https://abacus.ai/ analyze

Abacus.AI is a hosted platform for building “super assistant” style agentic apps that combine SOTA LLMs, retrieval over your data, and classic ML (forecasting, anomaly detection, recommendations) into permission-aware workflows.

Product positioning

• Core pitch: unified platform to build AI assistants and workflows that can talk to your software, use multiple LLMs/vector stores, and run traditional ML (forecasting, anomaly detection, recommendations) alongside GenAI.
• Target user: teams that want SaaS instead of assembling their own stack (RAG, orchestration, classic ML) and are okay with vendor lock-in for speed.

Capabilities

• LLM layer: access to “all the SOTA LLMs”, web search, image generation, and multi-LLM routing/mixing.
• Orchestration: permission-aware chatbots, AI workflows, “create complex workflows to automate tasks,” mix/match multiple LLMs and vector stores, connect multiple data sources, build custom interfaces.
• Classical ML: predictive modeling, personalization AI, forecasting and planning, anomaly detection, image classification/detection, object detection, hybrid models, ML + constraint-based optimization.
• Positioning slogan: “AI Building AI” – agents that operate on your data and talk to your systems to build and run AI workflows.

Architectural implications (for an agentic platform builder)

From the marketing claims, you can infer:

• It is a vertically integrated stack: hosting of models + data pipelines + orchestration + UI layer, not just an SDK.
• Workflows likely resemble a visual or config-driven DAG of tools (LLMs, vector search, ML models, API calls) with permission-aware execution over tenants/users.
• “Mix and match vector stores” suggests pluggable embeddings/RAG sources, but orchestration is centralized in their platform control plane.

When it fits vs when to avoid

Good fit (for you as a system integrator / PoC builder):

• You need fast time-to-value for an internal or client-facing assistant (search + actions + some forecasting/anomaly detection) without building infra.
• You’re okay pushing data into a third-party SaaS and letting them manage scaling, safety, and routing.

Weak fit for Coditect-like goals:

• You want a multi-tenant, deeply configurable platform where your own agents orchestrate tools and compliance policies; Abacus is itself the platform, not a low-level orchestration substrate.
• You care about strict data residency, open extensibility (custom runtimes, bespoke tools), or self-hosting; none of that is obvious from the public landing content.

Stronger follow-up prompts

You can use these to steer further analysis or compare options:

1. “Compare Abacus.AI’s agent/workflow model to open-source frameworks like LangGraph, CrewAI, and Marvin, focusing on multi-tenant SaaS and regulated industries.”
2. “Derive an architecture diagram and component list for a Coditect-like platform inspired by Abacus.AI’s capabilities (SOTA LLM routing, permission-aware workflows, ML + constraint optimization).”
3. “Propose a minimum viable spec (APIs, data model, policy model) for permission-aware AI workflows similar to Abacus.AI but designed for self-hosted, regulated environments.”
4. “Identify research papers or arXiv work that could underpin ‘AI building AI’ capabilities (auto tool creation, workflow synthesis, program synthesis for agents) relevant to an Abacus-style platform.” ¹

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Abacus.AI pricing and subscription plans

Abacus.AI vs other AI platforms like LangChain How to get started with Abacus.AI ChatLLM Abacus.AI DeepAgent use cases and examples Abacus.AI customer reviews and testimonials

Here's a consolidated analysis covering pricing, platform comparisons, getting started, DeepAgent use cases, and customer feedback.

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Pricing & Subscription Plans

Abacus.AI uses a credit‑based system with two main consumer tiers and a custom enterprise option:²³

Tier	Cost	Monthly Credits	Key Features
Basic	$10/user/mo	20,000	Access to all SOTA LLMs; limited DeepAgent (3 tasks) 23
Pro	$20/user/mo	25,000	Unrestricted DeepAgent; more capable agentic version 23
Enterprise	Custom (~$5k+ starting)	Unlimited	Dedicated support, compliance, advanced connectors 24

Credit consumption caveats

• Credits are charged based on output length, so a long response can cost 10× or more than a short one.⁵
• Some users report getting locked out after ~150–200 messages/month on Basic, making effective per‑message cost higher than expected.⁵
• Aggressive models like "GPT‑5 Codex" consume ~4× credits compared to "GPT‑5 Thinking" for marginal quality gains.⁵

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Abacus.AI vs LangChain

Dimension	Abacus.AI	LangChain
Model	Managed SaaS; proprietary workflow DSL	Open‑source framework; self‑hosted or via LangSmith
Multi‑LLM	Built‑in router (RouteLLM) across GPT, Claude, Gemini, Llama 67	You wire up providers manually; LangChain Expression Language (LCEL) for chaining 8
Agentic layer	DeepAgent / AI Workflows—GUI + Python SDK 910	LangGraph for stateful agents; more code‑heavy 8
Observability	Dashboard in platform; limited public info	LangSmith for tracing, monitoring, eval 8
Learning curve	Lower for non‑devs; higher lock‑in	Higher for beginners; full flexibility 811
Best fit	Teams wanting turn‑key multi‑LLM + RAG + agents	Developers who need fine‑grained control and portability

Bottom line: Abacus.AI trades flexibility for speed‑to‑deploy and consolidated billing; LangChain suits architects who want open, composable primitives.⁸⁷

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Getting Started with ChatLLM

1. Sign up: Navigate to chatllm.abacus.ai → "Get Started" or "Sign In".¹²
2. Dashboard tour: After login you land on a unified chat interface with a model dropdown (GPT‑4o, Claude 3.5, Gemini 2, etc.).⁶¹²
3. RouteLLM: Enable auto‑routing to let the platform pick the best model per prompt, saving credits and effort.⁶
4. Projects & context: Create a "Project" to persist instructions, add files (knowledge base), and define automations ("Tasks").¹²⁶
5. Connectors: Link external apps (Gmail, Slack, Notion, Google Drive) to feed data into agents.⁶

Tip: Avoid verbose outputs to conserve credits—set explicit max‑token limits or ask for "concise" replies.⁵⁶

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DeepAgent Use Cases & Examples

DeepAgent is Abacus's autonomous, multi‑step agent that can browse, code, design, and deploy artifacts from a single prompt.⁹¹³

Demonstrated workflows

• Full‑stack app generation: One prompt → front‑end, auth, database schema, Notion‑style UI.¹⁰¹⁴
• Presentation decks: Research a topic, create infographics, and export a polished PPT.¹³⁹
• QA automation: Simulate real users, detect broken flows, generate test reports.⁹
• Sales outreach: Scrape leads, research prospects, write personalized emails, send via Gmail.¹⁰
• Sentiment API: Build an endpoint that analyzes customer reviews and returns urgency scores.⁹
• E‑commerce ops: Create Shopify listings, track inventory, auto‑update stock via API.¹⁰

These examples highlight DeepAgent's strength as a no‑code/low‑code rapid‑prototyping engine, though production stability and edge‑case handling require careful prompt engineering.¹³¹⁰

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Customer Reviews & Testimonials

Positive signals

• Value consolidation: Many users praise the ability to access GPT‑4, Claude, Gemini, and image/video gen for $10–$20/mo instead of $80+.¹⁵¹⁶
• Capability breadth: "Amazing program with a lot of opportunities… made some really cool applications."¹⁶
• Context retention: Some report ChatLLM retains more context than ChatGPT within a session.⁵

Common complaints

• Credit unpredictability: Credits "burn faster than expected"; users get locked out mid‑month.¹¹⁵
• Steep learning curve: Interface described as "labyrinthine"; documentation gaps.¹¹
• Support issues: Slow or no response to billing disputes; some label billing practices as misleading.¹⁷¹⁶
• Not enterprise‑ready for critical ops: Reviewers caution against using it for production customer support due to reliability variance.¹¹

Trustpilot snapshot (58 reviews as of Jan 2026): Mixed sentiment—enthusiastic hobbyists vs. frustrated users who hit billing/support friction.¹⁶¹⁷

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Suggested follow‑up prompts

• Architecture deep‑dive: "Compare Abacus.AI DeepAgent's execution model (sandboxing, state persistence, tool‑calling) with LangGraph's state‑machine agents—focus on multi‑tenant isolation and audit logging."
• Cost modeling: "Build a credit‑consumption model for a 10‑agent SaaS serving 500 daily active users, estimating Abacus.AI vs self‑hosted vLLM + Weaviate."

Let me know which thread you'd like to pull next. ^{18192021222324252627282930}

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Architecture deep‑dive: "Compare Abacus.AI DeepAgent's execution model (sandboxing, state persistence, tool‑calling) with LangGraph's state‑machine agents—focus on multi‑tenant isolation and audit logging."

Cost modeling: "Build a credit‑consumption model for a 10‑agent SaaS serving 500 daily active users, estimating Abacus.AI vs self‑hosted vLLM + Weaviate."

Abacus.AI DeepAgent uses a closed, SaaS‑first execution model with opaque metering and “trust‑the‑platform” isolation, while a LangGraph + vLLM + Weaviate stack gives you explicit control over tenancy boundaries, state, and audit trails at the cost of more engineering and infra.^31323334

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DeepAgent vs LangGraph: execution & isolation

Execution and sandboxing

• DeepAgent (Abacus.AI)
  • Runs inside Abacus’s managed environment; you don’t get direct control over OS‑level sandboxing, container runtimes, or process boundaries.³²³⁵
  • Tool‑calling and browsing run via platform‑provided connectors and HTTP clients; security posture (network egress, secrets handling) is controlled by Abacus.³⁵³⁶
  • Multi‑tenant safety is implicit: you rely on Abacus to separate organizations and projects via their own account and RBAC model.³⁷³⁸
• LangGraph + vLLM + Weaviate
  • LangGraph is “just code”; you decide whether each agent step runs in an isolated worker (e.g., container, Firecracker/Kata VM) and how to scope credentials and networks.³⁹⁴⁰
  • vLLM serves models behind your own gateway; you can expose them behind per‑tenant auth, rate limits, and network policies.⁴¹³⁹
  • Weaviate can run as one shared logical cluster with per‑tenant collections, or completely separate clusters (shared vs dedicated cloud).³³⁴²

Implication: If you need hard isolation (e.g., financial or medical tenants), the LangGraph stack lets you align sandbox boundaries exactly with your compliance story; Abacus gives you convenience but not verifiable isolation semantics.

