code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
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def _generate_thread(self, idx: int):
"""Step token generation and insert prefills from backlog."""
logging.info("---------Spinning up generate thread %d.---------", idx)
generate_engine = self._generate_engines[idx]
my_slots = self._generate_slots[idx]
my_generate_backlog = self._generate_backlogs[... | Step token generation and insert prefills from backlog. | _generate_thread | python | xjdr-alt/entropix | entropix/orchestrator.py | https://github.com/xjdr-alt/entropix/blob/master/entropix/orchestrator.py | Apache-2.0 |
def _detokenize_thread(self, idx: int):
"""Detokenize sampled tokens and returns them to the user."""
# One of these per generate engine.
# For all filled my_slots, pop the sampled token onto the relevant
# requests return channel. If it done, place it back onto free slots.
my_detokenize_backlog = s... | Detokenize sampled tokens and returns them to the user. | _detokenize_thread | python | xjdr-alt/entropix | entropix/orchestrator.py | https://github.com/xjdr-alt/entropix/blob/master/entropix/orchestrator.py | Apache-2.0 |
def decode(self, t: Sequence[int]) -> str:
"""
Decodes a list of token IDs into a string.
Args:
t (List[int]): The list of token IDs to be decoded.
Returns:
str: The decoded string.
"""
# Typecast is safe here. Tiktoken doesn't do anything list-related with the sequence.
re... |
Decodes a list of token IDs into a string.
Args:
t (List[int]): The list of token IDs to be decoded.
Returns:
str: The decoded string.
| decode | python | xjdr-alt/entropix | entropix/tokenizer.py | https://github.com/xjdr-alt/entropix/blob/master/entropix/tokenizer.py | Apache-2.0 |
def take_nearest_length(lengths: list[int], length: int) -> int:
"""Gets the nearest length to the right in a set of lengths."""
pos = bisect_left(lengths, length)
if pos == len(lengths):
return lengths[-1]
return lengths[pos] | Gets the nearest length to the right in a set of lengths. | take_nearest_length | python | xjdr-alt/entropix | entropix/token_utils.py | https://github.com/xjdr-alt/entropix/blob/master/entropix/token_utils.py | Apache-2.0 |
def tokenize_and_pad(
s: str,
vocab,
is_bos: bool = True,
prefill_lengths: Optional[List[int]] = None,
max_prefill_length: Optional[int] = None,
jax_padding: bool = True,
) -> Tuple[Union[jax.Array, np.ndarray], int]:
"""Tokenize and pads a string.
Args:
s: String to tokenize.
vocab: Vocabulary... | Tokenize and pads a string.
Args:
s: String to tokenize.
vocab: Vocabulary to tokenize with.
is_bos: Whether or not this is the beginning of a sequence. Default to yes
as prefill is typically used when beginning sequences.
prefill_lengths: Buckets to pad the sequence to for static compilation.
... | tokenize_and_pad | python | xjdr-alt/entropix | entropix/token_utils.py | https://github.com/xjdr-alt/entropix/blob/master/entropix/token_utils.py | Apache-2.0 |
def pad_tokens(
tokens: np.ndarray,
bos_id: int,
pad_id: int,
is_bos: bool = True,
prefill_lengths: Optional[List[int]] = None,
max_prefill_length: Optional[int] = None,
jax_padding: bool = True,
) -> Tuple[Union[jax.Array, np.ndarray], int]:
"""Pads tokens to the nearest prefill length that is equal to... | Pads tokens to the nearest prefill length that is equal to or greater
than the token length.
Args:
tokens: Tokens.
bos_id: Bos ID.
pad_id: Pad ID.
is_bos: Add a beginning of sequence token if this is ture.
prefill_lengths: Buckets to pad the sequence to for static compilation.
max_prefil... | pad_tokens | python | xjdr-alt/entropix | entropix/token_utils.py | https://github.com/xjdr-alt/entropix/blob/master/entropix/token_utils.py | Apache-2.0 |
def process_result_tokens(
tokenizer: Tokenizer,
slot: int,
slot_max_length: int,
result_tokens: ResultTokens,
complete: np.ndarray,
is_client_side_tokenization: bool = False,
debug: bool = False,
) -> Tuple[List[ReturnSample], np.ndarray]:
"""Processes a result tokens into a list of strings, handling m... | Processes a result tokens into a list of strings, handling multiple
samples.
Args:
slot: The slot at which to draw tokens from.
slot_max_length: Max length for a sample in the slot.
result_tokens: The tokens to access by slot.
complete: Array representing the completion status of each sample in t... | process_result_tokens | python | xjdr-alt/entropix | entropix/token_utils.py | https://github.com/xjdr-alt/entropix/blob/master/entropix/token_utils.py | Apache-2.0 |
def is_byte_token(s: str) -> bool:
"""Returns True if s is a byte string like "<0xAB>"."""
# Bytes look like "<0xAB>".
if len(s) != 6 or s[0:3] != "<0x" or s[-1] != ">":
return False
return True | Returns True if s is a byte string like "<0xAB>". | is_byte_token | python | xjdr-alt/entropix | entropix/token_utils.py | https://github.com/xjdr-alt/entropix/blob/master/entropix/token_utils.py | Apache-2.0 |
def create_mesh(device_count: int) -> jax.sharding.Mesh:
"""Creates device mesh for distributed execution."""
devices = jax.devices()
mesh_shape = (device_count, 1)
device_mesh = jax.experimental.mesh_utils.create_device_mesh(mesh_shape)
return jax.sharding.Mesh(device_mesh, ("mp", "fsdp")) | Creates device mesh for distributed execution. | create_mesh | python | xjdr-alt/entropix | entropix/weights.py | https://github.com/xjdr-alt/entropix/blob/master/entropix/weights.py | Apache-2.0 |
def load_weights(
ckpt_dir: Path, model_params, weight_config: Optional[WeightConfig] = None
) -> Tuple[XfmrWeights, jax.sharding.Mesh]:
"""Load and shard model weights across devices."""
weight_config = weight_config or WeightConfig()
mesh = create_mesh(jax.device_count())
w = {}
layer_weights = []
for... | Load and shard model weights across devices. | load_weights | python | xjdr-alt/entropix | entropix/weights.py | https://github.com/xjdr-alt/entropix/blob/master/entropix/weights.py | Apache-2.0 |
def aggregate_results(
single_eval_results: list[SingleEvalResult],
default_stats: tuple[str] = ("mean", "std"),
name2stats: dict[str, tuple[str]] | None = None,
) -> EvalResult:
"""
Aggregate results from multiple evaluations into a single EvalResult.
"""
name2stats = name2stats or {}
name2values = def... |
Aggregate results from multiple evaluations into a single EvalResult.
| aggregate_results | python | xjdr-alt/entropix | evals/common.py | https://github.com/xjdr-alt/entropix/blob/master/evals/common.py | Apache-2.0 |
def map_with_progress(f: callable, xs: list[Any], num_threads: int = 50):
"""
Apply f to each element of xs, using a ThreadPool, and show progress.
