القائمة
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.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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.
Evaluation provider abstraction for dataset experiments (Langfuse today). Use when running an experiment, adding a new eval backend, or wiring evals into CI.
| name | echo-sdk-overview |
| description | 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. |
Echo is a framework-agnostic Python SDK for LLM agents. Multi-provider, async-first, Pydantic v2. Each submodule has a tight responsibility — pick the right one before editing.
| Module | Public entry point | Skill | Diagram |
|---|---|---|---|
agents/ | from echo.agents import BaseAgent, GenericAgent, Skill, AgentConfig | echo-sdk/agents | agent-skill-lifecycle.md |
llm/ | from echo.llm import get_llm, LLMConfig, LLMResponse, StreamEvent | echo-sdk/llm | llm-invoke-flow.md |
tools/ | import per subpackage: from echo.tools.core import BaseTool, ElicitationResponse; from echo.tools.mcp import MCPTool, MCPConnectionManager; from echo.tools.elicitation import BaseElicitationTool (see layout note) | echo-sdk/tools | tools-mcp-connection.md |
models/ | from echo.models import ConversationContext, Message, ToolCall, ToolResult | echo-sdk/models | conversation-context-er.md |
prompts/ | from echo.prompts import get_prompt_provider, FetchedPrompt | echo-sdk/prompts | — |
evals/ | from echo.evals import get_eval_provider | echo-sdk/evals | — |
audio/ | from echo.audio import get_transcriber, AudioInput | echo-sdk/audio | — |
databases/ | from echo.databases.postgres import PgQueryTool, PostgresClient | echo-sdk/databases | — |
utils/ | helpers; minimal | — | — |
For the big picture: .claude/diagrams/architecture-overview.md and .claude/diagrams/data-flow-end-to-end.md.
tools/ holds the tool framework only, organized into a foundation + categories:
core/ — the foundation: BaseTool (universal contract) + shared schemas
(directive/result types, elicitation payloads, ToolOutput). Dependency root;
imports nothing else from echo.elicitation/ — BaseElicitationTool category (PAUSE/VISIBLE fixed).mcp/ — MCPTool, MCPConnectionManager, and MCP-private schemas
(MCPServerConfig, errors, transport). Pulls optional mcp/httpx.system/ — SystemTool (echo-internal, __init_subclass__-guarded, not re-exported).Concrete domain tools live with their domain and import the framework:
LoadSkillTool/UnloadSkillTool → echo.skills; PgQueryTool → echo.databases.postgres.
Import policy (tools/): each subpackage owns and exposes its own public API;
the top-level tools/__init__ does NOT re-aggregate them — import from the owning
subpackage (echo.tools.core, .mcp, .elicitation, .system). The sole top-level
convenience re-export is BaseTool. This keeps import echo.tools lean and avoids
dragging the optional mcp/httpx deps. Schemas follow the lowest-common-ancestor
rule: cross-category types live in core, category-private types in their category
(mcp/schemas.py). tools/__init__ never imports skills/databases — graph stays acyclic.
| Task | Route to |
|---|---|
| Add a new LLM provider | echo-sdk/adding-a-provider → then echo-sdk/llm |
| Add a new transcriber / eval / prompt provider | echo-sdk/adding-a-provider → then module skill |
| Build a new agent | echo-sdk/agents |
| Attach a skill to an agent | echo-sdk/adding-a-skill-to-an-agent |
| Write a new tool | echo-sdk/tools (+ echo-sdk/models for tool_context) |
| Wrap an MCP server | echo-sdk/tools + diagram tools-mcp-connection.md |
| Modify conversation state shape | echo-sdk/models — careful, immutability is load-bearing |
| Fetch prompts from Langfuse | echo-sdk/prompts |
| Run an experiment / eval | echo-sdk/evals |
| Query Postgres from a tool | echo-sdk/databases |
| Transcribe audio | echo-sdk/audio |
These python skills apply to every task in this repo — load them when relevant:
python/async-discipline — no blocking calls in async codepython/orjson-only — never stdlib jsonpython/optional-deps — guard provider importspython/pydantic-v2 — config/schemaspython/memory-safety — close async resources (MCP managers, asyncpg pools, httpx clients)ConversationContext is immutable from the caller — return updated, don't mutate.get_*() factory; never wire concrete classes into callers.try/except ImportError with a clear install hint.orjson not json.AsyncGenerator[StreamEvent].system_context.tool_context) is injected by the LLM layer — tools receive these as kwargs; the LLM never sees them in input_schema.BaseAgent subclasses → use get_llm(LLMConfig(...)).import boto3 at module top → wrap in optional-deps guard.context.messages inside a tool → tools return values; the LLM layer appends.BaseTool.to_<provider>_schema().user_id in tool args → put it in system_context.tool_context, it's injected.