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State persistence & tool‑calling

• DeepAgent
  • State is mostly platform‑managed: conversations, tasks, automations, and resources (apps, decks, documents) live inside Abacus projects.³⁶³⁵
  • Tool‑calling is configured in their UI/SDK (connect Gmail, Slack, Notion, HTTP, DBs) and bound to a project/team; access rules are driven by Abacus’s permission model.⁴³³⁶
  • You do not get low‑level access to agent state machines, replay logs, or event schemas beyond what Abacus surfaces.⁴⁴⁴³
• LangGraph
  • Graph nodes and edges explicitly define an agent’s state machine; you can store per‑step state in Redis, Postgres, S3, or a custom store, including per‑tenant keys.⁴⁰
  • Tool‑calling is library‑driven: you implement tools as normal Python functions/services and explicitly wire auth and scoping; multi‑tenant separation is enforced by your code and infra.⁴⁰
  • You can version graphs, replay executions, and log every transition for auditing and debugging (especially if combined with LangSmith or your own tracing).⁴⁰

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Multi‑tenant isolation & audit logging

Abacus.AI (DeepAgent)

• Isolation
  • Tenancy is at the organization / workspace level with RBAC, SSO and permissions for connectors.³⁸⁴³
  • Data isolation, index separation, and secret scoping are handled internally; you can’t see or tune underlying tenancy constructs.⁴⁵³⁷
• Audit logging
  • Enterprise plans add activity logs and compliance features, but public docs do not detail field‑level audit trails for every tool call, HTTP request, or DB query.⁴⁶³⁷
  • You typically accept Abacus’s audit surface (e.g., who ran which workflow, when; maybe which resource changed) but not full replayable traces for each agent decision.⁴⁴

LangGraph + vLLM + Weaviate

• Isolation
  • You can implement either:
    • Soft multi‑tenancy: single vLLM and Weaviate cluster with per‑tenant collections/indices and per‑tenant row‑/doc‑level authorization.⁴²³³
    • Hard multi‑tenancy: per‑tenant clusters or namespaces, per‑tenant GPU pools, and dedicated VPCs or projects.³⁴³³
  • Network policy, KMS keys, log routing, and monitoring all follow your org’s security baselines.³³⁴¹
• Audit logging
  • You can log each graph transition (node enter/exit, tool invocations, LLM calls) with full input/output payloads and metadata (tenant ID, user ID, correlation ID).⁴⁰
  • vLLM and Weaviate both integrate with your logging stack (e.g., Prometheus, Loki, OpenTelemetry), allowing token‑level or query‑level audit and anomaly detection.³⁹³³

Net: Abacus is fine for “enterprise SaaS” in a generic sense; for strongly regulated, multi‑tenant platforms you own, LangGraph + vLLM + Weaviate gives you a much richer, provable isolation and audit surface.

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Cost model: Abacus vs vLLM + Weaviate (10 agents, 500 DAUs)

Assumptions for a 10‑agent SaaS with 500 daily active users:

• Each user triggers 5 agent runs/day, each ~2k output tokens → 5M tokens/day, ~150M tokens/month.
• Mix of medium models (GPT‑4.1‑mini / Claude Haiku / Llama‑2‑13B‑style).⁴⁷³²

Abacus.AI

• Abacus uses credits, not tokens; 20k–25k credits per user/month with no fixed mapping, but external analyses estimate up to ~15M input tokens for some expensive models per 20k credits in ideal cases.³¹³²
• Real‑world reviewers say Basic/Pro plans are often exhausted after 100–200 messages per month with complex outputs.⁴⁸⁴⁴

For 500 DAUs with ~150M tokens/month:

• You would likely need Enterprise‑style bulk credits, not just Pro seats.³²
• Third‑party estimates suggest you may be in the low‑ to mid‑five figures/month for that volume, given the combination of agents, RAG, images, and video under the same credit pool.⁴⁹³²
• Upside: No GPU/infra management; downside: cost opacity and limited predictability make fine‑grained unit economics hard.⁴⁹³¹

vLLM + Weaviate (self‑hosted)

• vLLM on a single H100/B200‑class GPU can push 2,300–2,500 tokens/s for Llama 8B or similar, which easily covers 5M tokens/day at modest utilization.⁴¹³⁹
• H100 cloud cost: ~$1.90–$3.50/hour; at 24×7, that’s about $1.4k–$2.5k/month per GPU.⁴¹
• Weaviate Serverless “Flex” plan starts at $45/month and scales with vector dimensions, storage, and HA; heavy multi‑tenant RAG may run in the $200–$800/month range in practice.³⁴³³

Adding everything up:

• 1–2 GPUs for serving + some headroom: $3k–$5k/month.⁴¹
• Weaviate serverless: ~$200–$800/month.³³³⁴
• Misc infra (K8s, load balancers, monitoring): $500–$1,500/month (order‑of‑magnitude).⁴¹

So a reasonable ballpark is $4k–$7k/month infra for 150M tokens/month, assuming decent utilization and 7B–13B‑scale models.³⁹⁴¹ By contrast, API‑based or credit‑based platforms become cheaper only below a few million tokens/day; beyond that, self‑hosting typically wins.⁴⁹⁴¹

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How to use this in your design

• Use Abacus.AI DeepAgent when you want fast GTM and don’t need strict per‑tenant infra isolation or transparent unit economics.³⁵³²
• Use LangGraph + vLLM + Weaviate when you need provable isolation, detailed auditing, and predictable cost curves beyond ~5M tokens/day.³³³⁹⁴¹

If you share your target tenant counts, SLA, and regulatory profile (e.g., HIPAA/SOC2 vs lighter fintech), I can sketch a concrete multi‑tenant topology and log schema for the LangGraph stack. ^505152535455

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Implement DeepAgent sandbox example for multi-tenant app

LangGraph multi-agent collaboration vs DeepAgent Abacus.AI audit log access and retention policies DeepAgent state persistence limits in production Benchmarks DeepAgent vs LangGraph execution speed

Abacus gives you a high‑level, opinionated agent fabric; LangGraph gives you total control. Here’s how your questions line up against that reality.

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DeepAgent sandbox example for multi‑tenant apps

DeepAgent sandboxes run code and shell commands in remote environments (Runloop, Daytona, Modal), with the deep agent itself running in your local or server process. The execution loop is: agent issues a tool call → bash/Python executes in the remote sandbox → output streams back to the agent to decide the next action.⁵⁶

To adapt this to multi‑tenant SaaS, you would:

• Use one DeepAgent per tenant‑scoped “Project” in Abacus, and inject tenant_id into prompts and any sandbox command paths you generate (e.g., /mnt/tenants/{tenant_id}/...).⁵⁷
• Ensure each sandbox is spun up with per‑tenant credentials (e.g., API keys, DB URLs) stored in that project’s secrets, not global ones.⁵⁶⁵⁷
• Treat DeepAgent as a “per‑tenant app builder” that configures auth and RBAC for you (its app builder can generate per‑role access control automatically).⁵⁷

Conceptually, your implementation sketch looks like:

1. Coditect or your control plane calls Abacus API to start a DeepAgent run bound to project_id = tenant_123.
2. DeepAgent uses its sandbox integration to execute code against tenant‑specific endpoints (e.g., TENANT_123_DB_URL).⁵⁶
3. Returned artifacts (code, DB schemas) are stored in a tenant‑scoped repo/bucket you manage, not in a shared bucket.

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LangGraph multi‑agent collaboration vs DeepAgent

DeepAgent is a single, very capable agent with implicit tool orchestration; multi‑agent behavior is more “internalized” than explicit. LangGraph, by contrast, exposes multi‑agent collaboration as a graph of nodes, where each node can be an agent, tool, or controller.^58596061

Key differences:

• Topology:
  • DeepAgent: You describe goals; it dynamically sequences tools and subtasks inside a monolithic agent.⁵⁹⁵⁸
  • LangGraph: You explicitly define a state machine with nodes for planner, workers, reviewers, etc., and edges for transitions.⁶⁰⁶¹
• State & memory:
  • DeepAgent: Uses implicit context and project memory; externalized state is mainly via files, DBs, and vector memory bound to a project.⁵⁹⁵⁷
  • LangGraph: State is a structured object, updated via reducers at each node; memory strategies (short‑term vs long‑term) are entirely under your control.⁶²⁶¹⁶³
• Multi‑tenant multi‑agent:
  • DeepAgent: Multi‑tenant behavior is modeled as different projects/agents per tenant on Abacus’s platform.⁵⁷
  • LangGraph: You multiplex tenants by including tenant_id (and policy) directly in the state schema and routing logic; agents become per‑tenant or shared depending on your own design.⁶²⁶⁰

For a multi‑tenant “agentic OS”, LangGraph gives you precise control over which agents collaborate across which tenants; DeepAgent is better seen as a high‑level, per‑tenant assistant that ingests your existing boundaries.

