{"slug": "pydantic-ai", "name": "Pydantic AI", "category": "ai", "category_name": "AI tools and apps", "description": "", "recommenders": 10, "commenters": 19, "warners": 3, "score": 6.108, "rising": -1.95, "first": "2025-11-03", "latest": "2026-09-01", "why": "I really like https://ai.pydantic.dev/ as it builds on existing developer knowledge and there is not much to learn in the framework itself.", "why_is_quote": true, "has_verdict": false, "sentiment": {"recommend": 9, "neutral": 9, "compare": 1, "warn": 4}, "spark": [4, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0], "listed": true, "loved": [], "caveat": "", "short_quote": "I really like https://ai.pydantic.dev/ as it builds on existing developer knowledge and there is not much to learn in the framework itself.", "kind": "", "icon": true, "domain": "pydantic.dev", "github_repo": null, "official_url": "https://ai.pydantic.dev", "pricing": "", "aliases": ["Pydantic AI", "PydanticAI", "pydantic-ai", "Pydantic-AI"], "verdict": null, "praise": [{"label": "", "summary": "", "quotes": [{"id": 786973992, "item_id": 49185874, "url": "https://news.ycombinator.com/item?id=49185874", "by": "Gayithri2026", "date": "2026-08-05", "version": null, "stance": "recommend", "quote": "multi-provider (OpenAI/Claude/Gemini/Bedrock), swappable in one line via PydanticAI.", "excerpt": "Location: San Jose, CA (SF Bay Area) Remote: Yes Willing to relocate: No Technologies: Python, FastAPI, PostgreSQL/pgvector, RAG, LangChain, PydanticAI, Celery, Redis, Docker, AWS, Langfuse, Java, Spring Boot R\u00e9sum\u00e9/CV: https://github.com/Gayithri606/resume/blob/main/GayithriPonnapalli-Resume.pdf Email: gayithri@pojoai.com AI engineer building RAG chatbots and AI-driven workflow automation. Five years of production J", "context": "AI engineer building RAG chatbots", "use_case": "multi-provider LLM agents", "switched_from": "", "story": "Ask HN: Who wants to be hired? (August 2026)"}, {"id": 770125473, "item_id": 48132842, "url": "https://news.ycombinator.com/item?id=48132842", "by": "gorkaerana", "date": "2026-05-14", "version": null, "stance": "recommend", "quote": "pydantic-ai has been a superb addition to the AI engineering toolkit", "excerpt": "I have only had positive experiences using pydantic tools. They keep iterating on the pydantic validation tool and are planning on a v3 at some point, and pydantic-ai has been a superb addition to the AI engineering toolkit; which makes me hopeful and confident about the work they will do with httpx2. I look forward to explore their eval and observability tools. I do wonder about pydantic being a VC-backed company an", "context": "", "use_case": "", "switched_from": "", "story": "Pythons new httpx fork: httpx2"}, {"id": 765809265, "item_id": 47863079, "url": "https://news.ycombinator.com/item?id=47863079", "by": "eleventen", "date": "2026-04-22", "version": null, "stance": "recommend", "quote": "migrate an app from Claude SDK to Pydantic AI to get off claude API pricing", "excerpt": "I just installed it and continued about my business. I don't have a carefully tuned claude code setup. It has some skills, some CLAUDE.md files, and not much else. I did also migrate an app from Claude SDK to Pydantic AI to get off claude API pricing. https://pydantic.dev/docs/ai/overview/", "context": "", "use_case": "Replacement for Claude SDK app", "switched_from": "Claude SDK", "story": "Claude Code removed from Anthropic's Pro plan"}, {"id": 761198912, "item_id": 47574932, "url": "https://news.ycombinator.com/item?id=47574932", "by": "kacper-vstorm", "date": "2026-03-30", "version": null, "stance": "recommend", "quote": "We maintain 5 open-source libraries built on Pydantic AI", "excerpt": "We maintain 5 open-source libraries built on Pydantic AI: a guardrails/shields library, a subagent delegation library, a context summarization library, a filesystem backend, and a full-stack template. When Pydantic AI shipped capabilities in v1.71+, we migrated all of them. The biggest lesson: we deleted our entire middleware abstraction layer (MiddlewareAgent, MiddlewareChain, ParallelMiddleware, pipeline compiler, ", "context": "", "use_case": "", "switched_from": "", "story": null}, {"id": 753557280, "item_id": 