"""
if os.getenv("debug"):
return list(map(f, tqdm(xs, total=len(xs))))
else:
with ThreadPool(min(num_threads, len(xs))) as pool:
return list(tqdm(p... |
Apply f to each element of xs, using a ThreadPool, and show progress.
| map_with_progress | python | xjdr-alt/entropix | evals/common.py | https://github.com/xjdr-alt/entropix/blob/master/evals/common.py | Apache-2.0 |
def message_to_html(message: Message) -> str:
"""
Generate HTML snippet (inside a <div>) for a message.
"""
return jinja_env.from_string(_message_template).render(
role=message["role"],
content=message["content"],
variant=message.get("variant", None),
) |
Generate HTML snippet (inside a <div>) for a message.
| message_to_html | python | xjdr-alt/entropix | evals/common.py | https://github.com/xjdr-alt/entropix/blob/master/evals/common.py | Apache-2.0 |
def make_report(eval_result: EvalResult) -> str:
"""
Create a standalone HTML report from an EvalResult.
"""
return jinja_env.from_string(_report_template).render(
score=eval_result.score,
metrics=eval_result.metrics,
htmls=eval_result.htmls,
) |
Create a standalone HTML report from an EvalResult.
| make_report | python | xjdr-alt/entropix | evals/common.py | https://github.com/xjdr-alt/entropix/blob/master/evals/common.py | Apache-2.0 |
def make_report_from_example_htmls(htmls: list[str]):
"""
Create a standalone HTML report from a list of example htmls
"""
return jinja_env.from_string(_report_template).render(
score=None, metrics={}, htmls=htmls
) |
Create a standalone HTML report from a list of example htmls
| make_report_from_example_htmls | python | xjdr-alt/entropix | evals/common.py | https://github.com/xjdr-alt/entropix/blob/master/evals/common.py | Apache-2.0 |
def normalize_response(response: str) -> str:
"""
Normalize the response by removing markdown and LaTeX formatting that may prevent a match.
"""
return (
response.replace("**", "")
.replace("$\\boxed{", "")
.replace("}$", "")
.replace("\\$", "")
.replace("$\\text{", "")
.replace("$", ""... |
Normalize the response by removing markdown and LaTeX formatting that may prevent a match.
| normalize_response | python | xjdr-alt/entropix | evals/common.py | https://github.com/xjdr-alt/entropix/blob/master/evals/common.py | Apache-2.0 |
def _align_bags(predicted: List[Set[str]], gold: List[Set[str]]) -> List[float]:
"""
Takes gold and predicted answer sets and first finds the optimal 1-1 alignment
between them and gets maximum metric values over all the answers.
"""
scores = np.zeros([len(gold), len(predicted)])
for gold_index, gold_item i... |
Takes gold and predicted answer sets and first finds the optimal 1-1 alignment
between them and gets maximum metric values over all the answers.
| _align_bags | python | xjdr-alt/entropix | evals/drop_eval.py | https://github.com/xjdr-alt/entropix/blob/master/evals/drop_eval.py | Apache-2.0 |
def get_drop_metrics(
predicted: Union[str, List[str], Tuple[str, ...]],
gold: Union[str, List[str], Tuple[str, ...]],
) -> Tuple[float, float]:
"""
Takes a predicted answer and a gold answer (that are both either a string or a list of
strings), and returns exact match and the DROP F1 metric for the predictio... |
Takes a predicted answer and a gold answer (that are both either a string or a list of
strings), and returns exact match and the DROP F1 metric for the prediction. If you are
writing a script for evaluating objects in memory (say, the output of predictions during
validation, or while training), this is the fu... | get_drop_metrics | python | xjdr-alt/entropix | evals/drop_eval.py | https://github.com/xjdr-alt/entropix/blob/master/evals/drop_eval.py | Apache-2.0 |
def answer_json_to_strings(answer: Dict[str, Any]) -> Tuple[Tuple[str, ...], str]:
"""
Takes an answer JSON blob from the DROP data release and converts it into strings used for
evaluation.
"""
if "number" in answer and answer["number"]:
return tuple([str(answer["number"])]), "number"
elif "spans" in an... |
Takes an answer JSON blob from the DROP data release and converts it into strings used for
evaluation.
| answer_json_to_strings | python | xjdr-alt/entropix | evals/drop_eval.py | https://github.com/xjdr-alt/entropix/blob/master/evals/drop_eval.py | Apache-2.0 |
def evaluate_functional_correctness(
sample: dict[str, str],
completions: list[str],
n_workers: int = 4,
timeout: float = 3.0,
):
"""
Evaluates the functional correctness of generated samples, and writes
results to f"{sample_file}_results.jsonl.gz"
"""
import copy
# Check the generated samples agai... |
Evaluates the functional correctness of generated samples, and writes
results to f"{sample_file}_results.jsonl.gz"
| evaluate_functional_correctness | python | xjdr-alt/entropix | evals/humaneval_eval.py | https://github.com/xjdr-alt/entropix/blob/master/evals/humaneval_eval.py | Apache-2.0 |
def setup_logger(
level: int = logging.ERROR,
rich_tracebacks: bool = True,
log_format: str | None = None,
propagate: bool = False,
**kwargs: Any,
) -> None:
"""Configure the any_agent logger with the specified settings.
Args:
level: The logging level to use (default: logging.INFO)
... | Configure the any_agent logger with the specified settings.
Args:
level: The logging level to use (default: logging.INFO)
rich_tracebacks: Whether to enable rich tracebacks (default: True)
log_format: Optional custom log format string
propagate: Whether to propagate logs to parent l... | setup_logger | python | mozilla-ai/any-agent | src/any_agent/logging.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/logging.py | Apache-2.0 |
def get_evidence_from_spans(self) -> str:
"""Get a summary of what happened in each step/span of the agent trace.
This includes information about the input, output, and tool calls for each step.
Returns:
str: The evidence of all the spans in the trace
"""
evidence ... | Get a summary of what happened in each step/span of the agent trace.
This includes information about the input, output, and tool calls for each step.
Returns:
str: The evidence of all the spans in the trace
| get_evidence_from_spans | python | mozilla-ai/any-agent | src/any_agent/evaluation/agent.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/evaluation/agent.py | Apache-2.0 |
def from_yaml(cls, evaluation_case_path: str) -> EvaluationCase:
"""Load a test case from a YAML file and process it."""
with open(evaluation_case_path, encoding="utf-8") as f:
evaluation_case_dict = yaml.safe_load(f)
if "ground_truth" in evaluation_case_dict:
# remove t... | Load a test case from a YAML file and process it. | from_yaml | python | mozilla-ai/any-agent | src/any_agent/evaluation/evaluation_case.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/evaluation/evaluation_case.py | Apache-2.0 |
def evaluate_checkpoints(
model: str,
trace: AgentTrace,
checkpoints: Sequence[CheckpointCriteria],
) -> list[EvaluationResult]:
"""Verify each checkpoint against the trace data using LLM.
Args:
model: The model to use for evaluation
trace: The trace data to evaluate
checkpo... | Verify each checkpoint against the trace data using LLM.