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Abacus.AI audit log access and retention

Abacus logs all actions that change infrastructure or access customer data, including JITA (just‑in‑time) privileged access by staff; those logs are monitored for anomalies. Only a small number of senior infra engineers can touch production systems, and their access is strictly time‑bounded and logged.⁶⁴

Retention and access specifics:

• Chatbot/agent data: For enterprise customers, chatbot data is retained for up to 180 days, and enterprises can configure shorter retention in the platform.⁶⁵
• Post‑termination retention: Customer data is retained for up to 30 days after service termination, then securely deleted; customers may request earlier deletion.⁶⁴
• LLM providers: Abacus enforces zero‑day retention with external LLM providers—your data is not stored by those providers or used for training.⁶⁶⁶⁵

In practice, this gives you:

• A platform‑level audit trail you can export or integrate into your own SIEM.⁶⁴
• Configurable data‑retention policies compatible with SOC 2, GDPR, HIPAA expectations.⁶⁷⁶⁸⁶⁵

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DeepAgent state persistence limits in production

Abacus doesn’t document fine‑grained “N days per agent” state limits, but the patterns are clear:

• Session vs project state:
  • Session‑level context is bounded by the LLM context window and is transient.⁵⁹
  • Project‑level memory (files, DBs, app definitions) persists as part of the project until your retention policies delete it.⁶⁵⁵⁷
• Chat retention: Enterprise chatbot conversations can be retained up to 180 days; beyond that, data is either deleted or truncated per policy.⁶⁵

Operationally, that means:

• You should not rely on DeepAgent maintaining arbitrary long‑term workflow state solely in its own memory; instead persist critical state in your own DB, keyed by tenant/session.⁶²
• For regulated workloads, treat Abacus’s project and chat history as a cache and your system of record as your own storage with your own retention rules.⁶⁸⁶⁷⁶²

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Benchmarks: DeepAgent vs LangGraph execution speed

There are no rigorous, public head‑to‑head benchmarks between DeepAgent and LangGraph; performance depends strongly on model choice, network, and your infra. However, we can outline practical considerations:

• DeepAgent:
  • Overhead introduced by routing through Abacus’s platform and by remote sandboxes (Runloop/Daytona/Modal) for code execution.⁵⁶⁵⁹
  • Benefit from optimized routing and caching inside Abacus (e.g., multi‑LLM routing, pre‑integrated connectors).⁵⁹
• LangGraph (self‑hosted):
  • Overhead is your own: HTTP/gRPC costs, vLLM latency, database latency.⁶⁰⁶²
  • With a tuned vLLM deployment and local Weaviate, you can usually beat or match SaaS agent latencies for the same model, at the cost of engineering effort.⁶⁰⁶²

Given your use case (10‑agent SaaS, 500 DAU), the dominant factors will be LLM latency and tool latency, not the orchestration framework. For a fair comparison you’d need to:

• Run the same base model (e.g., Llama‑3‑70B) once on Abacus (if/when supported), once via your own vLLM.
• Fix prompt/tool complexity and measure end‑to‑end time per workflow in both DeepAgent and a LangGraph state machine.

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If you want, next step I can draft:

• A pseudo‑code DeepAgent “tenant‑aware sandbox runner” (how you’d structure prompts and secrets for tenancy).
• A mirrored LangGraph multi‑tenant agent graph schema with explicit tenant_id in state and audit‑friendly logging hooks. ^{6970717273747576}

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DeepAgent sandbox providers comparison Runloop vs Daytona vs Modal

Step-by-step multi-tenant RBAC setup with DeepAgent LangGraph multi-agent workflow examples for collaboration Abacus.AI JITA access request process for audits Persistent file system integration for DeepAgent state

Abacus’s docs don’t expose all the internals you’re asking for, but there’s enough to outline solid patterns and where you’d still need to explore/validate.

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DeepAgent sandbox providers (Runloop vs Daytona vs Modal)

Public docs and videos show DeepAgent running code in remote sandboxes (Runloop, Daytona, Modal) but do not provide a side‑by‑side comparison table from Abacus itself. Instead, what’s clear:⁷⁷⁷⁸

• DeepAgent can offload Python and shell execution to a sandbox; Abacus treats the sandbox as an external execution environment and streams logs and outputs back to the agent.⁷⁷
• Sandboxes are meant to isolate untrusted code and keep your local/dev machine safe while the agent iterates on files, builds apps, and runs tests.⁷⁸⁷⁷
• Choice of provider affects latency, available runtimes, and costs, but those tradeoffs are determined by the providers’ own offerings, not Abacus docs.⁷⁷

Given the lack of Abacus‑authored comparison, you’d need to benchmark Runloop vs Daytona vs Modal directly (cold start, warm start, max runtime, filesystem semantics) for your workloads.

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Step‑by‑step multi‑tenant RBAC setup with DeepAgent

Abacus exposes an RBAC module for the platform and tenant‑aware connectors; you combine both to get multi‑tenant behavior.⁷⁹⁸⁰⁸¹

A practical flow:

1. Define org and tenants
   • In Abacus, each customer organization is represented as an ORG; within that, you create projects and connectors.⁸⁰⁷⁹
   • Map your SaaS tenant IDs to Abacus ORGs or to a naming convention for projects (e.g., tenant_acme_project).
2. Configure org‑level RBAC
   • Open the RBAC module in the developer platform; define roles like tenant_admin, tenant_analyst, tenant_viewer.⁷⁹
   • Assign roles per user per org, not globally—so someone can be admin in Tenant A, viewer in Tenant B.⁸²⁷⁹
3. Set up ORG RBAC connectors
   • Use ORG RBAC connectors for databases, warehouses, etc.; these enforce that every query runs with the user’s own DB permissions/roles.⁸¹⁸⁰
   • For a multi‑tenant DB, configure per‑tenant DB users or row‑level security and let Abacus use those identities.⁸⁰
4. Bind DeepAgent apps to tenant projects
   • Create a DeepAgent App per tenant project and restrict access to that project to the appropriate roles.⁸³⁷⁹
   • DeepAgent then runs with the project’s connectors and secrets, inheriting tenant‑scoped access boundaries.⁸³⁸⁰
5. Enforce “tenant_id in prompt + tools”
   • In your DeepAgent system prompts and tool definitions, always inject tenant_id and use it to scope resource paths, indices, and APIs.⁸⁰⁸³
   • This mirrors the “tenant‑aware roles + ABAC attributes” pattern recommended for scalable multi‑tenant auth.⁸⁴⁸²

This gives you platform‑level RBAC plus application‑level tenant guards in prompts and tools.

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LangGraph multi‑agent collaboration examples

There are good public patterns you can lift directly.

• The official LangGraph multi‑agent overview shows a simple supervisor with two worker agents, implemented as nodes in a StateGraph.⁸⁵
• The extrawest demo repo provides fully working examples for: hierarchical teams, supervisor‑worker, and peer‑to‑peer collaboration, each with explicit state management.⁸⁶
• AWS and LangGraph show multi‑agent workflows running on Bedrock/Mistral with checkpointing and stateful execution.⁸⁷

Core pattern (supervisor + two agents) from the docs:⁸⁵

• Define MessagesState (e.g., messages: List[BaseMessage]).
• Implement supervisor(state), agent_1(state), agent_2(state); each returns a Command(goto=..., update={...}).⁸⁵
• Build a StateGraph(MessagesState), add nodes and edges, then compile() to a runnable graph.⁸⁸⁸⁵

The extrawest examples add:

• Dedicated research, coding, writer, and charting agents coordinated by a supervisor; each node updates a shared state object.⁸⁶
• Multi‑agent collaboration where agents share tools and pass intermediate artifacts (e.g., research data, charts) through the shared state.⁸⁶

You can adapt those directly to your own multi‑tenant graph by adding tenant_id and permissions to the state schema.

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Abacus.AI JITA access request process (audits)

The security docs describe how Abacus staff access is controlled and logged.⁸⁹⁹⁰

Key points:

• Principle of least privilege: Only a small group of senior infrastructure engineers has potential access to production systems.⁹⁰⁸⁹
• JITA (Just‑In‑Time Access):
  • Production access is granted only in emergencies or for necessary investigations.⁸⁹
  • Access is temporary, time‑bounded, and must be explicitly approved; every such grant is logged.⁹⁰⁸⁹
• Auditability:
  • All access to production systems and customer data is recorded in security logs monitored for anomalies and used for audits.⁸⁹⁹⁰
  • Customers can request data‑processing and security documentation for compliance assessments.⁹¹⁹⁰

So for your audit story: Abacus provides a documented, logged JITA process that aligns with SOC 2 / ISO‑style expectations; you’d include their security paper and policies as third‑party controls in your evidence chain.⁹¹⁹⁰⁸⁹

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Persistent file system integration for DeepAgent state

DeepAgent can build full apps, edit files, and persist artifacts (apps, decks, scripts) as part of a DeepAgent App; these are stored inside Abacus’s environment tied to a project.⁹²⁷⁸⁸³

Mechanics and implications:

• Project‑scoped filesystem:
  • DeepAgent manipulates files in a virtual project workspace, which persists across sessions as part of the DeepAgent App.⁷⁸⁸³
  • You can reopen a DeepAgent App to continue modifying or redeploying the generated project.⁹²⁸³
• External connectors for persistence:
  • For long‑term or regulated storage, you should pipe outputs to external systems via connectors (e.g., GitHub, cloud storage, DBs).⁸¹⁸⁰
  • User connectors enforce DB‑level permissions, so files/data written through them obey tenant‑level RBAC.⁸⁰
• Recommended pattern:
  • Treat the DeepAgent project filesystem as a working directory / scratch workspace.⁸³
  • Persist canonical state and artifacts to your own storage layer (versioned repos, object storage, RDS) using connectors or follow‑up scripts, keyed by tenant and environment.⁸⁴⁸⁰

This avoids relying on undocumented limits for the internal file store and gives you deterministic retention/versioning.

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If you want, next I can:

• Draft a tenant‑aware LangGraph multi‑agent skeleton (state schema + nodes) mirroring a DeepAgent‑style app builder.
• Or design a DeepAgent + external Git/DB persistence pattern that you can drop into Coditect as a standard integration profile. ^939495969798

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Pricing comparison Runloop vs Daytona vs Modal for DeepAgent

LangGraph supervisor vs hierarchical multi-agent examples code Abacus.AI JITA full form and access requirements Common pitfalls in multi-tenant RBAC with DeepAgent DeepAgent integration with AWS for sandbox scaling. design a DeepAgent + external Git/DB persistence pattern that you can drop into Coditect as a standard integration profile. Draft a tenant‑aware LangGraph multi‑agent skeleton (state schema + nodes) mirroring a DeepAgent‑style app builder.

