⚡ Scoreboard · August 6, 2026
AI Agents Scoreboard
Agent frameworks and end-user AI agent apps ranked by verified-data score. Built for builders who need a current shortlist, not a directory dump.
Ranked by AIDiveForge's verified-data score: data completeness, verification recency, community rating, and real visitor engagement. How we rank · No tool can pay for placement.
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1
NoInfra
The vendor delivers pre-configured, hosted agents across a set of templated use cases: study summarization, job application tracking, spreadsheet cleanup, meeting prep, and sandbox code execution. You open a workspace and the agent is already running — no keys, no servers, no config files. The managed runtime abstracts token provisioning server-side, so users see a balance and status indicator rather than provider credentials. Where this model breaks: the compute ceiling is fixed per tier, and teams whose workloads outgrow the allocated vCPU and RAM have no self-hosted escape hatch — they either upgrade or leave.
Paid $19.99/mo and upVerified Jul 9, 202610.9 score -
2
Nimbus
Nimbus runs a ReAct planning loop that maps a natural-language request to actual cloud actions: querying live AWS or GCP telemetry, generating infrastructure changes, opening PRs on connected repositories, and updating shared architecture diagrams. Approval gates sit between the agent's plan and execution, so nothing ships without a human sign-off. That model works well for incident diagnosis and routine cost optimizations. Where it strains is on cross-account, deeply custom IAM environments — the agent's tool set reflects the scaffolding its maintainers have wired up, and anything outside that surface area requires you to extend it yourself. Self-hosting via Docker or source install keeps sensitive cloud credentials off third-party infrastructure, which is the primary reason platform teams choose it over a SaaS alternative.
Paid Open source APISelf-hostedVerified Jul 8, 202610.0 score -
3
Open-Kritt
The tool runs parallel AI agents across a codebase, so vulnerability discovery that would serialize into hours on a single-context scan distributes across concurrent analysis threads. It targets security researchers and bug bounty teams who need to sweep repositories at scale, not review a function at a time. Self-hosting is supported under AGPL-3.0, which means your code and findings never leave your infrastructure — a requirement for any org with compliance constraints. The open-source core is inspectable and forkable, but managed scans are a paid-only feature, so teams that want the hosted workflow face a significant spend threshold. The page describes GitHub integration as a first-class path, making it a practical fit for teams already running security workflows inside existing CI infrastructure.
Paid Open source Self-hostedVerified Jul 21, 202610.0 score -
4
Xalgorix
The core loop is detect, chain, verify: the agent runs reconnaissance through injection through authentication testing, then executes a dedicated validation phase before anything reaches your report. On a public deliberately-vulnerable target, the vendor documents 9 verified findings including a CVSS 9.8 RCE in 17 minutes. The REST API and cron-style scheduling let security teams wire scans directly into CI/CD gates, so releases block on verified findings rather than scanner noise. Where the architecture shows its limits: scan depth and concurrency are credit-gated, and teams running continuous coverage across a wide attack surface will need to budget credits carefully. Self-hosted deployment is listed as an option for teams with data-residency requirements.
Paid Open source from $1 per scanAPISelf-hostedVerified Jul 8, 202610.0 score -
5
adris.tech
adris is a desktop app for Windows and Linux that bundles eight modules — AI agents, automation, a code editor, local model hosting, a credential vault, DNS-level threat blocking, cross-machine RAM pooling, and a shared knowledge graph — under one login. The agents (called Krew) research prospects and verify contacts in a live browser, then hand results directly to automations that push to Slack, Sheets, or Notion on schedule. Everything stores locally in SQLite; credentials never leave the device. The ceiling appears when you need a public API to connect adris to an existing internal system — the vendor does not list one. Teams that need to pipe agent output into a custom backend will hit that wall fast.
Paid from ₹0Self-hostedVerified Jul 20, 20269.6 score -
6
exployt.ai
exployt is a multi-AI orchestration platform built specifically for software developers who need to run coding agents from Anthropic, OpenAI, Google, and local models in parallel rather than in sequence. The core workflow lets a single developer assign tasks to multiple agents simultaneously, monitor their progress, and ship output without context-switching between provider dashboards. The product is in Early Access, which means the feature surface is still forming — vendor documentation confirms this explicitly. Teams that need stable, battle-tested orchestration for production systems will feel that immaturity. At this stage, exployt fits exploratory workflows better than it fits pipelines where a Monday morning spike cannot break anything.
