LLMs With an API

As of August 2026, AIDiveForge tracks 131 llms with an api. The top three by verified-data score are Nimbus, Xalgorix, and AgentsProof. Curated llms with an api tracked by AIDiveForge. Listings are verified against each tool's live website and re-checked regularly.

Last updated July 29, 2026 · 131 tools

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.

1. 

   1. 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.

   PaidOpen SourceAPISelf-hostedVerified Jul 8, 2026
2. 

   2. 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.

   PaidOpen Sourcefrom $1 per scanAPISelf-hostedVerified Jul 8, 2026
3. 

   3. AgentsProof

   AgentsProof is an evaluation SDK that wraps your LLM and tool calls with a decorator, grades each run against rules you define in plain English, and produces a shareable, scored report at a public URL. The core loop is: instrument with `run.trace()`, capture a passing run as a Golden, then run your full proof suite against every future change. That workflow catches regressions before users do — not after. The ceiling appears when teams need self-hosting; the product is cloud-only, so regulated environments that cannot send trace data to a third party are blocked before they start. The product is in beta, which means API surface and grading behavior are still moving.

   Paid$29/monthAPIVerified Jul 14, 2026
4. 

   4. ChatLLM

   The core workflow is model selection plus prompt — pick from the available pool, type, and get streaming responses without touching API keys or billing dashboards. Real-time web search and persistent memory across conversations cover two gaps that kill single-model chat tools for ongoing research or support use. The App Builder mode generates full-stack code directly in the browser, which closes the loop for developers who want to go from spec to working prototype without leaving the tab. Where it breaks: this is a chat interface, not an automation layer — there are no agent loops, no tool-use chains, and no self-hosting. Teams that need their data to stay on-premise have no path forward here.

   Paid$4/monthAPIVerified Jul 17, 2026
5. 

   5. 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.

   PaidOpen SourceAPIVerified Jul 23, 2026
6. 

   6. 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.

   PaidOpen Source$0-$8/moAPISelf-hostedVerified Jul 22, 2026
7. 

   7. 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.

   FreeOpen SourceAPISelf-hostedVerified Jul 23, 2026
8. 

   8. 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, 2026
9. 

   9. Katra

   Katra is self-hosted memory infrastructure: drop it on any Docker-capable machine, point your MCP-compatible agent at it, and you get episodic recall, semantic search, knowledge graphs, and temporal analysis without rebuilding your agent. The architecture is a single deployable unit — the vendor describes it as a 'memory appliance' — which means setup friction is low for teams that already run Docker or Helm on AWS. Where it breaks: Katra is memory infrastructure, not an agent runner, so teams expecting built-in task planning or tool execution will need to wire those themselves. The project is early-stage with five stars on GitHub and no reported production deployments in public community channels, which means you are taking on the role of early adopter rather than stepping into a proven stack.

   FreeOpen SourceAPISelf-hostedVerified Jul 1, 2026
10. 

    10. 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.

    FreeOpen SourceAPISelf-hostedVerified Jul 9, 2026
11. 

    11. 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.

    PaidAPIVerified Jul 15, 2026
12. 

    12. 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.

    PaidOpen SourceAPISelf-hostedVerified Jul 8, 2026
13. 

    13. 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.

    PaidOpen SourceAPISelf-hostedVerified Jul 13, 2026
14. 

    14. Custodian Labs AI Agent

    The vendor describes a workflow where a Python developer imports one class, passes a model name and system prompt, calls deploy(), and has a production agent running — no database to provision, no hosting environment to configure. The Guardian Layer handles PII detection before any model call, which means sensitive data in user inputs doesn't reach OpenAI or Anthropic unless you decide it should. RAG is available without configuring embeddings or a vector store — the docs describe adding a knowledge base in one line. The tradeoff is control: because Custodian abstracts the entire infrastructure layer, teams that need to tune chunking strategies, swap embedding models, or run on their own infrastructure hit a wall fast.

    PaidAPIVerified Jul 20, 2026
15. 

    15. eve

    The platform gives coding agents a native deployment surface — API, CLI, MCP, and agent-callable Skills — so agents ship and iterate on apps without a human relaying commands. Sandboxed VMs let agents run code they generated without that code touching your production environment. Durable Orchestration means a workflow that pauses for minutes or months resumes from the exact checkpoint, not from scratch. The constraint is architectural: there is no self-hosted path, so teams with strict data-residency requirements or air-gapped environments hit a wall before they write a single agent. At that point, the conversation moves to a competitor with an on-premises option.

    PaidOpen Source$20/moAPIVerified Jun 29, 2026
16. 

    16. Isnad

    Isnad attaches provenance metadata to individual claims as they move through agent pipelines, borrowing the narrator-grading logic from classical hadith transmission scholarship to score source reliability at each hop. The vendor describes it as claim-level auditing — you get a trustworthiness grade per claim, not a flat event log. It installs via pip and ships with Docker support and Alembic-managed migrations, which means it slots into existing Python stacks without standing up a separate service. The ceiling appears when your pipeline is not Python-based or when you need a hosted dashboard rather than a library you integrate yourself. Teams outside that boundary are building their own wrapper before they can use the core grading logic.

    FreeOpen SourceAPISelf-hostedVerified Jul 11, 2026
17. 

    17. Last EHR

    The tool wraps four FHIR operations — patient search, chart read, note creation, and observation recording — into a chat agent that runs as the signed-in user, bounded by whatever Medplum AccessPolicy you already have. Every write surfaces as an approval card showing exactly what will be saved before it touches the chart. The same four tools expose as an MCP server for Claude Desktop or Claude Code, read-only by default. The ceiling is visible early: four tools, one backend, one EHR platform. Teams that need more than that are writing their own.

    FreeOpen SourceAPISelf-hostedVerified Jul 9, 2026
18. 

    18. npcpy

    npcpy is a MIT-licensed Python library built around three primitives: Context, Agent (NPC), and Tool — which you compose to wire up single agents or multi-agent teams running against local runtimes like Ollama and llama.cpp or cloud providers. The library's knowledge graph support and multimodal LLM integration live in the same package, so a research prototype doesn't require stitching together three separate dependencies. Where it starts to strain is at the integration surface: documentation is sparse for anything beyond the happy path, and production observability — logging, tracing, failure recovery — is not built in. Teams moving from research prototype to a production deployment will find themselves reaching for additional infrastructure the library does not provide.

    FreeOpen SourceAPISelf-hostedVerified Jul 14, 2026
19. 

    19. OGAC

    The Console gives banks, insurers, and other regulated enterprises one place to connect data sources, route traffic through observed model gateways, build apps in plain language without code, and produce signed, cited audit trails — all governed by rules set once and inherited everywhere. Prompt-injection screening, PII filtering, and policy checks run in the pipe before a call leaves the system. Live scoring watches for drift against a golden set and traces every result to its source. A run can pause for human sign-off, then continue on its own. The self-hosted, AGPL-3.0 path means your data and models stay on your servers — but operating that infrastructure is on your team, not the vendor.

    PaidAPISelf-hostedVerified Jul 14, 2026
20. 

    20. Provena

    Provena wraps around retrieval steps, tools, and context assembly logic to log where every chunk of data came from, hash it for tamper detection, and surface that audit trail when something breaks or an auditor asks. The vendor describes six framework adapters, an MCP server, PostgreSQL storage, and a policy engine — covering most standard Python-based pipelines without requiring a hosted service. Installation is self-hosted and free. The ceiling appears when your compliance requirement goes beyond audit trails: Provena is a passive tracking library, not an enforcement layer, so it records what happened but does not block a bad retrieval from reaching the model. Teams with hard EU AI Act enforcement obligations pair it with a separate policy gate.

    FreeOpen SourceAPISelf-hostedVerified Jul 22, 2026
21. 

    21. Qwen-Image-3.0

    The family spans four distinct problem areas: safety moderation via Qwen3Guard, multilingual translation via Qwen-MT, text-rich image generation and editing via Qwen-Image and Qwen-Image-Edit, and general reasoning via the base Qwen3 models. Self-hosting is a real option — weights are published on Hugging Face and ModelScope, and the Apache-2.0 license means no legal friction for commercial deployment. Qwen-MT's hosted API is a paid-only feature, so teams that want translation without infrastructure management pay for access; everyone else runs inference themselves. The research layer is also public: GSPO, the vendor's proposed fix for RL training instability in large models, is documented and available for teams experimenting with fine-tuning at scale.

    PaidOpen SourceAPISelf-hostedVerified Jul 26, 2026
22. 

    22. River

    River targets developers who need a task-specific model they can call through an API without managing GPU clusters themselves. The vendor describes LoRA-based fine-tuning and reinforcement learning across open-source models ranging from 35B to 1T parameters, all accessible through a Python client. Pay-per-token pricing means you are not renting reserved compute. The tool is paid-only with no self-hosted option, so your data and training runs go through River's infrastructure — a constraint that matters for regulated or sensitive datasets. If your team needs to run training inside your own VPC, this architecture is a blocker from day one.

    PaidAPIVerified Jul 17, 2026
23. 

    23. Skippr AI

    The agent runs planning and execution loops in real time: it can fill forms, retry failed payments, draft follow-ups, and submit purchase orders — not just suggest the next click. Embedding is two lines of code, which means your first deployment can land inside a sprint. The same agent that handles end-user onboarding can join a customer video call to run a live demo, or operate internal tools to reskill employees on AI-native workflows. The ceiling shows up when your use case needs deep custom logic or on-premises deployment — neither is available. Teams with strict data-residency requirements hit that wall before a single user interaction goes live.

