PydanticAI Under the Hood: A Source Tour of Type-Safe Agents
Ask a typical agent framework to extract a structured record and it hands you a dictionary — whatever the model happened to emit, passed straight through. That is convenient right up until it is not: a missing field becomes a...
Mem0 Under the Hood: A Source Tour of the Memory Layer for AI Agents
Ask your coding agent next Monday what it learned about you last Monday, and watch it blink. Most agents are goldfish: every session restarts from a blank context, and the standard workaround — replaying the whole transcript into the prompt...
LlamaIndex Under the Hood: A Source Tour of the RAG Engine Powering Data-Awar...
Ask an LLM about your internal wiki, your support tickets, or last quarter’s contracts and it will answer fluently — and wrong, because it has never seen any of them. The obvious fix is to dump your documents into a...
LangGraph Under the Hood: A Source Tour of the State Machine Running Modern A...
Almost every demo agent works, and almost every production agent eventually does not. The model calls the same tool in a loop until the context window fills, a crash at step fourteen throws away the previous thirteen, and there is...
LangChain Under the Hood: A Source Tour of the Framework Behind Millions of L...
Every LLM application eventually turns into the same pile of glue. You concatenate prompts, wrap a provider SDK call in retry logic, parse replies with string hacks, feed tool results back into the conversation, and stitch retrieved documents from a...
Haystack Under the Hood: A Source Tour of the Type-Safe RAG Orchestration Engine
Every LLM application eventually hits the same failure: stage two was refactored, stage three still expects the old shape, and nothing tells you until 3 a.m. in production when the retriever hands the prompt builder a string instead of a...
DSPy Under the Hood: A Source Tour of the Compiler That Optimizes Your Prompts
Hand-tuned prompts rot. A wording tweak that squeezed an extra five points out of one model silently underperforms after the next model swap, and every fix re-breaks something else: the few-shot examples drift out of date, the output format hint...
CrewAI Under the Hood: A Source Tour of the Role-Based Multi-Agent Framework
Ask a plain chat loop to “research the market, then analyze it, then write the report” and it will happily blur all three jobs into one context window. Nothing in a bare while True: ask the model loop knows who...
AutoGen Under the Hood: A Source Tour of Microsoft's Multi-Agent Runtime
One agent with one while-loop is fine until the day your task needs a researcher, a coder, and a critic at the same time. Then you either cram all three personalities into one context window and hope, or you hand-roll...
Agno Under the Hood: A Source Tour of the Full-Stack Agent Runtime
Ask any team what it takes to ship one production agent, and the answer is usually a shopping list: a model client for your provider, a vector database for document retrieval, a memory store for user facts, somewhere durable to...
TiDB Under the Hood: A Source Tour of the MySQL-Compatible Distributed SQL Layer
Every ML platform eventually hits the same wall: the feature store, the event log, or the experiment tracker outgrows a single MySQL instance, and the team is forced to choose between sharding by hand and rewriting the app for some...
SQLite Under the Hood: A Source Tour of the World's Most Deployed Database
SQLite is almost certainly running within arm’s reach of you right now. It is the database inside every phone, every major browser, most operating systems, and an enormous share of embedded devices. It has no server process, no network protocol,...
RocksDB Under the Hood: A Source Tour of the LSM Storage Engine Behind Modern...
RocksDB is the storage engine you have probably used today without noticing. It sits underneath Flink’s checkpointed operator state, Kafka Streams’ local state stores, TiKV (the transactional key-value layer that powers TiDB), and a growing family of feature stores that...
PostgreSQL Under the Hood: A Source Tour of the Engine Powering Your AI Data ...
Ask around any serious ML team and you will hear the same answer to the question “where does your data actually live?”: PostgreSQL. The vector search boom did not replace it — it extended it, with pgvector and friends turning...
Neo4j Under the Hood: A Source Tour of the Graph Database Engine
Every large language model application eventually hits the same wall: text alone cannot answer “how is entity A related to entity B through three intermediaries?” Vector search gives you semantic neighbors, but relationships — who funded whom, which user reviewed...
Inside MySQL: A Source-Level Tour of InnoDB, the Optimizer, and Replication
MySQL is the quiet backbone of the internet. Written in C and C++, maintained by Oracle in the open on GitHub, it has spent three decades being the default transactional database behind forums, e-commerce carts, payment ledgers, and SaaS dashboards....
LevelDB Under the Hood: A Source Tour of the LSM Engine That Started It All
Yesterday we walked the RocksDB source tree — the heavily forked engine that powers Flink state stores, Kafka Streams, TiKV, and the local segments of countless vector databases. But RocksDB did not appear out of nothing. It is a fork...
Dolt Under the Hood: A Source Tour of Git for Data
Every serious ML effort eventually collides with the same question: which exact rows did this model train on? Training data is not static. Labels get corrected, schemas grow columns, bad batches are rolled back, and feature-engineering experiments fork the dataset...
CockroachDB Under the Hood: A Source Tour of the Distributed SQL Engine Built...
If you are serving AI features to users in several regions at once, you eventually hit the same wall: a single-node Postgres works beautifully until you need writes to survive a datacenter failure, or until feature data, embeddings metadata, and...
BadgerDB Under the Hood: A Source Tour of the Pure-Go LSM Key-Value Store
Most storage engines we have toured in this series descend from the same C++ lineage: RocksDB and LevelDB pack keys and values into the same LSM tree and pay for it with compaction write amplification. BadgerDB, maintained by the Dgraph/Hypermode...
Velox: Inside Meta's C++ Vectorized Execution Engine Behind Presto and Spark
Most engineers meet Velox indirectly. If you have run a query on Presto’s C++ worker (Prestissimo) or accelerated Spark through Apache Gluten, Velox is the library that actually executed your plan. Created at Meta and now developed with partners including...
Inside Apache Spark: How One Line of SQL Becomes a Cluster Job
The best description of this project is the one its authors wrote: the README's first line calls Apache Spark "a unified analytics engine for large-scale data processing." That phrasing undersells what the repository actually holds. Behind that sentence sits a...
Presto Under the Hood: A Source Tour of the Distributed SQL Engine That Queri...
Presto describes itself in one line in its own README: “Presto is a distributed SQL query engine for big data.” That modest sentence hides one of the most influential architectures in data infrastructure. Born at Facebook to let engineers query...
Apache Paimon: Inside the Streaming Lakehouse's LSM-Tree Table Format
Modern ML systems are hungry for fresh tabular data. Feature tables churn all day: a user’s click counts change, an item’s embedding ages, a fraud score drifts. Classically you had to choose between a streaming log like Kafka, which is...