Agent Commands

Manage LLM agents with YAML/Markdown configuration files.

bash

tdx agent <command>   # Full command
tdx agents            # Shorthand alias for `tdx agent list`

Overview

The tdx agent commands provide complete management of LLM agents:

Command	Description
`list`	List agents in current project
`show`	Show agent details
`pull`	Export agents from an LLM project to local YAML/Markdown files
`push`	Import local agent files to an LLM project
`clone`	Clone a project to a new project
`test`	Run automated tests against an agent
`create`	Create a new agent (prefer `pull`/`push` workflow)
`update`	Update an existing agent (prefer `pull`/`push` workflow)
`delete`	Delete an agent

Typical Usage

bash

# 1. Create a new LLM project (or use an existing one)
tdx llm project create "My LLM Project" --description "Data analysis agents"

# 2. Pull project to local files (auto-sets context)
tdx agent pull "My LLM Project"
# Creates: agents/my-llm-project/

# 3. Edit YAML/Markdown files locally
# - Edit agents/my-llm-project/my-agent/prompt.md for system prompt
# - Edit agents/my-llm-project/my-agent/agent.yml for configuration

# 4. Preview changes before pushing
tdx agent push --dry-run

# 5. Push changes to the project
tdx agent push

# 6. Add tests and run them
# - Create agents/my-llm-project/my-agent/test.yml
tdx agent test ./agents/my-llm-project/my-agent/

# 7. Clone to another environment
tdx agent clone ./agents/my-llm-project/ --name "My Project (Staging)" --profile staging

Recommended Workflow

Use the pull/push workflow for agent development:

Edit agents as YAML/Markdown files in your favorite editor
Version control with Git for history and collaboration
Use --dry-run to preview changes before pushing
Add test.yml to validate agent behavior with automated tests

The create/update commands are available for quick one-off changes but the file-based workflow is recommended for maintainability.

Folder Structure

When you pull agents from a project, the following folder structure is created:

agents/
└── {project-name}/                    # Normalized project name (kebab-case)
    ├── tdx.json                       # Project configuration
    ├── {agent-name}/                  # Normalized agent name
    │   ├── prompt.md                  # System prompt (editable markdown)
    │   ├── agent.yml                  # Agent configuration
    │   ├── starter_message.md         # Optional: multiline starter message
    │   └── test.yml                   # Optional: automated test definitions
    ├── knowledge_bases/               # Knowledge base definitions
    │   ├── {kb-name}.yml              # Table-based KB (queries TD database)
    │   └── {kb-name}.md               # Text-based KB (plain text content)
    ├── prompts/                       # Prompt definitions
    │   └── {prompt-name}.yml
    └── integrations/                  # Chat integration definitions
        └── {service-type}.yml         # e.g., chat_generic.yml

Name Collisions

If multiple agents have names that normalize to the same folder name (e.g., "My Agent" and "my-agent" both become my-agent), the pull operation appends numeric suffixes to avoid conflicts: my-agent, my-agent-2, my-agent-3, etc. The actual agent name is preserved in the name: field of agent.yml.

Integration Support

Safe integrations (Generic Chat, Agent Console, Parent Segment) are included in pull/push/clone operations. These are stored in integrations/{service-type}.yml.

Sensitive integrations (Slack, Webhook) are not synced because they contain secrets (Slack signing secrets, webhook credentials) that should not be version controlled. Configure these manually in the TD console for each environment.

File Formats

tdx.json

Project configuration file located in the project root folder.

json

{
  "llm_project": "My LLM Project"
}

agent.yml

Agent configuration file with model settings, tools, outputs, and variables.

yaml

name: My Support Agent
description: Customer support assistant

# Model configuration
model: claude-4.5-sonnet
temperature: 0.7
max_tool_iterations: 5
reasoning_effort: medium    # none, minimal, low, medium, high

# Starter message (short inline, or use starter_message.md for long text)
starter_message: Hello! How can I help you today?