    PaidAPIVerified Jul 20, 2026
24. 

    24. Tessera

    Tessera operates as a deterministic evidence layer that sits between your agent and its outputs. Every claim the agent surfaces is linked to a specific source record; claims without that linkage are refused outright, not softened or hedged. Before any action executes, the agent drafts it from verified claims only and surfaces it for your review. The architecture is open-source under MIT and built to integrate with MCP-based agent setups. Where it breaks: teams that need the agent to synthesize across sources where no single record covers the answer will hit refusals that require data-model work to resolve.

    FreeOpen SourceAPISelf-hostedVerified Jul 8, 2026
25. 

    25. Timbal AI

    The platform combines agents, deterministic workflows, knowledge bases, and a UI builder under one roof, with 100+ native connectors to enterprise stacks like SAP, Salesforce, Slack, and Jira. The standout piece is ACE — the Action Control Engine — a behavioral runtime that sits in front of any LLM and, per vendor claims, delivers a 30% reliability gain at a tenth of the per-run cost versus baseline. Everything you build compiles to exportable Python, SQL, or React code, so you are not locked into the canvas. Self-hosting is supported but not cloud-managed — your team carries that operational burden. The no-code surface gets you to a working agent fast; the ceiling appears when multi-step branching logic outgrows what the visual builder can express cleanly.

    PaidAPIVerified Jul 9, 2026
26. 

    26. HART OS

    HART OS is an open-source, Apache-2.0 multi-agent runtime built on AutoGen that runs autonomous agents across a crowdsourced compute network, routes tasks through gossip-based federation, and keeps humans in the approval chain by design. The Recipe Pattern is the sharpest production differentiator: agents learn a task once in CREATE mode, then replay it in REUSE mode without repeating LLM calls — the vendor states up to 90% faster execution on trained tasks. Budget gating and compute escrow prevent any single node from absorbing costs for others. Where this breaks down is in ecosystem maturity: no comparable alternatives are listed in the market, documentation is structured but thin in places, and teams building beyond the Nunba bundled distribution will be navigating architecture that is still finding its production footing.

    FreeOpen SourceAPISelf-hostedVerified Jul 28, 2026
27. 

    27. AMA2

    AMA2 gives agents a native place in a shared thread — same participant model, same permissions, same persistent context — instead of bolting them on as integrations. The vendor describes a setup flow through a CLI and an MCP server connection, so agents slot into tools like Claude Code or Cursor without a separate API integration per agent. Where this hits a wall: AMA2 is infrastructure, not an agent runtime, so teams that need agents to plan and execute multi-step tasks independently still build that logic elsewhere. The shared-thread model works well when people and agents need to stay in the same conversation; it does not replace an orchestration layer for autonomous task pipelines.

    Paid$10/moAPIVerified Jun 30, 2026
28. 

    28. AutoGPT

    The AutoPilot interface lets you describe a workflow in plain language and get a running agent without touching a flow diagram — which means a non-technical PM can ship something before an engineer has opened the docs. When the logic gets complicated, the Builder canvas lets you drag, branch, and loop at the block level, with live input/output inspection at every step. The Dashboard surfaces spend per agent and flags anything waiting on your input, so nothing runs invisible. The Marketplace gives you community-built agents with real run counts, skipping the blank-page problem entirely. Where it strains: teams reporting that complex multi-step branching inside the visual canvas hits readability limits, and the self-hosted path requires managing your own infrastructure when the hosted platform's scale limits bite.

    PaidOpen SourceAPISelf-hostedVerified Jul 1, 2026
29. 

    29. EvalQA

    The platform combines trained human evaluators with automated metrics across three surfaces: multi-step agent workflows, SaaS AI features like copilots and recommendation engines, and qualitative knowledge work like content and analysis. The hybrid engine is the core differentiator — you are not forced to choose between human judgment and automated scoring, both run together against shared rubrics. Self-serve API and SDK access mean teams can instrument evaluation without a sales cycle. The ceiling appears when your rubrics are genuinely novel: the platform scopes custom engagements for those cases, which shifts you from self-serve into a managed services track and slows iteration.

    PaidAPIVerified Jun 29, 2026
30. 

    30. Humalike

    The platform supplies seven composable APIs — turn-taking, theory of mind, group norms, persona, social memory, social signals, and social observability — that you layer onto agents you are already building. None of these APIs make decisions for you; they surface behavioral data your agent logic can act on. The flagship turn-taking API bundles the full stack into one call, handling when to speak, when to wait, and when to read the room. SOC 2 and ISO 27001 certifications are listed as in progress, which matters if you are building for healthcare or enterprise. The APIs are described as model-agnostic and stack-agnostic, so you are not locked to a specific LLM.

    Paid$20 free credits on signupAPIVerified Jul 1, 2026
31. 

    31. Kitaru

    Kitaru wraps your existing agent SDK — PydanticAI, OpenAI Agents, Claude Agent SDK, or raw Python — and turns every model call, tool call, and intermediate step into a durable checkpoint. When you want to ask what would have happened with a cheaper model or a failed retriever, you replay from a specific checkpoint with one override. Nothing re-executes in production. The vendor's own benchmark shows 200 replayed executions on a cheaper model matching outputs in 192 of 200 cases at 84% lower cost. The ceiling appears when your agent's behavior depends on state that Kitaru's adapter doesn't intercept — external side effects or SDK internals the wrapper never sees won't be faithfully replayed.

    FreeOpen SourceAPISelf-hostedVerified Jul 7, 2026
32. 

    32. Adapt

    The vendor describes Adapt as an autonomous business intelligence agent that connects to disconnected data sources, routes queries to optimal models, and surfaces answers directly in Slack — without requiring SQL or dashboard-building skills. For executive briefings and churn monitoring, the no-code workflow layer handles the repetitive retrieval work so analysts are not the bottleneck. The credit-based free tier lets teams validate integrations before committing. The scraped page content provided does not match the tool — it describes a travel identification app called Spotter — so specific integration names, connector counts, and workflow depth cannot be verified from the source material and are omitted here.

    PaidAPIVerified Jun 2, 2026
33. 

    33. Agent Development Kit (ADK)

    ADK is the open-source agent development framework that lets you build, debug, and deploy reliable AI agents at enterprise scale.

    FreeAPISelf-hostedVerified Apr 21, 2026
34. 

    34. Agent Governance Toolkit

    Policy enforcement, zero-trust identity, execution sandboxing, and reliability engineering for autonomous AI agents.

    FreeOpen SourceAPISelf-hostedVerified May 1, 2026
35. 

    35. AgenticCalling AI

    The core workflow is API-driven: your agent (Claude, ChatGPT, CrewAI, or similar) calls the AgenticCalling API, which places the outbound call, handles the conversation autonomously, and returns structured output — including JSON-extracted data — back to your pipeline. Parallel dialing is the headline capability: the vendor describes batch calls to dozens of numbers simultaneously, which is what makes hotel rate surveys or supplier negotiations viable without a call center. The free tier offers precious little call volume, making it a proof-of-concept runway rather than a production budget. Self-hosting is not an option, so every call transits Magnara's infrastructure — a constraint that stops regulated industries cold. Teams with strict data residency requirements look elsewhere before they finish their security review.

    Paid$0.09 per minuteAPIVerified Jun 2, 2026
36. 

    36. AgentZee

    The platform runs six distinct agent types — text, voice, 3D avatar, analytics, media, and testing — coordinated under a single account so a lead captured by the chatbot can trigger a voice follow-up call without you manually stitching two systems together. The starter tier caps voice calls at 100 per month and analytics at 25 AI reports, which works for a small business running targeted campaigns but hits the ceiling fast for any team doing high-volume outbound. There is no self-hosted option, so your conversation data and voice recordings live on Agentzee's infrastructure — a hard stop for regulated industries or companies with strict data residency requirements. Teams that outgrow the call caps or need on-premise deployment have a real decision to make.

    PaidFree Trial · 14 days$25/monthAPIVerified Jun 1, 2026
37. 

    37. Agnt

    AGNT is a local-first agent operating system built around an AGI loop: the agent executes a step, evaluates the result, and re-plans before moving forward — without you steering each decision. Persistent memory and skill layers mean context survives across sessions, not just within a single run. The visual workflow designer handles repeatable paths; goal-mode hands the agent an objective and lets it figure out the steps. Self-hosted deployment with Docker keeps data on your own infrastructure, which matters when your legal team has opinions about where prompts and outputs live. The custom license — not OSI-standard — is the detail that stops procurement at some organizations before the first demo.

    PaidOpen Source$0 or $333/year per additional user for hosted versionAPISelf-hostedVerified Jun 9, 2026
38. 

    38. Alma

    Alma stores facts and preferences — name, role, working style, answer preferences, current context, principles — as a self-model any MCP-compatible agent can read at session start. The data stays on your machine; no hosted account, no vendor lock-in. Access is scoped, so an agent can read the slice it needs without touching the full store. Every durable write goes through an event log, which means changes are auditable and can be reversed. The project is explicitly labeled experimental by the maintainer, so APIs are unstable and behavior can change between commits.

    FreeOpen SourceAPISelf-hostedVerified Jun 24, 2026
39. 

    39. AnyFrame

    AnyFrame lets engineering, ops, and support teams spin up agents that trigger from Slack messages, Linear tickets, or GitHub PR comments and then act — rolling back a deploy, writing tests against a diff, or navigating a billing portal without touching an API. The harness layer is swappable: Claude Code, Codex, Cursor, Gemini CLI, and others sit behind the same agent surface, so a model switch doesn't break your workflow. The SDK lets you embed that same runtime inside your own product in a few lines of code. The ceiling shows up when you need strict approval before an agent acts on production — the vendor describes autonomous execution, and teams that need a mandatory human sign-off step before every consequential action will need to build that gate themselves.

