Optimize n8n with Python for Complex Automations

I made LiteLLM 3x faster. With one line of code. I was running LiteLLM (YC W23) in production, simulating and handling thousands of requests per second. The Python overhead was killing us. Connection pooling was the bottleneck. Rate limiting was eating CPU cycles. So we did something unconventional: We rewrote the hot paths in Rust. The results?  - 3.2x faster connection pooling  - 1.6x faster rate limiting  - 42x more memory efficient for high-cardinality workloads  - Zero code changes required Here's how you use it: import fast_litellm # That's it. One line. import litellm # Everything just works, but faster No configuration. No migration. No breaking changes. Just add fast-litellm to your requirements.txt and you're done. The secret? PyO3 + DashMap for lock-free concurrency. We kept the Python API you love but replaced the internals with Rust where it matters. What we learned:  1. Not everything needs to be rewritten in Rust  2. FFI overhead is real - small operations don't benefit  3. The biggest wins are in concurrent data structures  4. Production safety matters - we built in automatic fallback I am open-sourcing everything. MIT licensed. Works on Linux, macOS, Windows. Python 3.8-3.13. Link in comments.  ---  Building something that needs LLM performance at scale? Let's connect. #OpenSource #Rust #Python #LLM #Performance #AI #MachineLearning #SoftwareEngineering

#LangChain v1 createAgent --- #LangChain v1 changed how we build agents. Before — `create_react_agent` from #LangGraph. Any customization meant new nodes, new edges, rewriting the graph. Want different tools per user? Good luck. Now — `create_agent` with a #middleware system. You intercept every model call and change agent behavior at runtime: → different models based on user expertise → different tools based on request context → dynamic system prompt per user No graph changes. Just middleware. Under the hood it's still LangGraph. But on the surface — clean, readable code with zero boilerplate. An agent with memory, a custom prompt, and tool limits is now 10 lines, not 50. --- #LangChain #AIAgents #Python #LLM #AIEngineering #GenerativeAI #MLOps #SoftwareEngineering #LangGraph

🚀 Day 24/60 — LeetCode Discipline Problem Solved: Symmetric Tree (Revision) Difficulty: Easy Today’s practice focused on revisiting a classic binary tree problem — checking whether a tree is symmetric around its center. The key idea is to treat the left and right subtrees as mirror images and recursively compare their corresponding nodes. By verifying both structure and node values simultaneously, the algorithm determines whether the tree maintains perfect symmetry. Problems like this highlight how recursion naturally fits tree-based structures and helps simplify complex comparisons. 💡 Focus Areas: • Strengthened recursive tree traversal • Practiced mirror comparison of subtrees • Improved understanding of binary tree symmetry • Reinforced recursive problem-solving patterns • Focused on writing clean and structured logic ⚡ Performance Highlight: Achieved 0 ms runtime (100% performance) on submission. Consistent practice across arrays, strings, stacks, bit manipulation, and now tree structures continues to deepen my overall algorithmic intuition. #LeetCode #60DaysOfCode #100DaysOfCode #DSA #BinaryTree #Recursion #Algorithms #DataStructures #ProblemSolving #CodingJourney #SoftwareEngineering #Programming #Developers #TechCareers #Java

𝗞𝗮𝗳𝗸𝗮 + 𝗜𝗱𝗲𝗺𝗽𝗼𝘁𝗲𝗻𝗰𝘆 Most distributed systems don’t fail loudly. They fail silently with duplicate processing. I learned this the hard way. In event-driven systems, retries are inevitable: • Consumer crashes • Network glitches • Reprocessing 👉 Result: Same event processed multiple times The fix is not “prevent retries” The fix is design for idempotency Simple rules: • Use unique event IDs • Maintain processed state • Make operations repeat-safe If your system breaks on duplicate events, it’s not fault-tolerant yet. 𝗥𝗲𝗮𝗹 𝘀𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝘀𝘁𝗮𝗿𝘁𝘀 𝘄𝗵𝗲𝗻 𝗱𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲𝘀 𝘀𝘁𝗼𝗽 𝗵𝘂𝗿𝘁𝗶𝗻𝗴 𝘆𝗼𝘂. Even if producer retries sending same message multiple times, system handle duplicates by using the simple rules stated, aka 𝗗𝗲𝘀𝗶𝗴𝗻 𝗳𝗼𝗿 𝗜𝗱𝗲𝗺𝗽𝗼𝘁𝗲𝗻𝗰𝘆 #Kafka #DistributedSystems #BackendEngineering #SystemDesign #Microservices #SoftwareEngineering #Scalability #TechLeadership #Python #EventDriven

