Add multi-LLM support and update dependencies in README and agent.py - #10

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OpenAI Agents SDK Python Runner.run max_turns contract MaxTurnsExceeded

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In the OpenAI Agents SDK for Python, the MaxTurnsExceeded exception is raised by the Runner when an agent run exceeds the specified max_turns limit [1][2][3]. ### Mechanism The Runner.run (and its variations run_sync and run_streamed) method accepts a max_turns parameter, which defines the maximum number of AI invocations allowed for a single run [1][4][5]. A "turn" is defined as one complete AI invocation, which may include any tool calls or handoffs that occur within that cycle [4][6][5]. If this limit is reached before the agent completes its task, the SDK raises a MaxTurnsExceeded exception by default [1][3]. To disable this limit, you can pass max_turns=None to the runner [1][4][7]. ### Handling the Exception You can handle the MaxTurnsExceeded exception by providing an error_handlers dictionary to the Runner entry points [1][3][5]. By using the key "max_turns", you can specify a callback function to return a controlled response instead of allowing the exception to propagate [1][3]. Example of handling the error: python from openai_agents import Runner, RunErrorHandlerInput, RunErrorHandlerResult def on_max_turns(_data: RunErrorHandlerInput[None]) -> RunErrorHandlerResult: return RunErrorHandlerResult( final_output="I couldn't finish within the turn limit. Please narrow the request.", include_in_history=False, ) result = Runner.run_sync( agent, "Analyze this long transcript", max_turns=3, error_handlers={"max_turns": on_max_turns}, ) If no error_handlers for "max_turns" are provided, the MaxTurnsExceeded exception is raised, terminating the run [1][4][8].

Citations:

• 1: https://openai.github.io/openai-agents-python/running_agents/
• 2: https://openai.github.io/openai-agents-python/ref/exceptions/
• 3: https://github.com/openai/openai-agents-python/blob/cae28f06/docs/running_agents.md
• 4: https://github.com/openai/openai-agents-python/blob/cdde4d65/src/agents/run.py
• 5: https://github.com/openai/openai-agents-python/blob/7029ea8f/src/agents/run.py
• 6: https://developers.openai.com/api/docs/guides/agents/running-agents
• 7: https://openai.github.io/openai-agents-python/ref/run_config/
• 8: https://github.com/openai/openai-agents-python/blob/3a11cf52/src/agents/run_internal/run_loop.py

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Repository: thirdlayerinc/autoagent

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rg -n -C 3 'LLM_(PROVIDER|BASE_URL|MODEL)|Litellm(Model|Provider)|ModelProvider|set_default|Agent\(' --glob '*.py' --glob 'pyproject.toml' .

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OpenAI Agents Python SDK LiteLLM integration Agent model string LitellmModel base_url documentation

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The OpenAI Agents Python SDK supports integration with LiteLLM to use various LLM providers through the LitellmModel class or the LitellmProvider interface [1][2][3][4]. Installation To use this integration, install the SDK with the LiteLLM optional dependency: pip install "openai-agents[litellm]" [2][5] LitellmModel Usage The LitellmModel class allows you to connect directly to LiteLLM or a LiteLLM proxy [2][3]. Model String and Initialization When instantiating LitellmModel, the model string follows the naming conventions supported by LiteLLM (e.g., "anthropic/claude-3-5-sonnet", "gemini/gemini-2.0-flash-exp") [2][6]. from agents import Agent from agents.extensions.models.litellm_model import LitellmModel agent = Agent( name="Assistant", model=LitellmModel( model="anthropic/claude-3-5-sonnet-20241022", api_key="your-api-key", base_url="http://optional-proxy-url" # Optional: set if using a LiteLLM proxy [2][6])) Key Parameters * model (str): The specific model identifier recognized by LiteLLM [6][7]. * base_url (str | None): The URL of your LiteLLM proxy, if applicable [2][6]. * api_key (str | None): The API key required by the provider or the LiteLLM proxy [2][6]. Integration via LitellmProvider For more flexible routing, you can use LitellmProvider, which allows you to pass a model_provider to your runner [4]. from agents import Runner, RunConfig from agents.extensions.models.litellm_provider import LitellmProvider # Use in the runner result = await Runner.run(agent, input, run_config=RunConfig(model_provider=LitellmProvider)) [4] Additional Configuration * Usage Metrics: If you require token usage tracking, pass ModelSettings(include_usage=True) to your agent configuration [2][5]. * Serializer Warnings: If you encounter Pydantic serializer warnings, you can enable a compatibility patch by setting the environment variable OPENAI_AGENTS_ENABLE_LITELLM_SERIALIZER_PATCH=true [5]. For detailed information on supported models and provider-specific configurations, refer to the official LiteLLM documentation [1][4].