Args:
model: The model to use for evaluation
trace: The trace data to evaluate
checkpoints: List of checkpoint criteria to verify
processor: Trace processor to extract evidence
Returns:
List of evaluation results
... | evaluate_checkpoints | python | mozilla-ai/any-agent | src/any_agent/evaluation/evaluators.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/evaluation/evaluators.py | Apache-2.0 |
def _calculate_f1_score(prediction: str, ground_truth: str) -> float:
"""Calculate F1 score between prediction and ground truth strings."""
# Normalize strings: lowercase and roughly split into words
pred_tokens = set(prediction.lower().split())
truth_tokens = set(ground_truth.lower().split())
if n... | Calculate F1 score between prediction and ground truth strings. | _calculate_f1_score | python | mozilla-ai/any-agent | src/any_agent/evaluation/evaluators.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/evaluation/evaluators.py | Apache-2.0 |
def evaluate_final_output(
final_output: str,
ground_truth_answer: GroundTruthAnswer,
) -> EvaluationResult:
"""Compare answers using simple string matching and F1 score."""
ground_truth_text = str(ground_truth_answer["value"])
# Check for exact match (case-insensitive)
exact_match = final_outp... | Compare answers using simple string matching and F1 score. | evaluate_final_output | python | mozilla-ai/any-agent | src/any_agent/evaluation/evaluators.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/evaluation/evaluators.py | Apache-2.0 |
def score(self) -> float:
"""Calculate the score based on the evaluation results."""
if self.ground_truth_result is not None:
all_results = [*self.checkpoint_results, self.ground_truth_result]
else:
all_results = self.checkpoint_results
total_points = sum([result.... | Calculate the score based on the evaluation results. | score | python | mozilla-ai/any-agent | src/any_agent/evaluation/schemas.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/evaluation/schemas.py | Apache-2.0 |
def _get_model(self, agent_config: AgentConfig) -> "Model":
"""Get the model configuration for an Agno agent."""
model_type = agent_config.model_type or DEFAULT_MODEL_TYPE
return model_type(
id=agent_config.model_id,
api_base=agent_config.api_base,
api_key=ag... | Get the model configuration for an Agno agent. | _get_model | python | mozilla-ai/any-agent | src/any_agent/frameworks/agno.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/frameworks/agno.py | Apache-2.0 |
def create(
cls,
agent_framework: AgentFramework | str,
agent_config: AgentConfig,
) -> AnyAgent:
"""Create an agent using the given framework and config."""
return run_async_in_sync(
cls.create_async(
agent_framework=agent_framework,
... | Create an agent using the given framework and config. | create | python | mozilla-ai/any-agent | src/any_agent/frameworks/any_agent.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/frameworks/any_agent.py | Apache-2.0 |
async def run_async(
self, prompt: str, instrument: bool = True, **kwargs: Any
) -> AgentTrace:
"""Run the agent asynchronously with the given prompt.
Args:
prompt: The user prompt to be passed to the agent.
instrument: Whether to instrument the underlying framework
... | Run the agent asynchronously with the given prompt.
Args:
prompt: The user prompt to be passed to the agent.
instrument: Whether to instrument the underlying framework
to generate LLM Calls and Tool Execution Spans.
If `False` the returned `AgentTrace` w... | run_async | python | mozilla-ai/any-agent | src/any_agent/frameworks/any_agent.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/frameworks/any_agent.py | Apache-2.0 |
def serve(self, serving_config: A2AServingConfig | None = None) -> None:
"""Serve this agent using the protocol defined in the serving_config.
Args:
serving_config: Configuration for serving the agent. If None, uses default A2AServingConfig.
Must be an instance of ... | Serve this agent using the protocol defined in the serving_config.
Args:
serving_config: Configuration for serving the agent. If None, uses default A2AServingConfig.
Must be an instance of A2AServingConfig.
Raises:
ImportError: If the `serving` depende... | serve | python | mozilla-ai/any-agent | src/any_agent/frameworks/any_agent.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/frameworks/any_agent.py | Apache-2.0 |
async def serve_async(
self, serving_config: A2AServingConfig | None = None
) -> tuple[asyncio.Task[Any], uvicorn.Server]:
"""Serve this agent asynchronously using the protocol defined in the serving_config.
Args:
serving_config: Configuration for serving the agent. If None, use... | Serve this agent asynchronously using the protocol defined in the serving_config.
Args:
serving_config: Configuration for serving the agent. If None, uses default A2AServingConfig.
Must be an instance of A2AServingConfig.
Returns:
A tuple containing:
... | serve_async | python | mozilla-ai/any-agent | src/any_agent/frameworks/any_agent.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/frameworks/any_agent.py | Apache-2.0 |
async def _run_async(self, prompt: str, **kwargs: Any) -> str | BaseModel:
"""To be implemented by each framework.""" | To be implemented by each framework. | _run_async | python | mozilla-ai/any-agent | src/any_agent/frameworks/any_agent.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/frameworks/any_agent.py | Apache-2.0 |
def _get_model(self, agent_config: AgentConfig) -> "BaseLlm":
"""Get the model configuration for a Google agent."""
model_type = agent_config.model_type or DEFAULT_MODEL_TYPE
model_args = agent_config.model_args or {}
if self.config.output_type:
model_args["tool_choice"] = "r... | Get the model configuration for a Google agent. | _get_model | python | mozilla-ai/any-agent | src/any_agent/frameworks/google.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/frameworks/google.py | Apache-2.0 |
async def _load_agent(self) -> None:
"""Load the Google agent with the given configuration."""
if not adk_available:
msg = "You need to `pip install 'any-agent[google]'` to use this agent"
raise ImportError(msg)
tools, _ = await self._load_tools(self.config.tools)
... | Load the Google agent with the given configuration. | _load_agent | python | mozilla-ai/any-agent | src/any_agent/frameworks/google.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/frameworks/google.py | Apache-2.0 |
def _get_model(self, agent_config: AgentConfig) -> "LanguageModelLike":
"""Get the model configuration for a LangChain agent."""
model_type = agent_config.model_type or DEFAULT_MODEL_TYPE
model_args = agent_config.model_args or {}
return cast(
"LanguageModelLike",
... | Get the model configuration for a LangChain agent. | _get_model | python | mozilla-ai/any-agent | src/any_agent/frameworks/langchain.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/frameworks/langchain.py | Apache-2.0 |
async def _load_agent(self) -> None:
"""Load the LangChain agent with the given configuration."""
if not langchain_available:
msg = "You need to `pip install 'any-agent[langchain]'` to use this agent"
raise ImportError(msg)
imported_tools, _ = await self._load_tools(self... | Load the LangChain agent with the given configuration. | _load_agent | python | mozilla-ai/any-agent | src/any_agent/frameworks/langchain.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/frameworks/langchain.py | Apache-2.0 |
def _get_model(self, agent_config: AgentConfig) -> "LLM":
"""Get the model configuration for a llama_index agent."""
model_type = agent_config.model_type or DEFAULT_MODEL_TYPE
return cast(
"LLM",
model_type(
model=agent_config.model_id,
api... | Get the model configuration for a llama_index agent. | _get_model | python | mozilla-ai/any-agent | src/any_agent/frameworks/llama_index.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/frameworks/llama_index.py | Apache-2.0 |
async def _load_agent(self) -> None:
"""Load the LLamaIndex agent with the given configuration."""
