nit: standardize naming (#849)

use correct project name in a few places that were missed

examples/text-to-sql-agent/.gitignore

@@ -16,7 +16,7 @@ __pycache__/
 *.so
 .Python
 
-# DeepAgent filesystem (if using local backend)
+# Deep Agent filesystem (if using local backend)
 .deepagent_fs/
 agent_files/
 agent_workspace/

examples/text-to-sql-agent/README.md

@@ -26,18 +26,21 @@ Uses the [Chinook database](https://github.com/lerocha/chinook-database) - a sam
 ### Installation
 
 1. Clone the deepagents repository and navigate to this example:
+
 ```bash
 git clone https://github.com/langchain-ai/deepagents.git
 cd deepagents/examples/text-to-sql-agent
 ```
 
-2. Download the Chinook database:
+1. Download the Chinook database:
+
 ```bash
 # Download the SQLite database file
 curl -L -o chinook.db https://github.com/lerocha/chinook-database/raw/master/ChinookDatabase/DataSources/Chinook_Sqlite.sqlite
 ```
 
-3. Create a virtual environment and install dependencies:
+1. Create a virtual environment and install dependencies:
+
 ```bash
 # Using uv (recommended)
 uv venv --python 3.11
@@ -45,18 +48,21 @@ source .venv/bin/activate  # On Windows: .venv\Scripts\activate
 uv pip install -e .
 ```
 
-4. Set up your environment variables:
+1. Set up your environment variables:
+
 ```bash
 cp .env.example .env
 # Edit .env and add your API keys
 ```
 
 Required in `.env`:
+
 ```
 ANTHROPIC_API_KEY=your_anthropic_api_key_here
 ```
 
 Optional:
+
 ```
 LANGCHAIN_TRACING_V2=true
 LANGSMITH_ENDPOINT=https://api.smith.langchain.com
@@ -100,15 +106,14 @@ result = agent.invoke({
 print(result["messages"][-1].content)
 ```
 
-
-## How DeepAgent Works
+## How the Deep Agent Works
 
 ### Architecture
 
 ```
 User Question
      ↓
-DeepAgent (with planning)
+Deep Agent (with planning)
      ├─ write_todos (plan the approach)
      ├─ SQL Tools
      │  ├─ list_tables
@@ -129,15 +134,17 @@ Formatted Answer
 
 ### Configuration
 
-DeepAgent uses **progressive disclosure** with memory files and skills:
+Deep Agents uses **progressive disclosure** with memory files and skills:
 
 **AGENTS.md** (always loaded) - Contains:
+
 - Agent identity and role
 - Core principles and safety rules
 - General guidelines
 - Communication style
 
 **skills/** (loaded on-demand) - Specialized workflows:
+
 - **query-writing** - How to write and execute SQL queries (simple and complex)
 - **schema-exploration** - How to discover database structure and relationships
 
@@ -146,25 +153,30 @@ The agent sees skill descriptions in its context but only loads the full SKILL.m
 ## Example Queries
 
 ### Simple Query
+
 ```
 "How many customers are from Canada?"
 ```
+
 The agent will directly query and return the count.
 
 ### Complex Query with Planning
+
 ```
 "Which employee generated the most revenue and from which countries?"
 ```
+
 The agent will:
+
 1. Use `write_todos` to plan the approach
 2. Identify required tables (Employee, Invoice, Customer)
 3. Plan the JOIN structure
 4. Execute the query
 5. Format results with analysis
 
-## DeepAgent Output Example
+## Deep Agent Output Example
 
-DeepAgent shows its reasoning process:
+The Deep Agent shows its reasoning process:
 
