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Conversation data model — ConversationContext, Message, ToolCall/ToolResult, polymorphic content. Use when reading/writing context, adding a new content type, or debugging tool-call pairing.
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Baseado na classificação ocupacional SOC
Router for any non-trivial echo-sdk task. Maps user intent to the right module skill and diagram. Invoke FIRST for any change to src/echo/ before reaching for a specific module skill.
Recipe for adding a new provider (LLM, transcriber, eval, prompt). Use whenever adding Cohere, Mistral, Deepgram, Phoenix, Promptlayer, etc. — anything that plugs into an existing get_*() factory.
End-to-end recipe for defining a Skill bundle and wiring it into a GenericAgent with the right activation mode and tool registry. Use when extending agent capability via swappable skills rather than baking tools in.
Building agents with BaseAgent / GenericAgent, including skill registration, activation modes (llm vs manual), and system-prompt composition. Use when editing src/echo/agents/ or building a new agent subclass.
Audio transcription provider abstraction (Gemini, EkaCare). Use when transcribing audio, adding a new transcriber, or shaping AudioInput.
Postgres support — asyncpg client, schema-aware binder, PgQueryTool surface. Use when querying postgres from a tool, modifying the binder, or adding a new DB engine.
| name | echo-sdk-models |
| description | Conversation data model — ConversationContext, Message, ToolCall/ToolResult, polymorphic content. Use when reading/writing context, adding a new content type, or debugging tool-call pairing. |
ConversationContext — root container; has messages: list[Message] and system_context.Message — has role: MessageRole and content: list (polymorphic).TextMessage, ImageContent, DocumentContent, ToolCall, ToolResult.MessageRole: user, assistant, system, tool.ContentSourceType — for image/document provenance.Provider enum in providers.py.Public API: src/echo/models/__init__.py. Implementation: user_conversation.py.
invoke() and invoke_stream() return a new ConversationContext — never mutate the input. Tools must not touch context.messages.ToolCall has a tool_call_id; the matching ToolResult must reuse the same tool_call_id. Providers rely on this.system_context.tool_context is a dict of hidden params injected into tool kwargs by the LLM layer. Not visible to the LLM. Add new hidden params here, not to tool input_schema.content: a single message can carry text + image + document + tool calls/results. Order matters for some providers (Anthropic in particular).model_validate, model_dump. For serialization to JSON, orjson.dumps(ctx.model_dump()) — never stdlib json.user_conversation.py (or a new file imported there).Message.content union (and any discriminator).src/echo/llm/<provider>.py to translate the new type to the provider's native shape — or skip it cleanly if the provider doesn't support it.tests/.context.messages from a tool → tools return values; the LLM layer appends.ConversationContext between two concurrent agent runs → make a copy or use separate contexts.Message.content → they go in system_context.tool_context (hidden) or LLM config.tool_call_id matching → provider rejects the next turn..claude/diagrams/conversation-context-er.md[[echo-sdk-tools]], [[echo-sdk-llm]]