A Groq-Powered Agentic Research Assistant with LangGraph, Calling Tools, Sub-Agents, and Agentic Memory: Let’s Build

import subprocess, sys
def _pip(*a): subprocess.check_call([sys.executable,"-m","pip","install","-q",*a])
_pip("langgraph>=0.2.50", "langchain>=0.3.0", "langchain-openai>=0.2.0",
    "langchain-community>=0.3.0", "ddgs", "requests", "beautifulsoup4",
    "tiktoken", "pydantic>=2.0")


import os, getpass
if not os.environ.get("GROQ_API_KEY"):
   os.environ["GROQ_API_KEY"] = getpass.getpass("GROQ_API_KEY (free at console.groq.com/keys): ")


os.environ["OPENAI_API_KEY"]  = os.environ["GROQ_API_KEY"]
os.environ["OPENAI_BASE_URL"] = "


MODEL_NAME = "llama-3.3-70b-versatile"


import json, re, io, contextlib, pathlib
from typing import Annotated, TypedDict, Sequence, Literal, List, Dict, Any
from datetime import datetime, timezone
from langchain_openai import ChatOpenAI
from langchain_core.messages import (
   SystemMessage, HumanMessage, AIMessage, ToolMessage, BaseMessage)
from langchain_core.tools import tool
from langgraph.graph import StateGraph, END
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode

SANDBOX = pathlib.Path("/content/deerflow_sandbox").resolve()
for sub in ["uploads","workspace","outputs","skills/public","skills/custom","memory"]:
   (SANDBOX/sub).mkdir(parents=True, exist_ok=True)


def _safe(p: str) -> pathlib.Path:
   full = (SANDBOX/p.lstrip("/")).resolve()
   if not str(full).startswith(str(SANDBOX)):
       raise ValueError(f"path escapes sandbox: {p}")
   return full


SKILLS: Dict[str, Dict[str,str]] = {}
def register_skill(name, description, content, location="public"):
   d = SANDBOX/"skills"/location/name; d.mkdir(parents=True, exist_ok=True)
   (d/"SKILL.md").write_text(content)
   SKILLS[name] = {"description": description, "content": content,
                   "path": str(d/"SKILL.md")}


register_skill("research",
   "Conduct multi-source web research on a topic and produce structured notes.",
   """# Research Skill
## Workflow
1. Decompose the question into 3-5 sub-questions.
2. For each sub-question call `web_search` and pick 2 authoritative URLs.
3. `web_fetch` those URLs; extract concrete facts, numbers, dates.
4. Cross-reference for consensus vs. disagreement.
5. Append findings to `workspace/research_notes.md`: claim → evidence → URL.
## Best practices
- Prefer primary sources. Note dates. Never fabricate URLs or numbers.""")


register_skill("report-generation",
   "Synthesize research notes into a polished markdown report in outputs/.",
   """# Report Generation Skill
## Workflow
1. file_read('workspace/research_notes.md').
2. Outline: exec summary, key findings, analysis, conclusion, sources.
3. file_write('outputs/report.md', ...).
## Structure
- # Title
- ## Executive Summary  (3–5 sentences)
- ## Key Findings       (bullets)
- ## Detailed Analysis  (sections)
- ## Conclusion
- ## Sources            (numbered URL list)""")


register_skill("code-execution",
   "Run Python in the sandbox for computation, data wrangling, charts.",
   """# Code Execution Skill
1. Plan in plain language first.
2. python_exec the code; persistent artifacts go to /outputs/.
3. Verify before quoting results.""")


MEM = SANDBOX/"memory/long_term.json"
if not MEM.exists():
   MEM.write_text(json.dumps({"facts":[],"preferences":{}}, indent=2))
def _load_mem(): return json.loads(MEM.read_text())
def _save_mem(m): MEM.write_text(json.dumps(m, indent=2))

@tool
def list_skills() -> str:
   """List all skills with one-line descriptions. Call this first for complex tasks."""
   return "n".join(f"- {n}: {s['description']}" for n,s in SKILLS.items())


@tool
def load_skill(name: str) -> str:
   """Load full SKILL.md for `name`. Call before running its workflow."""
   if name not in SKILLS: return f"Unknown. Available: {list(SKILLS)}"
   return SKILLS[name]["content"]


