How much does a LangGraph project with Idun Group cost?

We do not publish prices: every estimate follows our analysis and is shared during a call. What we sell is a fixed-price engagement whose scope is set by the discovery phase. The Idun open source platform is released under the GPL v3 licence, with public and auditable code.

How quickly is the first agent up and running?

The timeline depends on the scope defined in the discovery phase. We define the MVP with your business lead, build the agent, test it in real conditions, then put it into production. The exact pace is set at the start of the engagement.

Do our teams need in-house LangGraph skills?

No. Idun Group handles the entire development, from scoping to deployment. We train your teams in parallel if you want to bring maintenance in-house over time. The LangGraph learning curve, 10 to 14 developer days, is absorbed by our engineers.

How do you guarantee the security and sovereignty of our data?

On-premise deployment is possible with the Idun open source platform. Your data stays in your infrastructure. We are aligned with the EU AI Act, whose transparency obligations have applied since 2 August 2026, with compliance for Annex III high-risk systems due by 2 December 2027, as well as with OWASP Agentic AI and GDPR. The platform's guardrails include PII detection and prompt injection defences.

Does LangGraph integrate with our existing IT systems?

Yes. The Idun open source platform exposes a standard FastAPI API, compatible with OAuth2/SSO (Okta, Auth0, Azure AD), with PostgreSQL backends and Docker/Kubernetes. Integration with your existing IT systems is planned from the scoping phase.

How is Idun Group different from other agencies working with LangGraph?

Others measure usage, fix a friction point in the interface or push you to build agents. We make the calls on your AI portfolio (industrialise, consolidate, cut) with a quantified return on investment and a roadmap you can take to your steering committee. Our advisory work is backed by tooling, and the Idun open source platform provides the technical credibility.

Can we take the project in-house after the implementation phase?

Yes: that is our goal. Full ownership means you receive the code, the documentation and the access rights on delivery. The Idun open source platform is released under GPL v3: your teams can maintain and extend it without depending on us. Training can support the handover to your internal team.

LangGraph for the enterprise: production-ready AI agents with Idun Group

TL;DR: LangGraph has become the reference runtime for AI agents in production (76% success on complex tasks vs 71% for CrewAI). MIT open source, stateful, native human-in-the-loop, deployable on-premise. Idun Group supports you from scoping to deployment: the code we develop belongs to you. Book a 30-minute discovery call to identify your most profitable use cases.

In 2026, 61% of large enterprises run at least one AI agent system in production, up from 18% two years earlier (Gartner 2026). LangGraph has become the reference runtime for companies that want to build durable agents without vendor lock-in. Idun Group, a specialist AI consultancy since late 2023, supports you from proof of concept to deployment, with full ownership of your technology. In a 30-minute discovery call, we pinpoint your two most profitable opportunities.

Why LangGraph is the go-to choice for enterprise AI agents

An open-source framework built for production, not for PoCs

LangGraph is a low-level orchestration framework for building agents that can manage persistent state, conditional loops and long-running interactions. Where a Python script stops at the first exception or loses context between two calls, a LangGraph agent recovers its state, corrects its course and picks up where it left off.

Released under the MIT licence, LangGraph reached version 1.0 in late 2025. Companies such as Klarna, Uber, Replit and Elastic have been running agents on it in production for several months. It is no longer an experimental framework: it is the infrastructure on which enterprise AI teams are building their business agents for the decade ahead.

LangChain vs LangGraph: two complementary layers, two distinct roles

The distinction often escapes teams discovering the ecosystem. LangChain is the integration layer: it connects your LLMs to tools, databases and APIs. LangGraph is the orchestration layer: it defines how agents chain together, make decisions and remember. The two coexist in the same application: one does not replace the other.

For a business, the distinction matters: a LangChain project on its own is enough for a simple chain. As soon as the need involves conditional decisions, several agents or memory across sessions, LangGraph becomes the central building block.

Stateful, multi-actor, human-in-the-loop: the capabilities that change the game

Organisations that have tried AI agents without a structured framework often come back with the same findings: context lost between calls, runaway loops, no human oversight. LangGraph addresses all three problems:

- Persistent memory: the agent recovers the state of each session (conversation, decision, intermediate result) - Conditional edges: the workflow branches based on responses, errors or human approvals - Native human-in-the-loop: the agent pauses, submits a decision to an operator, then resumes without losing the thread

LangGraph vs the alternatives: CrewAI, AutoGen, Google ADK

Performance on complex tasks: what the 2026 benchmarks reveal

AI agent framework comparisons have been piling up since 2025. On complex tasks (those that involve multiple steps, decision-making and error recovery), LangGraph reaches a 76% success rate, compared with 71% for CrewAI and 68% for AutoGen (pooya.blog, 2026 benchmarks). A 5 to 8 point gap that translates directly into reliability at production scale.

Across 6 decision criteria for enterprise teams, the table below summarises what the 2026 benchmarks and the official documentation reveal: