docs: cleanup README and improve agent guides (#17003)

* docs: cleanup README and improve AI agent guides

- Remove obsolete version warnings (openai>=1.0.0, pydantic>=2.0.0)
- Add note about Responses API in README
- Add GitHub templates section to CLAUDE.md, GEMINI.md, and AGENTS.md
- Remove temporary test file test_pydantic_fields.py

* update files

* update Gemini file

AGENTS.md

@@ -98,6 +98,25 @@ LiteLLM supports MCP for agent workflows:
 
 Use `poetry run python script.py` to run Python scripts in the project environment (for non-test files).
 
+## GITHUB TEMPLATES
+
+When opening issues or pull requests, follow these templates:
+
+### Bug Reports (`.github/ISSUE_TEMPLATE/bug_report.yml`)
+- Describe what happened vs. expected behavior
+- Include relevant log output
+- Specify LiteLLM version
+- Indicate if you're part of an ML Ops team (helps with prioritization)
+
+### Feature Requests (`.github/ISSUE_TEMPLATE/feature_request.yml`)
+- Clearly describe the feature
+- Explain motivation and use case with concrete examples
+
+### Pull Requests (`.github/pull_request_template.md`)
+- Add at least 1 test in `tests/litellm/`
+- Ensure `make test-unit` passes
+
+
 ## TESTING CONSIDERATIONS
 
 1. **Provider Tests**: Test against real provider APIs when possible

CLAUDE.md

@@ -28,6 +28,22 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
 ### Running Scripts
 - `poetry run python script.py` - Run Python scripts (use for non-test files)
 
+### GitHub Issue & PR Templates
+When contributing to the project, use the appropriate templates:
+
+**Bug Reports** (`.github/ISSUE_TEMPLATE/bug_report.yml`):
+- Describe what happened vs. what you expected
+- Include relevant log output
+- Specify your LiteLLM version
+
+**Feature Requests** (`.github/ISSUE_TEMPLATE/feature_request.yml`):
+- Describe the feature clearly
+- Explain the motivation and use case
+
+**Pull Requests** (`.github/pull_request_template.md`):
+- Add at least 1 test in `tests/litellm/`
+- Ensure `make test-unit` passes
+
 ## Architecture Overview
 
 LiteLLM is a unified interface for 100+ LLM providers with two main components:

GEMINI.md

@@ -25,6 +25,25 @@ This file provides guidance to Gemini when working with code in this repository.
 - `poetry run pytest tests/path/to/test_file.py -v` - Run specific test file
 - `poetry run pytest tests/path/to/test_file.py::test_function -v` - Run specific test
 
+### Running Scripts
+- `poetry run python script.py` - Run Python scripts (use for non-test files)
+
+### GitHub Issue & PR Templates
+When contributing to the project, use the appropriate templates:
+
+**Bug Reports** (`.github/ISSUE_TEMPLATE/bug_report.yml`):
+- Describe what happened vs. what you expected
+- Include relevant log output
+- Specify your LiteLLM version
+
+**Feature Requests** (`.github/ISSUE_TEMPLATE/feature_request.yml`):
+- Describe the feature clearly
+- Explain the motivation and use case
+
+**Pull Requests** (`.github/pull_request_template.md`):
+- Add at least 1 test in `tests/litellm/`
+- Ensure `make test-unit` passes
+
 ## Architecture Overview
 
 LiteLLM is a unified interface for 100+ LLM providers with two main components:

README.md

@@ -48,10 +48,6 @@ Support for more providers. Missing a provider or LLM Platform, raise a [feature
 
 # Usage ([**Docs**](https://docs.litellm.ai/docs/))
 
-> [!IMPORTANT]
-> LiteLLM v1.0.0 now requires `openai>=1.0.0`. Migration guide [here](https://docs.litellm.ai/docs/migration)
-> LiteLLM v1.40.14+ now requires `pydantic>=2.0.0`. No changes required.
-
 <a target="_blank" href="https://colab.research.google.com/github/BerriAI/litellm/blob/main/cookbook/liteLLM_Getting_Started.ipynb">
   <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
 </a>
@@ -114,6 +110,8 @@ print(response)
 }
 ```
 
+> **Note:** LiteLLM also supports the [Responses API](https://docs.litellm.ai/docs/response_api) (`litellm.responses()`)
+
 Call any model supported by a provider, with `model=<provider_name>/<model_name>`. There might be provider-specific details here, so refer to [provider docs for more information](https://docs.litellm.ai/docs/providers)
 
 ## Async ([Docs](https://docs.litellm.ai/docs/completion/stream#async-completion))

test_pydantic_fields.py

@@ -1,42 +0,0 @@
-from litellm.proxy._types import GenerateKeyRequest
-
-# Test 1: Check if fields exist in model
-print("=== Test 1: Check model_fields ===")
-print(
-    f"rpm_limit_type in model_fields: {'rpm_limit_type' in GenerateKeyRequest.model_fields}"
-)
-print(
-    f"tpm_limit_type in model_fields: {'tpm_limit_type' in GenerateKeyRequest.model_fields}"
-)
-
-# Test 2: Create instance with empty dict (simulating FastAPI parsing minimal request)
-print("\n=== Test 2: Create instance with minimal data ===")
-instance = GenerateKeyRequest()
-print(f"Instance created: {instance}")
-print(f"Instance dict: {instance.model_dump()}")
-
-# Test 3: Try to access the fields
-print("\n=== Test 3: Try direct attribute access ===")
-try:
-    print(f"instance.rpm_limit_type = {instance.rpm_limit_type}")
-    print(f"instance.tpm_limit_type = {instance.tpm_limit_type}")
-except AttributeError as e:
-    print(f"AttributeError: {e}")
-
-# Test 4: Try getattr
-print("\n=== Test 4: Try getattr ===")
-print(
-    f"getattr(instance, 'rpm_limit_type', None) = {getattr(instance, 'rpm_limit_type', None)}"
-)
-print(
-    f"getattr(instance, 'tpm_limit_type', None) = {getattr(instance, 'tpm_limit_type', None)}"
-)
-
-# Test 5: Check what fields are actually set
-print("\n=== Test 5: Check model_fields_set ===")
-print(f"model_fields_set: {instance.model_fields_set}")
-
-# Test 6: Check instance __dict__
-print("\n=== Test 6: Check instance __dict__ ===")
-print(f"'rpm_limit_type' in __dict__: {'rpm_limit_type' in instance.__dict__}")
-print(f"'tpm_limit_type' in __dict__: {'tpm_limit_type' in instance.__dict__}")