LLM application frameworks sit between your product and a model API, handling prompts, tools, memory, retrieval and agent loops. This guide ranks eleven of them for buyers who must live with the result.
It weighs setup effort, the real bill once tracing or hosting arrives, how easily you can leave, and whether a small vendor stands behind the code.
Vendors can pay for more visibility on this website, for example a higher place in a ranking or more room on a page. Commercial relationships can also affect which products we feature and how prominently. Every vendor link is marked the same, whatever the arrangement behind it. How that works.
In short
What LLM application frameworks software does
Libraries and platforms that let developers connect language models to tools, documents and memory, and organise the resulting prompts, agents and workflows as maintainable application code.
To rank LLM application frameworks tools we asked, in turn, how much work setup takes, what it truly costs, whether data can be taken back out, whether leaving is possible, and who the product suits. Smaller challengers are placed in the top three wherever the category has them; the well-known brands come after. Reasons for the choice of tests, and for skipping a score out of ten, can be read on the how we work page.
Typed Python agents that validate model output and retry on failure
Ranked #1 of 11 in Best LLM Application Frameworks in 2026.
Pricing on requestElsewhere
Agents run with run() or run_sync(), and typed function tools receive dependencies through RunContext. Tool arguments are validated before your code executes, and outputs are validated against the output type with a retry on failure.
Durable execution is offered through nine engines, five of them co-maintained with vendor teams, including Temporal and Prefect. Models range across OpenAI, Anthropic, Mistral, Groq and Ollama. Everything is code, so non-developers cannot contribute.
What stands out
Typed outputs
Durable execution engines
Model gateway
Where it costs you
The page names no legal entity or headquarters, so contract questions need a separate answer.
The Pydantic AI Gateway shows no prices, so its cost cannot be planned from the page.
Right for
Python teams that want validated, typed agent output with retries built in
Wrong for
Teams wanting a visual builder or a TypeScript-first toolkit
Not stated by the vendorMIT-licensed library; the page gives no price for it or the Gateway
Python agent framework with a runtime you host in your own cloud
Ranked #2 of 11 in Best LLM Application Frameworks in 2026.
Free tierSelf-hostablePublished pricingElsewhere
The Agno SDK is a Python framework for agents, teams and workflows. AgentOS is the runtime, and because it runs in your own cloud, serving over REST, MCP, Slack and Discord is included on every plan.
The control plane connects from the browser to that runtime. Pro covers three seats and one live connection; each extra seat, connection or SAML SSO is billed monthly on top, so the real bill depends on team size.
What stands out
Self-hosted AgentOS
Teams and workflows
Add-on SSO
Where it costs you
Pro includes one live AgentOS connection, and each extra connection is a further monthly charge.
SAML single sign-on is a separate monthly add-on rather than part of Pro.
Right for
Developers who want agents running in their own cloud with a managed console
Wrong for
Buyers needing a published enterprise price or a named owner
Not stated by the vendorPublished monthly plans with a Free local tier; seats and connections are paid add-ons
Stateful agents with persistent memory, from the creators of MemGPT
Ranked #3 of 11 in Best LLM Application Frameworks in 2026.
Free tierPublished pricingElsewhere
Plans step from Free, with three stateful agents, to Pro with twenty agents and monthly credits, then Teams Pro per seat for sharing agents. Memory repositories are capped at 5 MiB on Free and 50 MiB on Pro.
Server-side tools consume credits per second of CPU time, whereas remote MCP and client-side tools cost none, and built-in tools are free apart from web search and fetch. Letta Code installs through npm, and the repository carries the Apache-2.0 label.
What stands out
Persistent agent memory
Credit-metered tools
Agent sharing for teams
Where it costs you
The Free plan includes no model usage, so you must bring your own API keys.
Server-side tools are billed per second of CPU time, so spend follows usage.
Right for
Builders of long-lived agents whose memory must persist between sessions
Wrong for
Teams that want stateless pipelines or a fixed monthly bill
Not stated by the vendorTiered monthly plans with a Free level that needs your own model keys, plus credits for server-side tools
Workflows chain steps with .then(), .branch() and .parallel() and can pause and resume. Memory covers observational memory and semantic recall, a Harness handles tool approvals and subagents, and a Server exposes API endpoints.
Model routing reaches more than 90 providers. On the hosted Platform, Starter is capped at 24 CPU hours a month; Teams raises that to 250 at a flat monthly fee, with overage at $0.35 per CPU hour.
What stands out
Pause-resume workflows
Observational memory
Apache 2.0 core
Where it costs you
Enterprise features sit under the Mastra Enterprise License and are source available only.
Both legal pages gave HTTP 404 when checked, so no owner or country could be confirmed.