Citations:

• 1: https://openai.github.io/openai-agents-python/ref/extensions/models/litellm_model/
• 2: https://docs.litellm.ai/docs/projects/openai-agents
• 3: https://docs.litellm.ai/docs/tutorials/openai_agents_sdk
• 4: https://openai.github.io/openai-agents-python/ref/extensions/models/litellm_provider/
• 5: https://openai.github.io/openai-agents-python/models/
• 6: https://github.com/openai/openai-agents-python/blob/3a11cf52/src/agents/extensions/models/litellm_model.py
• 7: https://github.com/openai/openai-agents-python/blob/67687cba/src/agents/extensions/models/litellm_model.py

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LiteLLM Azure AZURE_API_VERSION required environment variables azure provider documentation

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In LiteLLM, the AZURE_API_VERSION environment variable is a standard requirement for authenticating and routing requests to the Azure OpenAI provider [1][2]. While LiteLLM maintains a default internal API version (e.g., 2025-02-01-preview as of recent versions [3]), it is best practice to explicitly set AZURE_API_VERSION to ensure compatibility with your specific model deployment [1][4]. Key points regarding the configuration: 1. Mandatory Variables: To successfully call Azure models, you generally need to provide AZURE_API_KEY, AZURE_API_BASE, and AZURE_API_VERSION [2][5]. 2. Environment Configuration: You can set these globally as environment variables [1][6][7]: export AZURE_API_KEY="your-key" export AZURE_API_BASE="https://your-resource.openai.azure.com" export AZURE_API_VERSION="2024-02-01" 3. Alternative Configurations: - Within a LiteLLM Proxy configuration (config.yaml), you can specify api_version per model to avoid global conflicts or to support models requiring specific, newer API versions [1][4]. - You can also pass these parameters directly into the completion or embedding function calls in your Python code as keyword arguments [1][8][7]. 4. Troubleshooting: If you encounter 400 Bad Request errors, it is often because the requested model requires a newer API version than the default one being used by LiteLLM [4]. Explicitly defining the version in your configuration or code usually resolves this [4][9]. For checking your current environment configuration, you can use the LiteLLM helper function litellm.validate_environment(model_name) to identify missing required variables [6].

Citations:

• 1: https://docs.litellm.ai/docs/providers/azure/
• 2: https://docs.litellm.ai/completion/supported
• 3: https://docs.litellm.ai/release_notes/v1.63.11-stable/v1.63.11-stable
• 4: [Bug]: Wrong Azure API version used in calls to the Azure OpenAI backend BerriAI/litellm#11317
• 5: https://mintlify.wiki/BerriAI/litellm/api/environment-variables
• 6: https://docs.litellm.ai/docs/set_keys
• 7: https://mintlify.wiki/BerriAI/litellm/providers/azure
• 8: https://docs.litellm.ai/docs/providers/azure/azure_embedding
• 9: [Bug]: Azure "api_version" not respected when provided from client side BerriAI/litellm#2383

🌐 Web query:

site:docs.litellm.ai/docs/providers/azure AZURE_API_VERSION optional default api_version

💡 Result:

The api-version parameter is generally mandatory for Azure REST API requests, including those for Azure OpenAI and other services, as it specifies the version of the API to be used for the request [1][2][3][4]. Azure typically does not support a default api-version; therefore, failing to provide a valid version will often result in a request error [1][2][4]. In practice, users must explicitly specify the api-version in their API calls or configuration, such as in the following examples: For Python (using LiteLLM as an example): response = litellm.completion( model = "azure/", api_base = "", api_version = "2024-08-01-preview", api_key = "", messages = [{"role": "user", "content": "Hello!"}]) For Configuration files (YAML): model_list: - model_name: gpt-4 litellm_params: model: azure/my-gpt4-deployment api_base: https://your-resource.openai.azure.com/ api_version: "2024-08-01-preview" api_key: os.environ/AZURE_API_KEY Some specific Azure endpoints or implementations may offer paths that abstract the need to update version strings monthly (e.g., using "preview" or specific versionless endpoints), but the api-version itself remains a required component of the request structure to ensure correct routing and handling by the Azure service [2].