@tool
def web_search(query: str, max_results: int = 5) -> str:
   """Search the web (DuckDuckGo). Returns titles, URLs, snippets."""
   from ddgs import DDGS
   out = []
   try:
       with DDGS() as d:
           for r in d.text(query, max_results=max_results):
               out.append(f"- {r.get('title','')}n  URL: {r.get('href','')}n  "
                          f"{(r.get('body') or '')[:220]}")
   except Exception as e:
       return f"search error: {e}"
   return "n".join(out) or "no results"


@tool
def web_fetch(url: str, max_chars: int = 4000) -> str:
   """Fetch a URL, return cleaned text (scripts/nav stripped)."""
   import requests
   from bs4 import BeautifulSoup
   try:
       r = requests.get(url, timeout=15,
                        headers={"User-Agent":"Mozilla/5.0 DeerFlow-Lite"})
       soup = BeautifulSoup(r.text, "html.parser")
       for s in soup(["script","style","nav","footer","aside","header"]): s.decompose()
       text = re.sub(r"ns*n", "nn", soup.get_text("n")).strip()
       return text[:max_chars] or "(empty page)"
   except Exception as e:
       return f"fetch error: {e}"


@tool
def file_write(path: str, content: str) -> str:
   """Write content to a sandbox path, e.g. 'workspace/notes.md' or 'outputs/x.md'."""
   p = _safe(path); p.parent.mkdir(parents=True, exist_ok=True)
   p.write_text(content)
   return f"wrote {len(content)} chars → {path}"


@tool
def file_read(path: str) -> str:
   """Read a sandbox file (first 8 KB)."""
   p = _safe(path)
   return p.read_text()[:8000] if p.exists() else f"not found: {path}"


@tool
def file_list(path: str = "") -> str:
   """List files under a sandbox dir."""
   base = _safe(path) if path else SANDBOX
   if not base.exists(): return "not found"
   items = []
   for c in sorted(base.rglob("*")):
       if "memory" in c.relative_to(SANDBOX).parts: continue
       items.append(f"  {'D' if c.is_dir() else 'F'}  {c.relative_to(SANDBOX)}")
   return "n".join(items[:60]) or "(empty)"


@tool
def python_exec(code: str) -> str:
   """Run Python in the sandbox. SANDBOX_ROOT is preset."""
   g = {"__name__":"__sb__", "SANDBOX_ROOT": str(SANDBOX)}
   buf = io.StringIO()
   try:
       with contextlib.redirect_stdout(buf), contextlib.redirect_stderr(buf):
           exec(code, g)
       return (buf.getvalue() or "(no stdout)")[:4000]
   except Exception as e:
       return f"{type(e).__name__}: {e}n{buf.getvalue()[:1500]}"


@tool
def remember(fact: str) -> str:
   """Persist a single fact to long-term memory (survives across runs)."""
   m = _load_mem()
   m["facts"].append({"fact": fact, "ts": datetime.now(timezone.utc).isoformat()})
   _save_mem(m)
   return f"remembered ({len(m['facts'])} total)"


@tool
def recall() -> str:
   """Retrieve everything in long-term memory."""
   m = _load_mem()
   if not m["facts"]: return "(memory empty)"
   return "n".join(f"- {f['fact']}" for f in m["facts"][-20:])

@tool
def spawn_subagent(role: str, task: str,
                  allowed_tools: str = "web_search,web_fetch,file_write,file_read") -> str:
   """Spawn an isolated sub-agent with a focused role and scoped tools.
   Returns its final report string. Use for parallelizable / focused subtasks."""
   bag = {t.name: t for t in BASE_TOOLS}
   sub_tools = [bag[n.strip()] for n in allowed_tools.split(",") if n.strip() in bag]
   sub_llm = ChatOpenAI(model=MODEL_NAME, temperature=0.2).bind_tools(sub_tools)
   sys_msg = SystemMessage(content=(
       f"You are a specialized sub-agent. Role: {role}.n"
       f"You operate in an ISOLATED context — no access to lead history.n"
       f"Tools: {', '.join(t.name for t in sub_tools)}.n"
       "End with a final assistant message starting 'FINAL REPORT:' "
       "containing a structured ≤700-word summary including any URLs."))
   msgs: List[BaseMessage] = [sys_msg, HumanMessage(content=task)]
   for _ in range(8):
       r = sub_llm.invoke(msgs); msgs.append(r)
       if not getattr(r, "tool_calls", None):
           return f"[sub-agent: {role}]n" + (r.content if isinstance(r.content,str) else str(r.content))
       for tc in r.tool_calls:
           t = bag.get(tc["name"])
           try:
               res = t.invoke(tc["args"]) if t else f"unknown tool {tc['name']}"
           except Exception as e:
               res = f"tool error: {e}"
           msgs.append(ToolMessage(content=str(res)[:3000], tool_call_id=tc["id"]))
   return f"[sub-agent: {role}] step-limit reached."


