GitDealFlow × LangChain

What you can build

Minimal LangChain agent (Python)

# pip install langchain langchain-openai langchain-core requests
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
import requests

A2A = "https://signals.gitdealflow.com/api/a2a"

@tool
def gitdealflow(skill: str, args: dict | None = None) -> dict:
    """Live VC engineering signals from GitDealFlow.

    skill: get_trending_startups | search_startups_by_sector |
           get_startup_signal | get_signals_summary | get_methodology
    args:  skill-specific dict (e.g. {"sector": "ai-ml"})
    """
    body = {
        "jsonrpc": "2.0", "id": 1,
        "method": "message/send",
        "params": {"message": {"role": "user", "parts": [
            {"kind": "data", "data": {"skill": skill, "args": args or {}}},
        ]}},
    }
    return requests.post(A2A, json=body, timeout=15).json()

agent = create_react_agent(
    ChatOpenAI(model="gpt-5.4"),
    tools=[gitdealflow],
)

result = agent.invoke({
    "messages": [("user", "Who's accelerating in fintech this week? Cite methodology.")]
})
print(result["messages"][-1].content)

LangGraph deal-memo pipeline

# Two-node graph: scout pulls signals, analyst writes a memo.
from langgraph.graph import StateGraph, END
from typing import TypedDict
import requests

A2A = "https://signals.gitdealflow.com/api/a2a"

class State(TypedDict):
    sector: str
    signals: list
    memo: str

def scout(state: State) -> State:
    body = {"jsonrpc":"2.0","id":1,"method":"message/send",
            "params":{"message":{"role":"user","parts":[
                {"kind":"data","data":{
                    "skill":"search_startups_by_sector",
                    "args":{"sector": state["sector"]}
                }},
            ]}}}
    data = requests.post(A2A, json=body, timeout=15).json()
    return {"signals": data["result"]["artifacts"][0]["parts"][0]["data"]["startups"][:5]}

def analyst(state: State) -> State:
    # ...your LLM-call here, given state["signals"]...
    return {"memo": f"Top 5 in {state['sector']}: " + ", ".join(s["name"] for s in state["signals"])}

graph = StateGraph(State)
graph.add_node("scout", scout)
graph.add_node("analyst", analyst)
graph.set_entry_point("scout")
graph.add_edge("scout", "analyst")
graph.add_edge("analyst", END)

app = graph.compile()
out = app.invoke({"sector": "ai-ml"})
print(out["memo"])

Five prompts to try first

• ›Who is trending in fintech this week? Cite the methodology.
• ›Compare commit velocity for Roboflow and Modular, which is shipping faster?
• ›Show me ai-ml startups with breakout signals; rank by contributor delta.
• ›What does engineering acceleration mean, is it the same as Y Combinator?
• ›Summarize the dataset: how many sectors, how many startups, how fresh.

FAQ

Gotchas

• create_react_agent occasionally calls tools with malformed args. Validate `skill` against the enum before forwarding the request.
• Sector slugs are normalized (crypto → web3, AI → ai-ml). Pass user free-text through search_startups_by_sector and let the endpoint resolve.
• If you stream tokens, fetch the tool result in a separate node so the model sees structured data, not a half-streamed JSON string.

References

Other frameworks

• CrewAI
• Letta
• Mastra
• Vercel AI SDK

When the free signal isn’t enough