diff --git a/agents/angela_briefing.py b/agents/angela_briefing.py index d8ed3f6..afc0715 100644 --- a/agents/angela_briefing.py +++ b/agents/angela_briefing.py @@ -9,6 +9,7 @@ CONFIG = { "person": "Angela", "agent_id": "angela-daily-briefing", "instance_id": INSTANCE_ID, + "user_id": 3, "wiki_parent_doc_id": "65966bd6-4ef8-4b79-9b79-e4aa62b94e96", "location": { "name": "Providence", diff --git a/agents/daily_briefing.py b/agents/daily_briefing.py index 930788b..542595f 100644 --- a/agents/daily_briefing.py +++ b/agents/daily_briefing.py @@ -81,6 +81,35 @@ def collect_sections(config): print(f" Reading List failed: {e}", file=sys.stderr) sections.append(("Reading List", "## Reading List\n\n*Unavailable.*\n", f"error: {e}")) + # --- Project Monitors (LLM-powered) --- + instance_id = config.get("instance_id", 0) + user_id = config.get("user_id", 0) + if user_id: + try: + # Fetch this user's project-monitor instances that are set to include in briefing + pm_instances = api_request( + f"{DASHBOARD_API}/api/instances/by-user/{user_id}?catalog_id=project-monitor", + retries=1, + ) + project_sections = [] + for pm in pm_instances: + pm_config = pm.get("config", {}) + if str(pm_config.get("include_in_briefing", "false")).lower() != "true": + continue + try: + from project_monitor import run as pm_run + md, summary = pm_run(pm_config, user_id=user_id, instance_id=pm.get("id")) + project_sections.append(md) + print(f" Project [{pm_config.get('project_name', '?')}]: {summary[:80]}") + except Exception as e: + print(f" Project [{pm_config.get('project_name', '?')}] failed: {e}", file=sys.stderr) + + if project_sections: + combined = "## Projects\n\n" + "\n\n".join(project_sections) + sections.append(("Projects", combined, f"{len(project_sections)} project(s)")) + except Exception as e: + print(f" Project monitors skipped: {e}", file=sys.stderr) + return sections diff --git a/agents/eric_briefing.py b/agents/eric_briefing.py index 7503fc2..a7e38e4 100644 --- a/agents/eric_briefing.py +++ b/agents/eric_briefing.py @@ -10,6 +10,7 @@ CONFIG = { "person": "Eric", "agent_id": "eric-daily-briefing", "instance_id": INSTANCE_ID, + "user_id": 2, "wiki_parent_doc_id": "2a891fe8-579b-450b-a663-de93915896b7", "location": { "name": "Providence", diff --git a/agents/llm_client.py b/agents/llm_client.py new file mode 100644 index 0000000..0f5c000 --- /dev/null +++ b/agents/llm_client.py @@ -0,0 +1,166 @@ +#!/usr/bin/env python3 +""" +LLM Client +Unified interface for calling Anthropic, OpenAI, and LiteLLM/compatible APIs. +Resolves the user's LLM config from the dashboard and routes accordingly. +""" + +import json +import sys +from urllib import request, error as urlerror +from shared import DASHBOARD_API, api_request + + +# Default models per provider +DEFAULT_MODELS = { + "anthropic": "claude-sonnet-4-5-20250514", + "openai": "gpt-4o-mini", + "litellm": "anthropic/claude-sonnet-4-5-20250514", + "ollama": "llama3", +} + +# Default API URLs per provider +DEFAULT_URLS = { + "anthropic": "https://api.anthropic.com", + "openai": "https://api.openai.com", + "litellm": "http://localhost:4000", + "ollama": "http://localhost:11434", +} + + +def get_llm_config(user_id): + """Get the resolved LLM config for a user from the dashboard API.""" + config = api_request(f"{DASHBOARD_API}/api/users/{user_id}/llm", retries=1) + if config.get("source") == "none": + raise RuntimeError("No LLM configured. Set one up via the LLM button in the dashboard.") + return config + + +def complete(user_id, prompt, system=None, max_tokens=4096): + """Send a completion request using the user's configured LLM. + + Args: + user_id: Dashboard user ID (for config resolution) + prompt: The user message / prompt text + system: Optional system message + max_tokens: Max response tokens + + Returns: + dict with keys: text (str), model (str), input_tokens (int), output_tokens (int) + """ + config = get_llm_config(user_id) + provider = config.get("provider_type", "anthropic") + api_url = config.get("api_url") or DEFAULT_URLS.get(provider, "") + api_key = config.get("api_key", "") + model = config.get("default_model") or DEFAULT_MODELS.get(provider, "") + + if provider == "anthropic": + return _call_anthropic(api_url, api_key, model, prompt, system, max_tokens) + elif provider in ("openai", "litellm"): + return _call_openai_compatible(api_url, api_key, model, prompt, system, max_tokens) + elif provider == "ollama": + return _call_openai_compatible(api_url, api_key, model, prompt, system, max_tokens, is_ollama=True) + else: + raise RuntimeError(f"Unknown LLM provider: {provider}") + + +def _call_anthropic(api_url, api_key, model, prompt, system, max_tokens): + """Call the Anthropic Messages API.""" + url = f"{api_url.rstrip('/')}/v1/messages" + body = { + "model": model, + "max_tokens": max_tokens, + "messages": [{"role": "user", "content": prompt}], + } + if system: + body["system"] = system + + headers = { + "x-api-key": api_key, + "anthropic-version": "2023-06-01", + "content-type": "application/json", + } + + data = json.dumps(body).encode() + req = request.Request(url, data=data, headers=headers, method="POST") + + try: + with request.urlopen(req, timeout=120) as resp: + result = json.loads(resp.read().decode()) + except urlerror.HTTPError as e: + err_body = e.read().decode() if e.fp else "" + raise RuntimeError(f"Anthropic API error {e.code}: {err_body[:500]}") + + text = "" + for block in result.get("content", []): + if block.get("type") == "text": + text += block["text"] + + usage = result.get("usage", {}) + return { + "text": text, + "model": result.get("model", model), + "input_tokens": usage.get("input_tokens", 0), + "output_tokens": usage.get("output_tokens", 0), + } + + +def _call_openai_compatible(api_url, api_key, model, prompt, system, max_tokens, is_ollama=False): + """Call an OpenAI-compatible API (works with OpenAI, LiteLLM, Ollama).""" + if is_ollama: + url = f"{api_url.rstrip('/')}/api/chat" + else: + url = f"{api_url.rstrip('/')}/v1/chat/completions" + + messages = [] + if system: + messages.append({"role": "system", "content": system}) + messages.append({"role": "user", "content": prompt}) + + body = { + "model": model, + "messages": messages, + "max_tokens": max_tokens, + } + + headers = {"content-type": "application/json"} + if api_key: + headers["authorization"] = f"Bearer {api_key}" + + data = json.dumps(body).encode() + req = request.Request(url, data=data, headers=headers, method="POST") + + try: + with request.urlopen(req, timeout=120) as resp: + result = json.loads(resp.read().decode()) + except urlerror.HTTPError as e: + err_body = e.read().decode() if e.fp else "" + raise RuntimeError(f"API error {e.code}: {err_body[:500]}") + + if is_ollama: + text = result.get("message", {}).get("content", "") + return {"text": text, "model": model, "input_tokens": 0, "output_tokens": 0} + + choice = result.get("choices", [{}])[0] + text = choice.get("message", {}).get("content", "") + usage = result.get("usage", {}) + return { + "text": text, + "model": result.get("model", model), + "input_tokens": usage.get("prompt_tokens", 0), + "output_tokens": usage.get("completion_tokens", 0), + } + + +if __name__ == "__main__": + # Quick test + import argparse + parser = argparse.ArgumentParser() + parser.add_argument("--user-id", type=int, required=True) + parser.add_argument("--prompt", default="Say hello in one sentence.") + args = parser.parse_args() + + result = complete(args.user_id, args.prompt) + print(f"Model: {result['model']}") + print(f"Tokens: {result['input_tokens']} in, {result['output_tokens']} out") + print(f"Response: {result['text']}") diff --git a/agents/project_monitor.py b/agents/project_monitor.py new file mode 100644 index 0000000..ed5ec9d --- /dev/null +++ b/agents/project_monitor.py @@ -0,0 +1,336 @@ +#!