Merge pull request #5058 from adenhq/fix/deprecation

fix(arch): remove all deprecated concepts and deadcodes

Author: TimothyZhang7

.claude/skills/hive-create/SKILL.md

@@ -492,7 +492,7 @@ AskUserQuestion(questions=[{
 - node_id (kebab-case)
 - name
 - description
-- node_type: `"event_loop"` (recommended for all LLM work) or `"function"` (deterministic, no LLM)
+- node_type: `"event_loop"` (the only valid type; use `client_facing: True` for HITL)
 - input_keys (what data this node receives)
 - output_keys (what data this node produces)
 - tools (ONLY tools that exist from Step 1 — empty list if no tools needed)
@@ -852,8 +852,7 @@ cd /home/timothy/oss/hive && PYTHONPATH=exports uv run python -m AGENT_NAME vali
 
 | Type         | tools param             | Use when                                |
 | ------------ | ----------------------- | --------------------------------------- |
-| `event_loop` | `'["tool1"]'` or `'[]'` | LLM-powered work with or without tools  |
-| `function`   | N/A                     | Deterministic Python operations, no LLM |
+| `event_loop` | `'["tool1"]'` or `'[]'` | All agent work (with or without tools, HITL via client_facing) |
 
 ---
 
@@ -1008,7 +1007,7 @@ Use this reference during STEP 2 to give accurate, honest assessments.
 | Sub-second responses | LLM latency is inherent | Traditional code, no LLM |
 | Processing millions of items | Context windows and rate limits | Batch processing + sampling |
 | Real-time streaming data | No built-in pub/sub or streaming input | Custom MCP server + agent |
-| Guaranteed determinism | LLM outputs vary | Function nodes for deterministic parts |
+| Guaranteed determinism | LLM outputs vary | Traditional code for deterministic parts |
 | Offline/air-gapped | Requires LLM API access | Local models (not currently supported) |
 | Multi-user concurrency | Single-user session model | Separate agent instances per user |
 

core/MCP_BUILDER_TOOLS_GUIDE.md

@@ -82,7 +82,7 @@ Register an MCP server as a tool source for your agent.
     "example_tool"
   ],
   "total_mcp_servers": 1,
-  "note": "MCP server 'tools' registered with 6 tools. These tools can now be used in llm_tool_use nodes."
+  "note": "MCP server 'tools' registered with 6 tools. These tools can now be used in event_loop nodes."
 }
 ```
 
@@ -149,7 +149,7 @@ List tools available from registered MCP servers.
     ]
   },
   "total_tools": 6,
-  "note": "Use these tool names in the 'tools' parameter when adding llm_tool_use nodes"
+  "note": "Use these tool names in the 'tools' parameter when adding event_loop nodes"
 }
 ```
 
@@ -246,7 +246,7 @@ Here's a complete workflow for building an agent with MCP tools:
     "node_id": "web-searcher",
     "name": "Web Search",
     "description": "Search the web for information",
-    "node_type": "llm_tool_use",
+    "node_type": "event_loop",
     "input_keys": "[\"query\"]",
     "output_keys": "[\"search_results\"]",
     "system_prompt": "Search for {query} using the web_search tool",

core/MCP_INTEGRATION_GUIDE.md

@@ -119,7 +119,7 @@ builder = WorkflowBuilder()
 builder.add_node(
     node_id="researcher",
     name="Web Researcher",
-    node_type="llm_tool_use",
+    node_type="event_loop",
     system_prompt="Research the topic using web_search",
     tools=["web_search"],  # Tool from tools MCP server
     input_keys=["topic"],
@@ -137,7 +137,7 @@ Tools from MCP servers can be referenced in your agent.json just like built-in t
     {
       "id": "searcher",
       "name": "Web Searcher",
-      "node_type": "llm_tool_use",
+      "node_type": "event_loop",
       "system_prompt": "Search for information about {topic}",
       "tools": ["web_search", "web_scrape"],
       "input_keys": ["topic"],