47097330, "url": "https://news.ycombinator.com/item?id=47097330", "by": "cjonas", "date": "2026-02-20", "version": null, "stance": "recommend", "quote": "I think the PydanticAI framework has the right approach of encouraging Agent Delegation & sequential workflow first", "excerpt": "This approach seems interesting, but in my experience, a single \"agent\" with proper context management is better than a complicated agent graph. Dealing with hand-off (+ hand back) and multiple levels of conversations just leaves too much room for critical information to get siloed. If you have a narrow task that doesn't need full context, then agent delegation (putting an agent or inference behind a simple tool call", "context": "", "use_case": "agent framework", "switched_from": "", "story": "Cord: Coordinating Trees of AI Agents"}]}], "caveats": [{"label": "", "summary": "", "quotes": [{"id": 781973809, "item_id": 48873363, "url": "https://news.ycombinator.com/item?id=48873363", "by": "reveriedev", "date": "2026-07-11", "version": null, "stance": "warn", "quote": "Getting those agent systems to behave reliably was often annoying and tricky.", "excerpt": "Back in the medieval age of 2025, I used frameworks like PydanticAI and LangGraph to build agents. Getting those agent systems to behave reliably was often annoying and tricky. Claude Code and Codex have since become capable general-purpose agents. They each have an SDK, but those SDKs expose different interfaces and inherit their respective platform\u2019s quirks. While building CodeAlmanac, I wanted one way to work with", "context": "", "use_case": "building agents", "switched_from": "", "story": null}, {"id": 761858324, "item_id": 47616145, "url": "https://news.ycombinator.com/item?id=47616145", "by": "Acacian", "date": "2026-04-02", "version": null, "stance": "warn", "quote": "them had it.", "excerpt": "LangChain, CrewAI, OpenAI Agents, Anthropic, LiteLLM, Pydantic AI, Google ADK \u2014 I went through 11 frameworks looking for basic runtime security: injection detection, PII masking, audit trails. None of them had it. So I built a monkey-patching layer that intercepts LLM calls and runs them through guardrails: import aegis; aegis.init() Patches whatever frameworks you have installed. ~2.6ms overhead. The nastiest find: ", "context": "", "use_case": "", "switched_from": "", "story": "Show HN: I tested 11 AI frameworks for basic security \u2013 none passed"}, {"id": 754205233, "item_id": 47137827, "url": "https://news.ycombinator.com/item?id=47137827", "by": "saberience", "date": "2026-02-24", "version": null, "stance": "warn", "quote": "Pydantic is a VC backed business now trying to funnel people into using their paid cloud services. No thanks.", "excerpt": "Would 1000% prefer to use Pi (https://github.com/badlogic/pi-mono) for building anything relating to AI agents. It's already battle-tested in super popular applications (like OpenClaw for example and many others), has 100s of extensions, is already built in a type-safe language, excellent community. And it also includes a full TUI and WebUI for interface support. Also Pydantic is a VC backed business now trying to fu", "context": "", "use_case": "", "switched_from": "", "story": "Build your own custom AI CLI with Pydantic AI"}]}], "who": [{"by": "Gayithri2026", "context": "AI engineer building RAG chatbots", "url": "https://news.ycombinator.com/item?id=49185874", "date": "2026-08-05"}, {"by": "djd20", "context": "hiring at a microinsurance SaaS startup", "url": "https://news.ycombinator.com/item?id=49165651", "date": "2026-08-04"}], "tags": [], "use_cases": ["agent framework", "multi-provider llm agents", "building agents", "replacement for claude sdk app", "agent framework base", "base agent framework"], "compared": [{"slug": "claude-code", "name": "Claude Code", "co": 2, "switched_from": 0, "switched_to": 2}, {"slug": "langchain", "name": "LangChain", "co": 3, "switched_from": 0, "switched_to": 0}, {"slug": "pydantic", "name": "Pydantic", "co": 3, "switched_from": 0, "switched_to": 0}, {"slug": "fastmcp", "name": "FastMCP", "co": 3, "switched_from": 0, "switched_to": 0}, {"slug": "dbos", "name": "DBOS", "co": 2, "switched_from": 0, "switched_to": 0}, {"slug": "langgraph", "name": "LangGraph", "co": 