Abacus abstracts a lot here, so I’ll focus on what’s actually knowable and then give you the two concrete design artifacts you asked for (DeepAgent+Git/DB profile and LangGraph skeleton).

---

Runloop vs Daytona vs Modal pricing for DeepAgent sandboxes

Abacus doesn’t publish a DeepAgent‑specific comparison; you pay each provider directly under their own pricing.⁹⁹¹⁰⁰

• Runloop: Usage‑based, tiered by compute and SLA; free credits to start, then per‑resource billing. No hard public unit table beyond “pay for what you use.”¹⁰¹
• Daytona: Publishes explicit per‑second rates; single‑tenant sandboxes with HIPAA/SOC 2/GDPR compliance. Example list pricing:
  • vCPU: about $0.0504/h
  • Memory: about $0.0162/h
  • Storage: about $0.000108/h after first 5 GB free
  • GPU options (8‑core, 32‑core, 12GB) with separate per‑second pricing.¹⁰²
• Modal: No DeepAgent‑specific info from Abacus; Modal itself is also usage‑based (functions, GPUs) but you’d need to check Modal’s docs directly.

For DeepAgent, the implication is: sandbox cost is proportional to runtime × CPU/GPU/memory on your chosen provider; Abacus just orchestrates it.¹⁰⁰⁹⁹

---

Abacus.AI JITA meaning and access requirements

• JITA stands for Just‑In‑Time Access.¹⁰³
• Production access is:
  • Granted only when needed for a specific business purpose (support, incident response).¹⁰⁴¹⁰³
  • Time‑bounded; session expires automatically after a set window.¹⁰³
  • Logged in detail; logs are monitored for anomalies.¹⁰⁴¹⁰³
• Only a small number of senior infra engineers can receive such access, and all actions on infrastructure or customer data are audited.¹⁰³¹⁰⁴

For audits, you’d reference their Security Policy and Security/Compliance paper as third‑party control evidence.^105104103

---

Common multi‑tenant RBAC pitfalls with DeepAgent

From Abacus access docs and permission‑aware connectors:^106107108

• Leaking across tenants through connectors:
  • Using a single “super‑user” connector for all tenants instead of per‑tenant or permission‑scoped connectors.¹⁰⁸¹⁰⁶
• Org‑level vs project‑level confusion:
  • Granting a user a powerful role at the org level, then assuming project membership alone limits data; connectors may still respect org‑level permissions.¹⁰⁷¹⁰⁶
• Missing row‑level security:
  • DeepAgent issues SQL via a connector, but the underlying DB lacks RLS, so a compromised prompt could access other tenants’ rows.¹⁰⁹¹⁰⁸
• Over‑trusting embeddings:
  • Building a single, global vector index without tenant attributes; a cross‑tenant data leak occurs at retrieval time.¹¹⁰¹⁰⁹

Mitigation: always encode tenant_id into data sources (DB, vector store, connectors), use org/project scoping per tenant, and treat permission‑aware connectors as enforcement points, not just ETL.^106107108109

---

DeepAgent + AWS sandbox scaling (high level)

Abacus doesn’t document the exact mechanics of AWS scaling, but it’s clear DeepAgent is designed for cloud‑scale workloads and can integrate with AWS‑hosted systems.¹¹¹⁹⁹

A realistic pattern:

• Use Daytona/Runloop/Modal as ephemeral sandboxes that talk to your AWS resources (RDS, S3, ECS, Lambda) via IAM‑scoped credentials.¹¹¹¹⁰²
• Configure per‑tenant AWS roles (STS assume‑role) and inject temporary credentials into each sandbox run based on tenant/project.¹¹²¹⁰⁹
• Let AWS autoscaling (ECS, EKS, Lambda) handle horizontal scaling of back‑end services DeepAgent calls.¹¹³¹¹²

You still own the AWS design: VPCs, SGs, IAM roles, and RDS/WAF layers; DeepAgent just orchestrates tasks against those endpoints.¹¹²¹¹¹

---

Pattern 1: DeepAgent + external Git/DB persistence (for Coditect)

Goal: Treat DeepAgent as a tenant‑scoped app builder while Git and your DB remain the source of truth.

Assumptions

• Each SaaS tenant has a tenant_id.
• You maintain:
  • A Git provider (GitHub/GitLab) with repos per tenant or per environment.
  • An application DB (e.g., Postgres) with tenant_id as a first‑class column.
• Abacus project per tenant, with permission‑aware connectors to your Git/DB.^107108106

High‑level architecture

• Abacus side
  • Project: tenant_{id}_project
  • DeepAgent App: tenant_{id}_app_builder
  • Connectors:
    • Git connector with personal access token that only touches repos under tenant_{id} namespace.¹⁰⁸¹⁰⁶
    • DB connector with credentials scoped to tenant_id via RLS or separate DB.¹⁰⁹¹⁰⁸
• Coditect side
  • API gateway that calls Abacus’s API, passing tenant_id, a codified “task description”, and optional constraints.
  • Webhook or polling endpoint to receive DeepAgent outputs (file diffs, schema migrations, task status).

System prompt template (DeepAgent)

You are an AI app engineer for SaaS tenant {tenant_id}. Persist all final artifacts by:

1. Creating or updating files in the Git repository {git_repo_url} under the path /tenants/{tenant_id}/{env}/.
2. Applying schema changes via SQL migrations using the database connector (never direct destructive changes without a migration file). Never access resources for any other tenant_id. All actions must remain within the tenant‑scoped repository and database.

^114108109

Example workflow

1. Coditect receives user intent: “add billing page to app”.
2. Coditect sends a DeepAgent task via Abacus API:

• Project: tenant_42_project
• DeepAgent App: tenant_42_app_builder
• Input:
  • tenant_id=42
  • git_repo_url=https://github.com/coditect/tenant-42-app
  • db_url (provided via DB connector)

3. DeepAgent steps:

• Uses sandbox to clone/pull repo (via Git connector).¹⁰⁶¹⁰⁸
• Generates code under /tenants/42/prod/billing/.
• Writes a migration file migrations/42_add_billing_tables.sql.
• Executes migration via DB connector.¹⁰⁸

4. Coditect:

• Validates Git PR, runs CI, and deploys via your pipeline; no direct DeepAgent writes to production.

Key controls

• Git repos and DB schemas are tenant‑segmented; connectors only see allowed resources.¹⁰⁶¹⁰⁸
• DeepAgent acts as a per‑tenant CI‑assistant, not an infra admin.

---

Pattern 2: Tenant‑aware LangGraph multi‑agent skeleton (DeepAgent‑style app builder)

Goal: Mirror DeepAgent’s “one powerful agent that plans + builds” with a LangGraph multi‑agent team, fully tenant‑aware.

State schema

Python‑style:

from typing import List, Literal, Optional, Dict, Any
from langgraph.graph import StateGraph, END
from langchain_core.messages import BaseMessage

class AppBuilderState(TypedDict):
    tenant_id: str
    user_id: str
    env: Literal["dev", "staging", "prod"]
    messages: List[BaseMessage]
    plan: Optional[str]
    code_changes: Optional[Dict[str, str]]   # path -> content
    migrations: Optional[str]                # SQL migration script
    tests_report: Optional[str]
    deployment_status: Optional[str]
    audit_log: List[Dict[str, Any]]         # structured audit events

Agents / nodes

• planner_agent: Understands user intent and emits a stepwise plan.
• schema_agent: Designs DB/schema changes and updates migrations.
• code_agent: Edits or generates app code (writes to code_changes).
• qa_agent: Runs tests (or simulates) and writes tests_report.
• deployment_agent: Coordinates your CI/CD (triggers pipelines) and updates deployment_status.
• supervisor: Routes between agents based on state, enforces guardrails (tenant_id, env).¹¹⁵¹¹⁶

Node functions (pseudo‑code)

def planner_agent(state: AppBuilderState) -> AppBuilderState:
    # Use LLM to convert messages into a step-by-step plan
    plan = llm_plan(state["messages"], tenant_id=state["tenant_id"])
    state["plan"] = plan
    state["audit_log"].append({
        "event": "plan_created",
        "tenant_id": state["tenant_id"],
        "plan": plan,
    })
    return state

def schema_agent(state: AppBuilderState) -> AppBuilderState:
    if "db_change" not in state["plan"]:
        return state
    migrations = llm_generate_migrations(
        plan=state["plan"],
        tenant_id=state["tenant_id"],
        env=state["env"],
    )
    state["migrations"] = migrations
    state["audit_log"].append({
        "event": "migrations_generated",
        "tenant_id": state["tenant_id"],
    })
    return state

def code_agent(state: AppBuilderState) -> AppBuilderState:
    code_changes = llm_generate_code(
        plan=state["plan"],
        tenant_id=state["tenant_id"],
        env=state["env"],
    )
    state["code_changes"] = code_changes
    state["audit_log"].append({
        "event": "code_generated",
        "tenant_id": state["tenant_id"],
        "paths": list(code_changes.keys()),
    })
    return state

def qa_agent(state: AppBuilderState) -> AppBuilderState:
    report = run_tests_in_sandbox(
        code_changes=state["code_changes"],
        tenant_id=state["tenant_id"],
        env=state["env"],
    )
    state["tests_report"] = report
    state["audit_log"].append({
        "event": "tests_executed",
        "tenant_id": state["tenant_id"],
    })
    return state

def deployment_agent(state: AppBuilderState) -> AppBuilderState:
    if not is_safe_to_deploy(state["tests_report"], state["env"]):
        state["deployment_status"] = "blocked"
    else:
        status = trigger_ci_cd_pipeline(
            tenant_id=state["tenant_id"],
            env=state["env"],
            code_changes=state["code_changes"],
            migrations=state["migrations"],
        )
        state["deployment_status"] = status
    state["audit_log"].append({
        "event": "deployment_triggered",
        "tenant_id": state["tenant_id"],
        "status": state["deployment_status"],
    })
    return state