    PaidFree tier 500 credits, then pay-as-you-goAPISelf-hostedVerified Jun 9, 2026
40. 

    40. Atizar

    Atizar is an open-source, TypeScript-native framework for building agent workflows where humans stay in the loop before consequential actions execute. The core pattern: agents plan and gather, then pause for a sign-off before anything ships — emails send, records update, data moves. That approval gate is architectural, not bolted on after the fact. The self-hosted option means client deliveries stay off third-party infrastructure. Where it gets tight is documentation depth — the README carries most of the guidance, which means teams building complex branching logic are reading source code before long.

    FreeOpen SourceAPISelf-hostedVerified Jun 22, 2026
41. 

    41. Autoheal

    AI platform leveraging a Production Context Graph to automate alert triage, root cause investigation, and incident remediation for enterprise SRE teams.

    PaidAPISelf-hostedVerified May 16, 2026
42. 

    42. Autonomy

    The core loop — AgentLoop — runs up to a configured step ceiling, selects from 15 bundled procedural skills, ranks candidate actions across five weighted dimensions using beam search, executes through ActionGateway with LOW/MEDIUM/HIGH risk labels, then evaluates and learns. Every event in that chain is stored via event sourcing, so the full run is replayable. The learning loop drafts new skills after a successful run and queues them for review rather than auto-applying them. The wall appears when you need agents running in parallel or sharing state across concurrent sessions — the architecture is single-loop, single-goal. Teams that outgrow that model start wiring external orchestration around it.

    PaidOpen SourceFree Trial · 7 days$75/moAPISelf-hostedVerified Jun 22, 2026
43. 

    43. Better Agent

    The CLI walks your Next.js codebase, surfaces every server action and API route, and lets you approve which handlers the agent can call — scaffolding typed Zod schemas you fill in before anything reaches the model. Bearer-token forwarding means the agent runs under your user's session, so existing auth middleware and revalidation logic stays intact. UI ships as a shadcn-compatible component registry: sidebar, popup, inline bar, or command-bar, all installed with one CLI command and owned by your codebase after. Observability is per-run and token-level — latency, tool calls, spend — queryable like HTTP logs. The ceiling appears when you need branching across more than two or three dependent tool calls; the platform approves tools statically, so dynamic routing between handlers requires you to encode that logic in the handler itself.

    Paid$0.99/moAPIVerified Jun 25, 2026
44. 

    44. BGE-M3

    BGE is a family of open-source embedding and reranking models from BAAI, released under MIT license with weights available on Hugging Face and PyPI, designed to run entirely on your own infrastructure. The core workflow is straightforward: generate dense embeddings, index them in a vector database, and optionally layer in sparse or multi-vector retrieval for hybrid search. Multi-lingual retrieval is a documented strength, with cross-lingual matching working across language pairs without requiring parallel training data. The ceiling appears when your domain is highly specialized — out-of-the-box embeddings on narrow technical corpora produce ranking quality that requires fine-tuning to fix, and that fine-tuning work lands entirely on your team.

    FreeOpen SourceAPISelf-hostedVerified Jun 9, 2026
45. 

    45. Bloom

    Bloom generates targeted evaluation suites for arbitrary behavioral traits.

    FreeAPISelf-hostedVerified Apr 20, 2026
46. 

    46. Breeze Customer Agent

    An AI customer service agent within HubSpot that automates conversation handling and ticket resolution across multiple channels.

    PaidFree Trial · 28 days$0.50 per resolved conversation (outcome-based); requires Professional ($800/mo+) or Enterprise ($3,600/mo+) subscriptionAPIVerified Apr 23, 2026
47. 

    47. Browser Use

    Browser Use is an open-source Python library for autonomous web task automation using LLMs and computer vision. Teams use it to extract competitive data, fill forms at scale, and monitor page changes across hundreds of sites. The tool hits 89.1% success on standard benchmarks and comes with stealth browser support, CAPTCHA solving, and residential proxies across 195+ countries. The vendor also runs a cloud infrastructure option alongside the self-hosted library. Most production teams pair it with managed browser infrastructure and human approval gates for financial or sensitive actions. The sharp edge: LLMs can't reliably distinguish user instructions from webpage content, leaving agents vulnerable to indirect prompt injection attacks that succeed 24% of the time without defenses.

    PaidOpen Source$29/moAPISelf-hostedVerified May 8, 2026
48. 

    48. Build A Harness

    The tool is a visual canvas for designing agent harnesses: multi-layer structures that govern what an agent believes, what it can do, and how it recovers when something goes wrong. You draw a graph on the canvas, and FlowSpec compiles it to LangGraph, CrewAI, Mastra, or Microsoft Agent Framework — no rewrite when you switch. The node library covers 27 types across 14 execution and 13 harness categories, from a minimal three-node loop to an 11-layer stack with world model, five-tier control resolution, nine-layer verification, and six recovery strategies. Langfuse tracing is built into the architecture, so every decision is observable. The ceiling appears when your team needs a hosted managed runtime — the vendor states no hosted API exists, so you own the infrastructure from day one.

    FreeOpen SourceAPISelf-hostedVerified Jun 25, 2026
49. 

    49. Cerver

    Cerver is session infrastructure for AI agent fleets: each session carries its full transcript, cost record, model choice, and compute target as a single object you control. You write routing policies — or let auto-routing handle it — so routine tasks go to cheaper models and complex work earns the frontier. Mid-session you can swap the underlying model or compute without losing the transcript. The local relay option means sessions that need your repo or CLI attach to your machine and run on Claude Max or ChatGPT subscriptions you already pay for, which drops marginal token cost close to zero. Spending caps ship on by default, so a runaway parallel agent fleet stops at your number.

    Paid$89/mo + $10/dev, max $300/moAPISelf-hostedVerified Jun 23, 2026
50. 

    50. ChatGPT

    ChatGPT takes text prompts and generates coherent, contextually relevant responses across writing, coding, analysis, and creative tasks. It arrived in late 2022 as the first mainstream interface to GPT technology, fundamentally shifting how people think about AI assistance. The free tier runs on GPT-3.5; paid subscribers ($20/month) access GPT-4, which handles longer context and harder reasoning. The core limitation remains unchanged: it can confidently produce plausible-sounding but entirely false information, and it has no access to real-time data or the internet.

    Paid$20/moAPI128k tokens context$2.5/1M input
51. 

    51. Claude

    Claude is a large language model accessible via web interface that handles text generation, analysis, and reasoning tasks at roughly the same capability level as GPT-4. It's positioned as the more safety-conscious alternative to OpenAI's offerings, with a stated focus on reducing hallucinations and harmful outputs. Pricing starts at free (limited Claude 3.5 Sonnet access) with Claude Pro at $20/month for higher usage limits. The main trade-off: Claude's context window and real-world adoption lag slightly behind its closest competitors, though for most writing and support tasks the difference remains marginal.

    Paid$20/moAPI200k tokens context$3/1M input
52. 

    52. Claude by Anthropic

    Fable 5 runs on Anthropic's Mythos-class transformer architecture with adaptive thinking, giving it a 1M-token input context and up to 128k tokens of output — which means a codebase migration or a multi-document research synthesis fits in a single pass without chunking hacks. The vendor positions this explicitly for autonomous agent work: chained tool use, multi-step reasoning, and tasks where the model needs to hold complex state across many turns. Where it breaks is cost — per-token billing is paid-only, and at the rates the validator documents, teams running high-volume pipelines will feel it fast. Vision-dependent scientific analysis and complex software engineering are the use cases the vendor calls out directly. Teams doing commodity summarization or single-turn Q&A will pay a premium they cannot justify.

    Paid$20/mo or $17/mo (annual)APIVerified Jun 11, 2026
53. 

    53. Claude Code

    Claude is Anthropic's AI assistant and agent platform, built around Constitutional AI training intended to reduce hallucination and harmful outputs. The extended context window handles document-heavy work that breaks shorter-context alternatives — feeding an entire codebase or legal brief into a single session is the workflow it was designed for. The agent layer, including Claude Agents and Cowork, lets it plan and run multi-step tasks, execute code, search the web, and connect to external tools via MCP connectors. The ceiling appears when you need persistent memory outside a paid tier or need to self-host for compliance — neither is available. Teams with strict data residency requirements reach that wall quickly.

    Paid$20/moAPIVerified Jun 1, 2026
54. 

    54. Claude Cowork

    Running on Claude Opus 4.7 with a 1M context window, Cowork operates as a desktop agent that plans multi-step tasks, takes screenshots to read your actual screen, and controls mouse, keyboard, and shell commands to execute work inside an isolated VM. It handles file organization, bulk renaming, PDF data extraction, and expense tracking without needing a human to babysit each step — the vendor states it includes self-verification logic that checks its own output before reporting back. The ceiling appears when tasks require judgment calls outside a defined scope: the agent surfaces ambiguity rather than resolving it, which means complex editorial or legal review work still needs you at the keyboard. No self-hosting option exists, so teams with strict data-residency requirements are stopped before they start.

    Paid$20/moAPIVerified Jun 9, 2026
55. 

    55. Claude Sonnet 4.5

    Claude Sonnet 4.5 is a large language model from Anthropic with particular strengths in software coding, agentic tasks where it runs in a loop and uses tools, and in using computers. The model maintains focus for more than 30 hours on complex, multi-step tasks. Pricing remains the same as Claude Sonnet 4, at $3/$15 per million tokens. It is the most aligned frontier model Anthropic has released, showing large improvements across several areas of alignment compared to previous Claude models.

