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(`python_ai_crewai_template`)\n\nA Copier template for bootstrapping production-ready AI agent projects using [CrewAI](https://github.com/crewAIInc/crewAI), with Anthropic / Bedrock / OpenAI provider support, a Streamlit chat UI, and a modern Python toolchain: `uv`, `ruff`, tests, docs, Docker, and releases.\n\n## Why this template\n- Start fast with a **role-based crew + sequential tasks** architecture out of the box.\n- Includes working examples of `@tool`-decorated functions, `Agent` factories, and `Crew` orchestration.\n- Streamlit chat UI with session management ready to go.\n- Provider-agnostic — flip `LLM_PROVIDER` between Anthropic API, AWS Bedrock, and OpenAI without code changes.\n- Keep quality automated with linting, formatting, type checking, and tests.\n\n## Technology stack\n- [CrewAI](https://github.com/crewAIInc/crewAI) for role-based multi-agent crews with sequential and hierarchical processes.\n- [crewai-tools](https://github.com/crewAIInc/crewAI-tools) for the `@tool` decorator.\n- [Streamlit](https://streamlit.io/) for the web chat UI.\n- [pydantic-settings](https://docs.pydantic.dev/latest/concepts/pydantic_settings/) for typed configuration from environment variables.\n- [Copier](https://copier.readthedocs.io/) for project scaffolding and updateable generation.\n- [uv](https://docs.astral.sh/uv/) for dependency management, virtual environments, lockfiles, and packaging via `uv_build`.\n- [ruff](https://docs.astral.sh/ruff/) for linting and formatting.\n- `pre-commit` to run hooks before commits.\n- `pytest` and `pytest-cov` for tests and coverage.\n- [MkDocs](https://www.mkdocs.org/) Material with mkdocstrings, mkdocs-gen-files, and friends for autogenerated API docs.\n- [Docker](https://www.docker.com/) for containerized builds (multi-stage, non-root user).\n- `AGENTS.md.jinja` to generate a project-specific `AGENTS.md` during scaffolding.\n\n## Usage\n\n```bash\nuvx copier copy Template/python_ai_crewai_template my_first_agent \\\n  --data package_name=my_first_agent \\\n  --data project_description=\"My first agent\" \\\n  --data github_username=YOU\n```\n\n## Quick start\n\n### 1. Install [`uv`](https://github.com/astral-sh/uv)\n\n### 2. Create the project using [copier](https://github.com/copier-org/copier)\n\n```bash\nuvx copier copy <path-to>/python_ai_crewai_template my-crewai-agent\n```\n\nYou will be prompted for:\n\n| Prompt | Description |\n|--------|-------------|\n| `package_name` | Name of the Python AI agent package |\n| `project_description` | Short description of the project |\n| `github_username` | GitHub username or organization name |\n\n> [!IMPORTANT]\n> Copier always generates a `.copier-answers.yml` file. Commit it and **never** edit manually.\n\n### 3. Setup\n\n```bash\ncd my-crewai-agent\ngit init --initial-branch=main\ncp .env.example .env\n# Edit .env — set ANTHROPIC_API_KEY (default provider is anthropic)\nmake install\ngit add . && git commit -m \"feat: first commit\"\n```\n\n### 4. Run the agent\n\n```bash\nmake run        # CLI: runs the default research-assistant query\nmake repl       # CLI: interactive REPL\nmake ui         # Streamlit UI\n```\n\n## Generated project structure\n\n```\nyour-project/\n├── app.py                              # Streamlit chat UI entry point\n├── main.py                             # CLI entry point (single query + REPL)\n├── pyproject.toml                      # Dependencies and tool config\n├── Makefile                            # Build targets\n├── .env.example                        # Environment variable template\n├── AGENTS.md                           # Coding-agent guidelines\n├── src/<package>/\n│   ├── config/\n│   │   ├── settings.py                 # pydantic-settings\n│   │   ├── llm_client.py               # crewai.LLM factory (provider/model strings)\n│   │   └── prompts.py                  # Backstories and goals\n│   ├── agents/\n│   │   ├── orchestrator.py             # Crew kickoff wrapper\n│   │   └── example_agent.py            # Researcher Agent factory\n│   ├── tools/\n│   │   └── example_tools.py            # @tool(\"name\") decorated functions\n│   └── ui/\n│       └── components.py               # Streamlit components\n├── tests/\n│   ├── conftest.py\n│   ├── test_example_tools.py           # Tool tests via tool.func(...)\n│   └── test_settings.py\n├── docker/\n├── docs/\n├── scripts/\n└── playground/notebook.py\n```\n\n## Architecture\n\n```\nUser Query\n    │\n    ▼\n┌──────────────────────┐\n│    Orchestrator       │  ← Crew(agents=[...], tasks=[...], process=sequential)\n│      (Crew)           │\n└──────────┬────────────┘\n           │\n           ▼\n┌──────────────────────┐\n│   Researcher Agent    │  ← Agent(role, goal, backstory, tools, llm)\n└──┬──────────────┬────┘\n   │              │\n   ▼              ▼\n┌────────┐  ┌────────────┐\n│ web_   │  │ calculator │   ← @tool(\"name\") functions\n│ search │  │            │\n└────────┘  └────────────┘\n```\n\n### Key patterns\n\n- **Role-based agents** — every `Agent` has `role`, `goal`, and `backstory`. The LLM uses these to drive behavior.\n- **Tasks** — units of work; `Task(description, expected_output, agent)`.\n- **Crew** — composes agents and tasks under a `Process` (sequential or hierarchical).\n- **Tool decorator** — `@tool(\"name\")` from `crewai.tools`. Test via `my_tool.func(args)`.\n- **LLM via model strings** — `LLM(model=\"anthropic/claude-sonnet-4-6\", api_key=...)` resolves through LiteLLM under the hood.\n- **Configuration** — `pydantic-settings` loads from `.env`.\n\n## Adding a new sub-agent\n\n1. **Create tools** in `src/<package>/tools/my_tools.py`:\n\n   ```python\n   from crewai.tools import tool\n\n   @tool(\"fetch_data\")\n   def fetch_data(query: str) -> dict:\n       \"\"\"Fetch data for the given query.\"\"\"\n       return {\"result\": \"...\"}\n   ```\n\n2. **Create agent** in `src/<package>/agents/my_agent.py`:\n\n   ```python\n   from crewai import Agent\n   from <package>.config.llm_client import build_llm\n   from <package>.tools.my_tools import fetch_data\n\n   def build_my_agent() -> Agent:\n       return Agent(\n           role=\"DataFetcher\",\n           goal=\"Fetch domain data on demand.\",\n           backstory=\"You are a specialist agent for fetching data.\",\n           tools=[fetch_data],\n           llm=build_llm(),\n           allow_delegation=False,\n       )\n   ```\n\n3. **Add to crew** in `orchestrator.py`:\n\n   ```python\n   from <package>.agents.my_agent import build_my_agent\n\n   def build_orchestrator():\n       researcher = build_researcher()\n       fetcher = build_my_agent()\n       def run(query):\n           t1 = Task(description=query, expected_output=\"Findings.\", agent=researcher)\n           t2 = Task(description=\"Fetch related data.\", expected_output=\"Data.\", agent=fetcher)\n           return Crew(agents=[researcher, fetcher], tasks=[t1, t2]).kickoff()\n       return run\n   ```\n\n4. **Write tests** in `tests/test_my_tools.py`:\n\n   ```python\n   def test_fetch():\n       assert \"result\" in fetch_data.func(\"foo\")\n   ```\n\n## Environment variables\n\n| Variable | Default | Description |\n|----------|---------|-------------|\n| `LLM_PROVIDER` | `anthropic` | One of `anthropic`, `bedrock`, `openai` |\n| `ANTHROPIC_API_KEY` | | Required when `LLM_PROVIDER=anthropic` |\n| `MODEL_ID` | `claude-sonnet-4-6` | Model ID for the active provider |\n| `AWS_REGION` | `us-east-1` | AWS region for Bedrock |\n| `BEDROCK_MODEL_ID` | `anthropic.claude-sonnet-4-6-v1:0` | Bedrock model ID |\n| `OPENAI_API_KEY` | | Required when `LLM_PROVIDER=openai` |\n| `OPENAI_MODEL_ID` | `gpt-4o-mini` | OpenAI model ID |\n| `MAX_TOKENS` | `2048` | Max response tokens |\n| `TEMPERATURE` | `0.7` | LLM temperature |\n| `LOG_LEVEL` | `INFO` | Logging level |\n\n## Makefile commands\n\n| Command | Description |\n|---------|-------------|\n| `make install` | Installs dependencies and pre-commit hooks |\n| `make run` | CLI: runs the default query |\n| `make repl` | CLI: interactive REPL |\n| `make ui` | Streamlit UI |\n| `make test` | pytest with coverage |\n| `make lint` | Ruff |\n| `make typecheck` | `ty` static type-checking |\n| `make format` | Ruff format |\n| `make docs` | MkDocs serve |\n| `make docker-build` | Builds Docker image |\n| `make docker-run` | Starts container |\n| `make clean` | Removes caches |\n\n## Documentation\n\n```bash\nmake docs\n```\n\n## Update an existing project\n\n```bash\nuvx copier update --defaults\n```\n","readmeExcerpt":"Python AI CrewAI Agent Template (python_ai_crewai_template) A Copier template for bootstrapping production-ready AI agent projects using $1, with Anthropic / Bedrock / OpenAI provider support, a Streamlit chat UI, and a modern Python toolchain: uv, ruff, tests, docs, Docker, and releases. Why this template - Start fast with a **role-based crew + sequential tasks** architecture out of the box. - Includes working examp","codeSnippets":[],"executableExamples":[{"language":"bash","snippet":"uvx copier copy Template/python_ai_crewai_template my_first_agent \\\n  --data package_name=my_first_agent \\\n  --data project_description=\"My first agent\" \\\n  --data github_username=YOU"},{"language":"bash","snippet":"uvx copier copy <path-to>/python_ai_crewai_template my-crewai-agent"},{"language":"bash","snippet":"cd my-crewai-agent\ngit init --initial-branch=main\ncp .env.example .env\n# Edit .env — set ANTHROPIC_API_KEY (default provider is anthropic)\nmake install\ngit add . && git commit -m \"feat: first commit\""},{"language":"bash","snippet":"make run        # CLI: runs the default research-assistant query\nmake repl       # CLI: interactive REPL\nmake ui         # Streamlit UI"},{"language":"text","snippet":"your-project/\n├── app.py                              # Streamlit chat UI entry point\n├── main.py                             # CLI entry point (single query + REPL)\n├── pyproject.toml                      # Dependencies and tool config\n├── Makefile                            # Build targets\n├── .env.example                        # Environment variable template\n├── AGENTS.md                           # Coding-agent guidelines\n├── src/<package>/\n│   ├── config/\n│   │   ├── settings.py                 # pydantic-settings\n│   │   ├── llm_client.py               # crewai.LLM factory (provider/model strings)\n│   │   └── prompts.py                  # Backstories and goals\n│   ├── agents/\n│   │   ├── orchestrator.py             # Crew kickoff wrapper\n│   │   └── example_agent.py            # Researcher Agent factory\n│   ├── tools/\n│   │   └── example_tools.py            # @tool(\"name\") decorated functions\n│   └── ui/\n│       └── components.py               # Streamlit components\n├── tests/\n│   ├── conftest.py\n│   ├── test_example_tools.py           # Tool tests via tool.func(...)\n│   └── test_settings.py\n├── docker/\n├── docs/\n├── scripts/\n└── playground/notebook.py"},{"language":"text","snippet":"User Query\n    │\n    ▼\n┌──────────────────────┐\n│    Orchestrator       │  ← Crew(agents=[...], tasks=[...], process=sequential)\n│      (Crew)           │\n└──────────┬────────────┘\n           │\n           ▼\n┌──────────────────────┐\n│   Researcher Agent    │  ← Agent(role, goal, backstory, tools, llm)\n└──┬──────────────┬────┘\n   │              │\n   ▼              ▼\n┌────────┐  ┌────────────┐\n│ web_   │  │ calculator │   ← @tool(\"name\") functions\n│ search │  │            │\n└────────┘  └────────────┘"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[],"languages":["python"],"docsSourceLabel":"GITHUB REPOS","editorialOverview":"Multi-agent orchestration template using CrewAI for complex task automation and role-playing agents. 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