Add API architecture documentation for Agent, Skill, and Tool pipeline
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# API Architecture — Agent + Skill + Tool Pipeline
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This document explains how the API routes user messages through the agent/skill/tool pipeline to produce responses.
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---
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## Overview
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```
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┌─────────────────────────────────────────────────────────────────┐
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│ OpenWebUI / Client │
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│ POST /v1/chat/completions { model, messages, stream } │
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└──────────────────────────────┬──────────────────────────────────┘
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│
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▼
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┌──────────────────────────────────────────────────────────────────┐
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│ api/v1/chat.py — chat_completions() │
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│ │
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│ 1. _resolve_agent(req.model) → Agent │
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│ 2. agent.build_system_prompt() → system prompt │
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│ 3. Build full_messages = [system] + req.messages │
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│ 4. run_agent_with_tools(client, messages, agent_id) │
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└──────────────────────────────┬───────────────────────────────────┘
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│
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▼
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┌──────────────────────────────────────────────────────────────────┐
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│ Tool-Calling Loop (run_agent_with_tools / run_agent_stream) │
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│ │
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│ while turns < max_turns: │
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│ response = LLM.chat(messages, tools=agent_tools) │
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│ if response has tool_calls: │
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│ for each tool_call: │
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│ result = execute_tool(skills, name, args) │
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│ append result to messages │
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│ else: │
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│ return response.text (stream tokens if streaming) │
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└──────────────────────────────────────────────────────────────────┘
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```
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---
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## Key Concepts
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### 1. Agent
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An **Agent** is a persona + skill bundle. Defined in `agents/`.
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```python
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# agents/media_agent.py
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Agent(
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agent_id="media-agent",
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description="Media assistant with Seerr integration",
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skills=["media_info", "seerr", "triage"],
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base_prompt="You are a media assistant...",
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)
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```
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- `agent_id` — unique name, exposed as a model in OpenWebUI
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- `skills` — list of skill names to load
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- `base_prompt` — starting system prompt, combined with skill fragments
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- `build_system_prompt()` — merges base_prompt + all skill prompt fragments
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Agents self-register at import time via `agents/__init__.py`'s `register()`.
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`main.py` calls `load_all_agents()` at startup to import all agent/skill modules.
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### 2. Skill
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A **Skill** is a capability bundle. Defined in `skills/`.
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```python
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# skills/seerr.py
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Skill(
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name="seerr",
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description="Seerr integration — trending, discover, request media, submit issues",
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prompt_fragment="## Seerr Media Tools\n...",
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tools=[...], # OpenAI function-calling schema
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execute=_execute, # async handler: tool_name + args → ToolResult
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)
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```
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- `prompt_fragment` — injected into the agent's system prompt. Teaches the LLM what tools are available and when to use them.
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- `tools` — list of OpenAI function definitions (name, description, parameters).
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- `execute` — async callable that routes tool calls to API handlers.
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### 3. Tool
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A **Tool** is a single function the LLM can call. Defined as part of a skill's `tools` list.
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```python
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{
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"type": "function",
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"function": {
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"name": "seerr_trending",
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"description": "Get trending movies and TV shows from Seerr...",
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"parameters": {
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"type": "object",
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"properties": {
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"kind": {"type": "string", "enum": ["movie", "tv", "all"]},
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"language": {"type": "string"},
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},
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"required": ["kind"],
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},
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},
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}
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```
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When the LLM responds with a tool call, the loop:
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1. Extracts `function.name` (e.g. `"seerr_trending"`) and `function.arguments` (e.g. `{"kind": "movie"}`)
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2. Calls `execute_tool(agent.skills, name, args)` which finds the owning skill and runs it
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3. Appends the result text to the message history
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4. Sends back to the LLM for a follow-up response
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---
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## Full Request Flow
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### Step-by-step: "What are trending movies?"
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```
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1. OpenWebUI sends:
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POST /v1/chat/completions
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{
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"model": "media-agent",
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"messages": [
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{"role": "user", "content": "What are trending movies?"}
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],
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"stream": false
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}
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2. chat_completions():
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→ _resolve_agent(model="media-agent")
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→ get_agent("media-agent") → Agent(skills=["media_info", "seerr", "triage"])
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→ tools = get_all_tools(["media_info", "seerr", "triage"])
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→ Returns 7 tool definitions from seerr.py
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→ system_prompt = agent.build_system_prompt()
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→ base_prompt + media_info fragment + seerr fragment + triage fragment
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3. run_agent_with_tools() — Turn 1:
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→ LLM receives: [system prompt with tools] + [user: "What are trending movies?"]
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→ LLM responds: tool_calls = [{"function": {"name": "seerr_trending", "arguments": {"kind": "movie"}}}]
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4. Execute tool:
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→ execute_tool(["media_info", "seerr", "triage"], "seerr_trending", {"kind": "movie"})
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→ Finds seerr skill → calls _execute("seerr_trending", ...) → _trending(args)
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→ GET /api/v1/discover/trending?mediaType=movie
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→ Returns formatted list with [tmdb:IDs]
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5. run_agent_with_tools() — Turn 2:
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→ LLM receives: previous messages + [tool: "Found 20 trending movies..."]
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→ LLM responds: text = "Here are the top trending movies! 🎬 ..."
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→ finish_reason="stop" → return the text
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6. chat_completions() returns:
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{ "choices": [{"message": {"content": "Here are the top trending movies!..."}}] }
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```
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### Step-by-step: "Request the 2026 one" (multi-turn context)
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```
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1. OpenWebUI sends the FULL history:
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{
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"model": "media-agent",
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"messages": [
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{"role": "user", "content": "What are trending movies?"},
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{"role": "assistant", "content": "Here are the top 10 trending movies!
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1. **Mortal Kombat II** (2026) [tmdb:931285] — ..."},
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{"role": "user", "content": "could request the mortal kombat one?"},
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{"role": "assistant", "content": "There are several Mortal Kombat entries! ..."},
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{"role": "user", "content": "the 2026 one"}
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]
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}
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2. chat_completions():
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→ req.messages contains the ENTIRE conversation history
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→ System prompt prepended → full_messages = [system] + 5 history messages
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→ LLM sees everything: the trending list with [tmdb:931285], the disambiguation, "the 2026 one"
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3. LLM reasons:
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- I previously listed Mortal Kombat II (2026) with [tmdb:931285]
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- The user said "request the mortal kombat one" → I searched and showed 4 options
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- Now they say "the 2026 one" → that matches Mortal Kombat II (2026) [tmdb:931285]
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- I should call seerr_request_media(kind="movie", title="Mortal Kombat II", tmdb_id=931285)
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4. Tool executes the request → ✅ Success
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```
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