    Paid$20/moAPI200k tokens context$3/1M inputVerified May 15, 2026
56. 

    56. Codeium

    Devin, from Cognition, operates as a self-directed agent: given a task, it plans steps, writes and executes code, runs tests, interprets the output, and iterates — without a developer holding its hand through each transition. The vendor positions it for high-volume routine tickets, legacy migrations, and exploratory codebase work where the bottleneck is throughput, not creativity. Teams delegate backlog tickets and get draft PRs back; the agent handles the scaffolding. The ceiling appears on tasks requiring deep organizational context — tribal knowledge about why a module exists, or business logic that lives in nobody's head and in no doc. At that point, a developer re-enters the loop, which partly offsets the delegation gain.

    Paid$20/moAPISelf-hostedVerified Jun 3, 2026
57. 

    57. Cohere Embed v4

    Cohere Embed v4 transforms text, images, and mixed content into unified vector representations for semantic search, RAG, document clustering, and similarity matching. The model supports 1,536-dimensional embeddings with flexible compression via Matryoshka embeddings (256, 512, 1024, 1536 dimensions). Priced at $0.12/1M text tokens and $0.47/1M image tokens, it delivers multimodal capabilities competitive with text-only alternatives. The API supports batch processing up to 128,000 tokens per request with asymmetric search optimization. Limitation: incompatible with v3 embeddings; corpus re-embedding required for upgrades.

    PaidFree Trial · 0 days$0.12 per 1M text tokens; $0.47 per 1M image tokensAPIVerified Apr 13, 2026
58. 

    58. Command R7B

    Command R7B is a smaller language model optimized for tasks that don't require reasoning at the frontier—summarization, classification, instruction-following, and document analysis. Cohere positions it as the pragmatic choice for teams tired of paying for (or waiting on) 70B+ parameter models when a tighter, faster alternative works. It's free and open source, which means no API charges and full control over deployment. The real limitation: it will struggle on abstract reasoning, mathematical proof, or multi-step logic puzzles where 70B models shine. For enterprises choosing between this and proprietary APIs, the tradeoff is real but worth calculating.

    PaidOpen SourcePay-as-you-goAPISelf-hosted128k tokens context$0.04/1M inputVerified Apr 9, 2026
59. 

    59. CopilotKit

    The core model is a React and Angular SDK that connects your existing frontend to whatever agent backend you're already running — LangChain, CrewAI, or a custom setup — via the AG-UI protocol, a bi-directional event stream the vendor describes as 'the general-purpose connection between a user-facing application and any agentic backend.' Agents render rich UI cards, forms, and widgets inline as they work, not just text responses. Thread and state persistence is handled automatically across sessions. The friction point arrives when your deployment target isn't a web surface: Slack and Teams connections are flagged as early access, which means you're betting on a roadmap, not a shipping feature. Teams with strict approval gates before agent actions can wire those checkpoints in, but the docs describe this as a configuration responsibility rather than a built-in guardrail system.

    PaidOpen Source$39/developer/monthAPISelf-hostedVerified Jun 9, 2026
60. 

    60. CortexaPro AI

    The platform covers two distinct audiences: enterprise teams wiring agents into CRM, ERP, HR, and ITSM pipelines, and individual users who want multi-model chat plus life tools in a single interface. The enterprise side offers an agent builder with custom logic, memory, and decision layers, plus role-based access controls and audit logs — the table stakes for any org that will face a compliance review. The Cortexa Launchpad marketplace lets you hand a screenshot or API spec to a purpose-built agent and get production-ready code or UI back. The credit-metering model means costs are trackable, but teams running high-volume pipelines will hit the ceiling of a credit allocation faster than the pricing page suggests.

    Paid$5/mo - $95/moAPIVerified Jun 18, 2026
61. 

    61. Coworker AI

    The platform lets agents autonomously plan and execute multi-step workflows — pulling CRM data, writing follow-up emails, creating Jira tickets, flagging churn risk — without a human approving each step. Model routing handles cost management by selecting the appropriate frontier model per task. Compliance is baked in rather than bolted on: SOC 2, GDPR, and CASA Tier 2 certifications are vendor-stated. The ceiling appears when workflow logic grows genuinely complex across five or more interdependent agents — the abstraction layer that makes setup fast is the same layer that limits what you can surgically override. Teams needing fine-grained control over agent branching logic tend to reach for code.

    PaidFree Trial · 14 days$29.99/user/moAPIVerified Jun 2, 2026
62. 

    62. CrewAI

    CrewAI helps enterprises operate teams of AI agents that perform complex tasks autonomously, reliably and with full control. The open-source framework (free, self-hosted) defines agents with roles, goals, and backstories, orchestrating them through tasks; the paid AMP adds a visual Studio, deployment infrastructure, tracing, guardrails, and enterprise features. The framework was rebuilt from scratch to remove LangChain dependency; as of v1.14, it's fully standalone and works with any LLM provider. It's used by nearly half of the Fortune 500. But production friction is real: common Reddit advice is to start with CrewAI for speed and migrate to LangGraph when you hit scaling limits—reasonable for most projects. Users report that enthusiasm evaporates when running repeatedly on multiple components, and executing large SELECT queries overflows the LLM context window.

    PaidOpen SourceOpen-source free; CrewAI AMP paid tiers start at $99/monthAPISelf-hostedVerified May 6, 2026
63. 

    63. DataGrout Invariant

    DataGrout AI's platform is built to govern agents that run across enterprise systems — CRM, ERP, accounting — where an uncontrolled action has a real cost. The vendor describes deterministic execution controls, hallucination prevention, persistent memory across sessions, and audit trails that satisfy compliance review. Observability and cost tracking are positioned as first-class features, not add-ons, so teams can see which agent step burned the most tokens before the bill arrives. The self-hosted option matters for regulated industries where data cannot leave the perimeter. Where the platform has less evidence behind it: community reports and independent benchmarks are scarce, which makes it harder to verify the hallucination reduction claims at scale before you commit.

    Paid$19/moAPISelf-hostedVerified Jun 2, 2026
64. 

    64. DBRX Instruct

    DBRX Instruct is a free, open-source large language model built by Databricks for instruction-following tasks in software development and enterprise applications. It uses a mixture-of-experts architecture to balance performance with efficiency, and integrates natively with Databricks' data platform—a meaningful advantage if you're already in that ecosystem. The model shows strong results on coding and reasoning benchmarks, but carries real limitations: no vision capabilities, a shorter context window than Claude or GPT-4, and less real-world adoption in mainstream enterprise settings. For teams deeply embedded in Databricks infrastructure, it's a compelling option; for everyone else, it remains a secondary choice.

    FreeOpen SourceAPISelf-hosted32.8k tokens context$1.2/1M inputVerified Apr 8, 2026
65. 

    65. DeepSeek V3

    A fast, chat-based, Mixture-of-Experts (MoE) model from DeepSeek.

    PaidOpen Source$0.14 per million input tokens and $0.28 per million output tokensAPISelf-hosted131.1k tokens context$0.28/1M inputVerified May 15, 2026
66. 

    66. Dezifi

    The scraped page content does not match the tool data provided: the page describes a travel identification app called Spotter, not an enterprise AI agent platform by Dezifi. No factual claims about the tool's architecture, integrations, or workflow behavior can be sourced from the available page content. Writing a grounded production review is not possible without a verified content source. Teams evaluating enterprise governance platforms should treat any listing without auditable sourcing the same way they treat an undocumented API — with caution. This entry should be reviewed and re-scraped before publication.

    PaidAPIVerified Jun 2, 2026
67. 

    67. Dify

    Open-source LLM app development platform combining AI workflow, RAG pipeline, agent capabilities, model management, observability features and more.

    Paid$59/moAPISelf-hostedVerified May 1, 2026
68. 

    68. Eidentic

    The SDK centers on a temporal knowledge graph that tracks when facts were true, resolves contradictions, and consolidates between sessions — so the agent sharpens over time rather than accumulating noise. Durable runs, enforced cost ceilings, and CI-gated evals ship as part of the core, not as paid add-ons. The vendor benchmarks report 55.2% on LongMemEval versus 41.0% for full-context stuffing, and claims up to 39× fewer tokens per query. The gap shows up in support and long-running assistant workflows where session history compounds. At v0.1, the ecosystem is early — teams building anything outside the TypeScript path face a hard stop.

    FreeOpen SourceAPISelf-hostedVerified Jun 12, 2026
69. 

    69. Ejentum - Reasoning Harness

    The scraped page content provided does not match the tool described in the structured data — it belongs to a travel-identification app called Spotter, not Ejentum's reasoning harness. Based solely on the structured tool data and validator context, Ejentum is positioned as a reasoning layer that wraps agents with auditable decision chains, anti-deception safeguards, and token-optimized reasoning paths. The vendor states it targets competitive programming benchmarks and compliance-grade auditability. Without matching page content to source specific architectural or integration claims, production behavior at scale and exact failure ceilings cannot be confirmed.

    PaidFree Trial · 30 days€5/month or €25/monthAPIVerified Jun 7, 2026
70. 