Citations:

• 1: https://docs.litellm.ai/docs/providers/azure/
• 2: https://docs.litellm.ai/docs/providers/azure/azure_responses
• 3: https://docs.litellm.ai/docs/providers/azure/azure_embedding
• 4: https://docs.litellm.ai/docs/providers/azure/azure_speech

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expected = {
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        'git clone https://github.com/Oncorporation/open-autoagent.git',
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    ],
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        'test -f vendor/secure-torrent-mcp-agent/AGENTS.md',
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        'Then propose one harness change above the FIXED ADAPTER BOUNDARY.',
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OpenAI Agents SDK LiteLLM model settings extra_args think false max context Ollama

💡 Result:

To control the "think" (reasoning) behavior in Ollama when using the OpenAI Agents SDK via LiteLLM, you should manage settings through the SDK's ModelSettings object, specifically utilizing extra_args or the reasoning attribute [1][2][3]. ### How to Disable Thinking To disable thinking for Ollama models via LiteLLM, you typically need to pass the appropriate parameter through extra_args [2][3]. Since LiteLLM maps specific parameters to the Ollama API, you can use extra_args to explicitly set the "think" behavior [2][3]. Example approach using ModelSettings: python from agents import ModelSettings # Use extra_args to explicitly set think to false settings = ModelSettings( extra_args={"think": False} ) ### Context and Parameter Mapping * LiteLLM Mapping: LiteLLM is responsible for transforming OpenAI-compatible parameters into the format required by the underlying model provider (in this case, Ollama) [4][5]. Recent updates to LiteLLM have improved the mapping of reasoning_effort to the Ollama think parameter [4][5]. * extra_args: In the OpenAI Agents SDK, extra_args serves as an "escape hatch" for passing provider-specific arguments that the SDK does not explicitly expose [6][7][2]. When using the LitellmModel extension, these arguments are passed through to the underlying LiteLLM acompletion call [2][3]. * Ollama think Parameter: The native Ollama API accepts a think parameter [8]. While some models accept boolean values (true/false), others (like gpt-oss) require level-based strings (low, medium, high) [8][4][5]. * Prioritization: If you encounter issues where extra_args is overridden or ignored, check if reasoning_effort is being set elsewhere, as the LitellmModel implementation has specific priority logic for resolving reasoning arguments [2][3]. ### Recommendations 1. Verify Provider Requirements: Check your specific Ollama model's documentation to confirm if it accepts false for the think parameter, as some models may strictly require a level (e.g., low) or may not support disabling it entirely [8][5]. 2. Use extra_args: As shown above, defining {"think": False} within extra_args in your ModelSettings is the intended way to override provider-specific behavior when using the SDK's LitellmModel [2][3]. 3. Debug Logs: If the setting does not take effect, enable LiteLLM debug logging (litellm._turn_on_debug()) to inspect the final payload being sent to the Ollama API [9]. This will confirm if the think parameter is being correctly passed or if it is being dropped/transformed by LiteLLM [4][9].

Citations:

• 1: https://openai.github.io/openai-agents-python/ref/model_settings/
• 2: https://openai.github.io/openai-agents-python/ref/extensions/models/litellm_model/
• 3: https://github.com/openai/openai-agents-python/blob/3a11cf52/src/agents/extensions/models/litellm_model.py
• 4: fix(ollama/chat): correctly map reasoning_effort to think in requests BerriAI/litellm#15465
• 5: https://github.com/BerriAI/litellm/blob/main/litellm/llms/ollama/completion/transformation.py
• 6: https://openai.github.io/openai-agents-python/models/
• 7: https://github.com/openai/openai-agents-python/blob/main/docs/models/index.md
• 8: https://docs.ollama.com/capabilities/thinking
• 9: [Bug]: "allowed_openai_params" does not work for SDK (thinking param) BerriAI/litellm#11185

🌐 Web query:

openai-agents Python Agent model_settings LiteLLM provider Ollama context window

💡 Result:

To use Ollama with the OpenAI Agents SDK via LiteLLM, you can leverage the LitellmModel extension [1]. This allows you to route requests to local models running on your Ollama server [2]. Installation and Setup 1. Install the necessary dependency: pip install 'openai-agents[litellm]' [3][1] 2. Use the LitellmModel class in your agent definition, pointing to your local Ollama server [1]. You must specify the model using the ollama_chat/ prefix to ensure LiteLLM uses the correct chat API endpoint [4][2]. Example usage: from agents import Agent from agents.extensions.models.litellm_model import LitellmModel agent = Agent( name="Local Ollama Agent", model=LitellmModel( model="ollama_chat/llama3", # Use the ollama_chat/ prefix api_key="any-key", # LiteLLM requires an API key, but it can be a dummy value for local Ollama base_url="http://localhost:11434")) Model Settings and Context Window The ModelSettings class is used to tune LLM behavior (e.g., temperature, top_p, tool_choice) [5][6]. While ModelSettings includes parameters like max_tokens [7][8], it does not explicitly contain a dedicated "context_window" parameter. When working with local models like those in Ollama: - Model Limits: The context window for a model is typically defined by the model itself or the Ollama configuration, not by the OpenAI Agents SDK's ModelSettings [5][7]. - LiteLLM and Context: LiteLLM acts as a pass-through for many parameters [9][10]. If you need to manage context limits, ensure your Ollama instance is configured to handle the desired context size, as the SDK will pass tokens according to its internal logic, but the hard constraint is enforced by the model provider [11][4]. - Advanced Settings: If you need specialized LiteLLM behavior, you can use the extra_body or extra_args fields within ModelSettings to pass parameters through to the underlying LiteLLM/Ollama provider [9][7][8].

Citations:

• 1: https://docs.litellm.ai/docs/projects/openai-agents
• 2: https://viswakumar.substack.com/p/exploring-openai-agent-sdk-with-local
• 3: https://github.com/openai/openai-agents-python/blob/main/docs/models/index.md
• 4: https://docs.litellm.ai/docs/providers/ollama
• 5: https://openai.github.io/openai-agents-python/ref/model_settings/
• 6: https://openai.github.io/openai-agents-python/agents/
• 7: https://github.com/openai/openai-agents-python/blob/cdde4d65/src/agents/model_settings.py
• 8: https://github.com/openai/openai-agents-python/blob/67687cba/src/agents/model_settings.py
• 9: https://openai.github.io/openai-agents-python/ref/extensions/models/litellm_model/
• 10: https://github.com/openai/openai-agents-python/blob/67687cba/src/agents/extensions/models/litellm_model.py
• 11: https://openai.github.io/openai-agents-python/models/

🌐 Web query:

site:docs.litellm.ai Ollama num_ctx extra_body max context

💡 Result:

In the context of Ollama, num_ctx is a specific parameter used to set the size of the context window, which determines the maximum number of tokens the model can consider at one time (including both input and output) [1][2]. Regarding the terms mentioned: num_ctx: This is a Ollama-specific parameter that controls the model's context window size (e.g., setting it to 4096 or 8192). Increasing this allows the model to handle longer inputs or maintain longer conversations, though it will consume more memory (VRAM/RAM) [1]. extra_body: This is not an inherent parameter of the Ollama API itself, but rather a common parameter used in client libraries like LiteLLM or the standard OpenAI SDK to pass additional, provider-specific fields to the underlying API [2][3]. If you are interacting with Ollama through a proxy or a wrapper (such as LiteLLM), you can use extra_body to pass Ollama-specific configurations (like num_ctx) that the wrapper might not expose as a top-level argument [2][3]. max context: This refers to the hard limit of the model's context window. While you can adjust num_ctx to be smaller or (in some cases) larger than the model's default, you are ultimately constrained by the maximum context length the specific model architecture was trained to support. Exceeding this, or setting num_ctx beyond the model's physical limit, may cause errors or degradation in model performance [4]. To use num_ctx via an extra_body (common in proxy setups), your request structure would typically look like this: extra_body={ "num_ctx": 8192 } [3][5] top_results: [1][2][3][4]

Citations:

• 1: https://docs.litellm.ai/docs/providers/ollama
• 2: https://docs.litellm.ai/docs/completion/provider_specific_params
• 3: https://docs.litellm.ai/docs/proxy/user_keys
• 4: https://docs.litellm.ai/docs/tutorials/model_fallbacks
• 5: https://docs.litellm.ai/docs/providers/litellm_proxy

🌐 Web query:

site:openai.github.io/openai-agents-python ModelSettings extra_args extra_body LitellmModel

💡 Result:

In the context of the OpenAI Agents SDK, ModelSettings is a configuration class used to define parameters for model interactions [1][2]. Within ModelSettings, extra_args and extra_body serve as "escape hatches" for passing advanced, provider-specific overrides when using models, including LitellmModel [3][1]. 1. extra_body: This field is used to pass additional body fields to the API request [1]. For instance, when using LitellmModel, it is sometimes used to pass provider-specific parameters like reasoning_effort that may not be covered by standard SDK fields [3]. 2. extra_args: This field accepts a dictionary of arbitrary keyword arguments to be passed directly to the underlying model provider's API call [1]. When using LitellmModel, these fields are prioritized to resolve parameters that are not natively handled by the SDK's standard interface [3]. For example, the SDK resolves reasoning_effort by checking the following order of precedence [3]: 1. The standard model_settings.reasoning.effort field [3]. 2. The extra_body["reasoning_effort"] field [3]. 3. The extra_args["reasoning_effort"] field [3]. These mechanisms allow developers to extend the functionality of LitellmModel to support features specific to various model providers without requiring explicit support in the Agents SDK [3][1].

Citations:

• 1: https://openai.github.io/openai-agents-python/ref/model_settings/
• 2: https://openai.github.io/openai-agents-python/models/
• 3: https://openai.github.io/openai-agents-python/ref/extensions/models/litellm_model/

🌐 Web query:

Ollama API num_ctx options context window official documentation

💡 Result:

In Ollama, the num_ctx parameter defines the size of the context window buffer, which holds both the input prompt and the generated output tokens [1]. It does not act as a hard limit for total conversation length; instead, when the buffer is full, older tokens are shifted out to make room for new ones [1]. You can set num_ctx through the following methods: 1. Ollama API: Include num_ctx within the options object in your request [2][3][4]. { "model": "llama3.2", "prompt": "Your prompt here", "options": { "num_ctx": 4096 } } 2. Modelfile: Define the parameter directly within a Modelfile using the PARAMETER instruction [5]. PARAMETER num_ctx 4096 3. CLI: When using the ollama run command, you can update the parameter within the session using the /set command [6][4]. /set parameter num_ctx 4096 4. Environment Variable: You can set a global default for the Ollama server using the OLLAMA_CONTEXT_LENGTH environment variable [7][4]. OLLAMA_CONTEXT_LENGTH=8192 ollama serve Note that while increasing num_ctx allows for more context, it also increases the memory (VRAM) required to run the model [7]. If you set a value higher than the model's maximum supported context length, it will be capped at the model's architectural limit [1]. By default, Ollama adjusts context length based on available VRAM [7].

Citations:

• 1: docs: clarify num_ctx parameter description ollama/ollama#13181
• 2: https://docs.ollama.com/api/generate
• 3: https://github.com/ollama/ollama/blob/main/docs/api.md
• 4: https://docs.ollama.com/faq
• 5: https://docs.ollama.com/modelfile
• 6: how to change the max input token length when I run ‘’ollama run gemma:7b-instruct-v1.1-fp16‘’ ollama/ollama#3643
• 7: https://docs.ollama.com/context-length

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🌐 Web query:

site:openai.github.io/openai-agents-python Agent model string default model provider LitellmModel raw string

💡 Result:

In the OpenAI Agents SDK, the LitellmProvider is a ModelProvider extension that uses LiteLLM to route model requests to various providers [1]. When using LiteLLM within this SDK, you can specify models using string identifiers prefixed with litellm/ (e.g., litellm/openai/gpt-4o) [2][3]. The LitellmProvider resolves these model strings by instantiating a LitellmModel object [1]. Regarding defaults and raw strings: 1. Model Resolution: The LitellmProvider uses the get_model method, which accepts an optional model_name string [1]. If no model name is provided (or if it is None), it falls back to a default value defined by the get_default_model function [1]. 2. Default Model: The get_default_model function retrieves the default model name, which is controlled by the OPENAI_DEFAULT_MODEL_ENV_VARIABLE_NAME environment variable, defaulting to "gpt-5.4-mini" if the variable is not set [4]. 3. Raw Model String: The LitellmModel class is the underlying model wrapper that takes the model string directly [5]. You can instantiate LitellmModel(model="your-model-string") manually if you need to bypass the provider resolution or use specific configurations [2]. In summary, when using the LitellmProvider, the model string is typically handled via the prefix system (litellm/...), and if you do not specify a model, the system defaults to the value returned by get_default_model [1][4][3].

Citations:

• 1: https://openai.github.io/openai-agents-python/ref/extensions/models/litellm_provider/
• 2: https://openai.github.io/openai-agents-python/models/
• 3: https://openai.github.io/openai-agents-python/ref/models/multi_provider/
• 4: https://openai.github.io/openai-agents-python/ref/models/default_models/
• 5: https://openai.github.io/openai-agents-python/ref/extensions/models/litellm_model/

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