Right for
Developers wanting agents, workflows and tracing in one framework with a self-hosted core
Wrong for
Buyers who need a named legal entity before signing
Not stated by the vendorMonthly platform tiers with overage metering, a self-hosted Free plan and custom enterprise pricing
Declare tasks as typed signatures and let optimisers tune prompts
Ranked #5 of 11 in Best LLM Application Frameworks in 2026.
Pricing on requestElsewhere
Tasks are written as dspy.Signature classes with InputField and OutputField, then run through modules: Predict, ChainOfThought, ReAct with tools, BestOfN, Refine, Parallel and more.
dspy.GEPA is the headline optimiser, and the API reference also lists MIPROv2, BootstrapFewShot, COPRO and SIMBA. MCP support, ColBERTv2 and LiteLLM logging controls are named integrations. Everything is code and the page states no price, so the cost is engineering time.
What stands out
Typed signatures
Prompt optimisers
Stanford NLP origin
Where it costs you
The page names no company, support contact or hosted offering behind the library.
Optimisers replace hand-written prompts, so teams must learn a different workflow before seeing results.
Right for
Engineers who would rather compile prompts with optimisers than edit them by hand
Wrong for
Buyers needing a visual builder, hosting or vendor support
Not stated by the vendorMIT-licensed Python library; the page states no pricing or hosted offering
Branching, loopable pipelines for LLM agents and retrieval, in Python
Ranked #6 of 11 in Best LLM Application Frameworks in 2026.
Open sourceSelf-hostablePricing on requestElsewhere
Pipelines are graphs that can branch and loop, and they serialise so they can be stored and reviewed. Prompts are Jinja-2 templates. Named integrations include OpenAI, Anthropic, Mistral, Hugging Face, Weaviate, Pinecone and Elasticsearch.
Guides cover logging, monitoring and Kubernetes-ready deployment. The Enterprise Platform builds on the framework for visual design, in cloud or on-prem, with a trial. The library alone comes with no hosted service described.
What stands out
Serialisable pipelines
Jinja-2 prompt templates
Kubernetes deployment guides
Where it costs you
The Enterprise Platform shows no price, and support pricing depends on company size.
The page does not state where deepset is based.
Right for
Teams building retrieval or agent pipelines who want serialisable, inspectable flows
Wrong for
Buyers wanting a visual builder without paying for the platform
Not stated by the vendorOpen source Apache-2.0 library; the paid platform has a trial and size-based support pricing
Low-code canvas for agentic and RAG apps with Python underneath
Ranked #7 of 11 in Best LLM Application Frameworks in 2026.
Open sourceSelf-hostablePricing on requestElsewhere
The builder connects hundreds of data sources, models and vector stores, with named connectors for OpenAI, Anthropic, Ollama, Mistral, Pinecone, Qdrant, Slack, Notion and Gmail. Components can be customised in Python.
The vendor says the open source and cloud versions are the same Langflow. Enterprise offers cover Premier Support and Professional Services, though no figures appear. Anything beyond the canvas means writing Python yourself.
What stands out
Visual flow builder
MCP server deployment
Hundreds of connectors
Where it costs you
Cloud costs could not be confirmed because langflow.org/pricing returned HTTP 404.
Owner and company location are not stated; the footer shows only a copyright line.
Right for
Teams that want to sketch agent and retrieval flows on a canvas first
Wrong for
Buyers who must see cloud prices before choosing
Not stated by the vendorOpen source to self-host, or a cloud account; no prices are shown
Python agent framework plus a visual node editor and hosted cloud
Ranked #8 of 11 in Best LLM Application Frameworks in 2026.
Pricing on requestElsewhere
The AI Framework is Python for agents, pipelines, workflows and RAG. Nodes adds a visual no-code interface that can still be scripted in Python, and AI Cloud supplies retrieval-ready databases and a serverless app runtime.
Samples cover a Slack event handler, LangChain code running on Griptape Cloud and ComfyUI nodes. The vendor states no limits. With three products sharing one brand, confirm which layer you are buying before building anything.
What stands out
Visual node editor
Hosted AI Cloud
Python pipelines
Where it costs you
Ownership is unclear, since the footer names Griptape, Inc while legal links point to foundry.com.
No plan or price could be checked because griptape.ai/pricing returned HTTP 404.
Right for
Developers wanting one vendor's framework, node editor and hosted runtime
Wrong for
Buyers who need published prices or a plain ownership story
Not stated by the vendorApache-2.0 framework; hosted cloud and Nodes list no prices
Credit-priced document parsing alongside an MIT-licensed retrieval framework
Ranked #9 of 11 in Best LLM Application Frameworks in 2026.