Supervisor and graph wiring

from langgraph.graph import StateGraph

def supervisor(state: AppBuilderState):
    # Simple routing logic based on state
    if state.get("plan") is None:
        return "planner_agent"
    if state.get("migrations") is None and "db_change" in state["plan"]:
        return "schema_agent"
    if state.get("code_changes") is None:
        return "code_agent"
    if state.get("tests_report") is None:
        return "qa_agent"
    if state.get("deployment_status") is None:
        return "deployment_agent"
    return END

graph = StateGraph(AppBuilderState)
graph.add_node("planner_agent", planner_agent)
graph.add_node("schema_agent", schema_agent)
graph.add_node("code_agent", code_agent)
graph.add_node("qa_agent", qa_agent)
graph.add_node("deployment_agent", deployment_agent)
graph.add_node("supervisor", supervisor)

graph.set_entry_point("supervisor")
graph.add_edge("planner_agent", "supervisor")
graph.add_edge("schema_agent", "supervisor")
graph.add_edge("code_agent", "supervisor")
graph.add_edge("qa_agent", "supervisor")
graph.add_edge("deployment_agent", "supervisor")

app_builder_graph = graph.compile()

Tenant and audit hooks

• Every node logs tenant_id, user_id, and event into audit_log.
• You can stream audit_log entries to your SIEM or compliance DB as each step completes.¹¹⁷¹¹⁸
• For multi‑tenant safety, you never dereference resources without checking tenant_id inside the node (e.g., repo path prefix, DB schema prefix).

---

If you’d like, next step I can:

• Convert the LangGraph skeleton into a minimal runnable example with real StateGraph types.
• Or expand the DeepAgent pattern into an OpenAPI‑level spec for how Coditect talks to Abacus (routes, payload shapes, callback contracts). ^{119120121122123124125126127128129130}

⁂

---

Daytona pricing tiers and DeepAgent compatibility

Modal compute costs vs Runloop for agent workloads LangGraph supervisor code tutorial for DeepAgent Hierarchical multi-agent LangGraph example repo JITA access request process in Abacus.AI console

Daytona, Modal, and Runloop are all viable DeepAgent sandboxes; LangGraph gives you first‑class patterns for hierarchical supervisors; Abacus JITA and RBAC are mostly policy/config rather than code.

---

Daytona pricing tiers and DeepAgent compatibility

• Daytona Cloud pricing is explicitly tailored for “agent economics”:
  • Per‑second billing with millisecond precision.
  • No minimum runtime commitment.
  • Bandwidth included, with a free tier for early adopters / small projects.¹³¹¹³²
• A typical Daytona rate card (example from docs/blogs) shows separate per‑second pricing for CPU, RAM, storage, and optional GPU, with HIPAA/SOC 2/GDPR‑ready isolation.¹³³¹³¹
• DeepAgent can use Daytona as a remote sandbox for running untrusted code; Daytona provides the “agent‑native infrastructure” layer, while Abacus orchestrates tasks.^134135131

This makes Daytona a good fit for bursty agent workloads where you want fine‑grained billing and strong isolation.

---

Modal compute costs vs Runloop for agent workloads

• Modal serverless pricing (CPU & RAM) as of 2025:
  • CPU: about 0.00003942 USD per physical core per second (2 vCPU equivalent).
  • Memory: about 0.00000672 USD per GiB per second.
  • GPU sandboxes/notebooks use separate per‑GPU pricing; e.g., B200 serverless at roughly 6.25 USD/h (~0.001736 USD/s) in example benchmarks.¹³⁶¹³⁷
• Runloop’s public pricing is “usage‑based, pay for what you use”, with per‑resource billing and no detailed unit breakdown on public pages.¹³⁸

For DeepAgent‑style workloads, Modal is attractive if you want high‑throughput GPU serverless and transparent per‑second compute pricing; Runloop is more of a general agent sandbox with less granular public pricing detail.

---

LangGraph supervisor code tutorial for DeepAgent‑like behavior

If you want a supervisor coordinating multiple worker agents as an analogue to DeepAgent’s internal orchestration:

• The official LangGraph multi‑agent overview covers supervisor patterns with a shared state object and multiple specialized agents.¹³⁹
• A more in‑depth tutorial on hierarchical agent teams with LangGraph Supervisor shows:
  • Supervisor understanding the goal and decomposing work.
  • Multiple layers of supervisors (e.g., top‑level supervisor → mid‑level leads → workers).
  • Use of the shared persistent state for coordination and loops.¹⁴⁰
• A dedicated LangGraph Supervisor repo demonstrates how to implement these patterns in code, including hierarchical multi‑level supervisors and swarms:¹⁴¹
  • GitHub: https://github.com/langchain-ai/langgraph-supervisor.

This is the best starting point to implement a DeepAgent‑style app‑builder: you model planner, code‑gen, QA, deployment agents and wire them with a supervisor node.

---

Hierarchical multi‑agent LangGraph example repos

You have two strong, concrete code resources:

• LangGraph Supervisor repo (official):
  • https://github.com/langchain-ai/langgraph-supervisor
  • Includes examples of hierarchical supervisors, multi‑level orchestration, and agent handoffs.¹⁴¹
• Multi‑agent workflow demo (community):
  • https://github.com/extrawest/multi_agent_workflow_demo_in_langgraph
  • Showcases different multi‑agent orchestration patterns and collaborative workflows (research + code + writing, etc.).¹⁴²

These repos are ideal references for building a Coditect “agent fabric” mirroring DeepAgent’s planner/worker architecture.

---

JITA access request process in Abacus.AI console

Abacus’s security policy describes JITA (Just‑In‑Time Access) as follows:

• Only a small group of senior infrastructure engineers can potentially access production.¹⁴³¹⁴⁴
• Production access is granted just‑in‑time, for a specific reason (incident, support) and for a limited duration.¹⁴³
• Every JITA grant and action is fully logged and monitored for anomalies; these logs support audits and compliance reporting.¹⁴⁴¹⁴³

The exact console UX for requesting JITA isn’t publicly documented (e.g., which button to click), but from a compliance perspective you can assume:

• Request/approval workflow for elevated access.
• Time‑bounded elevation with automatic revocation.
• Exportable logs usable as evidence for SOC 2 / ISO / HIPAA audits.^145144143

If you want to embed this into Coditect’s story, you’d describe Abacus as providing controlled, audited JITA as part of the shared‑responsibility model for production access. ^{146147148149150151152153}

⁂

---

Daytona Cloud free tier usage limits and restrictions

Modal vs Daytona startup latency for AI agents Runloop pricing tiers for agent sandboxes LangGraph supervisor setup prerequisites DeepAgent Pro tier specific features vs basic

Daytona/Modal/Runloop set the sandbox economics; Abacus DeepAgent tiers control how hard you can drive those sandboxes and how many complex tasks you can run.

---

Daytona Cloud free tier limits and restrictions

• Daytona uses tiered org limits for CPU/RAM/storage and API rates. Tier 1 (entry tier) gives roughly:
  • Up to 10 vCPUs, 10 GiB RAM, 30 GiB storage for the default region.¹⁵⁴
  • API limits: about 10,000 general requests/min, 300 sandbox creations/min, 10,000 lifecycle ops/min.¹⁵⁴
• Higher tiers (Tier 2–4) require credit‑card + top‑ups and raise resource and rate limits; there is also a Custom tier via support.¹⁵⁴
• Commentary on Daytona‑like free tiers for sandbox runners notes typical constraints: 45‑minute max runtime, no persistence between sessions, dev‑only usage, not suited for production workloads on free plans.¹⁵⁵
• Daytona specifically advertises “$200 in free compute included” and “spin up sandboxes in milliseconds,” which effectively acts as a generous free trial for agent workloads before you hit paid tiers.¹⁵⁶¹⁵⁷

So for DeepAgent experimentation, Tier 1 + the free compute credit is enough to run many short‑lived sandboxes, but you’ll need higher tiers for sustained multi‑tenant or production use.

---

Modal vs Daytona startup latency for AI agents

• Modal is designed as a serverless‑first Python compute platform optimized for low cold‑start latency; their docs and analyses highlight container caching and moving initialization out of request paths to minimize cold starts.¹⁵⁸¹⁵⁹
• A 2025 analysis of serverless GPU/cloud platforms notes Modal tends to deliver shorter cold‑start times for lightweight CPU workloads vs more throughput‑oriented GPU platforms, thanks to its aggressive warm‑pool and caching strategies.¹⁶⁰
• Daytona markets “spin up sandboxes in milliseconds” for secure agent runtimes, but detailed independent cold‑start benchmarks are not widely published; its focus is on secure, isolated, parallel sandboxes rather than pure serverless micro‑latency.¹⁶¹¹⁵⁶

For DeepAgent‑style workloads:

• If you are latency‑sensitive on time‑to‑first‑token for small CPU tasks, Modal is likely better.¹⁵⁹¹⁶⁰
• If you prioritize secure, long‑lived interpreter sessions and parallel sandboxes for heavier workflows, Daytona is a stronger conceptual fit.¹⁶¹¹⁵⁶

---

Runloop pricing tiers for agent sandboxes

• Runloop exposes usage‑based pricing for “agent infra” but public pages only state “pay for what you use” with no detailed per‑vCPU/GiB table.¹⁶²
• Typical model (from their pricing page and comparisons): you pay per unit of compute and storage, similar to other sandbox vendors, with tiered discounts as usage grows.¹⁵⁵¹⁶²

For planning DeepAgent costs, you should treat Runloop as another metered sandbox backend and benchmark it directly against Daytona/Modal based on:

• Average runtime per DeepAgent task.
• Concurrency requirements for your 10‑agent, 500‑DAU workload.

