All projects

agentgatewayGuardrails

A Rust data plane that sits between agents and everything they call — model providers, MCP servers, other agents — so authentication, RBAC, rate limits and OpenTelemetry export are configured once instead of reimplemented in every agent. Solo.io donated it to the Linux Foundation in August 2025 and it moved under the Agentic AI Foundation in June 2026, alongside MCP and goose. It is young for something that sits in the request path: per-user identity onto downstream MCP servers is still an open issue, so confirm the authentication model you need before putting it in front of production traffic.

AiderCoding Agents

Every change lands as a git commit, which makes an AI edit exactly as reviewable and revertible as a colleague's. Its repository map lets it work on codebases far larger than a context window. Terminal-only by design — a strength if you live there, a non-starter if you want it inside an editor.

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.

Browser UseComputer Use

Extracts the interactive elements of a page and hands the model a structured list, instead of asking it to guess click coordinates from a screenshot. That makes it markedly more reliable for form flows and scraping. It degrades on canvas-heavy applications and on sites with aggressive bot protection, where there is no clean DOM to read.

ChromaVector & Storage

The lowest-friction way to get retrieval working: no server to stand up, no cluster to size. That makes it excellent for prototypes and small production loads, and the point at which you outgrow it is a real consideration to plan for rather than discover.

ClickHouseObservability

A column-oriented engine for append-heavy, high-cardinality data, which is the shape agent telemetry takes: one row per model call, filtered later by model, cost or error. Langfuse moved its traces here from Postgres in December 2024 and ClickHouse acquired the project in January 2026, so a self-hosted observability stack increasingly means running this underneath. It is the wrong choice for anything transactional — updates and deletes rewrite whole column parts rather than edit rows — so treat it as where agent runs land, not as the database your application writes state to.

ClineCoding Agents

Runs in VS Code and asks for approval before each file write or terminal command, which keeps a human in the loop without breaking flow. MCP support lets it reach tools beyond the editor. The approval prompts are the safety mechanism — turning them all off removes the main reason to prefer it.

cloudflare-osSandboxes

Cloudflare OS pairs an agent chat with "Gadgets" — small apps the built-in coding agent writes for you, each running as a Dynamic Worker with its internet access disabled, its client confined to a sandboxed iframe that reaches the server only over Cap'n Web via `postMessage()`. External services are reached through Gatekeepers, per-service Workers that wrap an API in Cap'n Web, log every call, and simulate side-effecting actions so the agent keeps queueing work while approvals wait for you to review them in bulk. The commitment to Workers runs deep: every workspace is a Durable Object, every Gadget a Dynamic Worker Facet, and Facets and Dynamic Workers were added to the runtime specifically for this project — so hosting means a Cloudflare account, and the deploy-to-your-own-servers-on-`workerd` path is still marked COMING SOON in the README. The maintainers call the August 2026 v2 rewrite an early access release with many rough edges, and many Gatekeepers need configuration of their own — including OAuth client credentials per service — before GitHub or Google will connect.

codex-securityGuardrails

`npx @openai/codex-security scan .` walks a checkout and leaves JSON results on stdout, while `--mode deep` spreads discovery across workers and subagents until it stops turning up anything new. Across runs, `scans compare BEFORE_SCAN_ID AFTER_SCAN_ID` matches findings by root cause and labels them new, persisting, reopened, resolved or unknown, so a second scan reports movement instead of restating the whole report. It is a poor fit as a pre-commit gate: deep discovery runs until `--max-time-hours`, which defaults to 96. Access is also gated — the CLI needs access to Codex Security, and some cybersecurity requests and protected findings require Trusted Access for Cyber approval, so cloning the repo on its own scans nothing.

ContinueCoding Agents

Autocomplete, chat and edits driven by whichever model you point it at, including one running on your own hardware. That makes it one of the few realistic options where code cannot leave the building. Configuration is more involved than the hosted alternatives — the price of that flexibility.

Crawl4AIData

A permissively licensed, self-hosted alternative for the same job as the hosted crawlers, with extraction strategies you can define per site. Being a library rather than a service means you own the proxy handling and rate limiting yourself.