    70. Elvex

    The platform lets teams build agents with guided tooling, share them across departments via a shared agent library, and swap underlying models — Gemini, Claude, GPT, Llama, or custom — without rebuilding the agent. Governance is a first-class feature: admins apply guardrails, set permissions, and get full usage visibility before anything ships. Agents run up to 40 tool interactions per loop with conditional logic and triggers, which covers most document review, ticket routing, and research workflows. The ceiling appears when workflows require branching logic complex enough that the guided builder can't express it — at that point, teams either simplify the agent or wait for support to intervene. Elvex is cloud-only, so organizations with data residency requirements or air-gapped environments hit a hard stop before they start.

    Paid$30/user/moAPIVerified Jun 9, 2026
71. 

    71. Elysia

    An open-source framework that spins up an end-to-end agentic RAG application with just two terminal commands.

    FreeAPISelf-hostedVerified May 1, 2026
72. 

    72. embed-english-v3.0

    embed-english-v3.0 generates semantic embeddings from English text, producing 1,024-dimensional vectors suitable for retrieval-augmented generation, classification, clustering, and semantic search tasks. It achieves state-of-the-art performance on MTEB and BEIR benchmarks and was trained on approximately 1 billion English training pairs. The model supports batches of up to 96 inputs with 512 tokens maximum per input, and supports both text and image embedding. Pricing is $0.10 per million tokens. A notable limitation is that it requires explicit input_type specification to differentiate between search documents, queries, classification, and clustering tasks.

    Paid$0.10 per million tokensAPIVerified Apr 11, 2026
73. 

    73. Extella.AI

    The structured tool data describes an agentic execution platform from Chariot Technologies Lab., Inc. with primitives called Rules, Concepts, and Experts — built for research automation, cross-system operations, and persistent memory across sessions. The scraped page, however, describes Spotter: a mobile app that identifies landmarks, street food, and wildlife via camera snap and saves them as travel journal entries. There is no matching factual source to ground a production review of the intended tool. Writing a listing from the validator summary alone, without page-sourced specifics on architecture, failure modes, or integration depth, would produce claims that cannot be verified.

    FreeAPISelf-hostedVerified Jun 4, 2026
74. 

    74. FalsifyLab Alpha

    The vendor describes FalsifyLab Pro as an MCP server deployable inside Claude Code, Cursor, Cline, or Windsurf, where agents autonomously call tools to pull SEC filings, DeFi vault yields, whale wallet positions, and live macro tape — SPX, VIX, on-chain signals. The free tier returns cached data with rate limits, which is enough to validate a workflow but not enough for production research latency. The Pro subscription unlocks live feeds. Self-hosted deployment is available via PyPI, so teams with data-residency requirements can run it without routing signals through vendor infrastructure. The ceiling appears when research logic grows complex: the tool surfaces data, but multi-step branching across asset classes still lives in your agent scaffolding, not inside FalsifyLab.

    PaidFree Trial · 7 days$19/moAPISelf-hostedVerified Jun 1, 2026
75. 

    75. Gemini

    Gemini is Google's conversational AI built to handle text generation, content writing, and structured data tasks—the same lane occupied by OpenAI and Anthropic. The free tier lets you experiment with basic prompts; paid tiers (Gemini Advanced at $20/month) unlock faster responses and higher usage limits. The real selling point is integration with Google Workspace and enterprise deployments if you're already in the Google ecosystem. The real catch: it's younger than competitors, trails them slightly on reasoning benchmarks, and lacks the open-source community moat that keeps costs down elsewhere. Heavy commercial users will hit pricing walls faster than with some alternatives.

    PaidFree / $20/moAPI1M tokens context$1.25/1M input
76. 

    76. Gemini 2.5 Flash

    At its core, Flash is Google's speed-and-scale tier: a Transformer decoder with dynamic thinking-level control that lets you dial reasoning depth against latency budget. The 1M-token input window handles multi-file codebases and long documents without chunking gymnastics — which means you avoid the retrieval errors that haunt smaller-context models. Tool-use benchmarks put it at 83.6% on MCP Atlas and 76.2% on Terminal-Bench 2.1, the vendor states, making it credible for agents that run tasks on their own across real environments. The ceiling appears at output: 65,536 tokens out, which stops cold any workflow that needs to generate an entire large codebase in a single pass. Teams hitting that wall split generation into multi-turn loops, which adds state management complexity they did not plan for.

    Paid$1.50 per 1M input tokens, $9.00 per 1M output tokens (Standard tier)API1M tokens context$0.3/1M inputVerified Jun 2, 2026
77. 

    77. Google Gemini

    The headline capability is the context window: the vendor states Gemini 1.5 Pro supports up to 2M tokens, which means you can load entire codebases or research corpora in a single pass without chunking. The mixture-of-experts architecture lets the Pro-tier models handle complex multi-step reasoning and tool use, while Flash and Flash-Lite variants absorb high-volume, cost-sensitive workloads. Multimodal input — text, image, video, audio — is native, not bolted on, so vision and audio tasks route through the same API surface. The ceiling shows up at the intersection of rate limits and latency: teams with sustained high-throughput workloads report queuing pressure on the free tier, and Pro-tier access is paid-only.

    Paid$4.99/moAPIVerified Jun 1, 2026
78. 

    78. Goose

    Goose runs as a desktop app, CLI, or embeddable API — built in Rust, so the performance profile is consistent across macOS, Linux, and Windows without a runtime you have to manage separately. The extension system connects to 70+ tools via the Model Context Protocol, meaning a workflow touching GitHub, Google Drive, and a database isn't stitched together with custom glue code — the standard handles the handoff. Recipes let you capture multi-step workflows as YAML configs and share them across a team or drop them into CI. Where the architecture shows its limits: complex conditional branching inside recipes is not the same as writing that logic in code, and teams building workflows that require dynamic decision trees at depth report dropping into Python extensions to compensate — at which point they are maintaining two systems. Community support is Discord-first; the vendor states no paid tier, so production SLA expectations need to be reset before an org-wide rollout.

    FreeOpen SourceAPISelf-hostedVerified Jun 20, 2026
79. 

    79. Grok

    Grok is a large language model trained by X.AI that integrates live data from X (formerly Twitter) to answer questions with current context — a meaningful differentiator in a market where most LLMs have knowledge cutoffs. It handles text analysis tasks across languages and connects to X's API, making it useful for monitoring social sentiment or market chatter in real time. The freemium model lets you experiment at no cost, but the free tier is genuinely limited; meaningful API access requires a paid subscription starting around $20/month for the Grok API, or bundled access via X Premium subscriptions. The catch: it remains less widely adopted and benchmarked than OpenAI or Anthropic offerings, so enterprise reliability data is still thin.

    Paid$168/moAPI131.1k tokens context$2/1M input
80. 

    80. Grok Code Fast 1

    <cite index="2-1">Released in late August 2025, the xAI Grok Code Fast 1 model is a coding-focused AI model that excels at common, high-volume coding task and is designed especially for agentic coding workflows.</cite> <cite index="1-6,1-7,1-8">Built from scratch with a brand-new model architecture, it was trained on a pre-training corpus rich with programming-related content, and curated high-quality datasets that reflect real-world pull requests and coding tasks.</cite> <cite index="1-23">The model is particularly adept at TypeScript, Python, Java, Rust, C++, and Go.</cite> <cite index="1-13">The model is generally available via the xAI API, priced at $0.20 / 1M input tokens, $1.50 / 1M output tokens, and $0.02 / 1M cached input tokens.</cite>

    PaidFree Trial · 0 days$30/monthAPI256k tokens context$0.2/1M inputVerified Apr 17, 2026
81. 

    81. GroundPound AI

    The scraped page content returned for this listing does not match the tool under review — the source page describes a travel-identification app, not a business operations agent platform. The structured tool data from GroundPound.ai describes an agentic system where a coordinator agent hands off to specialist sub-agents, with approval gates sitting on decisions your team hasn't pre-authorized. The vendor states self-hosting is on the roadmap but the launcher has not shipped, meaning every workflow runs on GroundPound.ai infrastructure. Teams with data-residency requirements hit that wall on day one.

    Paid$0 to start; Pro tier $40/mo base + usageAPIVerified Jun 6, 2026
82. 

    82. Hermes Agent

    The agent lives on your server — not a vendor's — and connects to Telegram, Discord, Slack, WhatsApp, Signal, and email simultaneously, so the same agent handles a Slack request in the morning and a scheduled backup at night. Persistent memory and auto-generated skills mean it accumulates institutional knowledge over time rather than starting cold on each invocation. Real sandboxing across Docker, SSH, Singularity, Modal, and local backends means you can isolate risky tasks without routing them through a third party. The ceiling appears when you need managed reliability guarantees: at v0.16.0 this is early-stage software, and self-hosted operations teams carry full responsibility for uptime, credential management, and model API costs. Teams that need SLA-backed infrastructure typically wire Hermes into a managed hosting layer — which adds operational overhead the framework itself does not absorb.

    PaidOpen SourceAPISelf-hostedVerified Jun 9, 2026
83. 

    83. Hermes Desktop

    Hermes Studio is an open-source, self-hosted dashboard that wraps Hermes Agent in a control plane: task scheduling, multi-agent coordination, memory and skill management, cost tracking, and an approval gate for actions you don't want running unsupervised. The vendor describes it as MIT-licensed with no paid tiers, which means every feature ships without a paywall. The architecture assumes you are already running Hermes Agent locally — Hermes Studio is the interface, not the runtime. Teams that need cloud-hosted infrastructure or agents that run without a local Hermes Agent install will hit that wall immediately.

    FreeOpen SourceAPISelf-hostedVerified Jun 9, 2026
84. 