BASE_TOOLS = [list_skills, load_skill, web_search, web_fetch, file_write,
             file_read, file_list, python_exec, remember, recall]
ALL_TOOLS = BASE_TOOLS + [spawn_subagent]


LEAD_SYSTEM = f"""You are DeerFlow-Lite, a long-horizon super-agent harness.


Sandbox layout (relative to {SANDBOX}):
 uploads/    – user files
 workspace/  – your scratchpad
 outputs/    – final deliverables
 skills/     – capability modules (load_skill)


Principles:
 • For non-trivial tasks: list_skills → load_skill → execute.
 • Use spawn_subagent for focused subtasks (isolated context keeps lead lean).
 • Persist intermediates to workspace/, deliverables to outputs/.
 • Use remember(fact) for cross-session knowledge.
 • Finish with a short summary of what was produced and where.


Today: {datetime.now(timezone.utc).strftime('%Y-%m-%d')}."""


class AgentState(TypedDict):
   messages: Annotated[Sequence[BaseMessage], add_messages]


llm = ChatOpenAI(model=MODEL_NAME, temperature=0.3).bind_tools(ALL_TOOLS)


def call_model(state: AgentState):
   msgs = list(state["messages"])
   if not msgs or not isinstance(msgs[0], SystemMessage):
       msgs = [SystemMessage(content=LEAD_SYSTEM)] + msgs
   return {"messages": [llm.invoke(msgs)]}


def route(state: AgentState) -> Literal["tools","__end__"]:
   last = state["messages"][-1]
   return "tools" if getattr(last, "tool_calls", None) else END


g = StateGraph(AgentState)
g.add_node("agent", call_model)
g.add_node("tools", ToolNode(ALL_TOOLS))
g.set_entry_point("agent")
g.add_conditional_edges("agent", route, {"tools":"tools", END: END})
g.add_edge("tools", "agent")
APP = g.compile()

def run(task: str, max_steps: int = 25):
   print("="*78); print(f"🦌 TASK: {task}"); print("="*78)
   state = {"messages":[HumanMessage(content=task)]}
   n = 0
   for ev in APP.stream(state, {"recursion_limit": max_steps*2}, stream_mode="updates"):
       for node, payload in ev.items():
           for m in payload.get("messages", []):
               n += 1
               if isinstance(m, AIMessage):
                   if m.tool_calls:
                       for tc in m.tool_calls:
                           args = json.dumps(tc["args"], ensure_ascii=False)
                           args = args[:140] + ("…" if len(args)>140 else "")
                           print(f"[{n:02}] 🔧 {tc['name']}({args})")
                   else:
                       txt = m.content if isinstance(m.content,str) else str(m.content)
                       print(f"[{n:02}] 🦌 {txt[:800]}")
               elif isinstance(m, ToolMessage):
                   s = str(m.content).replace("n"," ")[:220]
                   print(f"[{n:02}] 📤 {s}")
   print("n"+"="*78); print("✅ COMPLETE — sandbox state:"); print("="*78)
   print(file_list.invoke({"path":""}))
   print("n🧠 Long-term memory:"); print(recall.invoke({}))
   for f in sorted((SANDBOX/"outputs").rglob("*")):
       if f.is_file():
           print(f"n--- 📄 {f.relative_to(SANDBOX)} (first 800 chars) ---")
           print(f.read_text()[:800])


run(
   "Give me a briefing on small language models (SLMs) in 2025. "
   "(1) discover skills; (2) spawn a researcher sub-agent to gather "
   "specifics on three notable SLMs from 2024-2025 with sizes, benchmarks, "
   "and use cases — sub-agent saves to workspace/slm_research.md; "
   "(3) load report-generation skill and write outputs/slm_briefing.md "
   "(~400 words) with a Sources section; (4) save the single most "
   "important takeaway to long-term memory; (5) summarize.",
   max_steps=25,
)