# Output definitions
outputs:
  - name: resolution_status
    function_name: get_resolution_status
    function_description: Returns the status of issue resolution
    json_schema: |
      {"type": "object", "properties": {"status": {"type": "string"}}}

# Tools - give the agent access to knowledge bases, other agents, etc.
tools:
  - type: knowledge_base
    target: '@ref(type: "knowledge_base", name: "support-kb")'
    target_function: SEARCH
    function_name: search_knowledge
    function_description: Search the support knowledge base

  - type: agent
    target: '@ref(type: "agent", name: "escalation-agent")'
    target_function: CHAT
    function_name: escalate_issue
    function_description: Escalate to senior support

# Variables (runtime inputs from knowledge base)
variables:
  - name: customer_context
    target_knowledge_base: '@ref(type: "knowledge_base", name: "customer-kb")'
    target_function: LOOKUP
    function_arguments: |
      {"query": "{{customer_id}}"}

Tools Configuration

Tools give agents access to external resources like knowledge bases, other agents, web search, and image generation. Each tool requires:

Field	Description
`type`	Tool type: `knowledge_base`, `agent`, `web_search`, `image_gen`
`target`	Reference to the resource using `@ref(...)` syntax
`target_function`	Function to call: `SEARCH`, `LOOKUP`, `CHAT`, `TEXT_TO_IMAGE`, etc.
`function_name`	Name exposed to the LLM (what the agent calls)
`function_description`	Description shown to the LLM
`output_mode`	Optional for agent tools: `RETURN` (default, returns complete response) or `SHOW` (shows response to user)

Knowledge Base Tool - Search or lookup data from a knowledge base.

The knowledge_base type works with both table-based and text-based knowledge bases. The system automatically resolves the correct type based on the name:

yaml

tools:
  # Table-based knowledge base (backed by TD table)
  - type: knowledge_base
    target: '@ref(type: "knowledge_base", name: "product-catalog")'
    target_function: SEARCH      # SEARCH, LOOKUP, LIST_COLUMNS
    function_name: search_products
    function_description: Search the product catalog for items

  # Text-based knowledge base (document/text content)
  - type: knowledge_base
    target: '@ref(type: "knowledge_base", name: "company-docs")'
    target_function: READ_TEXT   # READ_TEXT for text KBs
    function_name: read_docs
    function_description: Read company documentation

Agent Tool - Call another agent as a sub-agent:

yaml

tools:
  - type: agent
    target: '@ref(type: "agent", name: "sql-expert")'
    target_function: CHAT
    function_name: run_sql_query
    function_description: Execute SQL queries using the SQL expert agent
    output_mode: RETURN         # RETURN (default) or SHOW

Web Search Tool - Search the web for real-time information:

yaml

tools:
  - type: web_search
    target: '@ref(type: "web_search_tool", name: "web-search")'
    target_function: SEARCH
    function_name: search_web
    function_description: Search the web for current information

Image Generation Tool - Generate or modify images:

yaml

tools:
  - type: image_gen
    target: '@ref(type: "image_generator", name: "image-gen")'
    target_function: TEXT_TO_IMAGE  # TEXT_TO_IMAGE, OUTPAINT, INPAINT, IMAGE_VARIATION, REMOVE_BACKGROUND
    function_name: generate_image
    function_description: Generate an image from a text description

Variables Configuration

Variables inject data from knowledge bases into the agent's context at runtime:

yaml

variables:
  - name: user_profile           # Variable name used in prompt
    target_knowledge_base: '@ref(type: "knowledge_base", name: "users-kb")'
    target_function: LOOKUP      # LOOKUP for exact match, SEARCH for semantic
    function_arguments: |
      {"query": "{{user_id}}"}   # Template with runtime values

Use variables in prompt.md with syntax.

prompt.md

System prompt file containing the agent's instructions. This is a plain markdown file that can be edited with any text editor.

markdown

You are a helpful customer support agent for our e-commerce platform.