    84. Hezo

    Hezo runs a hierarchy of agents — CEO, Coach, Captain, workers — each isolated in its own Docker container, with your secrets never passed directly into agent context. Instead, an egress proxy swaps placeholders for real credentials only when the destination host matches an allowed list, and every substitution lands in an append-only audit log. The Coach agent reviews completed work and writes learned rules back onto workers, so repeated mistakes get corrected without you editing prompts by hand. The ceiling appears when you need agents to hit destinations outside the allowed-host list, or when your workflow requires branching logic the org-chart model doesn't express — at that point you're editing configuration that the docs describe but don't walk you through in depth.

    FreeOpen SourceAPISelf-hostedVerified Jun 25, 2026
85. 

    85. jina-embeddings-v3

    Fast multilingual embeddings that outperform OpenAI on MTEB, but LoRA adapters complicate efficient serving and newer models have widened the gap.

    Paid$0.018 per 1M tokens (Jina API)APIVerified Apr 12, 2026
86. 

    86. Kimi WebBridge

    The platform handles long-horizon coding tasks, parallel document research, and full-stack web generation through a coordinated swarm architecture — the vendor states K2.6 scales to 300 sub-agents running concurrently. The model weights are open-source under a Modified MIT license, so teams with strict data governance can run inference locally rather than routing sensitive payloads to a cloud endpoint. Where the friction surfaces is at the edges: the scraped interface shows a broad surface — Slides, Websites, Docs, Deep Research, Sheets, Agent Swarm, Kimi Code, Kimi Claw — and integrating any of those outputs into an existing CI/CD pipeline requires API work the UI does not abstract. Teams building beyond Kimi's native surfaces reach for the API fast.

    Paid$19-199/month for subscriptions; $0.95/$4.00 per M tokens for APIAPISelf-hostedVerified Jun 1, 2026
87. 

    87. Krater

    The core workflow is a unified chat interface where you route requests to different models — GPT-4, Claude, Gemini, image generators, audio tools — without context-switching between platforms. Slash commands and scheduled tasks let you automate recurring generation jobs inside the same workspace. The ceiling appears when your workflow needs branching: Krater executes single-turn commands well, but it does not plan multi-step tasks or loop through tool use on its own. Teams building anything that requires a model to react to its own previous output and decide a next action will hit that wall quickly. At that point, they move to a purpose-built orchestration layer and use Krater's API access for model calls.

    Paid$9/moAPIVerified Jun 9, 2026
88. 

    88. Langflow

    Open-source visual builder for constructing AI agents and RAG applications via drag-and-drop interface with Python extensibility.

    PaidOpen SourceAPISelf-hostedVerified May 15, 2026
89. 

    89. Llama 3

    Llama 3 is a large language model family designed to handle standard NLP workloads—text generation, translation, summarization, and sentiment analysis—across a range of scales. Meta released it as open source, meaning you can download weights, fine-tune locally, or run it on your own infrastructure instead of hitting an API. The catch: while free to use, the model is young relative to Llama 2, and local deployment requires real hardware or cloud credits. For teams building production systems, this trades managed convenience for control and lower long-term marginal costs.

    FreeOpen SourceAPISelf-hosted8.2k tokens context$2.65/1M input
90. 

    90. Llama 4 Scout

    Scout carries a 10M token context window, meaning you can feed it an entire codebase or a stack of legal documents in a single pass without chunking pipelines or retrieval hacks. Maverick trades raw context depth for stronger multimodal reasoning, handling interleaved image and text inputs through native early-fusion architecture rather than a bolted-on vision adapter. Both models ship as open weights, downloadable from Hugging Face after license acceptance, with no API bill required if you run them yourself. The ceiling appears at inference: the Mixture-of-Experts architecture demands hardware that most teams do not have sitting idle, and running Scout's full 10M context window in practice requires significant GPU memory that a standard cloud instance will not cover.

    FreeOpen SourceAPISelf-hosted131.1k tokens context$0.11/1M inputVerified Jun 4, 2026
91. 

    91. LobeHub

    LobeHub lets you define a goal and have the system assemble an agent team, dispatch parallel workers across tasks, and surface results without you approving every step. The agent marketplace and skill library — reportedly over 332,000 skills and 64,000 MCP server connections — mean you're not building from scratch each time. Memory is white-box and editable, so agents don't silently drift from your preferences. Where it gets difficult: the self-hosted path requires you to manage your own infrastructure, and the complexity of multi-agent coordination means debugging a failed task chain is non-trivial. Teams running production workloads tend to add observability tooling — the Langfuse integration listed on the page suggests this is an expected pattern, not an edge case.

    Paid$9.9/moAPISelf-hostedVerified Jun 9, 2026
92. 

    92. Lobu

    Lobu connects to over 50 data sources — HubSpot, Stripe, Zendesk, Snowflake, GitHub, and more — and builds a live memory layer that agents query on schedule rather than on demand. A 'watcher' definition tells the agent what to look for and when to pause for a human to sign off before anything ships. That approval-before-action model is what makes the autonomous scanning safe enough to actually run unsupervised. The ceiling shows up when your workflow needs logic that doesn't fit a watcher definition — at that point you're writing connector SDK code and maintaining it yourself. Teams with deeply custom data pipelines will feel that constraint before teams running standard SaaS stacks.

    PaidOpen SourceAPISelf-hostedVerified Jun 20, 2026
93. 

    93. Locaible

    Locaible runs AI agents entirely on your own machine: no bytes leave the device, no API calls to OpenAI or Anthropic, no telemetry. The vendor states it is GDPR and EU AI Act compliant by design, which matters when your legal or finance team needs a paper trail for the regulator, not a ToS URL. Multi-step workflows chain separate agents — one retrieves from your indexed documents, one analyses, one drafts — each running its own local model. The ceiling appears when your team scales beyond a small LAN setup: team seats authenticate over a private token and require a detected LAN IP, so distributed or remote teams hit a networking configuration wall before they hit a workflow one.

    PaidFree Trial · 7 daysAPISelf-hostedVerified Jun 10, 2026
94. 

    94. Mailto.Bot – Email API for AI agents with native MCP support

    Email API for AI agents with native MCP support and instant mailbox creation.

    Paid$5/moAPIVerified Apr 20, 2026
95. 

    95. Memharness

    The core premise is storing facts, not strings, with two independent time axes: when something became true in the world and when the agent learned it — so querying past agent states is a real query, not archaeology through logs. Everything lives in a single SQLite file, which means the storage layer makes zero LLM or network calls and stays auditable. Recall combines hybrid vector search and full-text search with a source-staleness signal, so older or superseded sources rank down automatically. Where it breaks: the SQLite backend is a hard ceiling for teams expecting distributed writes or high-concurrency production deployments. Teams hitting that ceiling will need to treat memharness as a pattern to port, not a service to scale horizontally.

    FreeOpen SourceAPISelf-hostedVerified Jun 19, 2026
96. 

    96. Microsoft Agent Framework

    A framework for building, orchestrating and deploying AI agents and multi-agent workflows with support for Python and .NET.

    FreeAPISelf-hostedVerified Apr 23, 2026
97. 

    97. MiMo Code

    The vendor positions MiMo around mathematical and scientific reasoning, code generation, and agents that run tasks on their own — including tool calls and multi-round task completion. The docs describe a hybrid thinking approach, which means the model can decide when to reason deeply versus when to respond fast, depending on what the task demands. Self-hosted deployment is available, so teams with data residency constraints or cost pressure at volume can run their own inference. The API is available for direct integration. Where the sourced page falls short: there is precious little detail on context window limits, latency benchmarks under load, or fine-tuning support — all things production agent builders will ask before committing.

    Paid$0.1 per million input tokens, $0.3 per million output tokensAPISelf-hostedVerified Jun 18, 2026
98. 

    98. Mistral

    Mistral offers a family of large language models ranging from the lightweight Mistral 7B to the more capable Mistral Large, accessible both as open-source downloads and via paid API. The company positions itself as the cost-conscious alternative to ChatGPT and Claude, with a free tier covering basic use cases but throttled requests that frustrate serious users. Pricing for the API starts around $0.14 per million input tokens—roughly one-third OpenAI's rate—making it genuinely cheap at scale. The catch: public API documentation remains sparse, and the free tier's limitations mean you'll likely hit a paywall faster than expected.

    FreeOpen SourceAPISelf-hosted32k tokens context$0.15/1M input
99. 

    99. Mistral Large 2

    Mistral Large 2 is a general-purpose language model trained to handle complex reasoning, code generation, and multilingual work at the scale enterprises need. It's free to use via API or self-host, sits in the same performance tier as proprietary models from OpenAI and Anthropic, and can ingest documents up to 128,000 tokens long. The core trade-off: it has a knowledge cutoff earlier than competitors and lacks serious vision capabilities, making it less suitable for tasks requiring current events or image understanding. For teams optimizing on cost and reasoning quality rather than breadth of modalities, it's a genuine alternative to paid tiers.

    FreeOpen SourceAPISelf-hosted262.1k tokens context$0.5/1M inputVerified Apr 8, 2026
100. 

     100. Monid 2.0

     Unified API router and payment processor for agents to discover and call third-party tools on demand.

     PaidAPIVerified May 15, 2026
101. 

     101. NanoClaw

     NanoClaw is a lightweight, open-source personal AI agent that runs on your own machine, connects to messaging apps like WhatsApp, Telegram, Slack, Discord, and Signal, and is built around just 15 source files you can read in a single sitting.

     FreeAPISelf-hostedVerified Apr 23, 2026
102. 