if not llama_index_available:
msg = "You need to `pip install 'any-agent[llama_index]'` to use this agent"
raise ImportError(msg)
instructions = self.config.instructions
... | Load the LLamaIndex agent with the given configuration. | _load_agent | python | mozilla-ai/any-agent | src/any_agent/frameworks/llama_index.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/frameworks/llama_index.py | Apache-2.0 |
def _get_model(
self,
agent_config: AgentConfig,
) -> "Model":
"""Get the model configuration for an OpenAI agent."""
model_type = agent_config.model_type or DEFAULT_MODEL_TYPE
return model_type(
model=agent_config.model_id,
base_url=agent_config.api_b... | Get the model configuration for an OpenAI agent. | _get_model | python | mozilla-ai/any-agent | src/any_agent/frameworks/openai.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/frameworks/openai.py | Apache-2.0 |
async def _load_agent(self) -> None:
"""Load the OpenAI agent with the given configuration."""
if not agents_available:
msg = "You need to `pip install 'any-agent[openai]'` to use this agent"
raise ImportError(msg)
if not agents_available:
msg = "You need to `... | Load the OpenAI agent with the given configuration. | _load_agent | python | mozilla-ai/any-agent | src/any_agent/frameworks/openai.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/frameworks/openai.py | Apache-2.0 |
def _filter_mcp_tools(self, tools: list[Any], mcp_servers: list[Any]) -> list[Any]:
"""OpenAI frameowrk doesn't expect the mcp tool to be included in `tools`."""
non_mcp_tools = []
for tool in tools:
if any(tool in mcp_server.tools for mcp_server in mcp_servers):
cont... | OpenAI frameowrk doesn't expect the mcp tool to be included in `tools`. | _filter_mcp_tools | python | mozilla-ai/any-agent | src/any_agent/frameworks/openai.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/frameworks/openai.py | Apache-2.0 |
def _get_model(self, agent_config: AgentConfig) -> Any:
"""Get the model configuration for a smolagents agent."""
model_type = agent_config.model_type or DEFAULT_MODEL_TYPE
model_args = agent_config.model_args or {}
kwargs = {
"model_id": agent_config.model_id,
"a... | Get the model configuration for a smolagents agent. | _get_model | python | mozilla-ai/any-agent | src/any_agent/frameworks/smolagents.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/frameworks/smolagents.py | Apache-2.0 |
async def _load_agent(self) -> None:
"""Load the Smolagents agent with the given configuration."""
if not smolagents_available:
msg = "You need to `pip install 'any-agent[smolagents]'` to use this agent"
raise ImportError(msg)
tools, _ = await self._load_tools(self.confi... | Load the Smolagents agent with the given configuration. | _load_agent | python | mozilla-ai/any-agent | src/any_agent/frameworks/smolagents.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/frameworks/smolagents.py | Apache-2.0 |
async def call_tool(self, request: dict[str, Any]) -> str:
"""Call the tool function.
Args:
request: The tool request with name and arguments
Returns:
Tool execution result
"""
try:
arguments = request.get("arguments", {})
if ha... | Call the tool function.
Args:
request: The tool request with name and arguments
Returns:
Tool execution result
| call_tool | python | mozilla-ai/any-agent | src/any_agent/frameworks/tinyagent.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/frameworks/tinyagent.py | Apache-2.0 |
def __init__(self, config: AgentConfig) -> None:
"""Initialize the TinyAgent.
Args:
config: Agent configuration
tracing: Optional tracing configuration
"""
super().__init__(config)
self.clients: dict[str, ToolExecutor] = {}
self.completion_params... | Initialize the TinyAgent.
Args:
config: Agent configuration
tracing: Optional tracing configuration
| __init__ | python | mozilla-ai/any-agent | src/any_agent/frameworks/tinyagent.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/frameworks/tinyagent.py | Apache-2.0 |
async def _load_agent(self) -> None:
"""Load the agent and its tools."""
wrapped_tools, mcp_servers = await self._load_tools(self.config.tools)
self._mcp_servers = (
mcp_servers # Store servers so that they don't get garbage collected
)
self._tools = wrapped_tools
... | Load the agent and its tools. | _load_agent | python | mozilla-ai/any-agent | src/any_agent/frameworks/tinyagent.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/frameworks/tinyagent.py | Apache-2.0 |
async def serve_a2a_async(
server: A2AStarletteApplication,
host: str,
port: int,
endpoint: str,
log_level: str = "warning",
) -> tuple[asyncio.Task[Any], uvicorn.Server]:
"""Provide an A2A server to be used in an event loop."""
uv_server = _create_server(server, host, port, endpoint, log_le... | Provide an A2A server to be used in an event loop. | serve_a2a_async | python | mozilla-ai/any-agent | src/any_agent/serving/server.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/serving/server.py | Apache-2.0 |
async def a2a_tool_async(
url: str, toolname: str | None = None, http_kwargs: dict[str, Any] | None = None
) -> Callable[[str], Coroutine[Any, Any, str]]:
"""Perform a query using A2A to another agent.
Args:
url (str): The url in which the A2A agent is located.
toolname (str): The name for ... | Perform a query using A2A to another agent.
Args:
url (str): The url in which the A2A agent is located.
toolname (str): The name for the created tool. Defaults to `call_{agent name in card}`.
Leading and trailing whitespace are removed. Whitespace in the middle is replaced by `_`.
... | a2a_tool_async | python | mozilla-ai/any-agent | src/any_agent/tools/a2a.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/tools/a2a.py | Apache-2.0 |
def a2a_tool(
url: str, toolname: str | None = None, http_kwargs: dict[str, Any] | None = None
) -> Callable[[str], str]:
"""Perform a query using A2A to another agent (synchronous version).
Args:
url (str): The url in which the A2A agent is located.
toolname (str): The name for the created... | Perform a query using A2A to another agent (synchronous version).
Args:
url (str): The url in which the A2A agent is located.
toolname (str): The name for the created tool. Defaults to `call_{agent name in card}`.
Leading and trailing whitespace are removed. Whitespace in the middle is ... | a2a_tool | python | mozilla-ai/any-agent | src/any_agent/tools/a2a.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/tools/a2a.py | Apache-2.0 |
def __init__(self, output_type: type[BaseModel]):
"""Create the function that will be used as a tool."""
self.output_type = output_type
# Set docstring for the callable object
self.__doc__ = f"""You must call this tool in order to return the final answer.
Args:
answe... | Create the function that will be used as a tool. | __init__ | python | mozilla-ai/any-agent | src/any_agent/tools/final_output.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/tools/final_output.py | Apache-2.0 |
def search_web(query: str) -> str:
"""Perform a duckduckgo web search based on your query (think a Google search) then returns the top search results.
Args:
query (str): The search query to perform.
Returns:
The top search results.
"""
ddgs = DDGS()
results = ddgs.text(query, ... | Perform a duckduckgo web search based on your query (think a Google search) then returns the top search results.
Args:
query (str): The search query to perform.