## Your Role

- Assist customers with order inquiries
- Provide product information
- Handle account issues

## Guidelines

Always be polite, professional, and empathetic.
If you cannot resolve an issue, escalate to a human agent.

starter_message.md

Optional starter message file for multiline starter messages. If the starter message is short, you can include it directly in agent.yml.

knowledge_bases/{name}.yml (Table-based)

Table-based knowledge base that queries a Treasure Data database.

yaml

name: Support KB
type: database
database: customer_data
tables:
  - name: faq
    td_query: SELECT * FROM faq
    enable_data: true
    enable_data_index: true

knowledge_bases/{name}.md (Text-based)

Text-based knowledge base containing plain text content. Uses YAML frontmatter for metadata.

markdown

---
name: Product FAQ
---

# Frequently Asked Questions

## What is your return policy?
We offer a 30-day return policy for all unused items...

## How do I track my order?
You can track your order by logging into your account...

Text KB Name

If the name field is omitted from the frontmatter, the filename (without .md) is used as the knowledge base name.

Both table-based and text-based knowledge bases can be referenced using the same @ref syntax:

yaml

tools:
  - type: knowledge_base
    target: '@ref(type: "knowledge_base", name: "Support KB")'      # Table-based
    # ...
  - type: knowledge_base
    target: '@ref(type: "knowledge_base", name: "Product FAQ")'     # Text-based
    # ...

prompts/{name}.yml

Prompt template configuration.

yaml

name: greeting-prompt
agent: '@ref(type: "agent", name: "support-agent")'
system_prompt: |
  Generate a personalized greeting...
template: |
  Customer Name: {{customer_name}}
json_schema_hint: |
  {"type": "object", "properties": {"customer_name": {"type": "string"}}}

integrations/{service-type}.yml

Chat integration configuration for Generic Chat, Agent Console, or Parent Segment integrations.

yaml

service_type: chat_generic
name: generic-chat-integration
chat_welcome_message: "Welcome! How can I help you today?"
chat_ignore_managed_actions: false
actions:
  - prompt: '@ref(type: "prompt", name: "support-prompt")'
    chat_widget_type: button
    chat_widget_label: Ask Support
    ui_tags:
      - "lang:en"
      - "lang:ja"

Field	Description
`service_type`	Integration type: `chat_generic`, `chat_agent_console`, or `chat_parent_segment`
`name`	Display name for the integration
`chat_welcome_message`	Welcome message shown when chat starts
`chat_ignore_managed_actions`	Whether to ignore managed actions
`actions`	List of actions/buttons shown in the chat widget

Supported Integration Types

Only safe integration types are synced:

chat_generic - Generic chat widget
chat_agent_console - Agent console integration
chat_parent_segment - Parent segment integration

webhook and slack integrations contain secrets and must be configured manually in the TD console.

Reference Syntax

Use @ref(...) to reference other resources by name:

yaml

# Reference a knowledge base
target: '@ref(type: "knowledge_base", name: "my-kb")'

# Reference another agent
target: '@ref(type: "agent", name: "my-agent")'

# Reference a prompt
prompt: '@ref(type: "prompt", name: "my-prompt")'

This allows resources to be referenced by name instead of UUID, making configurations portable across environments.

Commands

list

List agents in the current project.

bash

tdx agent list [pattern]
tdx agents [pattern]          # Shorthand alias

Options:

-w, --web: Show console URLs for each agent

Examples:

bash

# List all agents in current project (uses llm_project context)
tdx agent list
tdx agents                    # Same as above

# Filter agents by pattern
tdx agent list "support-*"
tdx agents "support-*"        # Same as above

# List agents in a specific project
tdx agent list "my-project/support-*"

# Show with console URLs
tdx agent list -w
tdx agents -w                 # Same as above

show

Show detailed information about a specific agent.

bash

tdx agent show <agent-name>

Examples:

bash

# Show agent details
tdx agent show "Support Agent"

# Show agent in JSON format
tdx agent show "Support Agent" --format json

create

Create a new agent in the current project.