Self-hostablePublished pricingElsewhere
Plans differ on credits and limits. Pay-as-you-go has 10K credits, 100 users, one project and five concurrent jobs per type; Pro offers 400K credits, a one-time 800K bonus for business email domains, five projects and twenty concurrent jobs.
Enterprise has 100 concurrent jobs, SSO and an account manager. Listed certifications are SOC 2 Type II, GDPR and HIPAA, with 130+ file formats supported and re-parsing cached documents costing nothing.
What stands out
Credit-metered parsing
Private VPC option
SOC 2 Type II
Where it costs you
Costs scale with credits consumed, so heavy parsing months can outrun the entry plan.
Pay-as-you-go limits concurrency to five parse, extract, classify and split jobs per type.
Right for
Teams feeding many documents into retrieval apps and happy to budget in credits
Wrong for
Buyers who need a fixed monthly cost whatever the document volume
Not stated by the vendorPublished per-credit rates on pay-as-you-go and Pro; Enterprise is custom
Agent framework whose LangSmith platform meters traces and deployments by seat
Ranked #10 of 11 in Best LLM Application Frameworks in 2026.
Self-hostablePublished pricingElsewhere
Plus costs per seat with unlimited seats, 10k base traces, one small serverless deployment and 37 Fleet units per organisation each month. Extended traces keep data for 180 days.
The platform names LangSmith Studio, an Assistants API with 30+ endpoints, cron scheduling, remote MCP support and Agent Authorization in beta. Venture-backed startups can get discounted rates and up to $10,000 in credits. Cloud data location is US or EU.
What stands out
LangSmith tracing
Hybrid or self-hosted option
MIT repository
Where it costs you
Hybrid and self-hosted deployment is tied to the Enterprise plan.
Base traces are retained for 14 days, so longer history needs extended traces.
Right for
Teams wanting tracing, Studio and hosted deployment from one vendor
Wrong for
Teams that want a flat price without metered units
Not stated by the vendorPublished per-seat LangSmith plans with a Developer tier, metered units and custom Enterprise terms
TypeScript toolkit for chat interfaces, agents and structured model output
Ranked #11 of 11 in Best LLM Application Frameworks in 2026.
Pricing on requestElsewhere
AI SDK Core generates text, structured objects and tool calls, and builds agents. AI SDK UI gives hooks for chat and generative interfaces that are not tied to one front-end framework.
AI SDK Harnesses adds HarnessAgent, with Claude Code, Codex and Pi as examples. Named runtimes are React, Next.js, Vue, Svelte and Node.js. It is a toolkit rather than a platform, so tracing, hosting and billing are outside what the page describes.
What stands out
TypeScript toolkit
Chat UI hooks
Framework-agnostic
Where it costs you
The introduction page lists no model providers, so coverage must be checked on a separate page.
No pricing or company location appears on the pages read.
Right for
Front-end and Node developers adding chat or agents to web apps
Wrong for
Python teams or buyers wanting a visual builder
Not stated by the vendorApache-2.0 toolkit; the introduction page states no pricing
Libraries and platforms that let developers connect language models to tools, documents and memory, and organise the resulting prompts, agents and workflows as maintainable application code. Rather than compare features, settle the following points one after another.
01
Check who publishes a price
The split is 5 published against 6 quoted. If you want to compare numbers first, start with Agno, Letta, Mastra, LlamaIndex, LangChain.
02
Decide how much the jurisdiction matters
1 regions are represented (Elsewhere 11). A vendor outside your own legal area is not a problem as such, but it changes who can compel it to hand over data.
03
Decide if open source matters
Open source here means Mastra, Haystack, Langflow. The price of self-hosting is the time your team spends maintaining it.
Library, canvas or platform: what an LLM application framework really is
The first split in this market is what you install. Pydantic AI, DSPy and Haystack are Python libraries added with pip, so the only cost is engineering time. Vercel AI SDK is the TypeScript equivalent for web teams. Langflow puts a visual canvas on top, and Griptape sells a framework, a node editor and a hosted cloud under one name.
LangChain is different again: the framework repository is MIT, but the paid product on its pricing page is LangSmith. Decide whether your team writes code daily or wants to draw flows, because moving between those two styles later means rebuilding rather than exporting. Ask what each tool lets you export.
Confirm whether the pricing page covers the framework or only a companion platform
Ask who will edit the logic: developers, or analysts on a canvas
Check that the language matches your stack, Python or TypeScript
Where the bill appears once agents leave the prototype stage
Libraries cost nothing to install, so the bill arrives through the services around them. LangChain meters usage in units at $1.00 each and keeps base traces for 14 days. Mastra charges overage per 100K observability events and per CPU hour. Letta bills server-side tools per second of CPU time, and LlamaIndex prices parsing in credits, with Pro lowering the per-1K rate.
