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https://github.com/openswarm-ai/openswarm.git
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[eric] lazy MCP connections + browser agent 9Router support
Performance fix: - MCP servers no longer spawned upfront for every message - Tool schemas loaded from cached metadata (instant, no subprocess) - MCP servers only connected when their tools are first called (lazy) - Browser agent MCP server still connects upfront (lightweight) - Reduces startup from 30+ seconds to <1 second for simple messages Browser agent 9Router support: - Browser agent now works through 9Router when no API key set - Uses OpenAI client format when base_url provided - Model ID mapping for 9Router (cc/ prefix) - Passes auth_token and base_url through run_browser_agents Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
ciregenz
co-authored by
Claude Opus 4.6
Claude Opus 4.6
parent
0a907e4bac
commit
36d79fd1d3
@@ -649,14 +649,46 @@ class AgentManager:
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"type": "stdio",
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}
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# Connect MCP servers and collect tool schemas
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# Lazy MCP: build tool schemas from saved metadata (no subprocess spawn)
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# Actual MCP servers are only connected when a tool is first called
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mcp_manager = MCPClientManager()
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await mcp_manager.__aenter__()
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mcp_tool_schemas = []
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for server_name, server_config in mcp_servers.items():
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tools = await mcp_manager.connect(server_name, server_config)
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mcp_tool_schemas.extend(tools)
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from backend.apps.agents.providers.base import ToolSchema
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mcp_tool_schemas: list[ToolSchema] = []
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_pending_mcp_servers: dict[str, dict] = dict(mcp_servers) # servers not yet connected
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_connected_servers: set[str] = set()
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# Build tool schemas from saved tool_permissions metadata (instant, no subprocess)
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for tool in load_all_tools():
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if not tool.mcp_config or not tool.enabled:
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continue
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server_name = _sanitize_server_name(tool.name)
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if server_name not in _pending_mcp_servers and server_name not in mcp_servers:
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continue
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tool_descs = tool.tool_permissions.get("_tool_descriptions", {})
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denied = _get_denied_tool_names(tool)
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for tn, desc in tool_descs.items():
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if tn not in denied:
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mcp_tool_schemas.append(ToolSchema(
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name=f"mcp__{server_name}__{tn}",
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description=desc,
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input_schema={"type": "object", "properties": {}},
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))
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# Always add browser agent tools (they're lightweight)
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browser_agent_name = "openswarm-browser-agent"
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if browser_agent_name in mcp_servers:
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try:
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ba_tools = await asyncio.wait_for(
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mcp_manager.connect(browser_agent_name, mcp_servers[browser_agent_name]),
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timeout=10.0,
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)
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mcp_tool_schemas.extend(ba_tools)
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_connected_servers.add(browser_agent_name)
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except Exception as e:
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logger.warning(f"Browser agent MCP connection failed: {e}")
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# Collect builtin + MCP tool schemas
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from backend.apps.agents.tools.registry import get_all_tool_schemas
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@@ -699,6 +731,19 @@ class AgentManager:
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parsed = mcp_manager.parse_mcp_tool_name(tool_name)
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if parsed:
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server_name, bare_name = parsed
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# Lazy connect: if this server hasn't been connected yet, connect now
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if server_name not in _connected_servers and server_name in _pending_mcp_servers:
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try:
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await asyncio.wait_for(
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mcp_manager.connect(server_name, _pending_mcp_servers[server_name]),
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timeout=15.0,
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)
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_connected_servers.add(server_name)
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except Exception as e:
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logger.warning(f"Lazy MCP connect failed for {server_name}: {e}")
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return [{"type": "text", "text": f"Failed to connect to {server_name}: {e}"}]
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result = await mcp_manager.call_tool(server_name, bare_name, tool_input)
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elapsed_ms = int((time.time() - t0) * 1000)
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_analytics("tool.called", {
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@@ -278,6 +278,8 @@ async def run_browser_agent(
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pre_selected: bool = False,
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initial_url: str | None = None,
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parent_session_id: str | None = None,
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auth_token: str | None = None,
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base_url: str | None = None,
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) -> dict:
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"""Run a browser sub-agent loop for a single browser card.