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.

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.

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.

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.

E2BSandboxes

Starts an isolated micro-VM in well under a second, which is what makes per-task sandboxing practical rather than theoretical. If an agent executes code it wrote itself, something like this is a requirement, not a nicety. Self-hosting is possible but meaningfully more work than the hosted path.

FirecrawlData

Renders JavaScript, follows links and strips the navigation and boilerplate that otherwise poison a retrieval corpus. Solves the unglamorous half of RAG properly. AGPL-licensed, so check the terms if you plan to expose it as a service.

GooseCoding Agents

Runs locally as a CLI or desktop app and gains capabilities through MCP extensions rather than a fixed tool list, so the same agent handles code, infrastructure and third-party services. Model-agnostic, which matters if you expect to switch providers.

hermes-agentTools & Integrations

Hermes Agent is Nous Research's own agent: a terminal UI plus a single gateway process that carries the same conversation into Telegram, Discord, Slack, WhatsApp, Signal and Email, with a cron scheduler, isolated subagents, and seven terminal backends from local and Docker to Modal and Vercel Sandbox. Its distinguishing piece is a closed learning loop — the agent curates its own memory on periodic nudges, creates skills after complex tasks, and searches past sessions through FTS5 with LLM summarization — which means the state that makes it useful accumulates under `~/.hermes`, outside your version control, and needs occasional pruning. It is an assistant you run and configure rather than a library you build on: the README documents `hermes` subcommands and slash commands, not an embedding API, so it is a poor fit if you wanted an agent loop to call from your own code. Putting a shell-capable agent behind chat platforms also makes the command-approval and DM-pairing settings load-bearing rather than optional.

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.

LangflowWorkflow

Useful for sketching an agent visually and for showing non-engineers what a pipeline actually does, then exporting it as an API. Visual editors get unwieldy as branching grows, so treat it as a design and demo surface rather than the final home for complex logic.

LangfuseObservability

Records what an agent actually did — every call, tool invocation and cost — and turns those traces into evaluation datasets. The managed service runs the same codebase you can self-host with Docker Compose, so migrating in either direction stays cheap. Some enterprise features sit outside the MIT core; check which tier you need before committing.

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.

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.

llama.cppModel Serving

The quantisation work here is what put usable models on hardware without a datacentre GPU, and much of the local-model ecosystem is built on top of it. Working with it directly means dealing with build flags and hardware specifics that higher-level wrappers hide from you.

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.

MCP Reference ServersProtocols

The clearest way to understand the Model Context Protocol is to read servers that implement it correctly, and this is that collection. Useful both as working connectors and as the template to copy when writing your own.

Mem0Memory & Context

Extracts durable facts from a conversation and retrieves the relevant ones on later runs, instead of replaying an ever-growing transcript into the context window. Useful wherever an assistant should remember a user between sessions. What gets stored is model-decided, so verify the extraction quality on your own data before relying on it.

MilvusVector & Storage

Separates storage from compute so indexing and querying scale independently — the architecture you want at billions of vectors. That capability comes with operational weight; below a few million vectors, something simpler will serve you better.

Model Context ProtocolProtocols

The document every MCP client and server is measured against: a TypeScript schema published alongside a JSON Schema, plus the prose defining what a conforming implementation must do. Anthropic donated it to the Linux Foundation's Agentic AI Foundation in December 2025, and the licence is mid-transition — new code and specification text under Apache-2.0, documentation under CC-BY-4.0, and contributions whose authors have not consented to relicensing still MIT. Read it when an SDK's behaviour surprises you, but it is a document rather than a dependency: shipping anything still means picking one of the language SDKs.

n8nWorkflow

Wires hundreds of services together on a visual canvas, and increasingly places LLM agents inside those flows. The licence is the thing to check first: it is source-available under the Sustainable Use License, not OSI-approved open source. Internal use and self-hosting are fine; reselling it as a hosted service is not.

numbatGuardrails

numbat is a single binary that watches supported desktop, CLI, IDE and gateway agents through hooks, plugins and OTLP/HTTP log exporters, normalizes their activity into one event model, and evaluates it with a local CEL rule engine that writes versioned NDJSON events, findings and enforcement decisions. It also reconstructs past sessions from on-disk artifacts via `numbat scan`, so you can investigate an agent that was never instrumented. Blocking is deliberately conservative: enforce mode is off by default and every shipped rule is monitor-only, so denying an action means copying the rule YAML into your own `--rules-dir`, keeping its id, adding `enforce: true` and bumping the version. It is a poor fit if you expect coverage of arbitrary agents or a definitive account of what ran — the coverage matrix decides what is observable per host and surface, at-rest reconstruction cannot recover activity an agent never persisted, and findings are rule matches rather than proof that an action completed.

OllamaModel Serving

Handles model download, quantisation and an OpenAI-compatible server so a local model is a single command away. The obvious starting point for development against local inference. For serving many concurrent users, a throughput-oriented server will do considerably better.

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.

openclawTools & Integrations

OpenClaw installs a Gateway on your own machine and makes it the local control plane for sessions, tools, events and channel connections, so the Control UI, CLI, TUI and every connected messaging channel drive the same assistant. Models can be hosted or local, and the project expects new capabilities to arrive as plugins built on the plugin SDK rather than as changes to the core. The constraints are stated plainly in the README: it is designed for a single operator, tools run on the host for the main session unless you configure sandboxing, and DM-capable channels leave the assistant reachable by unknown senders until you approve a pairing with `openclaw pairing approve <channel> <code>`. The README tells you to read the security, exposure and sandboxing guides before connecting other users or exposing the Gateway remotely, which is a fair signal that shared or multi-user deployment is not the case it was built for.

OpenHandsCoding Agents

Edits files, runs tests and browses documentation inside an isolated container, so a failed run cannot damage the host. Competitive on SWE-bench style benchmarks, but budget carefully before pointing it at a real repository — long autonomous runs consume a great deal of tokens and still need review before anything is merged.

PhoenixObservability

Built on OpenTelemetry, so traces are portable rather than locked to one vendor, and it will run locally next to the code you are debugging. Licensed under Elastic 2.0 — fine for internal use, restrictive if you intend to offer it as a managed service.

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.

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.

QdrantVector & Storage

Written in Rust and built so that metadata filters apply during search rather than after it — the difference that keeps results correct when an agent queries a narrow slice of a large collection. Runs from a single container for local development up to a distributed cluster.

SGLangModel Serving

Its prefix cache pays off precisely in the agent case, where many calls share a long, identical system prompt. Also strong at constrained decoding when responses must match a schema. The main alternative in this space is vLLM; benchmark both on your own traffic shape.

skillsTools & Integrations

Over a hundred skill directories of Google's own written guidance, installed selectively with `npx skills add google/skills`. The bulk sits under `skills/cloud` — GKE, BigQuery, Agent Platform, the six Well-Architected pillars — while `skills/ads` and `skills/analytics` cover the Google Ads API, the Mobile Ads and IMA SDKs and the two Google Analytics APIs. Depth is uneven: `skills/cloud/gke-*` accounts for twenty-nine directories on its own, Cloud Run and Firebase get one each, and the README labels the repository as under active development. A stack on neither Google Cloud nor Google's ads and analytics products gets nothing here, and app-side coverage stops at the Mobile Ads SDK — Android, Flutter, Dart, Genkit and ADK skills are separate repositories the README only links to.

SkyvernComputer Use

Combines vision with DOM analysis so a flow keeps working when a site is redesigned — the failure mode that kills conventional selector-based scripts. Note the AGPL licence: if you intend to offer it as a network service, read the terms before you build on it.

StagehandComputer Use

Lets you write deterministic Playwright for the steps you already know and drop into natural language only where the page is unpredictable. That mix is the pragmatic middle ground: fully AI-driven automation is slow and flaky, fully scripted automation breaks on redesigns.

vLLMModel Serving

Paged attention and continuous batching let one GPU serve far more concurrent requests than a naive loop, which matters because agents are unusually chatty. The OpenAI-compatible endpoint means most agent frameworks point at it with a base-URL change. Expect real operational work around GPU memory sizing and model loading times.