core/MCP_SERVER_GUIDE.md

@@ -103,39 +103,28 @@ Add a processing node to the agent graph.
 - `node_id` (string, required): Unique node identifier
 - `name` (string, required): Human-readable name
 - `description` (string, required): What this node does
-- `node_type` (string, required): One of: `llm_generate`, `llm_tool_use`, `router`, `function`
+- `node_type` (string, required): Must be `event_loop` (the only valid type)
 - `input_keys` (string, required): JSON array of input variable names
 - `output_keys` (string, required): JSON array of output variable names
-- `system_prompt` (string, optional): System prompt for LLM nodes
-- `tools` (string, optional): JSON array of tool names for tool_use nodes
-- `routes` (string, optional): JSON object of route mappings for router nodes
+- `system_prompt` (string, optional): System prompt for the LLM
+- `tools` (string, optional): JSON array of tool names
+- `client_facing` (boolean, optional): Set to true for human-in-the-loop interaction
 
-**Node Types:**
+**Node Type:**
 
-1. **llm_generate**: Uses LLM to generate output from inputs
-   - Requires: `system_prompt`
-   - Tools: Not used
-
-2. **llm_tool_use**: Uses LLM with tools to accomplish tasks
-   - Requires: `system_prompt`, `tools`
-   - Tools: Array of tool names (e.g., `["web_search", "web_fetch"]`)
-
-3. **router**: LLM-powered routing to different paths
-   - Requires: `system_prompt`, `routes`
-   - Routes: Object mapping route names to target node IDs
-   - Example: `{"pass": "success_node", "fail": "retry_node"}`
-
-4. **function**: Executes a pre-defined function
-   - System prompt describes the function behavior
-   - No LLM calls, pure computation
+**event_loop**: LLM-powered node with self-correction loop
+- Requires: `system_prompt`
+- Optional: `tools` (array of tool names, e.g., `["web_search", "web_fetch"]`)
+- Optional: `client_facing` (set to true for HITL / user interaction)
+- Supports: iterative refinement, judge-based evaluation, tool use, streaming
 
 **Example:**
 ```json
 {
   "node_id": "search_sources",
   "name": "Search Sources",
   "description": "Searches for relevant sources on the topic",
-  "node_type": "llm_tool_use",
+  "node_type": "event_loop",
   "input_keys": "[\"topic\", \"search_queries\"]",
   "output_keys": "[\"sources\", \"source_count\"]",
   "system_prompt": "Search for sources using the provided queries...",
@@ -198,7 +187,7 @@ Export the validated graph as an agent specification.
 
 **What it does:**
 1. Validates the graph
-2. Auto-generates missing edges from router routes
+2. Validates edge connectivity
 3. Writes files to disk:
    - `exports/{agent-name}/agent.json` - Full agent specification
    - `exports/{agent-name}/README.md` - Auto-generated documentation
@@ -252,47 +241,6 @@ Test the complete agent graph with sample inputs.
 
 ---
 
-### Evaluation Rules
-
-#### `add_evaluation_rule`
-Add a rule for the HybridJudge to evaluate node outputs.
-
-**Parameters:**
-- `rule_id` (string, required): Unique rule identifier
-- `description` (string, required): What this rule checks
-- `condition` (string, required): Python expression to evaluate
-- `action` (string, required): Action to take: `accept`, `retry`, `escalate`
-- `priority` (integer, optional): Rule priority (default: 0)
-- `feedback_template` (string, optional): Feedback message template
-
-**Condition Examples:**
-- `'result.get("success") == True'` - Check for success flag
-- `'result.get("error_type") == "timeout"'` - Check error type
-- `'len(result.get("data", [])) > 0'` - Check for non-empty data
-
-**Example:**
-```json
-{
-  "rule_id": "timeout_retry",
-  "description": "Retry on timeout errors",
-  "condition": "result.get('error_type') == 'timeout'",
-  "action": "retry",
-  "priority": 10,
-  "feedback_template": "Timeout occurred, retrying..."
-}
-```
-
-#### `list_evaluation_rules`
-List all configured evaluation rules.
-
-#### `remove_evaluation_rule`
-Remove an evaluation rule.
-
-**Parameters:**
-- `rule_id` (string, required): Rule to remove
-
----
-
 ## Example Workflow
 
 Here's a complete workflow for building a research agent:
@@ -320,7 +268,7 @@ add_node(
     node_id="planner",
     name="Research Planner",
     description="Creates research strategy",
-    node_type="llm_generate",
+    node_type="event_loop",
     input_keys='["topic"]',
     output_keys='["strategy", "queries"]',
     system_prompt="Analyze topic and create research plan..."
@@ -330,7 +278,7 @@ add_node(
     node_id="searcher",
     name="Search Sources",
     description="Find relevant sources",
-    node_type="llm_tool_use",
+    node_type="event_loop",
     input_keys='["queries"]',
     output_keys='["sources"]',
     system_prompt="Search for sources...",
@@ -359,10 +307,9 @@ The exported agent will be saved to `exports/research-agent/`.
 
 1. **Start with the goal**: Define clear success criteria before building nodes
 2. **Test nodes individually**: Use `test_node` to verify each node works
-3. **Use router nodes for branching**: Don't create edges manually for routers - define routes and they'll be auto-generated
-4. **Add evaluation rules**: Help the judge evaluate outputs deterministically
-5. **Validate early, validate often**: Run `validate_graph` after adding nodes/edges
-6. **Check exports**: Review the generated README.md to verify your agent structure
+3. **Use conditional edges for branching**: Define condition_expr on edges for decision points
+4. **Validate early, validate often**: Run `validate_graph` after adding nodes/edges
+5. **Check exports**: Review the generated README.md to verify your agent structure
 
 ---
 

core/README.md

@@ -73,7 +73,7 @@ To use the agent builder with Claude Desktop or other MCP clients, add this to y
 The MCP server provides tools for:
 - Creating agent building sessions
 - Defining goals with success criteria
-- Adding nodes (llm_generate, llm_tool_use, router, function)
+- Adding nodes (event_loop only)
 - Connecting nodes with edges
 - Validating and exporting agent graphs
 - Testing nodes and full agent graphs

core/demos/github_outreach_demo.py

@@ -68,7 +68,7 @@
 )
 from framework.graph.executor import GraphExecutor  # noqa: E402
 from framework.graph.goal import Goal  # noqa: E402
-from framework.graph.node import NodeSpec  # noqa: E402
+from framework.graph.node import NodeContext, NodeProtocol, NodeResult, NodeSpec  # noqa: E402
 from framework.llm.litellm import LiteLLMProvider  # noqa: E402
 from framework.runner.tool_registry import ToolRegistry  # noqa: E402
 from framework.runtime.core import Runtime  # noqa: E402
@@ -654,7 +654,7 @@ def list_data_files() -> dict:
         id="sender",
         name="Sender",
         description="Send approved campaign emails",
-        node_type="function",
+        node_type="event_loop",
         input_keys=["approved_emails"],
         output_keys=["send_results"],
     ),
@@ -823,11 +823,20 @@ def _send_email_via_resend(
         return {"error": f"Network error: {e}"}
 
 
+class SenderNode(NodeProtocol):
+    """Node wrapper for send_emails function."""
+
+    async def execute(self, ctx: NodeContext) -> NodeResult:
+        approved = ctx.input_data.get("approved_emails", "")
+        result_str = send_emails(approved_emails=approved)
+        ctx.memory.write("send_results", result_str)
+        return NodeResult(success=True, output={"send_results": result_str})
