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feat: enhance agent TUI and CLI with streaming responses and model selection improvements

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- Added a `_safe_render` function to escape Rich markup and convert markdown to Rich format.
- Implemented token-by-token streaming for agent responses in the TUI, improving user experience during interactions.
- Updated the CLI to allow selection of LLM providers and models, enhancing flexibility in agent creation.
- Refactored benchmark case paths to use a `tests` directory instead of `benchmarks`.
- Introduced a `last_stream_result` property in the `NewAgent` class to retrieve the latest streaming response.

These changes aim to provide a more interactive and user-friendly experience in managing agents within the CrewAI framework.
This commit is contained in:
Joao Moura
2026-05-12 16:03:50 -04:00
committed by alex-clawd
parent fe7f730546
commit 6cb29dce65
7 changed files with 235 additions and 87 deletions
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153
lib/cli/src/crewai_cli/create_agent.py
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@@ -225,8 +225,8 @@ def _bootstrap_project(base: Path, llm_model: str = "") -> None:
tools_dir = base / "tools"
tools_dir.mkdir(parents=True, exist_ok=True)
benchmarks_dir = base / "benchmarks"
benchmarks_dir.mkdir(parents=True, exist_ok=True)
tests_dir = base / "tests"
tests_dir.mkdir(parents=True, exist_ok=True)
config_path = base / "config.json"
if not config_path.exists():
@@ -319,24 +319,59 @@ def _run_uv_sync(base: Path) -> None:
def _create_benchmark_cases(base: Path, agent_name: str) -> None:
"""Create a starter benchmark cases file for the agent."""
cases_path = base / "benchmarks" / f"{agent_name}_cases.json"
cases_path = base / "tests" / f"{agent_name}_cases.json"
if cases_path.exists():
return
cases_path.parent.mkdir(parents=True, exist_ok=True)
cases_path.write_text(_STARTER_CASES, encoding="utf-8")
_POPULAR_MODELS: list[tuple[str, str]] = [
("openai/gpt-4o", "OpenAI GPT-4o"),
("openai/gpt-4o-mini", "OpenAI GPT-4o Mini (cheaper)"),
("openai/o3", "OpenAI o3 (reasoning)"),
("anthropic/claude-sonnet-4-6", "Anthropic Claude Sonnet 4.6"),
("anthropic/claude-haiku-4-5-20251001", "Anthropic Claude Haiku 4.5 (fast)"),
("gemini/gemini-2.5-pro-exp-03-25", "Google Gemini 2.5 Pro"),
("groq/llama-3.1-70b-versatile", "Groq Llama 3.1 70B (fast)"),
("ollama/llama3.1", "Ollama Llama 3.1 (local)"),
_PROVIDERS: list[tuple[str, str]] = [
("openai", "OpenAI"),
("anthropic", "Anthropic"),
("gemini", "Google Gemini"),
("groq", "Groq (fast inference)"),
("ollama", "Ollama (local)"),
]
_PROVIDER_MODELS: dict[str, list[tuple[str, str]]] = {
"openai": [
("gpt-5.5", "GPT-5.5"),
("gpt-5.5-pro", "GPT-5.5 Pro"),
("o4-mini", "o4-mini (reasoning, fast)"),
("o3", "o3 (reasoning)"),
("gpt-4.1-mini", "GPT-4.1 Mini (budget)"),
],
"anthropic": [
("claude-opus-4-6", "Claude Opus 4.6"),
("claude-sonnet-4-6", "Claude Sonnet 4.6"),
("claude-haiku-4-5-20251001", "Claude Haiku 4.5 (fast)"),
("claude-3-7-sonnet-20250219", "Claude 3.7 Sonnet"),
("claude-3-5-sonnet-20241022", "Claude 3.5 Sonnet"),
],
"gemini": [
("gemini-3-pro-preview", "Gemini 3 Pro (preview)"),
("gemini-2.5-pro-exp-03-25", "Gemini 2.5 Pro"),
("gemini-2.5-flash-preview-04-17", "Gemini 2.5 Flash"),
("gemini-2.0-flash-001", "Gemini 2.0 Flash"),
("gemini-1.5-pro", "Gemini 1.5 Pro"),
],
"groq": [
("llama-3.3-70b-versatile", "Llama 3.3 70B"),
("llama-3.1-70b-versatile", "Llama 3.1 70B"),
("llama-3.1-8b-instant", "Llama 3.1 8B (fast)"),
("deepseek-r1-distill-llama-70b", "DeepSeek R1 70B"),
("mixtral-8x7b-32768", "Mixtral 8x7B"),
],
"ollama": [
("llama3.3", "Llama 3.3"),
("llama3.1", "Llama 3.1"),
("deepseek-r1", "DeepSeek R1"),
("qwen2.5", "Qwen 2.5"),
("mistral", "Mistral"),
],
}
_POPULAR_TOOLS: list[tuple[str, str]] = [
("SerperDevTool", "Web search via Serper API"),
@@ -470,7 +505,7 @@ def create_agent(name: str | None = None) -> None:
base = Path.cwd()
# Directories are bootstrapped now, pyproject written after model selection
for d in ("agents", "tools", "benchmarks"):
for d in ("agents", "tools", "tests"):
(base / d).mkdir(parents=True, exist_ok=True)
dest = base / "agents" / f"{name}.jsonc"
@@ -517,51 +552,77 @@ def create_agent(name: str | None = None) -> None:
def _select_model() -> str:
"""Let the user pick an LLM model from popular options or type a custom one."""