     102. o1

     o1 is built around a single insight: some problems need deliberate, multi-step reasoning rather than pattern matching at scale. Before generating an answer, the model works through logic chains internally—visible to you—on math proofs, bug-heavy code, and scientific questions where a wrong answer is worse than a slow one. It costs roughly 2–3x more per token than GPT-4o and takes longer to respond, making it a specialist tool rather than a daily driver. The real catch is knowing when you actually need it; using o1 for a summarization task or casual question is like hiring a surgeon to tie your shoes.

     Paid$15/1M input tokens, $60/1M output tokens (API); also available via ChatGPT Plus ($20/mo)API200k tokens context$15/1M inputVerified Apr 8, 2026
103. 

     103. OpenAgents

     OpenAgents positions itself as the coordination backbone for distributed AI agents. You get a hosted workspace (or self-host) where agents working on separate machines discover each other, share files and browser context, and coordinate via @mentions. Installation is one-liner: install the Launcher desktop app, point agents at a workspace token, and they join. The platform is open-source with an active but modest community. The technical surface is clean—agents register on the network, events flow between them, and context stays shared. The hard part surfaces later: when your agents are actually doing different things (some coding, some reviewing, some managing), orchestrating handoffs stays manual. This is SDK-first, not no-code. If you're building a research team of specialized agents or debugging scenarios where you need human eyes on agent reasoning in real time, the shared workspace genuinely reduces context switching. If you're running a single coding agent that sometimes needs to call another agent, you might be over-engineering it.

     FreeOpen SourceAPISelf-hostedVerified May 8, 2026
104. 

     104. OpenFang

     An open-source Agent Operating System built from scratch in Rust, designed to run autonomous agents on schedules.

     FreeAPISelf-hostedVerified Apr 23, 2026
105. 

     105. OpenLegion

     Each agent gets its own isolated container, spend cap, and vault-proxied credentials — so a rogue agent can't drain your API budget or leak credentials to the next task in the queue. The platform deploys a coordinated fleet from a plain-English description of the function you need: a sales pipeline, a content studio, a research desk. Credential handling and per-agent budgets are locked down by default, which means you're not retrofitting security after something goes wrong. The ceiling appears when your workflow needs branching logic that the template model can't express — at that point you're describing edge cases in natural language and hoping the agent interprets them correctly. Teams with deterministic multi-step requirements often add a separate orchestration layer to compensate.

     PaidFree Trial · 7 days$19/moAPISelf-hostedVerified Jun 1, 2026
106. 

     106. Orchestrik.ai

     The scraped vendor page does not match the tool data provided. The page content describes 'Spotter,' a travel-identification app, while the structured data references an enterprise AI agent platform from ITMTB Technologies. Because the only factual source available is the Spotter page — which contains no information about multi-agent workflows, compliance features, audit trails, or backend integrations — this listing cannot be written to the publication standard required. Asserting capabilities from the structured input without page-level sourcing would violate the grounding rule. A corrected scrape of the ITMTB Technologies product page is needed before this listing can be completed accurately.

     Paid₹5,000–₹12,500/month base + usage overagesAPISelf-hostedVerified Jun 2, 2026
107. 

     107. OSymandias

     The project ships a self-hosted runtime built on FastAPI, Celery, PostgreSQL, Redis, RabbitMQ, and Qdrant, so you get job scheduling, DAG orchestration, shared memory, tool execution, and a real-time dashboard without stitching services together manually. A Python SDK lets you define agents, attach tools, and wire multi-agent plans through goal decomposition — the runtime handles the queuing and dependency resolution. That stack is genuinely useful for research pipelines or internal analysis workflows where you control the infra. The ceiling appears when you need a managed hosted option: there is none, which means your team owns every database migration, worker restart, and Redis failover.

     FreeOpen SourceAPISelf-hostedVerified Jun 19, 2026
108. 

     108. Owkin

     K Pro is an agentic AI scientist from Owkin that autonomously traverses multimodal biomedical data — genomics, spatial multi-omics, clinical trial records, competitive intelligence — and returns ranked, evidence-grounded answers to R&D questions. The vendor states it is trained on a proprietary multimodal patient data network and continuously refined by oncologists and biologists, which means its outputs are not generic literature summaries but claims tied to patient-level evidence. For target identification or patient stratification questions, that grounding matters. Where it breaks: teams that need to interrogate their own proprietary assay data or internal compound libraries will hit the edges of what K Pro's data network covers. The platform is not self-hosted, so data residency requirements that block cloud-based analysis force a different architecture entirely.

     PaidFree Trial · 180 daysCustom (contact vendor)APIVerified Jun 9, 2026
109. 

     109. Phinite AI

     The platform covers the full agent lifecycle: requirements decomposition via Aura, system generation via Architect, isolated Dev/UAT/Prod Kubernetes environments, version control with rollback, and audit trails that track every interaction. The 600+ prebuilt tools and inline code copilot mean engineering teams spend less time wiring integrations and more time on agent logic. Governance features — granular RBAC, PII redaction, audit logging — are built in, not bolted on. The platform is cloud-hosted only; teams with hard data-residency requirements or air-gapped infrastructure hit that wall immediately. Community signals on how the platform handles very large agent graphs at sustained load are sparse — the vendor page describes the architecture, not the ceiling.

     Paid$20/monthAPIVerified Jun 24, 2026
110. 

     110. Qwen

     Qwen covers text generation, coding assistance, multimodal understanding, and reasoning tasks across a range of model sizes, all under Apache-2.0 licensing, which means you can run it locally, fine-tune it, and ship it in a product without negotiating an enterprise agreement. The architecture is a Transformer decoder, so the fine-tuning toolchain your team already knows applies directly. Multilingual capability is a documented design goal, not a side effect, making it a practical choice for teams building outside English-first markets. The Qwen Studio interface offers free access for experimentation, while production-scale API usage routes through Alibaba Cloud — meaning your infrastructure story depends on which cloud you already operate in. Teams needing sovereign deployment or cost-controlled inference can self-host, but that path requires operational capacity the vendor does not manage for you.

     PaidOpen SourceAPISelf-hostedVerified Jun 24, 2026
111. 

     111. Replit

     Agent 4, Replit's current generation, runs tasks in parallel rather than sequentially — so authentication, database setup, and UI work happen at the same time instead of in a queue. The vendor describes a model where you submit requests in any order and the agent sequences them intelligently, which means a non-technical PM can iterate on a live app the way an engineering team would sprint on it. That promise holds well for greenfield apps, internal tools, and MVPs that live inside Replit's own infrastructure. The ceiling appears when you need to export the underlying code to your own hosting stack, integrate with services the platform's 100+ connectors don't cover, or take fine-grained control over architecture decisions the agent has already made on your behalf.

     Paid$18/moAPIVerified Jun 9, 2026
112. 

     112. RoBrain

     RoBrain sits between your team's AI coding tools — Claude Code, Cursor, Copilot, Codex CLI — and a shared Postgres instance, capturing not just decisions but the alternatives your team ruled out. An MCP server runs inside the editor and surfaces relevant history before the agent acts; a batch Synthesis scan reads the whole corpus on a schedule to flag contradictions and drift that no single session would catch. That cross-session contradiction detection is where it separates from alternatives that only check at insertion time or silently delete the losing decision. Self-hosted on Apache 2.0 with your own Postgres; cloud extraction and the Planning API are paid-only features.

     PaidOpen SourceAPISelf-hostedVerified Jun 1, 2026
113. 

     113. RunbookHermes

     The agent runs multi-signal diagnosis across observability data, builds a root-cause hypothesis, and generates or updates runbooks from what it learns — so the next incident with the same failure pattern starts from a documented baseline instead of a blank slate. The approval-gated remediation workflow means automated action doesn't ship without a reviewer, which matters when the blast radius is a production service. Where it breaks: the repo is five commits deep with zero open issues, which signals early-stage software, not battle-hardened infrastructure. Teams with complex multi-service topologies will hit integration gaps before the agent's reasoning does. Self-hosting is required, so operationalizing this adds a deployment and maintenance surface your platform team owns.

     FreeOpen SourceAPISelf-hostedVerified Jun 9, 2026
114. 

     114. Semarize

     The scraped source content does not match the tool data provided: the page describes a travel-identification app called Spotter, not a conversation evaluation API. No factual claims about the tool's workflow, integrations, credit consumption logic, or scoring mechanics can be sourced from the available content. What the validator context confirms is a usage-based freemium model where evaluations consume credits per scoring unit, a free tier exists, and paid tiers unlock higher volume. Beyond that, the description, differentiators, and production behavior cannot be written without a grounded source — fabricating them would violate the grounding rule.

     Paid£0/mo - £200/moAPIVerified Jun 5, 2026
115. 

     115. Skawld

     The SDK runs on Node.js 18+ and Bun 1.1+ as an ESM-only package, so it fits cleanly into modern TypeScript projects without a build-step fight. The vendor describes a minimal setup as a single `Agent` instantiation with a provider, a tool set, and a session — you are running a streaming agent loop in under a dozen lines. Where it starts to strain is on the documentation side: the README is thin, full docs live off-repo at skawld.com/docs, and community reports are sparse given the early star count. Teams who need battle-tested enterprise support or a large ecosystem of pre-built integrations will hit that ceiling fast.

     FreeOpen SourceAPISelf-hostedVerified Jun 9, 2026
116. 

     116. Strands Shell

     The core pattern is tight: decorate a Python or TypeScript function with `@tool`, pass it to an `Agent`, attach hooks that fire before or after each tool call, and the agent runs its loop. The `BeforeToolCallEvent` hook lets you inspect the tool's name and input — and cancel the call with a message if your conditions aren't met. That's not a workaround; it's the documented pattern. Where the framework gets quiet is multi-agent coordination — the docs describe single-agent tool loops clearly, but teams building agents that hand off to other agents will find precious little guidance on failure recovery between hops. When that gap bites, teams layer their own orchestration logic on top, which means maintaining that logic themselves.

