LangGraph Experts in Munich
in minutes from 15,000 CVs with the power of AIHire experts who design stateful LLM workflows, build agent orchestration with LangGraph and LangChain, and connect tools, memory, and guardrails for production systems. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Munich, who have recently used LangGraph
Karen Manukyan
Last position:
Personal AI Engineering Project — Croky AI at Crocky AI
Product:
- Built a production-ready AI platform for generating brand-aware marketing images and videos from product data, user requirements, and uploaded media.
- Own the platform architecture, technical roadmap, API design, security, deployment workflow, operational reliability, and model-provider strategy.
- Developed the core platform in .NET and built supporting AI and workflow prototypes in Python, applying language-independent API contracts and structured interfaces between services and model providers.
- Implemented reliable background processing with RabbitMQ, persisted workflow state, idempotent handling, retries, failure recovery, logging, secure storage, authorization, and credit accounting.
- Made pragmatic build-versus-buy and model-routing decisions based on reliability, latency, cost, and maintainability rather than novelty.
Agent Orchestration & RAG Systems
- Built and compared agent workflows using Microsoft Agent Framework, LangGraph, and LangChain, including tool use, conditional routing, clarification steps, state management, and hand-offs between agents.
- Implemented reusable .NET components for agents, prompts, tools, model providers, structured responses, and retrieval with pyvector, making it easier to change AI providers without rewriting the core workflow.
Giuseppe Abrignani
Last position:
Embedded Software Developer at Inheco
- AI Integration (LLM & RAG): Design and build of an internal intelligent RAG system (Retrieval-Augmented Generation) based on LLMs, n8n, and vector data for the automated analysis of technical documents and error logs.
- Design & Implementation: Design of a robust RS-232/UART communication interface for an SBC-based embedded device to control medical shaker systems.
- Architecture & Protocol Design: Implementation of a highly maintainable software structure (OOP, SOLID) and definition of hardware-close, resilient communication protocols including multithreading and advanced error handling.
- Quality Assurance & DevOps: Test automation using xUnit, integration tests directly on the hardware target, and maintenance of technical documentation according to strict medical technology standards via Azure DevOps.
Label: C#, .NET, LLMs, RAG, n8n, RS-232, UART, Multithreading, async/await, xUnit, gRPC/protobuf, Blazor, MudBlazor, EF Core, Visual Studio 2026, Azure DevOps
Mirza Klimenta
Last position:
Agentic AI for a DeepResearch project at Freelance
- Created a multi-agentic system supported by a knowledge graph to automate drafting of research papers
- Used multiple experts (OpenAI models) collaborating during document drafting
- Extracted useful information from the knowledge graph
- Technologies: LangChain, LangGraph, Smolagents, LlamaIndex, dspy
- Infrastructure: Terraform and GitHub Actions (CI/CD) on AWS
- Deployed initial application as a Streamlit app
Azadeh Tavassoli
Last position:
AI Engineering Fellow at Turing College
- Completed an intensive AI Engineering Program focused on LLM evaluation, retrieval, agent orchestration, and multimodal workflows.
- Designed retrieval pipelines with document ingestion, semantic search, and citation-aware outputs using LangChain and ChromaDB.
- Built LangGraph-based agent workflows with state handling, clarification loops, and human-in-the-loop approval steps.
- Applied prompt engineering and evaluation patterns, including scoring logic and guardrails, to improve output quality and reliability.
- Worked extensively with Python, FastAPI, OpenAI APIs, LangChain, LangGraph, and ChromaDB in end-to-end implementations.
Christian Schulz
Last position:
Data-Scientist/AI Engineer at The Marcom Engine GmbH & Co. KG
- Concept creation and implementing AI Agents in AWS Cloud
- Continuously alignment with stakeholders
- Collaborate with DevOps
- Technologies: Git, CI/CD (GitHub Actions), Python/ML, Streamlit, Deno/typescript, AWS SAM, AWS Bedrock, AWS Lambda, AWS Dynamo DB, AWS S3, AWS Event Bridge etc.
Nima Nooshi
Last position:
Co founding LLM Engineer at LLM Ventures
- Co-founded an AI venture focused on building production-grade LLM applications and agentic systems
- Designed and implemented multi-agent AI workflows for financial and trading applications
- Developed LLM-powered copilot architectures for portfolio analysis, trade management, and personalized user coaching
- Built on-device and edge-deployed inference applications, optimizing models for low latency, privacy, and resource-constrained environments
- Led system architecture decisions across model selection, orchestration, state management, and deployment
Caner Karaoğlu
Last position:
Synthetic Medical Dataset (MedGym) at MedTank
- Generated synthetic datasets for CXR, mammography, and distal radius fracture detection using GANs and diffusion, creating >50k synthetic images for benchmarking.