+
+
 def send_emails(approved_emails: str = "") -> str:
     """Send approved campaign emails via Resend, or log if unconfigured.
 
-    Called by FunctionNode which unpacks input_keys as kwargs.
-    Returns a JSON string (FunctionNode wraps it in NodeResult).
+    Returns a JSON string.
     """
     approved = approved_emails
     if not approved:
@@ -1780,7 +1789,7 @@ async def _run_pipeline(websocket, initial_message: str):
     )
     for nid, impl in nodes.items():
         executor.register_node(nid, impl)
-    executor.register_function("sender", send_emails)
+    executor.register_node("sender", SenderNode())
 
     # --- Event forwarding: bus → WebSocket ---
 

core/examples/manual_agent.py

@@ -4,8 +4,8 @@
 This example demonstrates how to build and run an agent programmatically
 without using the Claude Code CLI or external LLM APIs.
 
-It uses 'function' nodes to define logic in pure Python, making it perfect
-for understanding the core runtime loop:
+It uses custom NodeProtocol implementations to define logic in pure Python,
+making it perfect for understanding the core runtime loop:
 Setup -> Graph definition -> Execution -> Result
 
 Run with:
@@ -16,22 +16,33 @@
 
 from framework.graph import EdgeCondition, EdgeSpec, Goal, GraphSpec, NodeSpec
 from framework.graph.executor import GraphExecutor
+from framework.graph.node import NodeContext, NodeProtocol, NodeResult
 from framework.runtime.core import Runtime
 
 
-# 1. Define Node Logic (Pure Python Functions)
-def greet(name: str) -> str:
+# 1. Define Node Logic (Custom NodeProtocol implementations)
+class GreeterNode(NodeProtocol):
     """Generate a simple greeting."""
-    return f"Hello, {name}!"
 
+    async def execute(self, ctx: NodeContext) -> NodeResult:
+        name = ctx.input_data.get("name", "World")
+        greeting = f"Hello, {name}!"
+        ctx.memory.write("greeting", greeting)
+        return NodeResult(success=True, output={"greeting": greeting})
 
-def uppercase(greeting: str) -> str:
+
+class UppercaserNode(NodeProtocol):
     """Convert text to uppercase."""
-    return greeting.upper()
+
+    async def execute(self, ctx: NodeContext) -> NodeResult:
+        greeting = ctx.input_data.get("greeting") or ctx.memory.read("greeting") or ""
+        result = greeting.upper()
+        ctx.memory.write("final_greeting", result)
+        return NodeResult(success=True, output={"final_greeting": result})
 
 
 async def main():
-    print("🚀 Setting up Manual Agent...")
+    print("Setting up Manual Agent...")
 
     # 2. Define the Goal
     # Every agent needs a goal with success criteria
@@ -55,8 +66,7 @@ async def main():
         id="greeter",
         name="Greeter",
         description="Generates a simple greeting",
-        node_type="function",
-        function="greet",  # Matches the registered function name
+        node_type="event_loop",
         input_keys=["name"],
         output_keys=["greeting"],
     )
@@ -65,8 +75,7 @@ async def main():
         id="uppercaser",
         name="Uppercaser",
         description="Converts greeting to uppercase",
-        node_type="function",
-        function="uppercase",
+        node_type="event_loop",
         input_keys=["greeting"],
         output_keys=["final_greeting"],
     )
@@ -98,23 +107,23 @@ async def main():
     runtime = Runtime(storage_path=Path("./agent_logs"))
     executor = GraphExecutor(runtime=runtime)
 
-    # 7. Register Function Implementations
-    # Connect string names in NodeSpecs to actual Python functions
-    executor.register_function("greeter", greet)
-    executor.register_function("uppercaser", uppercase)
+    # 7. Register Node Implementations
+    # Connect node IDs in the graph to actual Python implementations
+    executor.register_node("greeter", GreeterNode())
+    executor.register_node("uppercaser", UppercaserNode())
 
     # 8. Execute Agent
-    print("▶ Executing agent with input: name='Alice'...")
+    print("Executing agent with input: name='Alice'...")
 
     result = await executor.execute(graph=graph, goal=goal, input_data={"name": "Alice"})
 
     # 9. Verify Results
     if result.success:
-        print("\n✅ Success!")
+        print("\nSuccess!")
         print(f"Path taken: {' -> '.join(result.path)}")
         print(f"Final output: {result.output.get('final_greeting')}")
     else:
-        print(f"\n❌ Failed: {result.error}")
+        print(f"\nFailed: {result.error}")
 
 
 if __name__ == "__main__":

core/examples/mcp_integration_example.py

@@ -122,7 +122,7 @@ async def example_4_custom_agent_with_mcp_tools():
         node_id="web-searcher",
         name="Web Search",
         description="Search the web for information",
-        node_type="llm_tool_use",
+        node_type="event_loop",
         system_prompt="Search for {query} and return the top results. Use the web_search tool.",
         tools=["web_search"],  # This tool comes from tools MCP server
         input_keys=["query"],
@@ -133,7 +133,7 @@ async def example_4_custom_agent_with_mcp_tools():
         node_id="summarizer",
         name="Summarize Results",
         description="Summarize the search results",
-        node_type="llm_generate",
+        node_type="event_loop",
         system_prompt="Summarize the following search results in 2-3 sentences: {search_results}",
         input_keys=["search_results"],
         output_keys=["summary"],