml --promote          # Promote to production
lettactl apply -f fleet.yaml --cleanup          # Remove canary agents
```

See `reference/canary-deployments.md`.

## Multi-Tenancy

Tag agents for B2B and B2B2C filtering:

```yaml
agents:
  - name: acme-support
    tags:
      - "tenant:acme-corp"
      - "role:support"
      - "env:production"
```

```bash
lettactl get agents --tags "tenant:acme-corp"
lettactl send --tags "tenant:acme-corp,role:support" "Policy update"
```

See `reference/multi-tenancy.md`.

## Self-Diagnosis

Analyze agent memory health fleet-wide:

```bash
lettactl report memory                  # Usage stats for all agents
lettactl report memory --analyze        # LLM-powered analysis per agent
```

Reports fill percentages, stale data, redundancy, missing knowledge, and split recommendations. See `reference/self-diagnosis.md`.

## Agent Calibration

Prime agents on creation with a boot message:

```yaml
agents:
  - name: support-agent
    first_message: "Review your persona and confirm you understand your role."
```

Recalibrate existing agents after updates:

```bash
lettactl apply -f fleet.yaml --recalibrate
lettactl apply -f fleet.yaml --recalibrate --recalibrate-tags "role:support"
```

See `reference/agent-calibration.md`.

## Template Mode

Apply configuration to existing agents matching a pattern:

```bash
lettactl apply -f template.yaml --match "*-draper"
```

Uses three-way merge: preserves user-added resources while updating managed ones. See `reference/template-mode.md`.

## SDK Usage

```typescript
import { LettaCtl } from 'lettactl';

const ctl = new LettaCtl({ lettaBaseUrl: 'http://localhost:8283' });

// Deploy from YAML
await ctl.deployFromYaml('./fleet.yaml');

// Programmatic fleet config
const config = ctl.createFleetConfig()
  .addSharedBlock({ name: 'kb', description: 'Knowledge', limit: 5000, from_file: 'kb.md' })
  .addAgent({
    name: 'support-agent',
    description: 'Support AI',
    system_prompt: { from_file: 'prompts/support.md' },
    llm_config: { model: 'google_ai/gemini-2.5-pro', context_window: 32000 },
    shared_blocks: ['kb'],
    tags: ['team:support'],
  })
  .build();

await ctl.deployFleet(config);

// Send message with callbacks
await ctl.sendMessage('agent-id', 'Hello', {
  onComplete: (run) => console.log('Done:', run.id),
});

// Template mode
await ctl.deployFromYaml('./template.yaml', { match: '*-prod' });
```

See `reference/sdk-usage.md` for full API.

Review the source

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
AI review approval is missing
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Stars/forks activity: 141 stars, 23 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

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Source & usage notes

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Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: letta-ai/skills
License: MIT
Version: Unknown
Last GitHub push: Sep 11, 2026
Registry updated: Sep 12, 2026
Instruction path: letta/fleet-management/SKILL.md @ 16352df0a3ce

Version reported in registry metadata; check source releases before relying on it.

Quality

63/100

Promising

Trust

63/100

Agent access

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open install API LLM text format Open manifest Auto-resolve Plan

More details

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  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "high-compliance environments without internal security review",
    "No OpenAgentSkill engagement data yet",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Financial research output is not financial advice; require human review before any live investment decision",
    "AI review approval is missing"
  ],
  "agent_contract": {
    "task_input": "Use letta-fleet-management in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 71/100 Manual review",
      "Audit: 75/100 Needs review",
      "Safety: 31/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "letta-ai-letta-fleet-management (letta-fleet-management)",
      "install_command": "npx skills add letta-ai/skills --skill letta-fleet-management",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "letta-ai-letta-fleet-management",
      "task": "Use letta-fleet-management in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/letta-ai-letta-fleet-management",
    "api": "https://www.openagentskill.com/api/agent/skills/letta-ai-letta-fleet-management",
    "audit": "https://www.openagentskill.com/skills/letta-ai-letta-fleet-management/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=letta-ai-letta-fleet-management&task=Use%20letta-fleet-management%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20letta-fleet-management%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20letta-fleet-management%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/letta-ai-letta-fleet-management/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/letta-ai-letta-fleet-management"
  }
}

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Creator: letta-ai
Source: letta-ai/skills
Indexed by: OpenAgentSkill community index

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