Topics
17 categories, each with a maintained overview
Agent Frameworks
Libraries that give an agent its control loop: how it plans, calls tools, keeps state and recovers from failure. The choice here shapes everything downstream, so weigh debuggability and lock-in alongside how fast the first demo comes together.
Tools & Integrations
Ready-made capabilities an agent can call: search, code execution, file handling and service integrations.
Coding Agents
Agents that read, write and run code against a real repository. Benchmark scores are a starting point, not an answer: sandboxing, token cost on large codebases and how reviewable the output is matter more day to day.
Observability & Evals
Seeing what an agent actually did — traces, costs, latencies — and turning those runs into evaluation sets. Without this, an agent in production is a black box you cannot improve.
Guardrails & Security
Constraining what an agent may say and do: input and output filtering, prompt-injection defence, and policy enforcement around tool calls.
Model Serving
Running models on your own hardware behind an OpenAI-compatible endpoint. Agents are unusually chatty, so throughput and concurrency matter more here than single-request latency.
Sandboxes & Runtimes
Isolated environments where agent-generated code can run without endangering the host. A prerequisite, not an optional extra, once an agent executes anything it wrote itself.
Vector & Storage
Storage engines for embeddings. Beyond raw search speed, look at metadata filtering, hybrid search and what happens to recall when the collection grows past what fits in memory.
Workflow & Low-code
Visual and low-code builders that place agents inside broader automation. Check the licence carefully: several popular options in this category are source-available rather than open source.
Computer & Browser Use
Driving a browser or desktop on the user's behalf. The dividing line is whether the agent reasons over structured elements or raw pixels — the former is far more reliable wherever a usable DOM exists.
Data & Ingestion
Getting real-world content into a form an agent can use — crawling, scraping, parsing PDFs and normalising documents.
Memory & Context
Persisting what an agent learns between sessions, instead of replaying an ever-growing transcript. The hard part is deciding what deserves to be remembered — verify extraction quality on your own data.
Multi-Agent Orchestration
Coordinating several agents or steps: routing, delegation, parallel execution and merging results. Worth reaching for only once a single agent has demonstrably hit its limit.
Protocols & Interop
Shared interfaces that let agents, tools and models from different vendors work together — MCP, A2A and the OpenAI-compatible surface most runtimes now expose.
Prompt & Optimization
Treating prompts and agent programs as something to be measured and improved automatically, rather than hand-tuned indefinitely.
RAG & Retrieval
Retrieval pipelines that ground an agent in your own documents: chunking, embedding, reranking and citation. Retrieval quality, not model choice, is usually what decides whether answers are trustworthy.
Voice Agents
Real-time speech agents. Latency budgets are brutal — interruption handling and turn-taking usually decide whether the result feels usable.