 ```
 Question: Which employee generated the most revenue by country?
@@ -230,6 +242,7 @@ All dependencies are specified in `pyproject.toml`:
 1. Sign up for a free account at [LangSmith](https://smith.langchain.com/)
 2. Create an API key from your account settings
 3. Add these variables to your `.env` file:
+
 ```
 LANGCHAIN_TRACING_V2=true
 LANGSMITH_ENDPOINT=https://api.smith.langchain.com
@@ -241,9 +254,10 @@ LANGCHAIN_PROJECT=text2sql-deepagent
 
 When configured, every query is automatically traced:
 
-![DeepAgent LangSmith Trace Example](text-to-sql-langsmith-trace.png)
+![Deep Agent LangSmith Trace Example](text-to-sql-langsmith-trace.png)
 
 You can view:
+
 - Complete execution trace with all tool calls
 - Planning steps (write_todos)
 - Filesystem operations
@@ -251,7 +265,7 @@ You can view:
 - Generated SQL queries
 - Error messages and retry attempts
 
-View your traces at: https://smith.langchain.com/
+View your traces at: <https://smith.langchain.com/>
 
 ## Resources
 

libs/deepagents/deepagents/middleware/__init__.py

@@ -1,4 +1,4 @@
-"""Middleware for the DeepAgent."""
+"""Middleware for the agent."""
 
 from deepagents.middleware.filesystem import FilesystemMiddleware
 from deepagents.middleware.memory import MemoryMiddleware

libs/harbor/README.md

@@ -1,8 +1,8 @@
-# Building DeepAgent Harnesses for Terminal Bench 2.0 with Harbor
+# Building Deep Agent Harnesses for Terminal Bench 2.0 with Harbor
 
 ## Overview
 
-This repository demonstrates how to evaluate and improve your DeepAgent harness using [Harbor](https://github.com/laude-institute/harbor) and [LangSmith](https://smith.langchain.com).
+This repository demonstrates how to evaluate and improve your Deep Agent harness using [Harbor](https://harborframework.com/) and [LangSmith](https://www.langchain.com/langsmith/observability).
 
 ### What is Harbor?
 
@@ -11,21 +11,22 @@ Harbor is an evaluation framework that simplifies running agents on challenging
 - **Sandbox environments** (Docker, Modal, Daytona, E2B, etc.)
 - **Automatic test execution** and verification
 - **Reward scoring** (0.0 - 1.0 based on test pass rate)
-- **Trajectory logging** in ATIF format (Agent Trajectory Interchange Format)
+- **Trajectory logging** in ATIF format [(Agent Trajectory Interchange Format)](https://harborframework.com/docs/trajectory-format)
 
 ### What is Terminal Bench 2.0?
 
 [Terminal Bench 2.0](https://github.com/laude-institute/terminal-bench-2) is an evaluation benchmark that measures agent capabilities across several domains, testing how well an agent operates using a computer environment, primarily via the terminal. The benchmark includes 90+ tasks across domains like software engineering, biology, security, gaming, and more.
 
 **Example tasks:**
+
 - `path-tracing`: Reverse-engineer C program from rendered image
 - `chess-best-move`: Find optimal move using chess engine
 - `git-multibranch`: Complex git operations with merge conflicts
 - `sqlite-with-gcov`: Build SQLite with code coverage, analyze reports
 
-### The DeepAgent Architecture
+### The Deep Agent Architecture
 
-The DeepAgent harness ships with design patterns validated as good defaults across agentic tasks:
+The Deep Agent harness ships with design patterns validated as good defaults across agentic tasks:
 
 1. **Detailed System Prompt**: Expansive, instructional prompts with tool guidance and examples
 2. **Planning Middleware**: The `write_todos` tool helps the agent structure thinking and track progress
@@ -148,6 +149,7 @@ Harbor supports multiple sandbox environments. Use the `--env` flag to select:
 - `runloop` - Runloop sandboxes
 
 Makefile shortcuts are available for common workflows:
+
 - `make run-terminal-bench-docker` - Run 1 task locally with Docker
 - `make run-terminal-bench-daytona` - Run 10 tasks on Daytona
 - `make run-terminal-bench-modal` - Run 4 tasks on Modal

libs/harbor/deepagents_harbor/deepagents_wrapper.py

@@ -156,7 +156,7 @@ class DeepAgentsWrapper(BaseAgent):
         environment: BaseEnvironment,
         context: AgentContext,
     ) -> None:
-        """Execute the DeepAgent on the given instruction.
+        """Execute the Deep Agent on the given instruction.
 
         Args:
             instruction: The task to complete