- Ensured GDPR-compliant workflows and reproducibility, enabling dataset adoption for internal validation and academic collaboration.
- Project highlighted in MedTank’s internal R&D showcase as a flagship synthetic data initiative.
Max Ritter
Last position:
Cloud (AWS) | AI | DevOps | Data at Boehringer Ingelheim
- Architected and implemented an enterprise-grade AI Agent Platform leveraging Retrieval Augmented Generation (RAG) architecture to enhance clinical data insights.
- Established robust CI/CD pipelines for LLM applications using CDK and Jenkins, significantly reducing deployment times.
- Implemented comprehensive observability solutions that increased agent reliability across pharmaceutical environments.
- Designed scalable AI workflows with advanced orchestration that optimized context handling for enterprise data sources.
- Technologies: AI Agents (LangChain, LangGraph, Bedrock, Smolagents, Streamlit); LLM Operations (Tracing, Testing, Evaluation, LangSmith, LangFuse); Infrastructure-As-Code (AWS CDK, Terraform, Typescript, Jenkins); Vectors, Embeddings, RAG (OpenSearch, pgvector, PDF Extraction)
Mohamed Saleh
Last position:
Machine Learning Engineer (Part Time) at E.ON Digital Technology
- Designed and implemented an advanced, agentic RAG pipeline using LangChain and LangGraph for structured data extraction from PDFs, utilizing tools, state management, and OpenAI LLMs (GPT-4) to improve accuracy and handle complex document structures.
- Developed a Google AI agent for extraction of structured information from PDF documents and deployed the agent on Vertex AI.
- Architected data pipelines using Azure Data Factory and Databricks to ingest data from Azure Blob Storage, process it with PySpark, and load it into Azure SQL Database via Linked Services.
- Containerized AI agents and services using Docker for consistent local development and deployment.
- Utilized PySpark and Dask for database querying in coordination with Azure Blob Storage and Document Storage.
- Created a ReAct agent that extracts structured data from PDF documents using tools and integrating Azure Document Intelligence.
- Contributed to the CPO invoices validation check project using Databricks to find existing CDRs and calculate total valid costs.
- Developed a conversational AI agent (chatbot) with a FastAPI backend, integrating RAG for precise tariff extraction and deployed the service using Azure Container Apps.
- Tools used: Azure, Azure OpenAI, Azure Document Intelligence, Azure Blob Storage, Google ADK, Google Cloud, Vertex AI, Gemini, Databricks, LangChain, LlamaIndex Ollama, Docker, PySpark, Azure SQL, Azure Data Factory, Azure AI Agent, Microsoft SQL Server
Abdul Khan
Last position:
Software Engineer at EdgeFirm
- Designed and developed end-to-end web and mobile products as a full-stack engineer, working across Python/FastAPI backends, databases, and React / React Native frontends.
- Built LLM- and agentic-AI systems using LangChain, LangGraph, CrewAI, Langfuse, and vector databases, focusing on reliability, observability, and clean abstractions.
- Developed a text-to-SQL assistant for the marketing team that lets non-technical users query a large retail-style dataset in natural language, returning clear analytics and campaign insights.
- Helped reduce ad-hoc SQL/reporting requests to engineering by 60% and cut time-to-insight for common marketing queries from hours to minutes.
- Created a full-stack mobile app where Apple Health data is processed and fed into an LLM to generate personalised, VO2-max–based health coaching and insights, owning architecture from frontend to backend and auth.
Jan Wahler
Last position:
Technical Consultant at AI Beratung (KMU)
- Evaluation of RAG for legal advisory (build or buy)
- Evaluation and POC of RAG for an ERP time tracking module
- Consulting on foundation model selection
- Setup AI development environment (eliminating shadow AI)
- AI strategy consulting
- AI-assisted code creation and context engineering make change sets larger
- Strong software engineering expertise, code reviews and safeguarding through pipelines and domain-specific automated test cases
Discover over 15,000 top freelancers
Statistics of experts using LangGraph
Aggregated from the professional profiles of matched freelancers.
Experience
12 years (Germany: 13 years)
Position duration
1.2 years (Germany: 1.8 years)
Positions per freelancer
13 (Germany: 9)
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Manufacturing, Energy
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
100%
Master's degree or higher
91% (Germany: 78%)
Doctorate
18% (Germany: 17%)
Certifications per freelancer
4 (Germany: 2)
Most common languages
English, German, Spanish
Speak two or more languages
100% (Germany: 94%)
Based on our profile pool as of 30 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Munich are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Munich using LangGraph
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What LangGraph does
LangGraph is a framework for building LLM applications as graphs of states and transitions. It fits agent workflows, multi-step reasoning, tool use, and review loops where a simple prompt chain is not enough. Teams use it for systems that must remember context and react to events.
