Expand description
Execution context system for Anda agents and tools.
Contexts carry the runtime capabilities exposed to anda_core agents and
tools: caller identity, request metadata, cancellation, scoped cache, object
storage, HTTP and canister calls, Web3 signing, remote engine access, and
per-session state. Contexts are hierarchical so each agent or tool receives a
scoped namespace while sharing the same underlying runtime resources.
Modules§
- mock
- Mock implementations for tests.
Structs§
- Agent
Ctx - Context for agent operations, providing access to models, tools, and other agents.
- Agent
Info - Contains descriptive and operational information about an AI agent.
- BaseCtx
- Completion
Runner - A iteration style executor for completion.
- Completion
Stream - Stream wrapper for
CompletionRunner. - Engine
Card - Information about the engine, including agent and tool definitions.
- Remote
Agent - Wraps a remote agent as a local agent.
- Remote
Engine Args - Arguments for registering a remote engine.
- Remote
Engines - Collection of remote engines.
- Remote
Tool - Wraps a remote tool as a local tool.
- TEEClient
- Client for interacting with Trusted Execution Environment (TEE) services
- TEEClient
Builder - Builder for constructing a Client instance with customizable parameters
- Tools
Output - Tools
Search - Searches the callable surface currently available to the model. The “tools_search” tool has been registered as a built-in agent with label “flash”.
- Tools
Search Args - Tools
Select - Loads a subset of callable definitions for the next reasoning turn. The “tools_select” tool has been registered as a built-in agent with label “flash”.
- Tools
Select Args - Web3
Client
Enums§
- Web3SDK
- Represents a Web3 client for interacting with the Internet Computer and other services.
Constants§
Statics§
Traits§
Functions§
- json_
candidates - Extracts JSON candidates from the input string by applying various heuristics, such as stripping markdown code blocks and extracting JSON-like substrings. This increases the chances of successfully parsing the model output even when it is not perfectly formatted.