Returns:
The top search results.
| search_web | python | mozilla-ai/any-agent | src/any_agent/tools/web_browsing.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/tools/web_browsing.py | Apache-2.0 |
def visit_webpage(url: str) -> str:
"""Visits a webpage at the given url and reads its content as a markdown string. Use this to browse webpages.
Args:
url: The url of the webpage to visit.
"""
try:
response = requests.get(url)
response.raise_for_status()
markdown_cont... | Visits a webpage at the given url and reads its content as a markdown string. Use this to browse webpages.
Args:
url: The url of the webpage to visit.
| visit_webpage | python | mozilla-ai/any-agent | src/any_agent/tools/web_browsing.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/tools/web_browsing.py | Apache-2.0 |
def search_tavily(query: str, include_images: bool = False) -> str:
"""Perform a Tavily web search based on your query and return the top search results.
See https://blog.tavily.com/getting-started-with-the-tavily-search-api for more information.
Args:
query (str): The search query to perform.
... | Perform a Tavily web search based on your query and return the top search results.
See https://blog.tavily.com/getting-started-with-the-tavily-search-api for more information.
Args:
query (str): The search query to perform.
include_images (bool): Whether to include images in the results.
... | search_tavily | python | mozilla-ai/any-agent | src/any_agent/tools/web_browsing.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/tools/web_browsing.py | Apache-2.0 |
def verify_callable(tool: Callable[..., Any]) -> None:
"""Verify that `tool` is a valid callable.
- It needs to have some sort of docstring that describes what it does
- It needs to have typed argument
- It needs to have a typed return.
We need these things because this info gets provided to the a... | Verify that `tool` is a valid callable.
- It needs to have some sort of docstring that describes what it does
- It needs to have typed argument
- It needs to have a typed return.
We need these things because this info gets provided to the agent so that they know how and when to call the tool.
| verify_callable | python | mozilla-ai/any-agent | src/any_agent/tools/wrappers.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/tools/wrappers.py | Apache-2.0 |
async def list_tools(self) -> list[T]:
"""List tools from the MCP server.""" | List tools from the MCP server. | list_tools | python | mozilla-ai/any-agent | src/any_agent/tools/mcp/mcp_connection.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/tools/mcp/mcp_connection.py | Apache-2.0 |
def _filter_tools(self, tools: Sequence[T]) -> Sequence[T]:
"""Filter the tools to only include the ones listed in mcp_tool['tools']."""
requested_tools = list(self.mcp_tool.tools or [])
if not requested_tools:
return tools
name_to_tool = {
tool.name if isinstan... | Filter the tools to only include the ones listed in mcp_tool['tools']. | _filter_tools | python | mozilla-ai/any-agent | src/any_agent/tools/mcp/mcp_connection.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/tools/mcp/mcp_connection.py | Apache-2.0 |
def _check_dependencies(self) -> None:
"""Check if the required dependencies for the MCP server are available."""
self.libraries = "any-agent[mcp,agno]"
self.mcp_available = mcp_available
super()._check_dependencies() | Check if the required dependencies for the MCP server are available. | _check_dependencies | python | mozilla-ai/any-agent | src/any_agent/tools/mcp/frameworks/agno.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/tools/mcp/frameworks/agno.py | Apache-2.0 |
def _create_tool_from_info(
self, tool: Tool, session: "ClientSession"
) -> Callable[..., Any]:
"""Create a tool function from tool information."""
tool_name = tool.name if hasattr(tool, "name") else tool
tool_description = tool.description if hasattr(tool, "description") else ""
... | Create a tool function from tool information. | _create_tool_from_info | python | mozilla-ai/any-agent | src/any_agent/tools/mcp/frameworks/tinyagent.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/tools/mcp/frameworks/tinyagent.py | Apache-2.0 |
async def tool_function(*args, **kwargs) -> Any: # type: ignore[no-untyped-def]
"""Tool function that calls the MCP server."""
# Combine args and kwargs
combined_args = {}
if args and len(args) > 0:
combined_args = args[0]
combined_args.update... | Tool function that calls the MCP server. | tool_function | python | mozilla-ai/any-agent | src/any_agent/tools/mcp/frameworks/tinyagent.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/tools/mcp/frameworks/tinyagent.py | Apache-2.0 |
def from_otel(cls, otel_span: Span) -> AgentSpan:
"""Create an AgentSpan from an OTEL Span."""
return cls(
name=otel_span.name,
kind=SpanKind.from_otel(otel_span.kind),
parent=SpanContext.from_otel(otel_span.parent),
start_time=otel_span.start_time,
... | Create an AgentSpan from an OTEL Span. | from_otel | python | mozilla-ai/any-agent | src/any_agent/tracing/agent_trace.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/tracing/agent_trace.py | Apache-2.0 |
def to_readable_span(self) -> ReadableSpan:
"""Create an ReadableSpan from the AgentSpan."""
return ReadableSpan(
name=self.name,
kind=self.kind,
parent=self.parent,
start_time=self.start_time,
end_time=self.end_time,
status=self.st... | Create an ReadableSpan from the AgentSpan. | to_readable_span | python | mozilla-ai/any-agent | src/any_agent/tracing/agent_trace.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/tracing/agent_trace.py | Apache-2.0 |
def serialize_final_output(self, value: str | BaseModel | None) -> Any:
"""Serialize the final_output and handle any BaseModel subclass."""
if value is None:
return None
if isinstance(value, str):
return value
if isinstance(value, BaseModel):
# This wi... | Serialize the final_output and handle any BaseModel subclass. | serialize_final_output | python | mozilla-ai/any-agent | src/any_agent/tracing/agent_trace.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/tracing/agent_trace.py | Apache-2.0 |
def _invalidate_usage_and_cost_cache(self) -> None:
"""Clear the cached usage_and_cost property if it exists."""
if "usage" in self.__dict__:
del self.tokens
if "cost" in self.__dict__:
del self.cost | Clear the cached usage_and_cost property if it exists. | _invalidate_usage_and_cost_cache | python | mozilla-ai/any-agent | src/any_agent/tracing/agent_trace.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/tracing/agent_trace.py | Apache-2.0 |
def add_span(self, span: AgentSpan | Span) -> None:
"""Add an AgentSpan to the trace and clear the usage_and_cost cache if present."""
if not isinstance(span, AgentSpan):
span = AgentSpan.from_otel(span)
self.spans.append(span)
self._invalidate_usage_and_cost_cache() | Add an AgentSpan to the trace and clear the usage_and_cost cache if present. | add_span | python | mozilla-ai/any-agent | src/any_agent/tracing/agent_trace.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/tracing/agent_trace.py | Apache-2.0 |
def duration(self) -> timedelta:
"""Duration of the parent `invoke_agent` span as a datetime.timedelta object.
The duration is computed from the span's start and end time (in nanoseconds).
Raises ValueError if:
- There are no spans.
- The invoke_agent span is not the la... | Duration of the parent `invoke_agent` span as a datetime.timedelta object.
The duration is computed from the span's start and end time (in nanoseconds).
Raises ValueError if:
- There are no spans.
- The invoke_agent span is not the last span.
