From a task to the components you need.

Let an agent read a Solana account balance

Plan a read-only tool that returns a documented RPC result to an agent.

Components

OpenAI Agents SDK

Agent runtime and a custom function tool

Solana

Read-only account data through JSON-RPC

A component has changed official documentation and is awaiting a source review. Read its current documentation before using this route.

How the interfaces connect

Wrap an HTTP request to getBalance in a Python function tool. This is a reference design using documented interfaces; it requires implementation and validation.

Before you start

• A Python environment and a pinned version of the Agents SDK.
• A chosen model, its API credentials and an explicit usage budget.
• The account’s public address and an RPC endpoint for the intended Solana cluster.

Implementation route

1. Follow the SDK function-tool guide and define a tool with a single public-address argument.
2. Implement the HTTP adapter with the documented getBalance request and a bounded timeout; return the RPC value and context.
3. Validate malformed addresses, RPC errors and the chosen cluster outside the agent before connecting the tool.
4. Give the agent only this read tool and check that its explanation agrees with the RPC result.

Limits to check

• Reading a balance does not sign or send a transaction.
• RPC limits and model usage are separate constraints.
• The documentation establishes the two interfaces; it does not establish a tested native Solana integration in the SDK.

Prepare a local model and an API client

Choose the installation, model and client route for LocalAI before adopting local inference.

Components

Localai

Local model runtime and documented API endpoints

Before you start

• A machine and installation method supported by the current LocalAI documentation.
• A suitable model, its documented resource requirements and its license.
• A client for the endpoint and modality you intend to use.

Implementation route

1. Follow the installation and quickstart route in order.
2. Install the selected model using a documented model source.
3. Use the endpoint example for the intended modality and inspect a first response.
4. Record the model, runtime version and hardware before comparing results or integrating another client.

Limits to check

• Local inference still consumes hardware resources and maintenance time.
• Support for a protocol endpoint does not prove every model supports every tool or modality.
• Check the selected model’s license separately from the runtime.

Choose stable asset IDs before reading market data

Prepare a CoinGecko market-data lookup using the provider’s own identifiers.

Components

CoinGecko

Asset identification and market-data API documentation

A component has changed official documentation and is awaiting a source review. Read its current documentation before using this route.

Before you start

• Access to the API plan and authentication method you intend to use.
• A specific asset and a way to disambiguate its identity.
• The currencies and market fields your application actually needs.

Implementation route

1. Use the coins/list documentation to resolve the provider’s asset ID rather than matching a symbol alone.
2. Follow the coins/markets documentation to request the required assets and currency.
3. Record the asset ID, request time and returned fields with your result.
4. Check rate limits and error responses before scheduling repeated requests.

Limits to check

• Symbols can collide across assets and networks.
• Market metadata does not verify the safety of a token or protocol.
• Provider access terms, freshness and limits must be checked for the selected plan.