Prefer pull/push Workflow

For complex agents with tools, knowledge bases, or long prompts, use the pull/push workflow instead:

Create a minimal agent with this command
Run tdx agent pull to export to YAML/Markdown
Edit files locally and tdx agent push to update

bash

tdx agent create <name> [options]

Options:

--system-prompt <text>: System prompt/instructions
--model <name>: Model type (default: claude-4.5-sonnet)
--starter-message <text>: Initial greeting message
--max-tool-iterations <n>: Maximum tool iterations (default: 4)
--temperature <n>: Temperature 0.0-2.0 (default: 0.7)

Examples:

bash

# Create basic agent
tdx agent create "My Agent"

# Create with system prompt
tdx agent create "SQL Expert" \
  --system-prompt "You are an expert in SQL and data analysis."

# Create with all options
tdx agent create "Data Analyst" \
  --system-prompt "Help users analyze data." \
  --model "claude-4.5-sonnet" \
  --starter-message "Hello! I can help you analyze your data." \
  --max-tool-iterations 8 \
  --temperature 0.5

# Create agent in a specific project
tdx agent create "MyProject/My Agent"

update

Update an existing agent.

Prefer pull/push Workflow

For significant changes, use the pull/push workflow instead:

bash

tdx agent pull "My Project"    # Export to YAML/Markdown
# Edit files locally
tdx agent push                  # Push changes

This provides better version control and allows editing complex prompts in your favorite editor.

bash

tdx agent update <agent-name> [options]

Options:

--name <text>: New agent name
--prompt <text>: Agent prompt/instructions
--description <text>: Agent description
--starter-message <text>: Starter message

Examples:

bash

# Update agent name
tdx agent update "Old Name" --name "New Name"

# Update system prompt
tdx agent update "My Agent" --prompt "Updated instructions..."

# Update multiple fields
tdx agent update "My Agent" \
  --description "Updated description" \
  --starter-message "New greeting!"

delete

Delete an agent.

bash

tdx agent delete <agent-name>

Examples:

bash

# Delete an agent
tdx agent delete "My Agent"

pull

Pull agents and resources from an LLM project to local files. Shows a YAML diff preview and asks for confirmation before writing files.

bash

# Pull from current directory (uses tdx.json or context)
tdx agent pull

# Pull all resources from a project
tdx agent pull <project> [options]

# Pull from an existing local directory
tdx agent pull <local-dir> [options]

# Pull a specific agent
tdx agent pull <project> <agent-name> [options]

Options:

-o, --output <dir>: Output directory (default: agents/{project-name}/)
--dry-run: Preview changes without writing files
-f, --force: Overwrite local changes without confirmation
-y, --yes: Skip confirmation prompts

Examples:

bash

# Pull from current directory (if inside a project folder with tdx.json)
tdx agent pull

# Pull entire project
tdx agent pull "My LLM Project"

# Pull to specific directory
tdx agent pull "My LLM Project" -o ./my-agents

# Pull from existing local directory
tdx agent pull ./agents/my-project/

# Pull specific agent
tdx agent pull "My LLM Project" "Support Agent"

# Preview what would be pulled (no files written)
tdx agent pull "My LLM Project" --dry-run

# Pull without confirmation prompt
tdx agent pull "My LLM Project" --yes

Output:

Pull summary for 'My LLM Project':
  + 4 new | ~ 3 changed | = 13 unchanged
  Agents: 1 new, 2 updated, 5 unchanged
  Knowledge Bases: 1 new, 0 updated, 2 unchanged
  Text Knowledge Bases: 1 new, 0 updated, 1 unchanged
  Prompts: 0 new, 1 updated, 3 unchanged
  Integrations: 1 new, 0 updated, 1 unchanged
  Target: agents/my-llm-project/

Changes to agent 'Support Agent':
────────────────────────────────────────────────────────────────
- temperature: 0.7
+ temperature: 0.5
────────────────────────────────────────────────────────────────

Write 7 files? [y/N]

push

Push local agent files to an LLM project. Shows a YAML diff preview comparing local vs remote, asks for confirmation, and displays the console URL after push.