- Any of the start/end ... | duration | python | mozilla-ai/any-agent | src/any_agent/tracing/agent_trace.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/tracing/agent_trace.py | Apache-2.0 |
def tokens(self) -> TokenInfo:
"""The current total token count for this trace. Cached after first computation."""
sum_input_tokens = 0
sum_output_tokens = 0
for span in self.spans:
if span.is_llm_call():
sum_input_tokens += span.attributes.get("gen_ai.usage.i... | The current total token count for this trace. Cached after first computation. | tokens | python | mozilla-ai/any-agent | src/any_agent/tracing/agent_trace.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/tracing/agent_trace.py | Apache-2.0 |
def cost(self) -> CostInfo:
"""The current total cost for this trace. Cached after first computation."""
sum_input_cost = 0.0
sum_output_cost = 0.0
for span in self.spans:
if span.is_llm_call():
cost_info = compute_cost_info(span.attributes)
if... | The current total cost for this trace. Cached after first computation. | cost | python | mozilla-ai/any-agent | src/any_agent/tracing/agent_trace.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/tracing/agent_trace.py | Apache-2.0 |
def enable_console_traces() -> None:
"""Enable printing traces to the console."""
has_console_exporter = any(
isinstance(getattr(p, "span_exporter", None), _ConsoleExporter)
for p in TRACE_PROVIDER._active_span_processor._span_processors
)
if not has_console_exporter:
TRACE_PROVI... | Enable printing traces to the console. | enable_console_traces | python | mozilla-ai/any-agent | src/any_agent/tracing/__init__.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/tracing/__init__.py | Apache-2.0 |
def disable_console_traces() -> None:
"""Disable printing traces to the console."""
with TRACE_PROVIDER._active_span_processor._lock:
TRACE_PROVIDER._active_span_processor._span_processors = tuple(
p
for p in TRACE_PROVIDER._active_span_processor._span_processors
if n... | Disable printing traces to the console. | disable_console_traces | python | mozilla-ai/any-agent | src/any_agent/tracing/__init__.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/tracing/__init__.py | Apache-2.0 |
def run_async_in_sync(coro: Coroutine[Any, Any, T]) -> T:
"""Run an async coroutine in a synchronous context.
Handles different event loop scenarios:
- If a loop is running, uses threading to avoid conflicts
- If no loop exists, creates one or uses the current loop
Args:
coro: The coroutin... | Run an async coroutine in a synchronous context.
Handles different event loop scenarios:
- If a loop is running, uses threading to avoid conflicts
- If no loop exists, creates one or uses the current loop
Args:
coro: The coroutine to execute
Returns:
The result of the coroutine ex... | run_async_in_sync | python | mozilla-ai/any-agent | src/any_agent/utils/asyncio_sync.py | https://github.com/mozilla-ai/any-agent/blob/master/src/any_agent/utils/asyncio_sync.py | Apache-2.0 |
async def echo_sse_server() -> AsyncGenerator[dict[str, str]]:
"""This fixture runs a FastMCP server in a subprocess.
I thought about trying to mock all the individual mcp client calls,
but I went with this because this way we don't need to actually mock anything.
This is similar to what MCPAdapt does i... | This fixture runs a FastMCP server in a subprocess.
I thought about trying to mock all the individual mcp client calls,
but I went with this because this way we don't need to actually mock anything.
This is similar to what MCPAdapt does in their testing https://github.com/grll/mcpadapt/blob/main/tests/test_... | echo_sse_server | python | mozilla-ai/any-agent | tests/conftest.py | https://github.com/mozilla-ai/any-agent/blob/master/tests/conftest.py | Apache-2.0 |
def configure_logging(pytestconfig: pytest.Config) -> None:
"""Configure the logging level based on the verbosity of the test run.
This is a session fixture, so it only gets called once per test session.
"""
verbosity = pytestconfig.getoption("verbose")
level = logging.DEBUG if verbosity > 0 else lo... | Configure the logging level based on the verbosity of the test run.
This is a session fixture, so it only gets called once per test session.
| configure_logging | python | mozilla-ai/any-agent | tests/conftest.py | https://github.com/mozilla-ai/any-agent/blob/master/tests/conftest.py | Apache-2.0 |
def mock_litellm_response() -> ModelResponse:
"""Fixture to create a standard mock LiteLLM response"""
return ModelResponse.model_validate_json(
'{"id":"chatcmpl-BWnfbHWPsQp05roQ06LAD1mZ9tOjT","created":1747157127,"model":"gpt-4o-2024-08-06","object":"chat.completion","system_fingerprint":"fp_f5bdcc3276... | Fixture to create a standard mock LiteLLM response | mock_litellm_response | python | mozilla-ai/any-agent | tests/conftest.py | https://github.com/mozilla-ai/any-agent/blob/master/tests/conftest.py | Apache-2.0 |
def mock_litellm_streaming() -> Callable[[Any, Any], AsyncGenerator[Any]]:
"""
Create a fixture that returns an async generator function to mock streaming responses.
This returns a function that can be used as a side_effect.
"""
async def _mock_streaming_response(
*args: Any, **kwargs: Any
... |
Create a fixture that returns an async generator function to mock streaming responses.
This returns a function that can be used as a side_effect.
| mock_litellm_streaming | python | mozilla-ai/any-agent | tests/conftest.py | https://github.com/mozilla-ai/any-agent/blob/master/tests/conftest.py | Apache-2.0 |
def pytest_addoption(parser: pytest.Parser) -> None:
"""
Add custom command-line options to pytest.
This hook adds the `--update-trace-assets` flag to pytest, which can be used when running integration tests.
When this flag is set, tests that generate trace asset files (aka the integration test that
... |
Add custom command-line options to pytest.
This hook adds the `--update-trace-assets` flag to pytest, which can be used when running integration tests.
When this flag is set, tests that generate trace asset files (aka the integration test that
produces agent traces) will update the asset files in the ... | pytest_addoption | python | mozilla-ai/any-agent | tests/integration/conftest.py | https://github.com/mozilla-ai/any-agent/blob/master/tests/integration/conftest.py | Apache-2.0 |
def _is_port_available(port: int, host: str = "localhost") -> bool:
"""Check if a port is available for binding.
This isn't a perfect check but it at least tells us if there is absolutely no chance of binding to the port.
"""
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
try:... | Check if a port is available for binding.
This isn't a perfect check but it at least tells us if there is absolutely no chance of binding to the port.
| _is_port_available | python | mozilla-ai/any-agent | tests/integration/conftest.py | https://github.com/mozilla-ai/any-agent/blob/master/tests/integration/conftest.py | Apache-2.0 |
def _get_deterministic_port(test_name: str, framework_name: str) -> int:
"""Generate a deterministic port number based on test name and framework.
This ensures each test gets a unique port that remains consistent across runs.
"""
# Create a unique string by combining test name and framework
unique_... | Generate a deterministic port number based on test name and framework.