Agno goes the other way: serving is included on every plan, but seats, connections and SAML SSO are monthly add-ons. Model a month of realistic traffic against each meter, because the entry plan rarely survives contact with production volumes. Pick the vendor whose meter tracks something you can forecast, such as seats, not something you cannot, such as agent loops.
Estimate monthly traces, CPU hours or credits before comparing plans
Find the add-on that unlocks SSO or extra seats
Check how long trace data is retained at your tier
Memory, retries and durability for agents that run for a long time
Agents that run for minutes or days fail in ways a single prompt does not. Pydantic AI validates tool arguments and retries when output breaks its declared type, and it hands long runs to durable engines such as Temporal or Prefect. Mastra workflows can pause and resume, with memory covering semantic recall.
Letta puts persistent memory at its centre and caps storage per memory repository by plan. Haystack pipelines can loop and be serialised, which helps when a flow has to be stored and reviewed. Kill a run halfway in a test and see what each tool restores. A framework that cannot resume forces you to build checkpointing yourself, which quietly becomes the largest hidden task in the project.
Test what happens when a run is stopped midway
Check memory limits per agent or repository on your plan
Look for retries on invalid model output
Licences and the open core: what stays open and what is sold
Most names here are open at the core, but the licence boundary matters. Mastra is Apache 2.0 for the core while its enterprise features are source available under a separate licence. Haystack is Apache-2.0, yet visual design and support sit in a commercial platform. Langflow and DSPy carry MIT labels on GitHub, and Griptape is Apache-2.0 with hosted layers on top.
Read the repository licence yourself, not only the marketing page, because a feature you depend on may sit on the paid side. Also note which vendors state no owner or country, since that affects contract and support questions. LangChain names a Delaware entity; several others name none at all, so ask before signing.
Read the licence file for every component you plan to use
List features sold under a separate enterprise licence
Record where the vendor does not name an owner or location
What goes wrong most often when buying LLM application frameworks software
Treating a free library as a free project and ignoring the metered tracing, hosting or parsing bill that follows.
Choosing a canvas builder like Langflow for logic that developers will later need to test and version in code.
Assuming one brand means one product, when Griptape and LangChain each sell separate framework and platform layers.
Skipping the licence file and discovering late that a needed feature is source available or sold in a paid platform.
07
Frequently asked questions
8 answers
What is the best LLM application frameworks in 2026?
Pydantic AI leads our ranking of 11. Pydantic AI is a Python library installed with pip or uv, built by the Pydantic team, with no legal entity or headquarters named on the page.
Output is checked against the declared type and the agent retries when that fails, which suits teams that treat model replies as data. The catch is scope: there is no visual builder, and the Gateway lists no prices to budget against.
Which LLM application frameworks tools publish their pricing?
5 out of 11 state a price model on their own site:
Agno: Published monthly plans with a Free local tier; seats and connections are paid add-ons.
Letta: Tiered monthly plans with a Free level that needs your own model keys, plus credits for server-side tools.
Mastra: Monthly platform tiers with overage metering, a self-hosted Free plan and custom enterprise pricing.
LlamaIndex: Published per-credit rates on pay-as-you-go and Pro; Enterprise is custom.
LangChain: Published per-seat LangSmith plans with a Developer tier, metered units and custom Enterprise terms.
The other 6 quote per organisation.
Is there a free LLM application frameworks tool?
Agno, Letta, Mastra offer a free tier or a free self-hosted edition.
Where are these LLM application frameworks vendors established?
The headquarters are spread over 1 countries and 1 regions, Elsewhere 11:
Pydantic AI: Not stated by the vendor.
Agno: Not stated by the vendor.
Letta: Not stated by the vendor.
Mastra: Not stated by the vendor.
DSPy: Not stated by the vendor.
Haystack: Not stated by the vendor.
Langflow: Not stated by the vendor.
Griptape: Not stated by the vendor.
LlamaIndex: Not stated by the vendor.
LangChain: Not stated by the vendor.
Vercel AI SDK: Not stated by the vendor.
Which LLM application frameworks tools are open source?
Mastra, Haystack, Langflow.
Which LLM application frameworks tools can you host yourself?
Agno, Mastra, Haystack, Langflow, LlamaIndex, LangChain. The remaining 5 run only on the vendor's servers.
What should you use instead of Pydantic AI?
Agno and Letta are the next two on this page. Agno is for Developers who want agents running in their own cloud with a managed console; Letta is for Builders of long-lived agents whose memory must persist between sessions.
Who should not buy Pydantic AI?
Teams wanting a visual builder or a TypeScript-first toolkit. The page names no legal entity or headquarters, so contract questions need a separate answer..
If a LLM application frameworks product of yours deserves a place among these 11, say what it is and who it serves. Inclusion rests with us alone. See software advice for the rules.