bash

# Push all resources from current directory
tdx agent push [path] [options]

# Push from specific directory
tdx agent push ./agents/my-project/ [options]

# Push specific agent
tdx agent push ./agents/my-project/support-agent/ [options]

Options:

--dry-run: Preview changes without pushing
-f, --force: Push without confirmation
-y, --yes: Skip confirmation prompts

Examples:

bash

# Push from current directory
tdx agent push

# Push from specific directory
tdx agent push ./agents/my-project/

# Push specific agent
tdx agent push ./agents/my-project/support-agent/

# Preview push changes (no changes made)
tdx agent push --dry-run

# Push without confirmation prompt
tdx agent push --yes

Output:

Push summary for 'My LLM Project':
  + 2 new | ~ 3 changed | = 15 unchanged
  Agents: 0 new, 2 updated, 8 unchanged
  Knowledge Bases: 1 new, 0 updated, 2 unchanged
  Text Knowledge Bases: 1 new, 0 updated, 1 unchanged
  Prompts: 0 new, 0 updated, 2 unchanged
  Integrations: 0 new, 1 updated, 1 unchanged
  Source: agents/my-llm-project/

Changes to agent 'Support Agent':
────────────────────────────────────────────────────────────────
- temperature: 0.5
+ temperature: 0.7
────────────────────────────────────────────────────────────────

Push 5 resources? [y/N]

✔ Pushed 5 resources to 'My LLM Project'
Project: https://console-next.us01.treasuredata.com/app/af/12345/ag

When pushing a single agent, the chat URL is shown:

✔ Agent updated successfully
Agent: Support Agent
Chat: https://console-next.us01.treasuredata.com/app/af/12345/ag/67890/tc

clone

Clone an LLM project to create a new project with all agents, knowledge bases, and prompts.

bash

# Clone from remote project
tdx agent clone <project> --name <new-name>

# Clone from local directory
tdx agent clone <local-dir> --name <new-name>

# Clone from context (llm_project)
tdx agent clone --name <new-name>

Options:

-n, --name <name>: Name for the new project (required)
--dry-run: Preview what would be cloned without making changes
-y, --yes: Skip confirmation prompts
--profile <name>: Target profile for the new project (for cross-profile cloning)

Examples:

bash

# Clone a project to a new name
tdx agent clone "Production Agents" --name "Staging Agents"

# Clone from local files (useful for cross-profile deployment)
tdx agent clone ./agents/my-project/ --name "New Project" --profile production

# Preview what would be cloned
tdx agent clone "My Project" --name "My Project Copy" --dry-run

# Clone without confirmation
tdx agent clone "My Project" --name "My Project Copy" --yes

Output:

Clone "Production Agents" to new project "Staging Agents"? [y/N] y
✔ Project cloned successfully
Source: Production Agents
New project: Staging Agents
New project ID: 12345

Summary:
  Agents: 5 created
  Knowledge Bases: 2 created
  Text Knowledge Bases: 1 created
  Prompts: 3 created
  Integrations: 1 created

Context set: llm_project = Staging Agents
Project: https://console-next.us01.treasuredata.com/app/af/12345/ag

Cross-Profile Cloning

To clone a project to a different profile (e.g., from default to production):

First pull the project locally: tdx agent pull "MyProject"
Then clone from local files: tdx agent clone ./agents/my-project/ --name "MyProject-Prod" --profile production

Integration Support in Clone

Safe integrations (Generic Chat, Agent Console, Parent Segment) are included in clone operations.

