Agent Frameworks

DifyWorkflow

A complete platform — prompt orchestration, retrieval, agent tools and an admin UI — that non-engineers can operate once a team has set it up. The licence is a modified Apache 2.0 with additional conditions on multi-tenant hosting and branding, so it is source-available rather than open source. Read it before building a product on top.

LangChainFrameworks

Its real value is coverage: whatever model, vector store or API you need, an adapter probably already exists, which shortens the distance from idea to prototype. The abstractions have a reputation for indirection, so many teams use it for the integrations and reach for something more explicit once the control flow gets complicated.

deepseek-harnessFrameworks

`dsh` is the agent harness DeepSeek AI develops itself. `npx @deepseek-ai/dsh web` brings up a Web UI on `http://127.0.0.1:3080`, and the architecture behind it is one where everything is a plugin, powered by the Cordis runtime, so it suits people who intend to extend a harness rather than only operate one. The caveat comes from the project itself: it is labelled a developer preview and warns in capitals that there will be compatibility-breaking changes, so plugins written today should budget for rework. It is also a poor fit if you want a documented scripted or embedded entry point right now, since the README covers only the `web` command and a source checkout, and links out to a development guide and architecture documentation without saying what either contains.

AutoGenOrchestration

Microsoft Research's take on multi-agent systems, where progress emerges from a structured conversation between specialised agents. Strong for exploration and research-flavoured problems. Two things to check before adopting: the API was substantially reworked between major versions, and GitHub reports the repository licence as CC-BY-4.0 — a content licence, not an OSI-approved software one. Confirm the terms with the maintainers before commercial use.

CrewAIOrchestration

Models a task as a crew of agents with named roles and goals that hand work to each other. The metaphor makes a multi-agent design readable at a glance, which is why it demos so well. Whether several role-playing agents beat one well-prompted agent on your task is worth measuring before committing — the answer is often no.

LlamaIndexRAG & Retrieval

Where most frameworks start from the agent loop, this one starts from the data: ingestion, indexing strategies, and the retrieval patterns that decide whether answers are actually grounded. Reach for it when the hard part of your problem is the corpus rather than the reasoning.

LangGraphFrameworks

Models an agent as an explicit graph of nodes and edges rather than a loop you cannot inspect. Durable checkpoints let a run pause for human approval and resume later, which is what makes it viable for workflows that touch production systems. The trade-off is verbosity: simple tool-calling agents need noticeably more setup here than elsewhere.

DSPyOptimization

You declare what each step should do and supply a metric; the optimiser searches for the prompts and few-shot examples that maximise it. The payoff is that improving a pipeline becomes measurable rather than superstitious. It requires an evaluation set — without one there is nothing to optimise against.

OpenAI Agents SDKFrameworks

Few primitives — agents, handoffs, guardrails, tracing — and little else. The small surface area is the appeal: there is not much to learn and not much to fight. It is built around OpenAI's own models first, so weigh that if multi-provider portability matters to you.

LettaMemory & Context

Treats the context window like RAM and everything else like disk, with the agent itself deciding what to page in and out. That framing makes indefinitely long-running agents tractable. It is a more opinionated commitment than bolting a memory library onto an existing stack.

Pydantic AIFrameworks

Brings the validation model that Python developers already trust to agent output, so a malformed response fails where you can see it rather than three layers downstream. Deliberately smaller in scope than the all-in-one frameworks; that is the point, and it is a good fit if your codebase is already typed throughout.

PipecatVoice Agents

Assembles the whole speech-to-speech loop — transcription, model, synthesis, interruption handling — as a pipeline of swappable services, which is the tedious part to get right. Latency is the entire game in voice, and turn-taking and barge-in are first-class here rather than bolted on. Running it well means caring about transport and network topology, not only which model you picked.