Paid €50/mo or €100/moVerified Jun 30, 20269.2 score -
7
AgentCaly
The core workflow is event-driven: you create or annotate a calendar event, and an agent runs a defined task at that time — pulling news, finding leads, checking flight fares, or drafting a blog outline — without you opening another app. The calendar-as-interface model removes the scheduling layer most automation tools require you to build separately. Where it strains is depth: agents suited to daily briefings and contact lookups hit their ceiling when tasks require conditional branching across more than a couple of steps. Teams that need complex multi-step decision logic will find themselves wanting a dedicated orchestration layer this tool does not provide.
Paid $15/month (PRO)Verified Jul 11, 20269.0 score -
8
Agently
Agently connects to 100+ tools via OAuth and builds a live graph of your company's activity, then runs a set of specialized agents — Researcher, Revenue, Growth, Support, Ops, Briefer — coordinated by an orchestrator called Jarvis. Agents post Slack threads, recover failed Stripe charges, flag renewal risks, and ship formatted documents without waiting for a prompt. The output is artifacts — sheets, docs, decks, gated pages — not chat transcripts. The ceiling appears when you need conditional branching that goes beyond the predefined agent roles; the vendor describes no mechanism for custom agent logic or self-hosted deployment. Teams with non-standard workflows will feel the constraint.
Paid $69/moVerified Jul 15, 20269.0 score -
9
Emem
emem stores facts as short, signed tokens — each one a content-addressed handle that any agent can carry through a summarization pass, hand to another agent on a different model or vendor, and resolve back to the exact signed bytes without trusting whoever sent them. The verify step is offline: recompute the hash and ed25519 signature yourself, no server call required. Cold resolution runs around 180 ms; warm cache hits around 10 ms, with every receipt reporting its own latency stats. The honest caveat from the vendor's own benchmarks: against a bare inline number, a single emem token costs 5.8x more context — the savings only appear when you bundle multiple facts into one round trip.
Paid Open source APIVerified Jul 23, 20269.0 score -
10
Genesys
Genesys stores what you share in a causal graph you own, then surfaces that context to any app that speaks MCP — so Claude already knows what you told ChatGPT, without you repeating yourself. The graph explains its own reasoning: ask why it remembers something and you get the actual chain of connections, not a confidence score with nothing behind it. Memories fade by a scoring formula tied to relevance and reactivation, so stale data drops out without silently deleting things that still matter. The free tier caps writes at 300 stores per month — heavy users or teams running MCP agents hit that ceiling, then face a choice.
Paid Open source $0-$8/moAPISelf-hostedVerified Jul 22, 20269.0 score -
11
Jaybase
Jaybase stores every agent-generated fact as an immutable, time-stamped record, which means the full sequence of what an agent wrote, when, and why is always recoverable. The vendor describes it as designed for accounting, compliance, and approval workflows where you cannot afford to lose the paper trail. Because it is append-only, there is no overwrite risk — replaying a sequence from any point is a native operation. The library is self-hostable and open-source under AGPL, so it runs inside your own infrastructure without a call home. The project has a small contributor footprint, which means production teams should expect to own gaps in documentation rather than wait for the maintainer to fill them.
Free Open source APISelf-hostedVerified Jul 23, 20269.0 score -
12
Loop me in
The workflow is mechanical by design: an expert publishes a named loop with a fixed rate, the required context, and the specific judgment they return. When an agent hits that loop, it assembles context automatically and holds until the expert answers — from wherever they are. The model works cleanly for agents with clear pause points: brand reviews mid-code-generation, onboarding drop-off calls during product iteration, incident root-cause reads from logs. Where it strains is any run that needs real-time back-and-forth rather than a single bounded exchange, or any team whose agent framework does not support MCP hosts. The expert-as-contractor model is early — the vendor states V1.0.0, so directory depth and answer latency are open questions.