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@@ -315,7 +317,17 @@ async def run_browser_agent(
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logger.info(f"Browser agent {session_id}: navigated to {initial_url}: {nav_result.get('text', nav_result.get('error', ''))}")
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api_model = MODEL_MAP.get(model, model)
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client = anthropic.AsyncAnthropic(api_key=api_key)
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# Use OpenAI client for 9Router, Anthropic client for direct API
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_use_openai_client = base_url is not None
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if _use_openai_client:
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from openai import AsyncOpenAI
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# Map to 9Router model IDs
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_9r_map = {"sonnet": "cc/claude-sonnet-4-6", "opus": "cc/claude-opus-4-6", "haiku": "cc/claude-haiku-4-5-20251001"}
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api_model = _9r_map.get(model, f"cc/{api_model}" if not api_model.startswith("cc/") else api_model)
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client = AsyncOpenAI(api_key=api_key, base_url=base_url)
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else:
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client = anthropic.AsyncAnthropic(api_key=api_key)
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messages: list[dict] = [{"role": "user", "content": task}]
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action_log: list[dict] = []
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@@ -330,30 +342,66 @@ async def run_browser_agent(
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try:
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for turn in range(MAX_TURNS):
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response = await client.messages.create(
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model=api_model,
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max_tokens=4096,
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system=SYSTEM_PROMPT,
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tools=BROWSER_TOOLS_SCHEMA,
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messages=messages,
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)
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if _use_openai_client:
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# OpenAI-compatible format (9Router)
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import json as _json
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oai_tools = [{"type": "function", "function": {"name": t["name"], "description": t["description"], "parameters": t["input_schema"]}} for t in BROWSER_TOOLS_SCHEMA]
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oai_messages = [{"role": "system", "content": SYSTEM_PROMPT}] + messages
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resp = await client.chat.completions.create(model=api_model, max_tokens=4096, tools=oai_tools, messages=oai_messages)
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choice = resp.choices[0]
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assistant_content = []
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text_parts = []
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tool_uses = []
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assistant_content = []
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text_parts = []
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tool_uses = []
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for block in response.content:
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if block.type == "text":
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text_parts.append(block.text)
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assistant_content.append({"type": "text", "text": block.text})
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elif block.type == "tool_use":
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tool_uses.append(block)
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assistant_content.append({
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"type": "tool_use",
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"id": block.id,
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"name": block.name,
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"input": block.input,
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})
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if choice.message.content:
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text_parts.append(choice.message.content)
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assistant_content.append({"type": "text", "text": choice.message.content})
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if choice.message.tool_calls:
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for tc in choice.message.tool_calls:
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try:
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inp = _json.loads(tc.function.arguments)
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except Exception:
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inp = {}
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# Create a simple object with .id, .name, .input
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class _TC:
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pass
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tool_obj = _TC()
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tool_obj.id = tc.id
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tool_obj.name = tc.function.name
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tool_obj.input = inp
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tool_uses.append(tool_obj)
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assistant_content.append({"type": "tool_use", "id": tc.id, "name": tc.function.name, "input": inp})
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stop_reason = "tool_use" if choice.message.tool_calls else "end_turn"
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else:
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# Anthropic format (direct API)
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response = await client.messages.create(
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model=api_model,
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max_tokens=4096,
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system=SYSTEM_PROMPT,
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tools=BROWSER_TOOLS_SCHEMA,
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messages=messages,
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)
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assistant_content = []
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text_parts = []
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tool_uses = []
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stop_reason = response.stop_reason
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for block in response.content:
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if block.type == "text":
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text_parts.append(block.text)
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assistant_content.append({"type": "text", "text": block.text})
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elif block.type == "tool_use":
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tool_uses.append(block)
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assistant_content.append({
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"type": "tool_use",
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"id": block.id,
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"name": block.name,
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"input": block.input,
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})
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if text_parts:
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asst_msg = Message(
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@@ -377,9 +425,16 @@ async def run_browser_agent(
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"message": tool_msg.model_dump(mode="json"),
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})
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messages.append({"role": "assistant", "content": assistant_content})
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if _use_openai_client:
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# OpenAI format: assistant message with tool_calls
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asst_api_msg: dict = {"role": "assistant", "content": choice.message.content}
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if choice.message.tool_calls:
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asst_api_msg["tool_calls"] = [{"id": tc.id, "type": "function", "function": {"name": tc.function.name, "arguments": tc.function.arguments}} for tc in (choice.message.tool_calls or [])]
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messages.append(asst_api_msg)
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else:
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messages.append({"role": "assistant", "content": assistant_content})
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if response.stop_reason != "tool_use":
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if stop_reason != "tool_use":
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break
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tool_results = []
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@@ -460,7 +515,16 @@ async def run_browser_agent(
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"message": result_msg.model_dump(mode="json"),
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})
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messages.append({"role": "user", "content": tool_results})
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if _use_openai_client:
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# OpenAI format: each tool result is a separate message
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for tr in tool_results:
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text_content = ""
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for block in (tr.get("content") or []):
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if isinstance(block, dict) and block.get("type") == "text":
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text_content += block.get("text", "")
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messages.append({"role": "tool", "tool_call_id": tr["tool_use_id"], "content": text_content or "Done."})
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else:
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messages.append({"role": "user", "content": tool_results})
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summary_parts = text_parts if text_parts else ["Task completed."]
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summary = "\n".join(summary_parts)
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@@ -563,6 +627,8 @@ async def run_browser_agents(
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dashboard_id: str | None = None,
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pre_selected_browser_ids: list[str] | None = None,
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parent_session_id: str | None = None,
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auth_token: str | None = None,
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base_url: str | None = None,
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) -> list[dict]:
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"""Run multiple browser sub-agents in parallel.
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@@ -590,6 +656,8 @@ async def run_browser_agents(
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pre_selected=is_pre_selected,
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initial_url=url if url and browser_id not in pre_selected else None,
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parent_session_id=parent_session_id,
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auth_token=auth_token,
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base_url=base_url,
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)
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results = await asyncio.gather(*[_run_one(t) for t in tasks], return_exceptions=True)
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+18
-3
@@ -145,16 +145,31 @@ async def browser_agent_run(request: Request):
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return JSONResponse({"error": "tasks array is required"}, status_code=400)
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settings = load_settings()
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if not settings.anthropic_api_key:
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return JSONResponse({"error": "Anthropic API key not configured"}, status_code=400)
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# Determine API credentials — check API key, then 9Router
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api_key = settings.anthropic_api_key
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auth_token = None
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base_url = None
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if not api_key:
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# Try 9Router
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from backend.apps.nine_router import is_running as _9r_running
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if _9r_running():
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api_key = "9router"
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base_url = "http://localhost:20128/v1"
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auth_token = None
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else:
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return JSONResponse({"error": "No AI provider configured. Set an API key or connect a subscription."}, status_code=400)
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results = await run_browser_agents(
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tasks=tasks,
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model=model,
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api_key=settings.anthropic_api_key,
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api_key=api_key,
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dashboard_id=dashboard_id or None,
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pre_selected_browser_ids=pre_selected_browser_ids,
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parent_session_id=parent_session_id or None,
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auth_token=auth_token,
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base_url=base_url,
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)
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return JSONResponse({"results": results})
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