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Announcing Turgon’s Series A Funding Round

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Nikhil Sama · Founder / CTO · Turgon AI
October 21, 2025
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10 min Read

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Why This Matters

Today, every serious AI product involves workflow orchestration over latent stochastic behavior.
That might sound academic, but it translates to something very real:

  • Your AI needs to do multiple steps
  • Possibly loop or retry along the way
  • Use external tools and memory
  • And recover when things go wrong

If you’re doing that, you’re building an agent.
And like any system, how you compose its logic matters — for observability, reliability, and scale.

Option 1: LangChain — The Swiss Army Knife

What it is:
LangChain is a batteries-included Python/JS framework for chaining LLM calls, memory, tools, and vectorstores.
It’s great for bootstrapping fast.

Strengths

  • Huge ecosystem of integrations (e.g., OpenAI, Pinecone, SerpAPI)
  • Easy to get started with Agents, Tools, and Memory
  • Great for rapid iteration or hackathons

Weaknesses

  • Opaque control flow — hard to debug or control multi-hop reasoning
  • Agents are often brittle at scale
  • Limited support for fine-grained retries, versioning, or step introspection

Best For

POCs, internal tools, RAG+tools workflows, light orchestration needs.

Option 2: LangGraph — Agentic DAGs, Done Right

What it is:
LangGraph is a batteries-included Python/JS framework for chaining LLM calls, memory, tools, and vectorstores.
It’s great for bootstrapping fast.

Strengths

  • Huge ecosystem of integrations (e.g., OpenAI, Pinecone, SerpAPI)
  • Easy to get started with Agents, Tools, and Memory
  • Great for rapid iteration or hackathons

Weaknesses

  • Opaque control flow — hard to debug or control multi-hop reasoning
  • Agents are often brittle at scale
  • Limited support for fine-grained retries, versioning, or step introspection

Best For

POCs, internal tools, RAG+tools workflows, light orchestration needs.

What Most Teams Get Wrong

Today, every serious AI product involves workflow orchestration over latent stochastic behavior.
That might sound academic, but it translates to something very real:

  • Your AI needs to do multiple steps
  • Possibly loop or retry along the way
  • Use external tools and memory
  • And recover when things go wrong

If you’re doing that, you’re building an agent.
And like any system, how you compose its logic matters — for observability, reliability, and scale.

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