Paid Verified Jul 24, 20269.0 score -
13
Lunen.ai
A subject-matter expert describes what they want in plain language; Lunen drafts a structured execution plan with named tools, scoped data, and a schedule — no canvas, no YAML. Every MCP tool connection becomes a per-tool policy decision: allow it to run unattended, or pause for a human sign-off before each call. User actions and agent actions land in the same audit log, which means security reviews have a single trail to pull. The ceiling appears when teams need conditional branching between agent steps — the plain-language plan model does not surface that logic visibly, so complex multi-step dependencies require workarounds the interface does not directly support.
Paid Self-hostedVerified Jul 20, 20269.0 score -
14
Scarlett.
The agent runs 24/7 across messaging channels, autonomously completing multi-step tasks: a patient asks about availability, Scarlett checks the schedule, books the slot, and fires a reminder — no staff action required. For a single-doctor practice or a business with two to ten staff, that coverage is the core value proposition. The vendor states HIPAA alignment, which matters the moment you are handling patient data in a regulated environment. The scraped page is thin on integration specifics, so verifying EHR or PMS connector depth before committing is non-negotiable. Multi-location teams should validate whether workflow logic scales cleanly across sites or requires per-location configuration.
Paid $97 USD / mo startingAPIVerified Jul 11, 20269.0 score -
15
Shepherd
SHEPHERD is a Python substrate from Stanford and Northeastern that turns an agent's execution into a Git-like, reversible trace — so a supervising meta-agent can observe, intercept, fork, and revert any step without rebuilding that capability from scratch each time. The vendor-published benchmark numbers are specific: a supervisor meta-agent lifted pair-coding pass rate from 28.8% to 54.7% on CooperBench; a counterfactual repair meta-agent beat MetaHarness on Terminal-Bench 2.0 by 12.8% while cutting wall-clock time by 58%. The framework is research-grade and open-source, installed via pip. Teams outside the specific use cases the paper targets — runtime intervention, counterfactual optimization, and agentic RL training — will find precious little guidance on how far the substrate stretches.
Free Open source Self-hostedVerified Jul 13, 20269.0 score -
16
firstmate
firstmate puts a single orchestrating agent — the 'first mate' — in front of you, while it spawns a crew of autonomous coding agents behind the scenes, each isolated in its own git worktree. You describe what needs doing; the crew splits the work in parallel and keeps collisions out of your main branch. The visible session backend means you can watch what each agent is doing without switching tabs. The architecture works cleanly for investigation tasks, parallel fixes, or supervised PR generation — the constraint is that there is no API surface, so anything requiring programmatic integration into an existing CI pipeline has to wire around the tool manually.
Free Open source Self-hostedVerified Jul 14, 20268.6 score -
17
AIfunc
The tool treats AI calls the way you already treat HTTP requests: stateless, typed, testable, and wired into your existing code with standard language control flow. No canvas, no orchestration runtime, no new mental model. The vendor states the target is the 80% of real-world AI work that is text-in, structured-data-out — sentiment analysis, summarization, classification. Multi-step workflows are composed with the same if-else and loops you already write. Where this breaks: anything requiring memory across turns, autonomous planning, or tool-use loops is outside the design scope entirely.
Free Open source APISelf-hostedVerified Jul 9, 20268.0 score -
18
ami
Ami builds a context graph in SQLite and markdown on disk — tracking entities, relationships, your past decisions, and your writing style — so the agent gets less hand-holdy the more you use it. It maintains a live to-do list and executes recurring busy work by learning how you handle tasks, not by following a static playbook. The self-hosting story is real: no data leaves your machine, no org-level sharing. Where it breaks is scale and surface area — the repo has 3 commits and 4 stars at time of writing, which means production edge cases are yours to discover and debug, not documented anywhere. Teams that need multi-user workflows, audit logs, or a managed API surface will hit the ceiling fast.
Free Open source Self-hostedVerified Jul 23, 20268.0 score -
19
Artificial Wit
The platform sits between your existing APIs, documents, and knowledge bases on one side and any LLM — Claude, ChatGPT, Gemini, or a local model — on the other. You connect REST or GraphQL endpoints, upload docs or point at a database, then the platform exposes every configured API as a Model Context Protocol tool, discoverable by any MCP-compatible client. No schema migration, no re-platforming. The free tier caps you at three API connections, which covers a proof of concept but hits the wall fast for a real ERP environment. Role-based access control is included, which matters the moment clinical documents or order data enter the picture.