2, "switched_from": 0, "switched_to": 0}], "makers": [{"id": 750726209, "item_id": 46920388, "url": "https://news.ycombinator.com/item?id=46920388", "by": "DouweM", "date": "2026-02-06", "version": null, "stance": "recommend", "quote": "we\u2019re implementing Code Mode in https://github.com/pydantic/pydantic-ai/pull/4153", "excerpt": "(Pydantic AI lead here) That\u2019s exactly what we built this for: we\u2019re implementing Code Mode in https://github.com/pydantic/pydantic-ai/pull/4153 which will use Monty by default, with abstractions to use other runtimes / sandboxes. Monty\u2019s overhead is so low that, assuming we get the security / capabilities tradeoff right (Samuel can comment on this more), you could always have it enabled on your agents with basically", "context": "Pydantic AI lead", "use_case": "", "switched_from": "", "story": "Python interpreter written in Rust for use by AI"}, {"id": 750725376, "item_id": 46920336, "url": "https://news.ycombinator.com/item?id=46920336", "by": "DouweM", "date": "2026-02-06", "version": null, "stance": "neutral", "quote": "We\u2019re implementing Code Mode in https://github.com/pydantic/pydantic-ai/pull/4153", "excerpt": "(Pydantic AI lead here) We\u2019re implementing Code Mode in https://github.com/pydantic/pydantic-ai/pull/4153 with support for Monty and abstractions to use other runtimes / sandboxes. The idea is that in \u201ctraditional\u201d LLM tool calling, the entire (MCP) tool result is sent back to the LLM, even if it just needs a few fields, or is going to pass the return value into another tool without needing to see the intermediate va", "context": "", "use_case": "", "switched_from": "", "story": "Python interpreter written in Rust for use by AI"}, {"id": 737753937, "item_id": 46109621, "url": "https://news.ycombinator.com/item?id=46109621", "by": "christophergs", "date": "2025-12-01", "version": null, "stance": "recommend", "quote": "one of the fastest growing agent frameworks", "excerpt": "Pydantic | Solutions Engineer | REMOTE (UTC-8 - UTC+1)| Full-Time | https://pydantic.dev If you write Python, you've probably heard of Pydantic (400m downloads per month) and if you do AI engineering you've probably heard of Pydantic AI (one of the fastest growing agent frameworks). We are also building Logfire - our commercial observability platform, and we are hiring our first Solutions Engineer to take ownership o", "context": "", "use_case": "AI agent framework", "switched_from": "", "story": "Ask HN: Who is hiring? (December 2025)"}, {"id": 736283632, "item_id": 46017727, "url": "https://news.ycombinator.com/item?id=46017727", "by": "DouweM", "date": "2025-11-22", "version": null, "stance": "neutral", "quote": "Hey munro, Douwe from Pydantic AI here.", "excerpt": "Hey munro, Douwe from Pydantic AI here. Our docs have a page dedicated to observability: https://ai.pydantic.dev/logfire/, which is all based on OpenTelemetry and its gen_ai conventions. The Logfire SDK that Pydantic AI uses is a generic OTel client that defaults to sending data to Pydantic Logfire, our SaaS observability platform: https://pydantic.dev/logfire, but can easily be pointed at any OTel sink: https://ai.p", "context": "", "use_case": "AI agent framework", "switched_from": "", "story": "Agent design is still hard"}], "threads": [{"url": "https://news.ycombinator.com/item?id=46013935", "title": "Agent design is still hard", "mentions": 4}, {"url": "https://news.ycombinator.com/item?id=46918254", "title": "Python interpreter written in Rust for use by AI", "mentions": 2}, {"url": "https://news.ycombinator.com/item?id=45800955", "title": "Why we migrated from Python to Node.js", "mentions": 2}, {"url": "https://news.ycombinator.com/item?id=49522897", "title": "Ask HN: Who is hiring? (September 2026)", "mentions": 1}, {"url": "https://news.ycombinator.com/item?id=49156682", "title": "Ask HN: Who wants to be hired? (August 2026)", "mentions": 1}, {"url": "https://news.ycombinator.com/item?id=49156683", "title": "Ask HN: Who is hiring? (August 2026)", "mentions": 1}], "sentiment_by_month": {"2025-11": {"recommend": 4, "warn": 1, "neutral": 3}, "2025-12": {"neutral": 1, "compare": 1}, "2026-01": {"neutral": 2}, "2026-02": {"recommend": 1, "warn": 