Add agent and skill system: implement Agent and Skill classes, register media and naked agents, and create media_info demo skill

agents/__init__.py

@@ -0,0 +1,64 @@
+"""
+Agent system — each agent combines a base LLM with optional skills
+to produce tailored system prompts and behavior.
+
+An Agent is a lightweight wrapper:
+- agent_id    : unique name (e.g. "naked", "media-agent")
+- description : human-readable summary
+- skills      : list of skill names to load
+- base_prompt : default system prompt (optional — falls back to generic)
+"""
+
+from dataclasses import dataclass, field
+from typing import Dict, List
+
+from skills import Skill, get_combined_prompt, list_all as list_all_skills
+
+
+@dataclass
+class Agent:
+    agent_id: str
+    description: str = ""
+    skills: List[str] = field(default_factory=list)
+    base_prompt: str = "You are a helpful agent."
+
+    def build_system_prompt(self) -> str:
+        """Combine base_prompt with all registered skills' prompt fragments."""
+        return get_combined_prompt(self.skills, base_prompt=self.base_prompt)
+
+    def __repr__(self) -> str:
+        sk = ", ".join(self.skills) if self.skills else "none"
+        return f"Agent(id={self.agent_id!r}, skills=[{sk}])"
+
+
+# ---------------------------------------------------------------------------
+# Global agent registry
+# ---------------------------------------------------------------------------
+_agent_registry: Dict[str, Agent] = {}
+
+
+def register(agent: Agent) -> None:
+    """Register an agent so it can be looked up by agent_id."""
+    _agent_registry[agent.agent_id] = agent
+
+
+def get(agent_id: str) -> Agent | None:
+    """Return a registered agent by id, or None."""
+    return _agent_registry.get(agent_id)
+
+
+def list_all() -> Dict[str, Agent]:
+    """Return a shallow copy of the registry."""
+    return dict(_agent_registry)
+
+
+def load_all_agents() -> None:
+    """
+    Import all agent modules so they self-register.
+    Call this once at startup.
+    """
+    import agents.naked         # noqa: F401
+    import agents.media_agent   # noqa: F401
+
+    # Also import skill modules so they self-register
+    import skills.media_info    # noqa: F401

agents/media_agent.py

@@ -0,0 +1,19 @@
+"""
+media-agent — an agent that knows how to handle media queries
+(Jellyfin / Sonarr / Seerr / subtitle requests).
+
+For now it only loads the *media_info* demo skill which teaches it
+a structured response format. Later you'll add real API-calling skills.
+"""
+
+from agents import Agent, register
+
+media_agent = Agent(
+    agent_id="media-agent",
+    description="Media assistant — handles movie/TV/subtitle/ticket requests. "
+    "Will eventually connect to Seerr, Sonarr, Jellyfin, etc.",
+    skills=["media_info"],
+    base_prompt="You are a media assistant. Help users with their media library.",
+)
+
+register(media_agent)

agents/naked.py

@@ -0,0 +1,15 @@
+"""
+naked agent — a barebone LLM with no extra skills attached.
+Just a thin wrapper that instructs the LLM to be a general helpful assistant.
+"""
+
+from agents import Agent, register
+
+naked_agent = Agent(
+    agent_id="naked",
+    description="A plain LLM — no extra skills, just a helpful assistant.",
+    skills=[],  # no skills
+    base_prompt="You are a helpful, general-purpose assistant.",
+)
+
+register(naked_agent)

api/v1/chat.py

@@ -6,6 +6,7 @@ import json
 import asyncio
 
 from api.dependencies import get_llm_client
+from agents import get as get_agent, list_all as list_all_agents
 
 router = APIRouter()
 
@@ -13,6 +14,7 @@ router = APIRouter()
 class ChatRequest(BaseModel):
     message: str
     session_id: str | None = None
+    agent_id: str | None = None   # which agent to use ("naked", "media-agent", …)
 
 
 class ChatCompletionRequest(BaseModel):
@@ -25,28 +27,59 @@ class ChatCompletionRequest(BaseModel):
 # Core helpers
 # ---------------------------------------------------------------------------
 
-def run_agent(client: OpenAI, message: str, session_id: str | None = None) -> str:
-    """Non-streaming: returns the full response as a single string."""
+def _resolve_agent(agent_id: str | None = None, model: str | None = None):
+    """
+    Look up the agent. Resolution order:
+    1. explicit agent_id
+    2. model name (OpenWebUI sends this — maps to agent_id if registered)
+    3. fallback to "naked"
+    """
+    lookup = agent_id or model
+    if lookup is None:
+        agent = get_agent("naked")
+    else:
+        agent = get_agent(lookup)
+        if agent is None:
+            agent = get_agent("naked")
+    return agent
+
+
+def run_agent(
+    client: OpenAI,
+    message: str,
+    session_id: str | None = None,
+    agent_id: str | None = None,
+    model: str | None = None,
+) -> str:
+    """Non-streaming: uses the chosen agent's system prompt."""
+    agent = _resolve_agent(agent_id, model)
     response = client.chat.completions.create(
         model="deepseek-chat",
         messages=[
-            {"role": "system", "content": "You are a helpful agent."},
+            {"role": "system", "content": agent.build_system_prompt()},
             {"role": "user", "content": message},
         ],
     )
     return response.choices[0].message.content
 
 
-async def run_agent_stream(client: OpenAI, message: str, session_id: str | None = None):
-    """Async generator that yields text tokens as they arrive from the LLM."""
+async def run_agent_stream(
+    client: OpenAI,
+    message: str,
+    session_id: str | None = None,
+    agent_id: str | None = None,
+    model: str | None = None,
+):
+    """Async generator — yields tokens using the chosen agent's system prompt."""
+    agent = _resolve_agent(agent_id, model)
+    system_prompt = agent.build_system_prompt()
     loop = asyncio.get_running_loop()
 
-    # OpenAI's sync streaming iterator must run in a thread so it doesn't block the event loop
     def _sync_stream():
         stream = client.chat.completions.create(
             model="deepseek-chat",
             messages=[
-                {"role": "system", "content": "You are a helpful agent."},
+                {"role": "system", "content": system_prompt},
                 {"role": "user", "content": message},
             ],
             stream=True,
@@ -56,7 +89,6 @@ async def run_agent_stream(client: OpenAI, message: str, session_id: str | None
             if delta and delta.content:
                 yield delta.content
 
-    # Run the sync generator in a thread, yield results back to the async world
     gen = _sync_stream()
     while True:
         token = await loop.run_in_executor(None, next, gen, None)
@@ -78,11 +110,12 @@ def root():
 async def chat(req: ChatRequest, client: OpenAI = Depends(get_llm_client)):
     """Streaming chat endpoint — returns Server-Sent Events."""
     async def event_stream():
-        async for token in run_agent_stream(client, req.message, req.session_id):
+        async for token in run_agent_stream(
+            client, req.message, req.session_id, req.agent_id,
+        ):
             payload = json.dumps({"token": token, "session_id": req.session_id})
             yield f"data: {payload}\n\n"
 
-        # Signal completion
         yield f"data: {json.dumps({'done': True, 'session_id': req.session_id})}\n\n"
 
     return StreamingResponse(
@@ -91,7 +124,7 @@ async def chat(req: ChatRequest, client: OpenAI = Depends(get_llm_client)):
         headers={
             "Cache-Control": "no-cache",
             "Connection": "keep-alive",
-            "X-Accel-Buffering": "no",   # Disable nginx buffering if behind a proxy
+            "X-Accel-Buffering": "no",
         },
     )
 
@@ -99,21 +132,38 @@ async def chat(req: ChatRequest, client: OpenAI = Depends(get_llm_client)):
 @router.post("/chat/sync")
 def chat_sync(req: ChatRequest, client: OpenAI = Depends(get_llm_client)):
     """Non-streaming fallback — returns the full response at once."""
-    response = run_agent(client, req.message, req.session_id)
+    response = run_agent(client, req.message, req.session_id, req.agent_id)
     return {"response": response, "session_id": req.session_id}
 
 
+@router.get("/agents")
+def list_agents():
+    """Return all registered agents with their ids, descriptions, and skills."""
+    return {
+        "agents": [
+            {
+                "agent_id": a.agent_id,
+                "description": a.description,
+                "skills": a.skills,
+            }
+            for a in list_all_agents().values()
+        ]
+    }
+
+
 @router.get("/models")
 def list_models():
+    """Return all registered agents as selectable models for OpenWebUI."""
     return {
         "object": "list",
         "data": [
             {
-                "id": "agent-model",
+                "id": a.agent_id,
                 "object": "model",
                 "created": 0,
                 "owned_by": "local-agent",
-            },
+            }
+            for a in list_all_agents().values()
         ],
     }
 
@@ -123,12 +173,17 @@ async def chat_completions(
     req: ChatCompletionRequest,
     client: OpenAI = Depends(get_llm_client),
 ):
-    """OpenAI-compatible /chat/completions — supports stream=True."""
+    """OpenAI-compatible /chat/completions — supports stream=True.
+    The last message's content is used as the user prompt; defaults to 'naked' agent.
+    """
     user_message = req.messages[-1]["content"]
 
+    # Resolve agent from the model field (OpenWebUI sends this)
+    agent = _resolve_agent(model=req.model)
+
     if req.stream:
         async def sse_stream():
-            async for token in run_agent_stream(client, user_message):
+            async for token in run_agent_stream(client, user_message, agent_id=agent.agent_id):
                 chunk = {
                     "id": "chatcmpl-local",
                     "object": "chat.completion.chunk",
@@ -141,7 +196,6 @@ async def chat_completions(
                     ],
                 }
                 yield f"data: {json.dumps(chunk)}\n\n"
-            # Final chunk with finish_reason
             final_chunk = {
                 "id": "chatcmpl-local",
                 "object": "chat.completion.chunk",
@@ -165,8 +219,9 @@ async def chat_completions(
             },
         )
 
-    # Non-streaming path
-    response = run_agent(client, user_message)
+    # Non-streaming path — resolve agent from model field
+    agent = _resolve_agent(model=req.model)
+    response = run_agent(client, user_message, agent_id=agent.agent_id)
     return {
         "id": "chatcmpl-local",
         "object": "chat.completion",

main.py

@@ -5,12 +5,18 @@ from api.v1.chat import router as v1_router
 from core.config import DEEPSEEK_API_KEY
 from core.llm import create_client
 
+# ---------------------------------------------------------------------------
+# Load all agents & skills so they self-register at startup
+# ---------------------------------------------------------------------------
+from agents import load_all_agents  # noqa: E402
 
+load_all_agents()
+
+# ---------------------------------------------------------------------------
+# App
+# ---------------------------------------------------------------------------
 app = FastAPI()
 
-# ---------------------------------------------------------------------------
-# Middleware
-# ---------------------------------------------------------------------------
 app.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],

skills/__init__.py

@@ -0,0 +1,50 @@
+"""
+Skill system — each skill is a piece of domain knowledge or a capability
+that can be attached to an agent to shape its behavior and system prompt.
+
+A Skill is a lightweight object with:
+- name        : short identifier (e.g. "media_info")
+- description : human-readable summary
+- prompt_fragment : extra text injected into the agent's system prompt
+"""
+
+from dataclasses import dataclass, field
+from typing import Dict
+
+
+@dataclass
+class Skill:
+    name: str
+    description: str
+    prompt_fragment: str = ""
+
+
+# ---------------------------------------------------------------------------
+# Global skill registry — populated at startup / import time
+# ---------------------------------------------------------------------------
+_skill_registry: Dict[str, Skill] = {}
+
+
+def register(skill: Skill) -> None:
+    """Register a skill so agents can look it up by name."""
+    _skill_registry[skill.name] = skill
+
+
+def get(name: str) -> Skill | None:
+    """Return a registered skill by name, or None."""
+    return _skill_registry.get(name)
+
+
+def list_all() -> Dict[str, Skill]:
+    """Return a shallow copy of the registry."""
+    return dict(_skill_registry)
+
+
+def get_combined_prompt(skill_names: list[str], base_prompt: str = "") -> str:
+    """Build a system prompt from a base prompt + requested skill fragments."""
+    parts = [base_prompt] if base_prompt else []
+    for name in skill_names:
+        s = get(name)
+        if s and s.prompt_fragment:
+            parts.append(s.prompt_fragment)
+    return "\n\n".join(parts)

skills/media_info.py

@@ -0,0 +1,45 @@
+"""
+Demo skill: media_info
+
+Gives the agent knowledge about how to respond to media-related queries
+(movie / TV / subtitle requests). This is intentionally simple — in the future
+you would add real API-calling skills here (Sonarr / Jellyfin / Seerr / etc.).
+"""
+
+from skills import Skill, register
+
+media_info_skill = Skill(
+    name="media_info",
+    description="Respond to media queries with a structured format "
+    "(movie / TV show requests, subtitles, tickets).",
+    prompt_fragment="""## Media Agent Instructions
+
+You are a media assistant. When users ask about movies, TV shows, subtitles,
+or media library requests, follow these rules:
+
+- If a user wants to **request** a movie or show, respond with a clear
+  confirmation using this format:
+
+  ```
+  [MEDIA REQUEST]
+  Title: <title>
+  Type: <movie | show>
+  Status: PENDING — this would be submitted to Seerr
+  ```
+
+- If a user asks about **subtitles**, acknowledge the request and respond with:
+
+  ```
+  [SUBTITLE REQUEST]
+  Media: <title>
+  Language: <language>
+  Status: PENDING — Bazarr would process this
+  ```
+
+- Otherwise, answer normally but always remind the user that media-backend
+  integrations (Seerr, Sonarr, Jellyfin) are not yet connected.
+
+This is a **demo** skill. Real API calls will be added later.""",
+)
+
+register(media_info_skill)