Paid APIVerified Jul 15, 20268.0 score -
20
Bike4Mind
The workbench routes across 60+ models from OpenAI, Anthropic, Google, and AWS Bedrock through a single interface and API, with a separate lane for open-weight models running on your own hardware via vLLM — the lane no lab can ever sell you or switch off. Sessions, prompts, and artifacts survive mid-conversation model swaps, so when a provider gates its best tier, the switch is a config change, not a rebuild. The agentic layer runs 'Quests' — long-running jobs with a code REPL, search, and MCP access under hard budget caps, so you fire a task and return to results rather than babysitting each step. Where the tool shows its edges: the source-available BSL 1.1 license means self-hosted deployments carry restrictions until the two-year Apache rollover, and teams that need branching multi-agent pipelines beyond single-Quest logic will hit the canvas ceiling fast.
Paid Open source APISelf-hostedVerified Jul 8, 20268.0 score -
21
Botchi
The core model is a 'swarm' of assistants and agents sharing the same company knowledge base, tool credentials, and approval layer — controlled from a single dashboard. A support agent touches tickets; a finance agent touches sheets; nothing crosses the boundary you set. Agents run on schedules, trigger from events, and write back to PDF or PNG when the output is a document. The self-improving loop is the differentiator the vendor leans on hardest: agents log what your team approves, edits, or rejects, and sharpen their behavior over time without retraining. Specialist agents are a paid-only feature, so teams that want more than one scoped agent hit that wall immediately.
Paid Verified Jul 17, 20268.0 score -
22
ClawLite
ClawLite extracts the reliability patterns from OpenClaw and strips the rest to roughly 500 lines of logic. You get lane-based serial execution so tool calls don't interleave, automatic context compaction at 80% capacity so small models don't hit the wall mid-task, and provider fallback so a dropped Ollama instance doesn't kill a pipeline. Skill behavior is configured via markdown files, not code. The ceiling appears fast: there is no API, no web UI, no parallel execution path you can opt into for tasks that actually need it, and the project sits at v0.1.0 — which means the surface area is deliberately small and the community footprint is thin.
Free Open source Self-hostedVerified Jul 24, 20268.0 score -
23
Cognee
The core workflow is three lines: install via pip, point Cognee at a data source, and your agents start recalling cited facts instead of hallucinating from scratch each session. Graph-structured memory means relationships between entities survive retrieval — not just keyword matches. First-party integrations cover Claude Code, Cursor, LangGraph, and an MCP server, so compatible agents read and write memory without custom glue code. The ceiling appears when your ontology needs get specific: custom data models and permissions controls are available, but tuning graph structure for a niche domain requires real configuration work. Teams that need a drop-in vector store with zero graph overhead typically reach for a simpler solution.
Paid Open source APISelf-hostedVerified Jul 13, 20268.0 score -
24
Conol
Conol runs agents on its own servers after you close the tab, files structured output into an editable notes layer, and pings you on Telegram or WeChat when the job is done. The core loop — give it a standing task, schedule it, receive the result — handles recurring research and reporting without you staying present. That loop is solid for daily briefs and long-form research drops. Where it strains: there is no API, so plugging Conol into an existing data pipeline or triggering it from another system is not supported. Teams that need Conol as one node in a larger automation stack hit that wall immediately and look elsewhere.
Paid Verified Jul 8, 20268.0 score -
25
Craft Agents
Built by the craft.do team for their own internal agent work, Craft Agents connects to external services like Slack or Linear, imports skills from Claude Code, and runs local MCP servers as subprocesses — all without requiring CLI fluency from the people reviewing agent output. The document-based interface lets you run multiple agents in parallel and share sessions, which removes the 'only the dev knows what happened' problem. Where it strains: the GitHub issues tracker shows 311 open issues and 146 open pull requests, signaling a project moving fast with rough edges still exposed. Teams expecting enterprise-grade stability at scale will feel those gaps quickly.
Free Open source Self-hostedVerified Jul 14, 20268.0 score
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