Draft: FROM ryaneggz/13-add-init-chat-example-ollama TO hwchase17/deepagents@master

.gitignore

@@ -56,7 +56,7 @@ cover/
 *.pot
 
 # Django stuff:
-*.log
+# *.log
 local_settings.py
 db.sqlite3
 db.sqlite3-journal
@@ -208,3 +208,4 @@ __marimo__/
 
 # LangGraph
 .langgraph_api
+**/logs/agent_*.log

README.md

@@ -187,3 +187,15 @@ as well as custom instructions.
 [] Create an example of a deep coding agent built on top of this
 [] Benchmark the example of [deep research agent](examples/research/research_agent.py)
 [] Add human-in-the-loop support for tools
+
+## Added InitChatModel [Ollama Qwen3](https://ollama.com/library/qwen3)
+
+Initial runs of out-of-box setup was took $1+, made mistake on first running `.invoke` instead of `.stream`
+
+```py
+init_chat_model(
+    model="ollama:qwen3:14b",
+    temperature=0.0,
+    max_tokens=40000,
+)
+```

examples/research/.vscode/launch.json

@@ -0,0 +1,31 @@
+{
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Debug Research Agent",
+            "type": "python",
+            "request": "launch",
+            "program": "${workspaceFolder}/research_agent.py",
+            "console": "integratedTerminal",
+            "cwd": "${workspaceFolder}",
+            "env": {
+                "PYTHONPATH": "${workspaceFolder}"
+            },
+            "justMyCode": false,
+            "python": "${workspaceFolder}/.venv/bin/python"
+        },
+        {
+            "name": "Debug Research Agent (External Terminal)",
+            "type": "python",
+            "request": "launch",
+            "program": "${workspaceFolder}/research_agent.py",
+            "console": "externalTerminal",
+            "cwd": "${workspaceFolder}",
+            "env": {
+                "PYTHONPATH": "${workspaceFolder}"
+            },
+            "justMyCode": false,
+            "python": "${workspaceFolder}/.venv/bin/python"
+        }
+    ]
+} 

examples/research/requirements.txt

@@ -1,3 +1,6 @@
 deepagents
 langgraph-cli[inmem]
 tavily-python
+langchain-openai
+langchain-ollama
+langchain-mcp-adapters

examples/research/research_agent.py

@@ -1,10 +1,43 @@
 import os
+import logging
 from typing import Literal
+from datetime import datetime
+from dotenv import load_dotenv
 
+from langchain.chat_models import init_chat_model
+from deepagents import create_deep_agent
 from tavily import TavilyClient
 
 
-from deepagents import create_deep_agent, SubAgent
+load_dotenv()
+
+# ─── Prepare logs directory ────────────────────────────────────────────────────
+logs_dir = "logs"
+os.makedirs(logs_dir, exist_ok=True)
+
+# Generate a new filename for this run
+timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+log_filename = os.path.join(logs_dir, f"agent_{timestamp}.log")
+
+# ─── Configure Logging ─────────────────────────────────────────────────────────
+logger = logging.getLogger("agent_logger")
+logger.setLevel(logging.INFO)
+
+formatter = logging.Formatter("%(asctime)s %(levelname)s: %(message)s",
+                              "%Y-%m-%d %H:%M:%S")
+
+# File handler → writes to a fresh file each run
+file_handler = logging.FileHandler(log_filename, mode="w", encoding="utf-8")
+file_handler.setLevel(logging.INFO)
+file_handler.setFormatter(formatter)
+
+# Console handler → still prints live to stdout
+console_handler = logging.StreamHandler()
+console_handler.setLevel(logging.INFO)
+console_handler.setFormatter(formatter)
+
+logger.addHandler(file_handler)
+logger.addHandler(console_handler)
 
 
 # Search tool to use to do research
@@ -155,9 +188,29 @@ def internet_search(
 Use this to run an internet search for a given query. You can specify the number of results, the topic, and whether raw content should be included.
 """
 
+
 # Create the agent
 agent = create_deep_agent(
     [internet_search],
     research_instructions,
     subagents=[critique_sub_agent, research_sub_agent],
-).with_config({"recursion_limit": 1000})
+    model=init_chat_model(
+        model="ollama:qwen3:14b",
+        temperature=0.0,
+        max_tokens=40000,
+    )
+).with_config({"recursion_limit": 100})
+
+
+# Stream the agent
+result = agent.stream({"messages": [{"role": "user", "content": "Provide a summary of https://github.com/hwchase17/deepagents. Review the README, Code, and Social Media."}]})
+
+for chunk in result:
+    if "agent" in chunk:
+        msg = chunk["agent"]["messages"][0].content.strip()
+        logger.info("=== Agent Message ===\n%s", msg)
+    if "tools" in chunk:
+        call = chunk["tools"]["messages"][0].content.strip()
+        logger.info("+++ Tool Call +++\n%s", call)
+
+logger.info("Finished streaming. Log saved to %s", log_filename)