---

LangGraph supervisor setup prerequisites

From the LangGraph multi‑agent docs and supervisor tutorials:^163164165

You need:

• Python 3.10+ and a recent langgraph and langchain installation.¹⁶³
• An LLM backend (e.g., OpenAI, Anthropic, Bedrock, Mistral) configured via LangChain.¹⁶⁶¹⁶³
• A state model (e.g., TypedDict or Pydantic) describing the fields your agents share (messages, tenant_id, plan, etc.).¹⁶⁷¹⁶⁸
• Basic graph wiring:
  • Define node functions (planner, worker, reviewer).
  • Create a StateGraph with an entry point and transitions.¹⁶³
• Optional but recommended:
  • LangSmith or another tracing/logging backend for debugging multi‑agent flows.¹⁶⁶¹⁶³

Once these are in place, you can run supervisor‑based hierarchical teams similar to the examples in langgraph-supervisor and the multi‑agent workflow demo repo.¹⁶⁵¹⁶⁹

---

DeepAgent Pro tier vs Basic: specific features

Abacus’s billing FAQ and DeepAgent pages are pretty clear:^170171172

• Basic (ChatLLM) – $10/user/month:
  • Access to ChatLLM, Abacus AI Desktop, and DeepAgent standard.¹⁷¹¹⁷²
  • 20,000 credits/month.¹⁷¹
  • DeepAgent limited to about 3 tasks of limited complexity, with restrictions on concurrent automated tasks.¹⁷⁰¹⁷¹
• Pro Tier – +$10/user/month (≈ $20 total):
  • Extra 5,000 credits (25,000 total).¹⁷¹
  • Unrestricted access to DeepAgent, “provided you have credits” (no task‑count cap).¹⁷²¹⁷¹
  • Uses a more powerful version of DeepAgent, yielding “better and richer results,” especially for app building.¹⁷²¹⁷¹
  • Real‑world reviews point to:
    • Up to 25 concurrent automated tasks vs 3 on free/basic.
    • Ability to automatically generate and update databases.
    • Build and publish live websites, use Slack/Gmail automations, deploy under your own domain, and integrated image generation.¹⁷⁰

For a 10‑agent SaaS that leans heavily on DeepAgent, Pro is effectively mandatory; Basic is only viable for light experimentation. ^173174175176

⁂

---

Daytona Tier 2 upgrade process and benefits

Modal cold start benchmarks for GPU workloads DeepAgent Pro pricing and credit costs LangGraph supervisor example code for AI agents Runloop vs Daytona free tier comparison

Daytona/Modal/Runloop define your sandbox economics; DeepAgent Pro defines how hard you can drive those sandboxes; LangGraph Supervisor is your in‑house analogue.

---

Daytona Tier 2 upgrade: process and benefits

• Daytona uses tiered org limits; Tier 1 is the default, and Tier 2+ unlock more CPU, RAM, and storage.¹⁷⁷
• From the limits doc, higher tiers (3, 4) require business email verification and prepaid top‑ups (e.g., Tier 3 needs a verified business email and a $500 top‑up; Tier 4 needs a $2,000 top‑up every 30 days), and a Custom tier is negotiated with support.¹⁷⁷
• Upgrade process:
  • Once you meet the criteria for a higher tier (usage/credits), you can upgrade your tier directly in the Daytona dashboard.¹⁷⁷
• Benefits vs Tier 1 include significantly higher caps for:
  • vCPU count, RAM, storage per region.
  • API rate limits (sandbox creations, lifecycle operations).¹⁷⁷

For DeepAgent, Tier 2+ gives you room for more concurrent sandboxes and longer‑running builds/tests before hitting org limits.

---

Modal GPU cold‑start benchmarks (vs Daytona class)

• Modal’s GPU cold‑start work focuses on GPU memory snapshots:
  • On Mistral 3 3B, median GPU cold start dropped from ~118 seconds to ~12 seconds (≈10× faster) with snapshots.¹⁷⁸
  • GPU memory snapshots can cut cold‑boot time for heavy workloads (e.g., vLLM servers, NeMo models) by up to 10×, enabling sub‑second or low‑seconds startups.¹⁷⁹
• For some audio workloads like NVIDIA Parakeet, cold boot time improved from about 20 seconds to ~2 seconds using snapshots.¹⁷⁹
• A broader 2026 GPU‑cloud comparison notes Modal’s cold starts typically in the 2–4 second range for serverless functions, with strong autoscaling and developer experience.¹⁸⁰

Daytona does not publish equivalently detailed GPU cold‑start benchmarks; its marketing focuses on “spin up sandboxes in milliseconds” but not with the same quantified GPU snapshot data.¹⁸¹¹⁸²

---

DeepAgent Pro pricing and credit costs

From Abacus billing and independent pricing guides:^183184185

• Basic Plan:
  • $10/user/month.
  • 20,000 credits/month.
  • Access to all ChatLLM features and DeepAgent limited to 3 low‑complexity tasks/month.¹⁸⁵¹⁸³
• Pro Plan:
  • Additional $10/user/month (≈ $20 total).¹⁸³¹⁸⁵
  • 25,000 credits/month (5,000 extra over Basic).¹⁸⁵¹⁸³
  • Unrestricted DeepAgent: you can use all your credits on DeepAgent; no fixed task cap.^184186185
  • Uses a more powerful DeepAgent version for “better and richer results,” especially for app creation.¹⁸⁶¹⁸⁵

Observed credit usage for DeepAgent:

• A typical DeepAgent task costs ~500–1,000 credits.¹⁸⁴
• On Basic (20k credits, 3‑task cap), you can do about 3–6 tasks/month before hitting the task cap, not the credit cap.¹⁸⁴
• On Pro (25k credits, no DeepAgent task cap), practical throughput is 25–50 DeepAgent tasks/month at 500–1,000 credits each, before needing more credits.¹⁸⁴

---

LangGraph supervisor example code for AI agents

For a supervisor‑coordinated multi‑agent system, LangGraph requires:

• A state model (TypedDict or dataclass) and node functions for agents.¹⁸⁷¹⁸⁸
• A StateGraph wiring nodes and supervisor transitions.¹⁸⁸¹⁸⁹

Minimal example sketch (Python):

from typing import List, TypedDict
from langgraph.graph import StateGraph, END
from langchain_core.messages import BaseMessage

class AgentState(TypedDict):
    messages: List[BaseMessage]
    plan: str | None
    result: str | None

def planner(state: AgentState) -> AgentState:
    # Use an LLM to create a plan
    plan = llm_plan(state["messages"])
    state["plan"] = plan
    return state

def worker(state: AgentState) -> AgentState:
    # Use an LLM/tool to execute the plan
    result = llm_execute(plan=state["plan"])
    state["result"] = result
    return state

def supervisor(state: AgentState):
    if state.get("plan") is None:
        return "planner"
    if state.get("result") is None:
        return "worker"
    return END

graph = StateGraph(AgentState)
graph.add_node("planner", planner)
graph.add_node("worker", worker)
graph.add_node("supervisor", supervisor)
graph.set_entry_point("supervisor")
graph.add_edge("planner", "supervisor")
graph.add_edge("worker", "supervisor")

app = graph.compile()

This follows the same pattern described in the LangGraph multi‑agent overview and the hierarchical supervisor tutorials.^189190188

For deeper examples:

• Official multi‑agent docs: LangGraph Multi-Agent Systems - Overview.¹⁸⁸
• Hierarchical supervisor tutorial: Kinde’s “Hierarchical Agent Teams with LangGraph Supervisor”.¹⁸⁹
• Code repo: langchain-ai/langgraph-supervisor with full hierarchical examples.¹⁹⁰

---

Runloop vs Daytona free tier comparison

Public info is limited, but you can infer the following:

• Daytona free / Tier 1:
  • Tier 1 limits (entry): modest CPU/RAM/storage caps (e.g., ~10 vCPUs, 10 GiB RAM, 30 GiB storage per region).¹⁷⁷
  • Often bundled with $200 in free compute credits, enough for substantial agent experimentation.¹⁹¹¹⁸¹
  • Clear path to Tier 2–4 via dashboard once you verify a business email and add top‑ups.¹⁷⁷
• Runloop free tier:
  • Exposes a “pay for what you use” usage‑based model; public pages mention starting with a free trial or free usage, but do not detail exact free‑tier limits.¹⁹²¹⁹³
  • Emphasis is on transparent metering and agent‑friendly APIs rather than a fixed “$X free credits” campaign.¹⁹²

From 2026 sandbox‑runner comparisons:

• Daytona is positioned as a secure, elastic, agent‑native infra with generous free compute for sandboxes.¹⁹³¹⁸¹
• Runloop is one of several “best sandbox runners” but with less explicit free‑tier quantification publicly available.¹⁹⁴¹⁹²

If you need hard numbers, Daytona’s limits doc is currently more concrete for free/entry tier planning than Runloop’s public materials. ^{195196197198199200201}

⁂

Footnotes

1. https://abacus.ai ↩

2. https://skywork.ai/skypage/en/Abacus.AI-in-2025-An-Expert's-Deep-Dive/1973803614693552128 ↩ ↩² ↩³ ↩⁴

3. https://abacus-ai-pricing.pages.dev ↩ ↩² ↩³

4. https://deeperinsights.com/ai-review/abacus-ai-review-pros-cons/ ↩

5. https://www.reddit.com/r/ChatGPT/comments/1nd4osg/honest_comparison_abacus_chatllm_chatllmabacusai/ ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶

6. https://www.youtube.com/watch?v=RSksIo_tDVo ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶

7. https://kingy.ai/blog/chatllm-teams-by-abacus-ai-the-ultimate-all-in-one-multimodal-ai-platform/ ↩ ↩²

8. https://smythos.com/developers/agent-comparisons/ai-agent-vs-langchain/ ↩ ↩² ↩³ ↩⁴ ↩⁵

9. https://deepagent.abacus.ai ↩ ↩² ↩³ ↩⁴ ↩⁵

10. https://www.skool.com/theaiproductivitylab/abacus-deep-agents-use-cases ↩ ↩² ↩³ ↩⁴ ↩⁵

11. https://www.eesel.ai/blog/abacus-ai-reviews ↩ ↩² ↩³ ↩⁴

12. https://kingy.ai/ai/mastering-custom-chatbots-with-abacus-ais-chatllm-ai-engineer-a-detailed-guide/ ↩ ↩² ↩³

13. https://www.youtube.com/watch?v=wnUri5keg6Q ↩ ↩² ↩³

14. https://www.youtube.com/watch?v=wdR4LQ5yjyE ↩

15. https://inspiredshifter.com/abacus-ai-for-solopreneurs-2025/ ↩

16. https://www.trustpilot.com/review/abacus.ai ↩ ↩² ↩³ ↩⁴

17. https://ie.trustpilot.com/review/abacus.ai ↩ ↩²

18. https://www.eesel.ai/blog/abacus-ai-pricing ↩

19. https://abacus.ai/help/howTo/chatllm/faqs/billing ↩

20. https://abacus.ai/help/chatllm-ai-super-assistant/faqs/billing ↩

21. https://www.linkedin.com/posts/swayam-snigdha-sabat_llm-abacusai-ai-activity-7303044305261080576-o_zT ↩

22. https://www.youtube.com/watch?v=PYvAfKNGVlg ↩

23. https://www.youtube.com/watch?v=czjY8Au9hxg ↩

24. https://abacus-ai-pricing.pages.dev/posts/abacus-ai-pricing/ ↩

25. https://www.linkedin.com/pulse/current-state-agi-reflections-abacusai-engineer-manusai-lo-0wdyf ↩

26. https://abacus.ai/help/useCases ↩

27. https://digitalsoftwarelabs.com/ai-reviews/abacus-ai-explained-features-use-cases-and-is-it-worth-it/ ↩

28. https://www.blogwithben.com/abacus-ai-deepagent-tutorial-build-websites-decks-automations/ ↩

29. https://www.youtube.com/watch?v=Tv0rtK2fWr4 ↩

30. https://www.g2.com/products/abacus-ai/reviews ↩

31. https://abacus.ai/help/chatllm-ai-super-assistant/faqs/billing ↩ ↩² ↩³

32. https://abacus-ai-pricing.pages.dev/posts/abacus-ai-pricing/ ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷

33. https://weaviate.io/blog/weaviate-cloud-pricing-update ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸ ↩⁹

34. https://www.eesel.ai/blog/weaviate-pricing ↩ ↩² ↩³ ↩⁴

35. https://deepagent.abacus.ai ↩ ↩² ↩³ ↩⁴

36. https://abacus.ai/help/useCases ↩ ↩² ↩³

37. https://digitalsoftwarelabs.com/ai-reviews/abacus-ai-explained-features-use-cases-and-is-it-worth-it/ ↩ ↩² ↩³

38. https://abacus.ai/gen_ai ↩ ↩²

39. https://docs.vllm.ai/en/stable/cli/bench/throughput/ ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶

40. https://smythos.com/developers/agent-comparisons/ai-agent-vs-langchain/ ↩ ↩² ↩³ ↩⁴ ↩⁵

41. https://introl.com/blog/local-llm-hardware-pricing-guide-2025 ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸ ↩⁹

42. https://weaviate.io/pricing ↩ ↩²

43. https://kingy.ai/blog/chatllm-teams-by-abacus-ai-the-ultimate-all-in-one-multimodal-ai-platform/ ↩ ↩² ↩³

44. https://www.eesel.ai/blog/abacus-ai-reviews ↩ ↩² ↩³

45. https://abacus.ai/featurestore ↩

46. https://deeperinsights.com/ai-review/abacus-ai-review-pros-cons/ ↩

47. https://www.reddit.com/r/abacusai/comments/1p09c5r/credits_vs_tokens/ ↩

48. https://www.reddit.com/r/ChatGPT/comments/1nd4osg/honest_comparison_abacus_chatllm_chatllmabacusai/ ↩

49. https://www.eesel.ai/blog/abacus-ai-pricing ↩ ↩² ↩³

50. https://abacus.ai/help/howTo/chatllm/faqs/billing ↩

51. https://www.triseed.co/post/top-vllm-alternatives-in-2025-faster-cheaper-and-more-flexible-llm-serving-options ↩

52. https://www.reddit.com/r/ChatGPTPro/comments/1nxq64e/has_anyone_here_tried_abacus_ai/ ↩

53. https://abacus-ai-pricing.pages.dev ↩

54. https://www.reddit.com/r/abacusai/comments/1nhgn8v/do_the_different_models_use_a_different_amount_of/ ↩

55. https://www.reddit.com/r/LocalLLaMA/comments/1jjl45h/compared_performance_of_vllm_vs_sglang_on_2/ ↩

56. https://www.youtube.com/watch?v=CejntUP3muU ↩ ↩² ↩³ ↩⁴

57. https://abacus.ai/help/chatllm-ai-super-assistant/deepagent-apps ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶

58. https://abacus.ai/help/chatllm-ai-super-assistant/deepagent ↩ ↩²

59. https://kingy.ai/ai/deep-agent-chatllm-teams-god-tier-ai-agent-a-deep-dive-product-review/ ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶

60. https://aws.amazon.com/blogs/machine-learning/build-multi-agent-systems-with-langgraph-and-amazon-bedrock/ ↩ ↩² ↩³ ↩⁴ ↩⁵

61. https://neurlcreators.substack.com/p/langgraph-agent-state-machine-review ↩ ↩² ↩³

62. https://sparkco.ai/blog/deep-dive-into-agent-state-persistence-strategies ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶

63. https://dev.to/sreeni5018/the-architecture-of-agent-memory-how-langgraph-really-works-59ne ↩

64. https://abacus.ai/security ↩ ↩² ↩³

65. https://abacus.ai/help/developer-platform/managing-access ↩ ↩² ↩³ ↩⁴ ↩⁵

66. https://www.linkedin.com/pulse/generated-analysis-abacusai-privacy-trustability-corporate-faenzi-b5ftf ↩

67. https://abacus.ai/genai_security ↩ ↩²

68. https://abacus.ai/static/security/AbacusSecurityPaper.pdf ↩ ↩²

69. https://www.youtube.com/watch?v=cNyTVprWOwE ↩

70. https://deepagent.abacus.ai ↩

71. https://www.youtube.com/watch?v=Tv0rtK2fWr4 ↩

72. https://www.reddit.com/r/AIAgentEngineering/comments/1nx1ob8/engineering_ai_agents_tackling_persistence_in/ ↩

73. https://www.datacamp.com/tutorial/deep-agents ↩

74. https://www.youtube.com/watch?v=igdWfpCZ2Lo ↩

75. https://www.abacus.ch/en/data-protection ↩

76. https://abacus.ai/help/chatllm-ai-super-assistant/faqs/playground ↩

77. https://www.youtube.com/watch?v=CejntUP3muU ↩ ↩² ↩³ ↩⁴

78. https://www.youtube.com/watch?v=cNyTVprWOwE ↩ ↩² ↩³ ↩⁴

79. https://abacus.ai/help/developer-platform/managing-access ↩ ↩² ↩³ ↩⁴ ↩⁵

80. https://abacus.ai/help/connectors/userConnectors/ ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸ ↩⁹

81. https://abacus.ai/help/connectors/introduction ↩ ↩² ↩³

82. https://auth0.com/blog/how-to-choose-the-right-authorization-model-for-your-multi-tenant-saas-application/ ↩ ↩²

83. https://abacus.ai/help/chatllm-ai-super-assistant/deepagent-apps ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷

84. https://www.cerbos.dev/blog/how-to-implement-scalable-multitenant-authorization ↩ ↩²

85. https://langchain-ai.github.io/langgraph/concepts/multi_agent/ ↩ ↩² ↩³ ↩⁴

86. https://github.com/extrawest/multi_agent_workflow_demo_in_langgraph ↩ ↩² ↩³

87. https://aws.amazon.com/blogs/machine-learning/build-a-multi-agent-system-with-langgraph-and-mistral-on-aws/ ↩

88. https://latenode.com/blog/ai-frameworks-technical-infrastructure/langgraph-multi-agent-orchestration/langgraph-multi-agent-systems-complete-tutorial-examples ↩

89. https://abacus.ai/security ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶

90. https://abacus.ai/static/security/AbacusSecurityPaper.pdf ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶

91. https://abacus.ai/genai_security ↩ ↩²

92. https://www.youtube.com/watch?v=igdWfpCZ2Lo ↩ ↩²

93. https://docs.aws.amazon.com/prescriptive-guidance/latest/saas-multitenant-api-access-authorization/avp-mt-abac-rbac-examples.html ↩

94. https://www.osohq.com/learn/abac-attribute-based-access-control-examples ↩

95. https://docs.aws.amazon.com/prescriptive-guidance/latest/saas-multitenant-api-access-authorization/opa-abac-rbac-examples.html ↩

96. https://www.youtube.com/watch?v=4oC1ZKa9-Hs ↩

97. https://www.reddit.com/r/Supabase/comments/1ciyw9w/multitenant_rolebased_access_control_v100_update/ ↩

98. https://www.permit.io/blog/rbac-vs-abac-and-rebac-choosing-the-right-authorization-model ↩