This ensures each test gets a unique port that remains consistent across runs.
| _get_deterministic_port | python | mozilla-ai/any-agent | tests/integration/conftest.py | https://github.com/mozilla-ai/any-agent/blob/master/tests/integration/conftest.py | Apache-2.0 |
def test_port(request, agent_framework):
"""Single fixture that provides a unique, deterministic port for each test."""
test_name = request.node.name
framework_name = agent_framework.value
port = _get_deterministic_port(test_name, framework_name)
# Ensure the port is available, if not, try nearby ... | Single fixture that provides a unique, deterministic port for each test. | test_port | python | mozilla-ai/any-agent | tests/integration/conftest.py | https://github.com/mozilla-ai/any-agent/blob/master/tests/integration/conftest.py | Apache-2.0 |
def assert_first_llm_call(llm_call: AgentSpan) -> None:
"""Checks the `_set_llm_inputs` implemented by each framework's instrumentation."""
assert llm_call.attributes.get("gen_ai.input.messages", None) is not None
# input.messages should be a valid JSON string (list of dicts)
input_messa... | Checks the `_set_llm_inputs` implemented by each framework's instrumentation. | assert_first_llm_call | python | mozilla-ai/any-agent | tests/integration/test_load_and_run_agent.py | https://github.com/mozilla-ai/any-agent/blob/master/tests/integration/test_load_and_run_agent.py | Apache-2.0 |
def assert_first_tool_execution(tool_execution: AgentSpan) -> None:
"""Checks the tools setup implemented by each framework's instrumentation."""
assert tool_execution.attributes.get("gen_ai.tool.args", None) is not None
# tool.args should be a JSON string (dict)
args = json.loads(tool_e... | Checks the tools setup implemented by each framework's instrumentation. | assert_first_tool_execution | python | mozilla-ai/any-agent | tests/integration/test_load_and_run_agent.py | https://github.com/mozilla-ai/any-agent/blob/master/tests/integration/test_load_and_run_agent.py | Apache-2.0 |
async def test_run_agent_twice(agent_framework: AgentFramework) -> None:
"""When an agent is run twice, state from the first run shouldn't bleed into the second run"""
model_id = "gpt-4.1-nano"
env_check = validate_environment(model_id)
if not env_check["keys_in_environment"]:
pytest.skip(f"{env... | When an agent is run twice, state from the first run shouldn't bleed into the second run | test_run_agent_twice | python | mozilla-ai/any-agent | tests/integration/test_load_and_run_agent_twice.py | https://github.com/mozilla-ai/any-agent/blob/master/tests/integration/test_load_and_run_agent_twice.py | Apache-2.0 |
def _assert_contains_current_date_info(final_output: str) -> None:
"""Assert that the final output contains current date and time information."""
now = datetime.datetime.now()
assert all(
[
str(now.year) in final_output,
str(now.day) in final_output,
now.strftime(... | Assert that the final output contains current date and time information. | _assert_contains_current_date_info | python | mozilla-ai/any-agent | tests/integration/test_load_and_run_multi_agent_a2a_tool.py | https://github.com/mozilla-ai/any-agent/blob/master/tests/integration/test_load_and_run_multi_agent_a2a_tool.py | Apache-2.0 |
def _assert_has_date_agent_tool_call(agent_trace: AgentTrace) -> None:
"""Assert that the agent trace contains a tool execution span for the date agent."""
assert any(
span.is_tool_execution()
and span.attributes.get("gen_ai.tool.name", None) == "call_date_agent"
for span in agent_trace.... | Assert that the agent trace contains a tool execution span for the date agent. | _assert_has_date_agent_tool_call | python | mozilla-ai/any-agent | tests/integration/test_load_and_run_multi_agent_a2a_tool.py | https://github.com/mozilla-ai/any-agent/blob/master/tests/integration/test_load_and_run_multi_agent_a2a_tool.py | Apache-2.0 |
async def test_load_and_run_multi_agent_a2a(agent_framework: AgentFramework) -> None:
"""Tests that an agent contacts another using A2A using the adapter tool.
Note that there is an issue when using Google ADK: https://github.com/google/adk-python/pull/566
"""
if agent_framework in [
# async a2... | Tests that an agent contacts another using A2A using the adapter tool.
Note that there is an issue when using Google ADK: https://github.com/google/adk-python/pull/566
| test_load_and_run_multi_agent_a2a | python | mozilla-ai/any-agent | tests/integration/test_load_and_run_multi_agent_a2a_tool.py | https://github.com/mozilla-ai/any-agent/blob/master/tests/integration/test_load_and_run_multi_agent_a2a_tool.py | Apache-2.0 |
def _run_server(
agent_framework_str: str,
port: int,
endpoint: str,
model_id: str,
server_queue: Queue,
):
"""Run the server for the sync test. This needs to be defined outside the test function so that it can be run in a separate process."""
date_agent_description = "Agent that can return ... | Run the server for the sync test. This needs to be defined outside the test function so that it can be run in a separate process. | _run_server | python | mozilla-ai/any-agent | tests/integration/test_load_and_run_multi_agent_a2a_tool.py | https://github.com/mozilla-ai/any-agent/blob/master/tests/integration/test_load_and_run_multi_agent_a2a_tool.py | Apache-2.0 |
def test_load_and_run_multi_agent_a2a_sync(agent_framework: AgentFramework) -> None:
"""Tests that an agent contacts another using A2A using the sync adapter tool.
Note that there is an issue when using Google ADK: https://github.com/google/adk-python/pull/566
"""
if agent_framework in [
# asyn... | Tests that an agent contacts another using A2A using the sync adapter tool.
Note that there is an issue when using Google ADK: https://github.com/google/adk-python/pull/566
| test_load_and_run_multi_agent_a2a_sync | python | mozilla-ai/any-agent | tests/integration/test_load_and_run_multi_agent_a2a_tool.py | https://github.com/mozilla-ai/any-agent/blob/master/tests/integration/test_load_and_run_multi_agent_a2a_tool.py | Apache-2.0 |
async def test_agent_serving_and_communication(test_port):
"""This test can be refactored to remove the need for multiproc, once we have support for control of the uvicorn server."""
# Start the agent in a subprocess
proc = multiprocessing.Process(target=run_agent, args=(test_port,), daemon=True)
proc.s... | This test can be refactored to remove the need for multiproc, once we have support for control of the uvicorn server. | test_agent_serving_and_communication | python | mozilla-ai/any-agent | tests/integration/test_serve_agent.py | https://github.com/mozilla-ai/any-agent/blob/master/tests/integration/test_serve_agent.py | Apache-2.0 |
def test_evaluate_runs_all_evaluators(
evaluation_case: EvaluationCase, agent_trace: AgentTrace
) -> None:
"""This unit test checks that all evaluators are called when evaluating a trace."""
#### Set up the mocks for the evaluators so that we don't actually call LLMs.
mock_checkpoint_evaluate = MagicMoc... | This unit test checks that all evaluators are called when evaluating a trace. | test_evaluate_runs_all_evaluators | python | mozilla-ai/any-agent | tests/unit/evaluation/test_evaluate.py | https://github.com/mozilla-ai/any-agent/blob/master/tests/unit/evaluation/test_evaluate.py | Apache-2.0 |
def test_evaluate_when_no_final_output(
evaluation_case: EvaluationCase, agent_trace: AgentTrace
) -> None:
"""This unit test checks that the hypothesis and qa evaluators are not called when there is no final output."""