Sensitive integrations (Slack, Webhook) are not cloned because they contain secrets. Configure these manually in the TD console for each environment.

test

Run automated tests against an agent using YAML test definitions. Tests are evaluated by a judge agent (Claude Sonnet 4.5) for binary pass/fail results.

bash

# Run tests from current agent directory
tdx agent test

# Run tests from a specific path
tdx agent test <path>

# Run tests from test.yml file
tdx agent test ./agents/my-project/my-agent/test.yml

Options:

-n, --name <name>: Filter to specific test(s) by name (can be repeated)
--dry-run: Parse and validate test definitions without running
--no-eval: Run conversations without evaluation (useful for debugging)
--reeval: Re-evaluate last test run with updated criteria (skip conversation generation)

Examples:

bash

# Run all tests in current agent directory
tdx agent test

# Run tests from specific agent
tdx agent test ./agents/my-project/my-agent/

# Run only specific tests
tdx agent test --name "greeting_test" --name "context_test"

# Validate test file without running
tdx agent test --dry-run

# Run without evaluation (just execute conversations)
tdx agent test --no-eval

# Re-evaluate last test run with updated criteria
tdx agent test --reeval

# Re-evaluate specific tests only
tdx agent test --reeval --name "greeting_test"

Output:

Running tests for my-agent...
Setting up evaluator agent...

Test 1/3: greeting_test
  Round 1: Hello → ✓ PASS

Test 2/3: calculation_test
  Round 1: What is 2+2? → ✓ PASS

Test 3/3: context_test
  Round 1: My name is Alice → ✓ PASS
  Round 2: What's my name? → ✓ PASS

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Results: 3 passed, 0 failed
Duration: 12.3s

Test File Format

Create a test.yml file in your agent directory alongside agent.yml:

agents/
└── my-project/
    └── my-agent/
        ├── agent.yml
        ├── prompt.md
        └── test.yml      # Test definitions

Flat Format (Single-Round Tests)

For simple tests with a single user input and criteria:

yaml

tests:
  - name: greeting_test
    user_input: Hello
    criteria: Should respond with a friendly greeting

  - name: calculation_test
    user_input: What is 2 + 2?
    criteria: Should respond with the correct answer (4)

  - name: help_request
    user_input: How do I reset my password?
    criteria: Should provide clear password reset instructions

Rounds Format (Multi-Round Tests)

For tests requiring multiple conversation turns:

yaml

tests:
  - name: context_memory_test
    rounds:
      - user_input: My name is Alice
        criteria: Should acknowledge the name
      - user_input: What's my name?
        criteria: Should remember and respond with "Alice"

  - name: multi_step_task
    rounds:
      - user_input: I want to analyze sales data
        criteria: Should ask clarifying questions about the data
      - user_input: It's in the sales_2024 table
        criteria: Should acknowledge and proceed with analysis
      - user_input: Show me top products
        criteria: Should provide a list of top-selling products

Mixed Format

You can combine both formats in the same file:

yaml

tests:
  # Simple single-round tests
  - name: basic_greeting
    user_input: Hi there!
    criteria: Should greet back politely

  - name: simple_question
    user_input: What time is it?
    criteria: Should explain it cannot tell time or ask for context

  # Complex multi-round test
  - name: data_analysis_workflow
    rounds:
      - user_input: I need help with customer segmentation
        criteria: Should ask about the data source and segmentation goals
      - user_input: Use the customers table, segment by purchase frequency
        criteria: Should propose a segmentation approach
      - user_input: Looks good, proceed
        criteria: Should execute the segmentation and show results

Writing Good Criteria

Criteria should be clear and specific about what constitutes a pass:

yaml

# ✓ Good - specific and measurable
criteria: Should respond with the number 4

# ✓ Good - describes expected behavior
criteria: Should ask for the customer's email address before proceeding

# ✓ Good - includes negative constraints
criteria: Should provide help without mentioning competitor products

# ✗ Bad - too vague
criteria: Should give a good response

# ✗ Bad - subjective
criteria: Should be helpful and friendly

How Evaluation Works

Test Execution: Each round sends the user_input to your agent and waits for a response
History Retrieval: After all rounds complete, the full conversation history is fetched from the API
Evaluation: For each round, the judge agent (Claude Sonnet 4.5) evaluates whether the agent's response meets the criteria
Results: Each round gets a PASS or FAIL result with a reason

The evaluator agent is automatically created in your default project (tdx_default_<username>) the first time you run tests.