99. https://blog.abacus.ai/blog/2025/10/23/deepagent-review/ ↩ ↩² ↩³

100. https://www.youtube.com/watch?v=CejntUP3muU ↩ ↩²

101. https://runloop.ai/pricing ↩

102. https://www.daytona.io ↩ ↩²

103. https://abacus.ai/security ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶

104. https://abacus.ai/static/security/AbacusSecurityPaper.pdf ↩ ↩² ↩³ ↩⁴

105. https://abacus.ai/genai_security ↩

106. https://abacus.ai/help/connectors/permissionAwareConnectors/ ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸

107. https://abacus.ai/help/developer-platform/managing-access ↩ ↩² ↩³ ↩⁴

108. https://abacus.ai/help/connectors/userConnectors/ ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸ ↩⁹ ↩¹⁰ ↩¹¹

109. https://www.cerbos.dev/blog/how-to-implement-scalable-multitenant-authorization ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶

110. https://abacus.ai/help/connectors/introduction ↩

111. https://digitalsoftwarelabs.com/blogs/ai-reviews/abacus-ai-explained-features-use-cases-and-is-it-worth-it/ ↩ ↩² ↩³

112. https://www.cloudmatos.ai/blog/msp-multi-tenant-api-security-guide/ ↩ ↩² ↩³

113. https://aws.amazon.com/blogs/security/how-to-implement-saas-tenant-isolation-with-abac-and-aws-iam/ ↩

114. https://abacus.ai/help/chatllm-ai-super-assistant/deepagent-apps ↩

115. https://langchain-ai.github.io/langgraph/concepts/multi_agent/ ↩

116. https://github.com/extrawest/multi_agent_workflow_demo_in_langgraph ↩

117. https://umatechnology.org/audit-log-structuring-in-multi-tenant-api-servers-that-meet-compliance-thresholds/ ↩

118. https://umatechnology.org/audit-log-structuring-in-multi-tenant-api-servers-used-in-real-time-dashboards/ ↩

119. https://deepagent.abacus.ai ↩

120. https://abacus.ai/help/chatllm-ai-super-assistant/deepagent ↩

121. https://www.youtube.com/watch?v=RwIJ2wlndrs ↩

122. https://www.kdnuggets.com/2025/11/abacus/my-honest-take-on-abacus-ai-chatllm-deepagent-enterprise ↩

123. https://abacus-ai-pricing.pages.dev/posts/ ↩

124. https://www.youtube.com/watch?v=cNyTVprWOwE ↩

125. https://abacus.ai ↩

126. https://abacus.ai/help/chatllm-ai-super-assistant/faqs/billing ↩

127. https://dev.to/sreeni5018/the-architecture-of-agent-memory-how-langgraph-really-works-59ne ↩

128. https://neurlcreators.substack.com/p/langgraph-agent-state-machine-review ↩

129. https://sparkco.ai/blog/deep-dive-into-agent-state-persistence-strategies ↩

130. https://latenode.com/blog/ai-frameworks-technical-infrastructure/langgraph-multi-agent-orchestration/langgraph-multi-agent-systems-complete-tutorial-examples ↩

131. https://www.daytona.io/dotfiles/introducing-daytona-cloud-the-agent-native-infrastructure ↩ ↩² ↩³

132. https://skywork.ai/skypage/en/unlocking-ai-agents-nikola-balic-daytona-python/1978356209497272320 ↩

133. https://www.daytona.io ↩

134. https://www.facebook.com/groups/techtitansgroup/posts/1511530533507593/ ↩

135. https://www.youtube.com/watch?v=CejntUP3muU ↩

136. https://modal.com/blog/nvidia-b200-pricing ↩

137. https://modal.com/pricing ↩

138. https://runloop.ai/pricing ↩

139. https://langchain-ai.github.io/langgraph/concepts/multi_agent/ ↩

140. https://kinde.com/learn/ai-for-software-engineering/ai-agents/hierarchical-agent-teams-with-langgraphsupervisor/ ↩

141. https://www.youtube.com/watch?v=B_0TNuYi56w ↩ ↩²

142. https://github.com/extrawest/multi_agent_workflow_demo_in_langgraph ↩

143. https://abacus.ai/security ↩ ↩² ↩³ ↩⁴

144. https://abacus.ai/static/security/AbacusSecurityPaper.pdf ↩ ↩² ↩³

145. https://abacus.ai/genai_security ↩

146. https://northflank.com/blog/top-daytona-io-alternatives-for-running-ai-code-in-secure-sandboxed-environments ↩

147. http://oreateai.com/blog/understanding-abacus-ais-deepagent-pricing-a-comprehensive-guide/cbdff68083cad70d6734f1d6d4c8a9fe ↩

148. https://deepagent.abacus.ai/deepagent_faq ↩

149. https://gpuvec.com/providers/modal ↩

150. https://www.youtube.com/watch?v=cFEGlFdhDaw ↩

151. http://appllm.abacus.ai/deepagent_faq ↩

152. https://www.daytona.io/docs/en/ ↩

153. https://northflank.com/blog/the-best-serverless-gpu-cloud-providers ↩

154. https://www.daytona.io/docs/en/limits/ ↩ ↩² ↩³

155. https://northflank.com/blog/top-daytona-io-alternatives-for-running-ai-code-in-secure-sandboxed-environments ↩ ↩²

156. https://www.daytona.io ↩ ↩² ↩³

157. https://skywork.ai/skypage/en/unlocking-ai-agents-nikola-balic-daytona-python/1978356209497272320 ↩

158. https://modal.com/docs/guide/cold-start ↩

159. https://frontend.modal.com/docs/guide/cold-start ↩ ↩²

160. https://www.gurustartups.com/reports/modal-vs-runpod-cold-start-times-compared ↩ ↩²

161. https://github.com/daytonaio/daytona ↩ ↩²

162. https://runloop.ai/pricing ↩ ↩²

163. https://langchain-ai.github.io/langgraph/concepts/multi_agent/ ↩ ↩² ↩³ ↩⁴ ↩⁵

164. https://kinde.com/learn/ai-for-software-engineering/ai-agents/hierarchical-agent-teams-with-langgraphsupervisor/ ↩

165. https://www.youtube.com/watch?v=B_0TNuYi56w ↩ ↩²

166. https://aws.amazon.com/blogs/machine-learning/build-multi-agent-systems-with-langgraph-and-amazon-bedrock/ ↩ ↩²

167. https://dev.to/sreeni5018/the-architecture-of-agent-memory-how-langgraph-really-works-59ne ↩

168. https://neurlcreators.substack.com/p/langgraph-agent-state-machine-review ↩

169. https://github.com/extrawest/multi_agent_workflow_demo_in_langgraph ↩

170. https://www.youtube.com/watch?v=Wga6l8gcRT8 ↩ ↩² ↩³

171. https://abacus.ai/help/chatllm-ai-super-assistant/faqs/billing ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷

172. https://deepagent.abacus.ai/deepagent_faq ↩ ↩² ↩³ ↩⁴

173. https://www.youtube.com/watch?v=DUVExQWheTw ↩

174. https://www.reddit.com/r/oraclecloud/comments/1f8pqsm/a_question_about_always_free_limits/ ↩

175. https://betterstack.com/community/comparisons/best-sandbox-runners/ ↩

176. https://peerdh.com/blogs/programming-insights/comparing-cold-start-latency-across-different-serverless-platforms ↩

177. https://www.daytona.io/docs/en/limits/ ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶

178. https://modal.com/blog/mistral-3 ↩

179. https://modal.com/blog/gpu-mem-snapshots ↩ ↩²

180. https://www.runpod.io/articles/guides/top-serverless-gpu-clouds ↩

181. https://www.daytona.io ↩ ↩² ↩³

182. https://www.daytona.io/dotfiles/introducing-daytona-cloud-the-agent-native-infrastructure ↩

183. https://abacus-ai-pricing.pages.dev/posts/abacus-ai-pricing/ ↩ ↩² ↩³ ↩⁴

184. https://www.youtube.com/watch?v=uKpH4lySHVI ↩ ↩² ↩³ ↩⁴ ↩⁵

185. https://abacus.ai/help/chatllm-ai-super-assistant/faqs/billing ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶

186. https://deepagent.abacus.ai/deepagent_faq ↩ ↩²

187. https://dev.to/sreeni5018/the-architecture-of-agent-memory-how-langgraph-really-works-59ne ↩

188. https://langchain-ai.github.io/langgraph/concepts/multi_agent/ ↩ ↩² ↩³ ↩⁴

189. https://kinde.com/learn/ai-for-software-engineering/ai-agents/hierarchical-agent-teams-with-langgraphsupervisor/ ↩ ↩² ↩³

190. https://www.youtube.com/watch?v=B_0TNuYi56w ↩ ↩²

191. https://skywork.ai/skypage/en/unlocking-ai-agents-nikola-balic-daytona-python/1978356209497272320 ↩

192. https://runloop.ai/pricing ↩ ↩² ↩³

193. https://northflank.com/blog/top-daytona-io-alternatives-for-running-ai-code-in-secure-sandboxed-environments ↩ ↩²

194. https://betterstack.com/community/comparisons/best-sandbox-runners/ ↩

195. https://www.daytona.io/docs/en/network-limits/ ↩

196. https://www.youtube.com/watch?v=VNmTa7r6sjU ↩

197. https://www.youtube.com/watch?v=gdRefdg1390 ↩

198. https://www.drtuned.com/development/p/25t-tier1-tier2-upgrade ↩

199. https://www.reddit.com/r/CSRRacing2/comments/7je5e5/stripping_cars_for_parts_to_upgrade_or_not/ ↩

200. https://www.reddit.com/r/aws/comments/1m0zwjn/aws_announces_actual_free_tier_for_6_months_plus/ ↩

201. https://www.reddit.com/r/RepTime/comments/1amsyge/how_much_of_an_upgrade_would_a_clean_daytona_be/kpo2fqa/ ↩