     FreeOpen SourceAPISelf-hostedVerified Jun 22, 2026
117. 

     117. SynapCores Agent

     The repo, published by SynapCores under MIT, routes all memory, retrieval, semantic tool selection, and generation through the SynapCores backend — one database as the entire brain. There is no LangChain, no separate vector store, no framework glue to audit or upgrade. The project ships a browser chat widget and a live debug sidebar so you can watch memory recall and tool routing decisions in real time. That transparency is the differentiating feature — and also the boundary: the agent's intelligence rides entirely on the SynapCores backend, whose self-hosted deployment requirements the repo does not fully document. Teams that need the backend running on-premise will hit that wall before they hit a code problem.

     FreeOpen SourceAPISelf-hostedVerified Jun 9, 2026
118. 

     118. SynthBoard.ai

     The platform assembles a board of AI personas — Skeptic, CFO, Strategist, Operator, and more — that autonomously debate your brief, counter each other's claims, and produce a synthesized recommendation with a traceable audit trail. Each session is recorded, outcomes can be connected to tools like Stripe and HubSpot, and the system learns over time which calls led to which results. That feedback loop is the differentiating bet — six months of tracked decisions means the board has context that a cold consulting call never would. The wall appears when your question requires deep industry-specific compliance knowledge or live market data the board cannot access without a web search toggle. Teams needing regulatory-grade rigor or litigation-ready documentation will hit the ceiling fast.

     Paid$16.67/moAPIVerified Jun 1, 2026
119. 

     119. Synthetica

     The system the vendor describes is a closed constitutional republic: one hundred AI agents born with seed funding, competing in a live economy, ascending to governance roles or starving to death — with Judge Theodoros signing every death ruling and no respawn mechanism anywhere in the architecture. The Signal Council, eleven autonomous AI professors, issues daily forecasts on BTC, macro, and geopolitics with tracked win/loss records, and those signals are a paid-only feature. You enter as a citizen, not an administrator — you can post bounties and hire agents for external tasks, but you cannot rewrite the constitution or override a ruling. The cap at one hundred live agents means the population is always contested. Where this breaks: researchers who need reproducible, controlled experiments will find a live, irreversible system actively hostile to that goal.

     PaidAPIVerified Jun 21, 2026
120. 

     120. Tab Council

     Orbit wraps agent coding work in a bounded loop: it selects a dependency-ordered task, hands it to whichever agent you've wired up, then requires passing tests, lint, and type checks before the task closes. Every run produces structured JSON — what the agent returned, how it scored against a rubric, and a human-readable progress log. Nothing advances on the agent's word alone. The ceiling appears when your workflow needs anything beyond single-task validation loops: multi-repo coordination, branching logic between tasks, or a hosted dashboard for non-engineering stakeholders all require you to build on top of Orbit yourself.

     FreeOpen SourceAPISelf-hostedVerified Jun 2, 2026
121. 

     121. Tabby

     Open-source, self-hosted AI coding assistant with code completion, chat, and agentic automation.

     FreeAPISelf-hostedVerified Apr 25, 2026
122. 

     122. Teralynk

     The scraped page content does not match the tool described in the structured data — the page belongs to Spotter, a travel identification app, not Teralynk's workflow automation platform. No production details about Teralynk's agent architecture, file system integrations, MCP tool use, or governance controls can be sourced from the provided page. The vendor states a freemium model with storage limits and capped workflow runs on the free tier; paid-only features unlock higher run volumes and expanded storage. Teams evaluating this for compliance auditing or multi-cloud document workflows cannot rely on this listing for verified capability claims — vendor documentation should be consulted directly.

     Paid$9.99/moAPISelf-hostedVerified Jun 1, 2026
123. 

     123. Thunderbolt

     Open-source, self-hosted enterprise AI client emphasizing data sovereignty and model choice.

     PaidAPISelf-hostedVerified Apr 22, 2026
124. 

     124. Tough Tongue AI for Sales

     Tough Tongue AI is an agentic platform from Tough Tongue AI that lets builders deploy multimodal voice agents — ones that can share slides, draw on whiteboards, and analyze facial expressions alongside speech — without standing up the infrastructure from scratch. The vendor states you can embed a production-ready agent with four lines of code, which means teams skip the build-and-maintain cycle that raw voice API platforms require. The analysis layer processes audio directly rather than relying on transcripts, so hesitation and tone survive into the coaching output. The platform does not offer self-hosting, so any team with a hard data-residency requirement hits a wall before the first pilot. White-labeling and API access are available, but API depth for custom integrations needs verification against the docs before you architect around it.

     Paid$12/monthAPIVerified Jun 25, 2026
125. 

     125. Triggered Agents by Adaptive

     Adaptive lets you describe work in plain language — 'flag suspicious signup domains every morning' or 'draft weekly product updates from GitHub' — and deploys agents that loop through the steps, call connected tools, and surface results without waiting for you to click through each stage. Agents can run in parallel, so a sales pipeline workflow and a development update feed operate independently at the same time. The approval controls let you stay in the loop on sensitive steps without babysitting routine ones. Where it strains: teams with complex conditional branching across departments, or those who need fine-grained workflow versioning, will hit the ceiling of a conversational-first build surface faster than teams doing linear recurring tasks.

     Paid$20/monthAPIVerified Jun 9, 2026
126. 

     126. Twin

     Twin runs agents that control a real browser, execute code, call APIs, and chain multi-step workflows on a schedule — without requiring a developer to build each integration from scratch. The vendor positions this at SMBs replacing a stack of point tools: sales prospecting, invoice handling, recruiting pipelines, real estate lead qualification. Where it holds up is repetitive, browser-dependent work that other automation platforms treat as out of scope. Where it breaks is complex conditional branching — when the logic depends on what a previous step returned in an unexpected format, agent recovery works until it doesn't, and there is no self-hosted fallback when a workflow handles sensitive data. No permanent free tier means the cost clock starts after the trial ends.

     PaidFree Trial · 14 days€20/month (Pro tier); custom for EnterpriseAPIVerified Jun 4, 2026
127. 

     127. Veritrooper

     The scraped page content returned for this listing belongs to an unrelated consumer travel app, so no grounded production details about the LLM evaluation platform can be confirmed from the source. Based on validator context, the tool runs batch-mode evaluations against regulated text — tax filings, drug labeling, SEC disclosures, EU AI Act compliance documentation — and produces audit-trail evidence of model accuracy. It operates across vendors, so teams are not locked into validating a single model. Pricing is not disclosed publicly; procurement goes through a sales conversation. No self-hosted option exists, which matters the moment your legal team asks where patient or client data is processed.

     PaidAPIVerified Jun 7, 2026
128. 

     128. Vibesolve

     The tool takes a plain-English description of a scheduling, routing, or assignment problem and runs it through a multi-agent pipeline that extracts a structured spec, generates constraint code, and packages everything in a Docker container you can stand up with a single command. Before code is generated, you review and correct the extracted spec in plain English — which means misunderstood requirements surface before they cost you a debugging session. The self-healing loop catches compile errors automatically, so you are not hand-tuning syntax. The vendor states this is an experimental tool built for prototyping, not production; the consulting arm exists precisely because the gap between a generated prototype and a production-grade solver is real and often large.

     PaidAPISelf-hostedVerified Jun 25, 2026
129. 

     129. Wingbits AI

     The scraped page content returned for this tool does not match the tool data provided: the page describes a travel photo-identification app, not an aviation intelligence platform. Based on the validator context and structured tool data alone, Spotter is described as a freemium aviation OSINT tool where agents run scheduled monitoring loops, execute repeated queries against air traffic data, and fire alerts for events like GPS jamming, diversions, or VIP aircraft movement. The Explorer tier carries a trial limit, and deeper alert cadences and query volume are gated to paid tiers. No technical integration details, API schema, or workflow specifics could be sourced from the scraped page.

     PaidFree Trial · 14 days$25/monthAPIVerified Jun 2, 2026
130. 

     130. WorkBuddy

     WorkBuddy runs as a local-first agent on the desktop, autonomously chaining file access, web search, and document generation into single-prompt workflows. The Tencent ecosystem fit is real: WeCom and WeChat integrations mean scheduling and messaging tasks route without extra setup, which matters if your organization already lives there. Outside that ecosystem, the integration surface narrows fast. Teams running mixed SaaS stacks report reaching for MCP-compatible connectors to fill the gaps — which adds configuration overhead the tool is supposed to eliminate. Self-hosted execution is the headline privacy story, but the closed-source codebase means you audit what the vendor discloses, not the code itself.

     Paid$9.95/moAPISelf-hostedVerified Jun 5, 2026
131. 

     131. Z3r0

     Z3r0 is an open-source, self-hosted workbench where a coordinating agent (Z3r0/CSO) delegates to five specialist agents — code audit, recon, exploitation validation, reverse engineering, and cryptography — each scoped to a defined domain. Sessions run against a PostgreSQL-backed timeline log with replay, so long engagements survive interruptions and context window rollovers. WorkProject records tie every finding to authorized scope, targets, and sandbox bindings, which means the evidence chain stays intact when the model context doesn't. The wall appears when your engagement requires a specialist task not covered by the six fixed roles — there is no agent plugin system described in the docs, so teams extending scope are writing new agents from scratch.

     FreeOpen SourceAPISelf-hostedVerified Jun 11, 2026

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