/usr/bin/env python3 +""" +Project Monitor Agent +Reads project data from wiki, Gitea, and custom URLs. +Uses LLM to generate intelligent status reports with analysis and next steps. +Posts full report to wiki, returns summary for daily briefing. +""" + +import json +import sys +from datetime import datetime +from urllib import request as urlreq, error as urlerror +from shared import ( + MT, DASHBOARD_API, WIKI_API, WIKI_TOKEN, + api_request, log_run, wiki_headers, +) +from llm_client import complete as llm_complete + +AGENT_ID = "project-monitor" + +GITEA_API = "http://192.168.1.204:3000/api/v1" +GITEA_TOKEN = "a03bb836c58010c4de35ac9c1f242292108c9776" + +SYSTEM_PROMPT = """You are a project analyst for a home lab and software development environment. +You review project documentation, code activity, and issues to produce clear, actionable status reports. +Be specific — reference actual document names, commit messages, and issue titles. +Be concise but thorough. Prioritize what matters most.""" + +ANALYSIS_PROMPT = """Review the current state of this project and generate a status report. + +Project: {project_name} + +{custom_notes} + +=== PROJECT DOCUMENTATION === +{wiki_content} + +=== CODE ACTIVITY (last 7 days) === +{gitea_content} + +=== ADDITIONAL CONTEXT === +{url_content} + +Generate a report with these sections: + +## Status Summary +2-3 sentences on overall health, momentum, and any blockers. + +## Recent Activity +What changed recently — commits, doc updates, new issues. + +## Open Issues / Blockers +Anything stalled, broken, or needing attention. + +## Recommended Next Steps +3-5 prioritized, actionable items. + +## Ideas & Opportunities +Things to consider — improvements, risks, opportunities. +""" + + +def fetch_wiki_docs(collection_id=None, doc_ids=None): + """Fetch wiki documents. Returns combined text content.""" + headers = wiki_headers() + texts = [] + + if doc_ids: + for doc_id in doc_ids: + doc_id = doc_id.strip() + if not doc_id: + continue + try: + result = api_request( + f"{WIKI_API}/documents.info", + data={"id": doc_id}, + headers=headers, + method="POST", + ) + doc = result.get("data", {}) + texts.append(f"### {doc.get('title', 'Untitled')}\n{doc.get('text', '')[:3000]}") + except Exception as e: + texts.append(f"### [Error fetching doc {doc_id}: {e}]") + + elif collection_id: + try: + result = api_request( + f"{WIKI_API}/documents.list", + data={"collectionId": collection_id, "limit": 25}, + headers=headers, + method="POST", + ) + for doc in result.get("data", [])[:15]: + # Fetch full content for each (up to limit) + try: + full = api_request( + f"{WIKI_API}/documents.info", + data={"id": doc["id"]}, + headers=headers, + method="POST", + ) + d = full.get("data", {}) + texts.append(f"### {d.get('title', 'Untitled')}\n{d.get('text', '')[:2000]}") + except Exception: + texts.append(f"### {doc.get('title', 'Untitled')}\n[Content not available]") + except Exception as e: + texts.append(f"[Error fetching collection: {e}]") + + return "\n\n".join(texts) if texts else "No wiki documentation configured." + + +def fetch_gitea_activity(repo): + """Fetch recent commits and open issues from a Gitea repo.""" + if not repo: + return "No Gitea repo configured." + + parts = [] + headers = {"Authorization": f"token {GITEA_TOKEN}"} + + # Recent commits + try: + url = f"{GITEA_API}/repos/{repo}/commits?limit=15&token={GITEA_TOKEN}" + req = urlreq.Request(url) + with urlreq.urlopen(req, timeout=15) as resp: + commits = json.loads(resp.read().decode()) + if commits: + parts.append("Recent commits:") + for c in commits[:10]: + msg = c.get("commit", {}).get("message", "").split("\n")[0][:80] + date = c.get("commit", {}).get("author", {}).get("date", "")[:10] + parts.append(f" - [{date}] {msg}") + except Exception as e: + parts.append(f"[Could not fetch commits: {e}]") + + # Open issues + try: + url = f"{GITEA_API}/repos/{repo}/issues?state=open&limit=20&token={GITEA_TOKEN}" + req = urlreq.Request(url) + with urlreq.urlopen(req, timeout=15) as resp: + issues = json.loads(resp.read().decode()) + if issues: + parts.append(f"\nOpen issues ({len(issues)}):") + for i in issues[:10]: + labels = ", ".join(l.get("name", "") for l in i.get("labels", [])) + parts.append(f" - #{i['number']}: {i['title']}" + (f" [{labels}]" if labels else "")) + except Exception as e: + parts.append(f"[Could not fetch issues: {e}]") + + return "\n".join(parts) if parts else "No recent Gitea activity." + + +def fetch_url_content(urls_text): + """Fetch text content from custom URLs.""" + if not urls_text: + return "No additional URLs configured." + + urls = [u.strip() for u in urls_text.strip().split("\n") if u.strip()] + if not urls: + return "No additional URLs configured." + + parts = [] + for url in urls[:5]: + try: + req = urlreq.Request(url, headers={"User-Agent": "AgentCommandCenter/1.0"}) + with urlreq.urlopen(req, timeout=15) as resp: + content = resp.read().decode("utf-8", errors="replace")[:2000] + parts.append(f"### {url}\n{content}") + except Exception as e: + parts.append(f"### {url}\n[Error: {e}]") + + return "\n\n".join(parts) + + +def post_report_to_wiki(report_md, project_name, collection_id): + """Post the full report to a wiki collection.""" + if not collection_id: + return None + + now = datetime.now(MT) + title = f"Project Status — {project_name} — {now.strftime('%Y-%m-%d')}" + headers = wiki_headers() + + # Check for existing report today + try: + search = api_request( + f"{WIKI_API}/documents.search", + data={"query": title, "collectionId": collection_id}, + headers=headers, + method="POST", + ) + for doc in search.get("data", []): + if doc.get("document", {}).get("title") == title: + api_request( + f"{WIKI_API}/documents.update", + data={"id": doc["document"]["id"], "text": report_md, "publish": True}, + headers=headers, + method="POST", + ) + return doc["document"]["id"] + except Exception: + pass + + # Create new + try: + result = api_request( + f"{WIKI_API}/documents.create", + data={"title": title, "text": report_md, "collectionId": collection_id, "publish": True}, + headers=headers, + method="POST", + ) + return result["data"]["id"] + except Exception as e: + print(f" Warning: could not post report to wiki: {e}", file=sys.stderr) + return None + + +def run(config, user_id=None, instance_id=None): + """Run the project monitor agent. + + Config keys: + project_name (str): Display name + wiki_collection_id (str): Outline collection to read + wiki_doc_ids (str): Comma-separated specific doc IDs + gitea_repo (str): owner/repo + custom_urls (str): Newline-separated URLs + custom_notes (str): Free text context + report_collection_id (str): Where to post the full report + include_in_briefing (str): "true"/"false" + + Returns: + (markdown_section, summary) for daily briefing integration + """ + project_name = config.get("project_name", "Unknown Project") + + if not user_id: + return f"## {project_name}\n\n*No user context for LLM.*\n", "error: no user_id" + + print(f" Collecting data for {project_name}...") + + # Collect data + wiki_collection = config.get("wiki_collection_id", "") + wiki_docs = config.get("wiki_doc_ids", "") + doc_ids = [d.strip() for d in wiki_docs.split(",") if d.strip()] if wiki_docs else None + + wiki_content = fetch_wiki_docs( + collection_id=wiki_collection if not doc_ids else None, + doc_ids=doc_ids, + ) + gitea_content = fetch_gitea_activity(config.get("gitea_repo", "")) + url_content = fetch_url_content(config.get("custom_urls", "")) + custom_notes = config.get("custom_notes", "") + if custom_notes: + custom_notes = f"Project context/goals:\n{custom_notes}" + + # Build prompt + prompt = ANALYSIS_PROMPT.format( + project_name=project_name, + custom_notes=custom_notes, + wiki_content=wiki_content[:8000], + gitea_content=gitea_content[:3000], + url_content=url_content[:3000], + ) + + print(f" Calling LLM for analysis...") + try: + result = llm_complete(user_id, prompt, system=SYSTEM_PROMPT, max_tokens=2000) + except RuntimeError as e: + err = str(e) + log_run(AGENT_ID, "failed", err=err, instance_id=instance_id) + return f"## {project_name}\n\n*LLM error: {err}*\n", f"error: {err}" + + report_md = result["text"] + model = result["model"] + tokens_in = result["input_tokens"] + tokens_out = result["output_tokens"] + + print(f" LLM: {model}, {tokens_in}+{tokens_out} tokens") + + # Post full report to wiki + report_collection = config.get("report_collection_id", "") or config.get("wiki_collection_id", "") + doc_id = None + if report_collection: + now = datetime.now(MT) + full_report = f"# Project Status — {project_name}\n\n" + full_report += f"**{now.strftime('%A, %B %d, %Y')}** | Generated by Project Monitor Agent\n\n" + full_report += f"---\n\n{report_md}\n\n" + full_report += f"---\n*Model: {model} | Tokens: {tokens_in} in, {tokens_out} out*\n" + doc_id = post_report_to_wiki(full_report, project_name, report_collection) + if doc_id: + print(f" Wiki report posted: {doc_id}") + + # Extract just the status summary for the briefing + summary_lines = [] + in_summary = False + for line in report_md.split("\n"): + if "## Status Summary" in line: + in_summary = True + continue + if in_summary and line.startswith("## "): + break + if in_summary and line.strip(): + summary_lines.append(line.strip()) + summary = " ".join(summary_lines)[:200] if summary_lines else report_md[:200] + + # Briefing section + section = f"### {project_name}\n\n{summary}\n" + + log_run(AGENT_ID, "success", output=f"{project_name}: {summary[:100]}", instance_id=instance_id, metadata={ + "project": project_name, + "model": model, + "tokens_in": tokens_in, + "tokens_out": tokens_out, + "wiki_report_id": doc_id, + }) + + return section, summary + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser() + parser.add_argument("--user-id", type=int, required=True) + parser.add_argument("--instance-id", type=int, default=0) + parser.add_argument("--project-name", required=True) + parser.add_argument("--wiki-collection", default="") + parser.add_argument("--gitea-repo", default="") + args = parser.parse_args() + + config = { + "project_name": args.project_name, + "wiki_collection_id": args.wiki_collection, + "gitea_repo": args.gitea_repo, + } + section, summary = run(config, user_id=args.user_id, instance_id=args.instance_id) + print(section) + print(f"\nSummary: {summary}") diff --git a/dashboard/app.py b/dashboard/app.py index e0d343e..837d297 100644 --- a/dashboard/app.py +++ b/dashboard/app.py @@ -328,6 +328,7 @@ def list_catalog(user: dict = Depends(require_auth), db: Session = Depends(get_d "category": e.category, "config_schema": e.config_schema or {}, "default_config": e.default_config or {}, "supports_schedule": e.supports_schedule, "is_sub_agent": e.is_sub_agent, + "requires_llm": e.requires_llm, "enabled": e.id in user_instance_ids, } for e in entries] @@ -381,6 +382,14 @@ def create_instance(data: InstanceCreate, user: dict = Depends(require_auth), db catalog = db.query(AgentCatalog).filter(AgentCatalog.id == data.catalog_id).first() if not catalog: raise HTTPException(status_code=404, detail="Agent type not found in catalog") + # Enforce LLM requirement + if catalog.requires_llm: + u = db.query(User).filter(User.id == user["user_id"]).first() + user_llm = u.llm_config or {} if u else {} + has_user_llm = bool(user_llm.get("api_key")) + has_system_llm = db.query(LLMProvider).filter(LLMProvider.is_default == True).first() is not None + if not has_user_llm and not has_system_llm: + raise HTTPException(status_code=400, detail="This agent requires an LLM provider. Configure one via the LLM button in the header.") config = {**(catalog.default_config or {}), **data.config} inst = AgentInstance( user_id=user["user_id"], @@ -482,11 +491,29 @@ def trigger_instance(instance_id: int, user: dict = Depends(require_auth), db: S subprocess.Popen(cmd, env=env, cwd=agent_dir) return {"status": "triggered", "message": f"Running {catalog_id} for {u.display_name}"} + if catalog_id == "project-monitor": + config_json = json.dumps(inst.config or {}).replace("'", "\\'") + cmd = ["python3", "-c", + f"import sys, json; sys.path.insert(0, '{agent_dir}'); " + f"from project_monitor import run; " + f"run(json.loads('{config_json}'), user_id={user['user_id']}, instance_id={instance_id})"] + subprocess.Popen(cmd, env=env, cwd=agent_dir) + return {"status": "triggered", "message": f"Running project monitor: {inst.name}"} + return {"status": "error", "message": f"Manual trigger not yet supported for {catalog_id}"} # --- Internal endpoints (no auth, for agent scripts) --- +@app.get("/api/instances/by-user/{user_id}") +def get_user_instances(user_id: int, catalog_id: str = None, db: Session = Depends(get_db)): + """Internal: get a user's instances, optionally filtered by catalog type.""" + query = db.query(AgentInstance).filter(AgentInstance.user_id == user_id, AgentInstance.status == "active") + if catalog_id: + query = query.filter(AgentInstance.catalog_id == catalog_id) + instances = query.all() + return [{"id": i.id, "catalog_id": i.catalog_id, "name": i.name, "config": i.config or {}} for i in instances] + @app.get("/api/instances/{instance_id}/config") def get_instance_config(instance_id: int, db: Session = Depends(get_db)): inst = db.query(AgentInstance).filter(AgentInstance.id == instance_id).first() diff --git a/dashboard/models.py b/dashboard/models.py index 05940cd..e3edcd7 100644 --- a/dashboard/models.py +++ b/dashboard/models.py @@ -30,6 +30,7 @@ class AgentCatalog(Base): default_config = Column(JSON, default=dict) supports_schedule = Column(Boolean, default=True) is_sub_agent = Column(Boolean, default=False) + requires_llm = Column(Boolean, default=False) instances = relationship("AgentInstance", back_populates="catalog_entry") diff --git a/dashboard/static/index.html b/dashboard/static/index.html index 62a2a0e..cfb5052 100644 --- a/dashboard/static/index.html +++ b/dashboard/static/index.html @@ -61,8 +61,9 @@ body{font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;b .config-section h3{font-size:.95rem;margin-bottom:1rem} .config-grid{display:grid;grid-template-columns:1fr 1fr;gap:.75rem} .config-field label{display:block;font-size:.75rem;font-weight:500;color:var(--text-dim);text-transform:uppercase;letter-spacing:.04em;margin-bottom:.3rem} -.config-field input,.config-field select{width:100%;padding:.5rem .65rem;background:var(--surface);border:1px solid var(--border);border-radius:6px;color:var(--text);font-size:.85rem;outline:none} -.config-field input:focus,.config-field select:focus{border-color:var(--accent)} +.config-field input,.config-field select,.config-field textarea{width:100%;padding:.5rem .65rem;background:var(--surface);border:1px solid var(--border);border-radius:6px;color:var(--text);font-size:.85rem;outline:none;font-family:inherit} +.config-field input:focus,.config-field select:focus,.config-field textarea:focus{border-color:var(--accent)} +.config-field textarea{min-height:80px;resize:vertical} .config-actions{display:flex;gap:.5rem;margin-top:1rem;align-items:center} .btn-save{padding:.5rem 1.25rem;background:var(--accent);color:#fff;border:none;border-radius:6px;font-size:.85rem;cursor:pointer} .btn-save:hover{background:var(--accent-hover)} @@ -187,9 +188,13 @@ function buildConfigForm(inst){ if(f.type==='select'){ html+=`
`; } else { - const inputType=f.type==='number'?'number':f.type==='password'?'password':'text'; - const step=f.step?`step="${f.step}"`:''; - html+=`
`; + if(f.type==='textarea'){ + html+=`
`; + } else { + const inputType=f.type==='number'?'number':f.type==='password'?'password':'text'; + const step=f.step?`step="${f.step}"`:''; + html+=`
`; + } } } @@ -276,7 +281,7 @@ async function saveInstanceConfig(id){ } const el=document.getElementById('cfg-'+f.key.replace(/\./g,'-')); if(!el)continue; - let val=el.value; + let val=el.tagName==='TEXTAREA'?el.value:el.value; if(f.type==='number')val=parseFloat(val)||0; setNestedVal(config,f.key,val); } @@ -340,7 +345,7 @@ async function showCatalog(){
${catalog.map(c=>`

${c.name}

${c.category}
-
${c.description}${c.is_sub_agent?' (sub-agent)':''}
+
${c.description}${c.is_sub_agent?' (sub-agent)':''}${c.requires_llm?' requires LLM':''}
${c.supports_schedule?'Schedulable':'Manual/sub-agent'} ${c.enabled?'':``} @@ -353,6 +358,7 @@ async function showCatalog(){ async function enableAgent(catalogId,name){ const res=await fetch(API+'/api/instances',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({catalog_id:catalogId,name:name})}); if(res.ok){closeModal();refresh()} + else{const err=await res.json();alert(err.detail||'Failed to enable agent')} } // --- LLM Settings ---