Re-evaluating Tests

When iterating on evaluation criteria, you can skip conversation generation and re-evaluate the last test run:

Initial Run: Execute tests normally to generate conversations
bash
```
tdx agent test
```
Edit Criteria: Modify test.yml to refine your criteria
Re-evaluate: Run with --reeval to test new criteria against cached conversations
bash
```
tdx agent test --reeval
```

The cached conversations are stored in .cache/tdx/last_agent_test_run.json relative to your working directory. The cache includes:

Chat IDs for conversation URLs
Agent responses for each round
Original test metadata

Handling new tests: If you add new tests to test.yml that weren't in the cached run, they will be executed normally (generating new conversations) while existing tests use the cache.

Criteria Development Workflow

Use --reeval to rapidly iterate on criteria without waiting for new conversations. Once criteria are stable, run a fresh test to verify end-to-end behavior.

Workflow

Initial Setup

Pull a project to create local files:
bash
```
tdx agent pull "My LLM Project"
```
This creates the folder structure with all agents and resources

Making Changes

Edit the YAML/Markdown files with your favorite editor
Preview changes:
bash
```
tdx agent push --dry-run
```
Push changes:
bash
```
tdx agent push
```

Testing Agents

Create a test.yml file in your agent directory:

yaml

tests:
  - name: basic_functionality
    user_input: Hello, can you help me?
    criteria: Should respond with a helpful greeting

Run tests:
bash
```
tdx agent test
```
Review results and iterate on your agent's prompt until tests pass

Version Control

The YAML/Markdown format is designed for version control:

Human-readable diffs
Easy code reviews
Branch and merge workflows
Git history for audit trails
Test definitions versioned alongside agent code

Comparison with Legacy Commands

Feature	`agent pull/push/clone`	`llm project backup/restore` (deprecated)
Format	YAML/Markdown	JSON
Human-editable	Yes	No
Git-friendly	Yes	No
Selective sync	Yes (single agent)	No (full project)
Cross-profile clone	Yes	No
Safe integrations	Included (Generic Chat, etc.)	Included
Sensitive integrations	Not included (Slack, Webhook)	Included
Use case	Development workflow	Legacy disaster recovery

Deprecated Commands

The tdx llm project backup/restore commands are deprecated. Use:

tdx agent pull instead of backup
tdx agent push instead of restore
tdx agent clone for copying projects

Agent Commands ​

Overview ​

Typical Usage ​

Folder Structure ​

File Formats ​

tdx.json ​

agent.yml ​

Tools Configuration ​

Variables Configuration ​

prompt.md ​

starter_message.md ​

knowledge_bases/{name}.yml (Table-based) ​

knowledge_bases/{name}.md (Text-based) ​

prompts/{name}.yml ​

integrations/{service-type}.yml ​

Reference Syntax ​

Commands ​

list ​

show ​

create ​

update ​

delete ​

pull ​

push ​

clone ​

test ​

Test File Format ​

Flat Format (Single-Round Tests) ​

Rounds Format (Multi-Round Tests) ​

Mixed Format ​

Writing Good Criteria ​

How Evaluation Works ​

Re-evaluating Tests ​

Workflow ​

Initial Setup ​

Making Changes ​

Testing Agents ​

Version Control ​

Comparison with Legacy Commands ​

Agent Commands

Overview

Typical Usage

Folder Structure

File Formats

tdx.json

agent.yml

Tools Configuration

Variables Configuration

prompt.md

starter_message.md

knowledge_bases/{name}.yml (Table-based)

knowledge_bases/{name}.md (Text-based)

prompts/{name}.yml

integrations/{service-type}.yml

Reference Syntax

Commands

list

show

create

update

delete

pull

push

clone

test

Test File Format

Flat Format (Single-Round Tests)

Rounds Format (Multi-Round Tests)

Mixed Format

Writing Good Criteria

How Evaluation Works

Re-evaluating Tests

Workflow

Initial Setup

Making Changes

Testing Agents

Version Control

Comparison with Legacy Commands