Low-code tools like n8n are great… until they become your biggest production bottleneck. I’ve seen this happen multiple times: You prototype fast 🚀 Everything works ✅ Then suddenly… • Workflows become hard to debug • Scaling becomes unpredictable • Teams start rewriting everything manually • “Temporary” logic becomes permanent tech debt At that point, low-code stops being an accelerator… and starts becoming friction. So, I asked a simple question: 👉 𝑊ℎ𝑎𝑡 𝑖𝑓 𝑤𝑒 𝑑𝑖𝑑𝑛’𝑡 𝑟𝑒𝑤𝑟𝑖𝑡𝑒 𝑤𝑜𝑟𝑘𝑓𝑙𝑜𝑤𝑠… 𝑏𝑢𝑡 𝑐𝑜𝑚𝑝𝑖𝑙𝑒𝑑 𝑡ℎ𝑒𝑚 𝑖𝑛𝑠𝑡𝑒𝑎𝑑? That’s how I built nCode, an open-source transpilation pipeline that converts n8n workflows into production-ready Python projects. ⚙️ Under the hood (high level) Instead of treating workflows as runtime configs, nCode treats them like a source language: • Parse & validate workflow JSON • Build DAG representation • Topological sort execution • Detect runtime (FastAPI vs script) • Generate Intermediate Representation (IR) • Emit clean, executable Python code 🧠 Design choices that mattered • Deterministic generation → same input, same output (debuggable & traceable) • Handler-based node system → plug-and-play extensibility • Shared context → manages imports, dependencies, variable flow • Expression translation layer → supports n8n-style expressions • Graceful fallback → unsupported nodes → TODO scaffolds (no hard failures) 🛠️ But here’s the real learning: Building OSS is not just writing code. It’s about: • CI pipelines • Security scanning • Documentation • Contribution workflows • Making it easy for strangers to trust and contribute 🤔 Now I’m exploring: • Deterministic vs AI-assisted code generation • Multi-language targets (Java, Go) • Handling complex node behaviors at scale If you’ve worked on: compilers / codegen / workflow engines 👉 I’d love your take: Would you prioritize determinism… or introduce AI into the generation pipeline? Also, if this sounds interesting, contributors are welcome 🙌 Repository URL: opsingh861/nCode Docs: Home | nCode #opensource #compilerdesign #systemdesign #backendengineering #python #fastapi #developertools #softwarearchitecture #n8n #buildinpublic #engineering

𝟱 𝗥𝗲𝗮𝘀𝗼𝗻𝘀 𝘆𝗼𝘂𝗿 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗻𝗲𝗲𝗱 𝗮 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 𝗠𝗲𝘀𝗵: 🕸️ 1️⃣ Retries on Autopilot: Let Envoy handle the 503s. Stop hardcoding retry logic in every service. 2️⃣ Mutual TLS (mTLS): Because "Zero Trust" isn't just a buzzword. Secure service-to-service traffic without touching your Java/Go code. 3️⃣ Traffic Splitting: Roll out that 5% Canary deployment with ease. Test in production without the "blast radius" anxiety. 4️⃣ Deep Tracing: Find exactly which service is causing the latency. No more "needle in a haystack" debugging. 5️⃣ Language Agnostic: It doesn't matter if Service A is Java and Service B is Python. The sidecar doesn't care. ⚠️ 𝗧𝗵𝗲 𝗖𝗮𝘁𝗰𝗵? It adds complexity. Don't use a Service Mesh if you only have 3-4 services. Use it when the "spider web" of connections starts to break your brain. Agree or Disagree? Does the overhead of a sidecar justify the benefits? Let’s hear your take below! ⬇️ #ServiceMesh #Microservices #SystemDesign #BackendDevelopment #Istio #Linkerd #SoftwareEngineering

#LangChain v1 SummarizationMiddleware Long conversations are killing your token budget. Every message stays in context. The longer the session — the more you pay, the slower it gets. And in v0, fixing that meant writing a custom summarization hook from scratch. Every. Single. Project. #LangChain v1 ships `SummarizationMiddleware`. You set a token threshold. The middleware kicks in automatically, compresses old messages into a summary, and the model gets a clean, shorter context — no changes to your agent logic needed. One line of middleware replaces dozens of lines of boilerplate. This is what the middleware pattern in v1 is about: stop solving the same infrastructure problems over and over. Focus on the agent behavior that actually matters. --- #LangChain #Python #LLM #AIAgents #AIEngineering #GenerativeAI #PromptEngineering #MLOps

Over the past few months I’ve been working on Pulse, an open‑source observability platform for AI apps. It brings everything your agents do into one place: Pulse captures every prompt, completion, token count, latency and cost, and organizes them into session timelines and traces. This makes it easy to debug, replay runs and understand how workflows behave. What Pulse includes • End‑to‑end trace capture and session timelines. • A trace‑ingestion server, interactive dashboard, CLI for agents, and TypeScript/Python SDKs that install quickly and can run locally or self‑host. Try it and help shape what’s next: https://usepulse.dev #Observability #AIAgents #LLM #DeveloperTools

🚀 Project: Multi-Client Chat Server Developed a scalable client-server chat application using Python that enables multiple users to communicate in real time over TCP. 🔧 Technical Highlights: • Implemented socket programming using TCP (AF_INET, SOCK_STREAM) • Used select() for efficient I/O multiplexing (handling multiple clients without threading) • Designed a broadcast mechanism for real-time messaging • Managed dynamic client connections and disconnections 📚 Key Learnings: • Deep understanding of networking fundamentals (TCP/IP, sockets) • Client-server architecture and concurrency handling • Error handling and connection management 🔮 Future Enhancements: • Private messaging system • File sharing support • End-to-end encryption #Python #ComputerNetworks #SocketProgramming #Backend

While revising Binary Search, I noticed a subtle but important issue with how we calculate the mid value 👀....... Using (low + high) / 2 can cause integer overflow for large inputs, leading to incorrect results. A safer approach is low + (high - low) / 2, which avoids this problem. It’s a small change, but it makes the implementation more robust 💡 Sometimes, it's not about learning new algorithms… It's about understanding the depth of what we already know. 😊 #Java #DSA #BinarySearch #Coding #SoftwareEngineering #Learning