labels = [f"{label} ({model_id})" for model_id, label in _POPULAR_MODELS]
labels.append("Other (enter manually)")
"""Two-step selection: provider first, then model."""
# Step 1: Pick provider
provider_labels = [label for _, label in _PROVIDERS]
provider_labels.append("Other (enter manually)")
click.echo()
click.secho(" LLM Model:", fg="cyan")
click.secho(" LLM Provider:", fg="cyan")
p_idx = _arrow_or_fallback(provider_labels)
if _is_interactive():
try:
_draw_single(labels, 0)
cursor = 0
total = len(labels)
while True:
key = _read_key()
if key == "up" and cursor > 0:
cursor -= 1
_draw_single(labels, cursor, clear=True)
elif key == "down" and cursor < total - 1:
cursor += 1
_draw_single(labels, cursor, clear=True)
elif key == "enter":
_clear_lines(total)
idx = cursor
break
except Exception:
idx = _select_model_fallback(labels)
else:
idx = _select_model_fallback(labels)
if idx == len(_POPULAR_MODELS):
if p_idx == len(_PROVIDERS):
custom = click.prompt(" Enter model (provider/model)", type=str)
return custom.strip()
selected = _POPULAR_MODELS[idx][0]
click.secho(f" → {selected}", fg="green")
return selected
provider_key, provider_name = _PROVIDERS[p_idx]
click.secho(f" → {provider_name}", fg="green")
# Step 2: Pick model from that provider
models = _PROVIDER_MODELS.get(provider_key, [])
model_labels = [f"{label} ({model_id})" for model_id, label in models]
model_labels.append("Other (enter model name)")
click.echo()
click.secho(f" {provider_name} Model:", fg="cyan")
m_idx = _arrow_or_fallback(model_labels)
if m_idx == len(models):
custom = click.prompt(f" Enter model name for {provider_key}/", type=str)
result = f"{provider_key}/{custom.strip()}"
else:
model_id = models[m_idx][0]
result = f"{provider_key}/{model_id}"
click.secho(f" → {result}", fg="green")
return result
def _select_model_fallback(labels: list[str]) -> int:
def _arrow_or_fallback(labels: list[str]) -> int:
"""Arrow-key select if interactive, numbered fallback otherwise."""
if _is_interactive():
try:
return _arrow_select_one(labels)
except Exception:
pass
return _numbered_select(labels)
def _arrow_select_one(labels: list[str]) -> int:
"""Arrow-key single-select. Returns selected index."""
cursor = 0
total = len(labels)
_draw_single(labels, cursor)
while True:
key = _read_key()
if key == "up" and cursor > 0:
cursor -= 1
_draw_single(labels, cursor, clear=True)
elif key == "down" and cursor < total - 1:
cursor += 1
_draw_single(labels, cursor, clear=True)
elif key == "enter":
_clear_lines(total)
return cursor
def _numbered_select(labels: list[str]) -> int:
"""Numbered fallback for non-TTY environments."""
for idx, label in enumerate(labels, 1):
click.echo(f" {idx}. {label}")
click.echo()
while True:
choice = click.prompt(" Select a model", type=str, default="1")
choice = click.prompt(" Select", type=str, default="1")
try:
num = int(choice)
if 1 <= num <= len(labels):
@@ -577,7 +638,7 @@ def _select_tools() -> list[str]:
labels.append("Add custom tool class names")
click.echo()
click.secho(" Tools (press Enter to skip):", fg="cyan")
click.secho(" Tools (space to select, enter to confirm):", fg="cyan")
if _is_interactive():
try: