Use Anaconda or conda to manage the project’s Python environment and dependencies; use an agent SDK to define and run the agent. A practical starting point is a separate conda environment, a small Python script using one agent framework, and runtime credentials stored outside your source code.
What Anaconda does—and what the agent framework does
Conda keeps a project’s Python version and packages separate from other projects. It can create and activate named environments, install dependencies, and export environment specifications for reuse. The agent framework provides the runtime: it defines how an agent receives instructions, calls tools, manages turns, or delegates work.
The OpenAI Agents SDK is one option for a hosted-provider workflow, not a requirement for building every Python agent. Anaconda AI is another optional route when you want its curated models or integrations; its documentation describes integrations with frameworks including LangChain, LlamaIndex, and Pydantic AI, but does not make Anaconda AI necessary for agents generally. See Anaconda AI documentation.
Create a project and its conda environment
Start with a dedicated environment so the agent’s dependencies do not alter another project’s setup. Conda’s project tutorial demonstrates keeping an environment.yml with project files, creating and activating the environment, and running a Python script: Conda environment management and creating an environment from an environment.yml file.
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Create and enter a project directory, then choose an environment name. The following commands use
my-agentas an example:mkdir my-agent
cd my-agent
conda create --name my-agent python
conda activate my-agent -
Check the current installation and Python compatibility requirements for your selected agent framework before pinning a Python version or package versions. Requirements can change, and no single Python version is established here as suitable for every framework.
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Install the chosen framework into the activated environment. For the OpenAI Agents SDK, the documented package-install command is:
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Build and run a minimal agent
The OpenAI Agents SDK quickstart uses an Agent to define instructions and Runner to run it. Start with one focused job; make sure the basic run works before adding tools or multi-agent behavior. Consult the OpenAI Agents SDK quickstart for the current code example and prerequisites.
For the quickstart’s OpenAI example, provide an OPENAI_API_KEY in the shell or other runtime configuration before running the script. The SDK’s configuration guide explains when it resolves the key: when the SDK first creates its OpenAI client. Keep the key out of checked-in source files and environment definitions. See SDK configuration.
Add tools, conversation state, and delegation as needed
A minimal agent can be enough for a single task. Add runtime features only to solve a concrete requirement:
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Function tools: Add these when the agent must call a function, use code, or interact with a service.
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Sessions or explicit conversation state: Use these when a task needs to continue across multiple turns rather than treating each run independently.
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Handoffs: Use them when a specialist agent should take over a part of the work.
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Guardrails and tracing: Apply guardrails to validate inputs or outputs where appropriate, and tracing to inspect how a run proceeded.
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The SDK documents these capabilities in its Python documentation. Their availability does not mean every agent needs all of them.
Save and share the environment
Keep a dependency definition with the project so you or a collaborator can recreate its environment. Conda supports YAML exports and platform-specific explicit exports; choose according to whether you need a more portable specification or an export tied to a particular platform. The exact export options are documented in the conda export command reference.
For a project-managed YAML file, define the environment and dependencies in environment.yml, then create the environment from that file with:
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conda env create --file environment.yml
For an existing environment, use the current conda export command and select the format appropriate to your sharing needs. Review the resulting file before committing it, and do not include API keys or other secrets.
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Useful when |
What it provides |
|---|---|---|
|
Conda environment plus an agent SDK |
You want an isolated, shareable Python setup and a framework runtime for a hosted-provider workflow. |
Conda manages Python and dependencies; the chosen SDK supplies agent features such as runs, tools, sessions, or tracing. |
|
Anaconda AI |
You specifically want Anaconda-curated models or its documented integrations. |
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These are not mutually exclusive categories: an environment manager and an agent runtime address different parts of a project. Compare provider and model access, required control over state and delegation, environment reproducibility, and deployment constraints. The cited setup documentation does not establish a universal framework winner or a head-to-head performance result.
Common setup problems to check
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Package installed in the wrong Python: Activate the intended environment before installing or running the project. Confirm the shell shows the expected environment name.
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Missing API key: Configure the provider credential in the runtime environment before the SDK creates its client. Do not put a real secret in source control or a shared environment file.
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Environment fails to recreate: Check that the environment definition matches the project’s actual dependencies and that the requested packages support the selected Python version and platform.
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Export is not portable enough: Choose the conda export format intentionally; a platform-specific explicit export and a portable YAML specification serve different reproducibility needs.
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