#### Set up the mocks for the evaluators so that we don't actually call LLMs.
mock_c... | This unit test checks that the hypothesis and qa evaluators are not called when there is no final output. | test_evaluate_when_no_final_output | python | mozilla-ai/any-agent | tests/unit/evaluation/test_evaluate.py | https://github.com/mozilla-ai/any-agent/blob/master/tests/unit/evaluation/test_evaluate.py | Apache-2.0 |
def test_trace_evaluation_result_score_calculation(agent_trace: AgentTrace) -> None:
"""Test that the score property of TraceEvaluationResult correctly calculates the ratio of passed points to total points."""
# Create evaluation results with different point values and pass status
checkpoint_results = [
... | Test that the score property of TraceEvaluationResult correctly calculates the ratio of passed points to total points. | test_trace_evaluation_result_score_calculation | python | mozilla-ai/any-agent | tests/unit/evaluation/test_evaluate.py | https://github.com/mozilla-ai/any-agent/blob/master/tests/unit/evaluation/test_evaluate.py | Apache-2.0 |
def create_agent_with_model_args(framework: AgentFramework) -> AnyAgent:
"""Helper function to create an agent with test model arguments"""
return AnyAgent.create(
framework,
AgentConfig(
model_id="gpt-4o",
model_args={
"temperature": TEST_TEMPERATURE,
... | Helper function to create an agent with test model arguments | create_agent_with_model_args | python | mozilla-ai/any-agent | tests/unit/frameworks/test_any_agent.py | https://github.com/mozilla-ai/any-agent/blob/master/tests/unit/frameworks/test_any_agent.py | Apache-2.0 |
def test_get_agent_card_with_explicit_skills(agent_framework: AgentFramework) -> None:
"""Test that when skills are explicitly provided in A2AServingConfig, they are used instead of inferring from tools."""
agent = MagicMock()
agent.config = AgentConfig(model_id="foo", description="test agent")
agent.fr... | Test that when skills are explicitly provided in A2AServingConfig, they are used instead of inferring from tools. | test_get_agent_card_with_explicit_skills | python | mozilla-ai/any-agent | tests/unit/serving/test_agent_card.py | https://github.com/mozilla-ai/any-agent/blob/master/tests/unit/serving/test_agent_card.py | Apache-2.0 |
def test_bad_functions(agent_framework: AgentFramework) -> None:
"""Test the verify_callable function with various bad functions."""
# Test missing return type
def missing_return_type(foo: str): # type: ignore[no-untyped-def]
"""Docstring for foo."""
return foo
with pytest.raises(Valu... | Test the verify_callable function with various bad functions. | test_bad_functions | python | mozilla-ai/any-agent | tests/unit/tools/test_unit_wrappers.py | https://github.com/mozilla-ai/any-agent/blob/master/tests/unit/tools/test_unit_wrappers.py | Apache-2.0 |
async def test_agno_client_session_timeout_passed():
"""Test that client_session_timeout_seconds parameter is properly passed to AgnoMCPTools (STDIO only)."""
custom_timeout = 15
stdio_params = MCPStdio(
command="echo",
args=["test"],
client_session_timeout_seconds=custom_timeout,
... | Test that client_session_timeout_seconds parameter is properly passed to AgnoMCPTools (STDIO only). | test_agno_client_session_timeout_passed | python | mozilla-ai/any-agent | tests/unit/tools/mcp/test_unit_agno_mcp.py | https://github.com/mozilla-ai/any-agent/blob/master/tests/unit/tools/mcp/test_unit_agno_mcp.py | Apache-2.0 |
async def test_langchain_client_session_timeout_passed():
"""Test that client_session_timeout_seconds parameter is properly passed to LangChain ClientSession (STDIO and SSE)."""
custom_timeout = 15.0
stdio_params = MCPStdio(
command="echo",
args=["test"],
client_session_timeout_secon... | Test that client_session_timeout_seconds parameter is properly passed to LangChain ClientSession (STDIO and SSE). | test_langchain_client_session_timeout_passed | python | mozilla-ai/any-agent | tests/unit/tools/mcp/test_unit_langchain_mcp.py | https://github.com/mozilla-ai/any-agent/blob/master/tests/unit/tools/mcp/test_unit_langchain_mcp.py | Apache-2.0 |
async def test_llamaindex_client_session_timeout_passed():
"""Test that client_session_timeout_seconds parameter is properly passed to LlamaIndex BasicMCPClient (STDIO only)."""
custom_timeout = 15.0
stdio_params = MCPStdio(
command="echo",
args=["test"],
client_session_timeout_secon... | Test that client_session_timeout_seconds parameter is properly passed to LlamaIndex BasicMCPClient (STDIO only). | test_llamaindex_client_session_timeout_passed | python | mozilla-ai/any-agent | tests/unit/tools/mcp/test_unit_llama_index_mcp.py | https://github.com/mozilla-ai/any-agent/blob/master/tests/unit/tools/mcp/test_unit_llama_index_mcp.py | Apache-2.0 |
def test_openai_mcpsse(
mcp_sse_params_no_tools: MCPSse,
) -> None:
"""This is a test kept for legacy purposes."""
agent_config = AgentConfig(model_id="gpt-4o", tools=[mcp_sse_params_no_tools])
agent = AnyAgent.create("openai", agent_config)
servers = agent._mcp_servers
assert servers
ser... | This is a test kept for legacy purposes. | test_openai_mcpsse | python | mozilla-ai/any-agent | tests/unit/tools/mcp/test_unit_openai_mcp.py | https://github.com/mozilla-ai/any-agent/blob/master/tests/unit/tools/mcp/test_unit_openai_mcp.py | Apache-2.0 |
def test_openai_client_session_timeout_passed():
"""Test that client_session_timeout_seconds parameter is properly passed to OpenAI MCPServerStdio and MCPServerSse."""
custom_timeout = 15.0
stdio_params = MCPStdio(
command="echo",
args=["test"],
client_session_timeout_seconds=custom_... | Test that client_session_timeout_seconds parameter is properly passed to OpenAI MCPServerStdio and MCPServerSse. | test_openai_client_session_timeout_passed | python | mozilla-ai/any-agent | tests/unit/tools/mcp/test_unit_openai_mcp.py | https://github.com/mozilla-ai/any-agent/blob/master/tests/unit/tools/mcp/test_unit_openai_mcp.py | Apache-2.0 |
def test_set_llm_input_missing_fields() -> None:
"""It should not fail when missing fields."""
span = MagicMock()
_set_llm_input([Message(role="user")], span)
span.set_attribute.assert_called_with(
"gen_ai.input.messages", '[{"role": "user", "content": null}]'
) | It should not fail when missing fields. | test_set_llm_input_missing_fields | python | mozilla-ai/any-agent | tests/unit/tracing/instrumentation/test_unit_agno_instrumentation.py | https://github.com/mozilla-ai/any-agent/blob/master/tests/unit/tracing/instrumentation/test_unit_agno_instrumentation.py | Apache-2.0 |
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