Agent Observability Python Provider Helper: Gemini
agento11y-gemini provides strict Gemini Models wrappers and mappers for agento11y.
Installation
pip install agento11y agento11y-gemini google-genai
Public API
- Wrappers:
models.generate_content(...)models.generate_content_async(...)models.generate_content_stream(...)models.generate_content_stream_async(...)models.embed_content(...)models.embed_content_async(...)
- Mappers:
models.from_request_response(...)models.from_stream(...)models.embedding_from_response(...)
Wrapper Mode (Sync)
from google.genai import types as genai_types
from agento11y import Client, ClientConfig
from agento11y_gemini import GeminiOptions, models
client = Client(ClientConfig())
model = "gemini-2.5-pro"
contents = [genai_types.Content(role="user", parts=[genai_types.Part(text="Hello")])]
config = genai_types.GenerateContentConfig(max_output_tokens=256)
response = models.generate_content(
client,
model,
contents,
config,
lambda req_model, req_contents, req_config: gemini_client.models.generate_content(
model=req_model,
contents=req_contents,
config=req_config,
),
GeminiOptions(conversation_id="conv-1", agent_name="assistant", agent_version="1.0.0"),
)
Wrapper Mode (Stream)
from agento11y_gemini import GeminiStreamSummary, models
summary = models.generate_content_stream(
client,
model,
contents,
config,
lambda req_model, req_contents, req_config: GeminiStreamSummary(
responses=list(gemini_client.models.generate_content_stream(
model=req_model,
contents=req_contents,
config=req_config,
))
),
)
Mapper Mode
generation = models.from_request_response(model, contents, config, response)
stream_generation = models.from_stream(model, contents, config, summary)
Embedding example
embedding_response = models.embed_content(
client,
"gemini-embedding-001",
contents,
None,
lambda req_model, req_contents, req_config: gemini_client.models.embed_content(
model=req_model,
contents=req_contents,
config=req_config,
),
)
Raw Provider Artifacts (Opt-In)
options = GeminiOptions(raw_artifacts=True)
Raw artifacts are default OFF and should only be enabled for diagnostics.
Provider metadata mapping
Gemini-specific fields are mapped as follows:
usage.thoughts_token_count-> normalizedusage.reasoning_tokensusage.tool_use_prompt_token_count-> metadataagento11y.gen_ai.usage.tool_use_prompt_tokensconfig.thinking_config.thinking_budget-> metadataagento11y.gen_ai.request.thinking.budget_tokensconfig.thinking_config.thinking_level-> metadataagento11y.gen_ai.request.thinking.level
Metadata
Release files for agento11y-gemini 0.17.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| agento11y_gemini-0.17.0.tar.gz | 11.2 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| agento11y_gemini-0.17.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 19.8 kB
Release files / agento11y_gemini-0.17.0.tar.gz
| Download URL | agento11y_gemini-0.17.0.tar.gz |
|---|---|
| Size | 11.2 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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| Size | 8.6 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.13
|
Provenance
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PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
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