Where it fits
- Agent orchestration with branching logic
- Tool calling and controlled retries
- Stateful workflows with checkpoints
- Human-in-the-loop review steps
- Long-running task flows and approvals
It is often used with LangChain, vector stores, message queues, and API backends. Strong projects also combine it with tracing, observability, and evaluation so the behavior stays understandable as the workflow grows.
Why companies hire help
Companies bring in freelance experts when a prototype needs to become a stable service. The work often involves cleaning up graph design, reducing brittle prompts, and making tool calls predictable. In Munich, this is common in software, mobility, industrial automation, and enterprise teams that need careful integration.
Skills that matter
A strong specialist understands graph state, conditional routing, retries, and memory patterns. They also know how to structure prompts, handle function calling, and connect external systems without turning the workflow into a tangle. Good experts write clear tests and can explain why each node exists.
Common project work
- Build a new agent flow from scratch
- Refactor a LangGraph proof of concept
- Add checkpoints, logging, and fallbacks
- Integrate models, tools, and retrieval
- Prepare a handoff for an internal team
The deliverables are usually workflow diagrams, working code, and guidance for future changes. The best freelancers keep the system small where possible and only add complexity when the use case truly needs it.
Signs you need LangGraph expertise
If your agent forgets context, loops, or makes unsafe tool calls, the graph design usually needs attention. If a LangChain setup has grown into many steps and branches, LangGraph can make the logic easier to control. For remote work, English is often enough, but on-site sessions in Munich can help when teams need fast alignment across product and engineering.
Frequently asked questions
Curious about LangGraph? Here are the answers that come up again and again.
LangGraph is used to build stateful LLM workflows that need branching, retries, memory, and tool use. Companies bring it in when a single prompt or simple chain cannot handle the full process. It is a good fit for agent systems, review flows, and long-running tasks that must stay under control.
LangGraph is often used together with LangChain, but it solves a different problem. LangChain gives building blocks for LLM apps, while LangGraph focuses on explicit state and graph-based control flow. If your logic needs loops, branches, or checkpoints, LangGraph is usually the better fit.
A company should hire LangGraph help when the workflow design is getting hard to reason about or when the prototype must become reliable. That often happens after the first agent demo works, but before the system is ready for users. A specialist can turn fragile logic into a clearer graph with safer tool use.
A strong LangGraph specialist usually also knows LangChain, Python, LLM APIs, and basic backend integration. Knowledge of vector search, prompt design, tracing, and evaluation is useful too. For production work, they should also understand testing and how to connect to existing services cleanly.
A small proof of concept can be handled by a generalist who knows Python and LLM apps, but production work needs a deeper LangGraph specialist. The more branching, memory, and tool calling you add, the more important graph design becomes. If the workflow affects customers or internal operations, expert review is worth it.
LangGraph work is often done remotely because the code, graphs, and tests are easy to share. On-site time in Munich can help during early workshops, especially when product, engineering, and domain experts need to shape the workflow together. Many teams use a mix of both.
A good LangGraph freelancer can explain the graph in plain language and show why each node, branch, and retry exists. Look for clear thinking about state, tool calls, failure handling, and tests, not just a polished demo. Strong experts also document trade-offs so the team can maintain the workflow later.
No, LangGraph is also useful for structured workflows that involve humans, tools, and approvals. Many teams use it for controlled process automation, not just open-ended agents. That makes it a good choice when you want flexibility without losing oversight.
The average hourly rate of freelancers in Munich, Germany who have used LangGraph in their recent projects is 96 €, which corresponds to a daily rate of about 771 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used LangGraph in their recent projects, 100% hold at least a Bachelor's degree, 91% hold at least a Master's degree, and 18% hold a doctorate.
On average, freelancers in Munich, Germany who have used LangGraph in their recent projects have 12 years of professional experience, with a single engagement typically lasting around 1.2 years.
The most common languages among freelancers in Munich, Germany who have used LangGraph in their recent projects are English (100%), German (91%), and Spanish (18%).
The most common industries among freelancers in Munich, Germany who have used LangGraph in their recent projects are Information Technology (91%), Manufacturing (64%), and Energy (55%).
The most common business areas among freelancers in Munich, Germany who have used LangGraph in their recent projects are Information Technology (100%), Product Development (100%), and Business Intelligence (82%).
Main locations of FRATCH Experts, who have recently used LangGraph
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Berlin Hamburg 
Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg 
Berlin Request a free demo
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We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