1}, "2026-03": {"recommend": 1}, "2026-04": {"neutral": 1, "warn": 1, "recommend": 1}, "2026-05": {"recommend": 1}, "2026-06": {}, "2026-07": {"warn": 1}, "2026-08": {"neutral": 1, "recommend": 1}, "2026-09": {"neutral": 1}, "2026-10": {}}, "versions": [], "version_unnamed": 0, "family_ref": null, "runs_on": null, "lines": [], "quotes": [{"id": 792409601, "item_id": 49525600, "url": "https://news.ycombinator.com/item?id=49525600", "by": "balerion-ai", "date": "2026-09-01", "version": null, "stance": "neutral", "quote": "Stack: Python, PydanticAI, Restate for durable execution", "excerpt": "Balerion AI | Software + FDE + AI Engineers | San Francisco, CA | ONSITE | Full-time | $250k+ base + seed equity | Visa sponsorship available | https://balerion.ai We're building agentic AI for mortgage lending \u2014 automating document processing and income qualification for loan originators. Underwriting a loan means pulling numbers out of a few hundred pages of paystubs, W-2s, bank statements, and tax returns, then ap", "context": "", "use_case": "Agent framework", "switched_from": "", "story": "Ask HN: Who is hiring? (September 2026)"}, {"id": 786973992, "item_id": 49185874, "url": "https://news.ycombinator.com/item?id=49185874", "by": "Gayithri2026", "date": "2026-08-05", "version": null, "stance": "recommend", "quote": "multi-provider (OpenAI/Claude/Gemini/Bedrock), swappable in one line via PydanticAI.", "excerpt": "Location: San Jose, CA (SF Bay Area) Remote: Yes Willing to relocate: No Technologies: Python, FastAPI, PostgreSQL/pgvector, RAG, LangChain, PydanticAI, Celery, Redis, Docker, AWS, Langfuse, Java, Spring Boot R\u00e9sum\u00e9/CV: https://github.com/Gayithri606/resume/blob/main/GayithriPonnapalli-Resume.pdf Email: gayithri@pojoai.com AI engineer building RAG chatbots and AI-driven workflow automation. Five years of production J", "context": "AI engineer building RAG chatbots", "use_case": "multi-provider LLM agents", "switched_from": "", "story": "Ask HN: Who wants to be hired? (August 2026)"}, {"id": 786650419, "item_id": 49165651, "url": "https://news.ycombinator.com/item?id=49165651", "by": "djd20", "date": "2026-08-04", "version": null, "stance": "neutral", "quote": "Python/Django/Pyramid/Pydantic AI hacker", "excerpt": "Democrance | https://democrance.com | Python/Django/Pyramid/Pydantic AI hacker | REMOTE (working in Dubai-UK timezones) We\u2019re a mature microinsurance SaaS startup, building software to help bridge the gap between insurance companies and those who are not insured or under-insured. High traffic, mobile first + SMS platform that requires some interesting compliance and security requirements with deployments in countries", "context": "hiring at a microinsurance SaaS startup", "use_case": "", "switched_from": "", "story": "Ask HN: Who is hiring? (August 2026)"}, {"id": 781973809, "item_id": 48873363, "url": "https://news.ycombinator.com/item?id=48873363", "by": "reveriedev", "date": "2026-07-11", "version": null, "stance": "warn", "quote": "Getting those agent systems to behave reliably was often annoying and tricky.", "excerpt": "Back in the medieval age of 2025, I used frameworks like PydanticAI and LangGraph to build agents. Getting those agent systems to behave reliably was often annoying and tricky. Claude Code and Codex have since become capable general-purpose agents. They each have an SDK, but those SDKs expose different interfaces and inherit their respective platform\u2019s quirks. While building CodeAlmanac, I wanted one way to work with", "context": "", "use_case": "building agents", "switched_from": "", "story": null}, {"id": 770125473, "item_id": 48132842, "url": "https://news.ycombinator.com/item?id=48132842", "by": "gorkaerana", "date": "2026-05-14", "version": null, "stance": "recommend", "quote": "pydantic-ai has been a superb addition to the AI engineering toolkit", "excerpt": "I have only had positive experiences using pydantic tools. They keep iterating on the pydantic validation tool and are planning on a v3 at some point, and pydantic-ai has been a superb addition to the AI engineering toolkit; which makes me hopeful and confident about the work they will do with httpx2. I look forward to explore their eval and observability tools. I do wonder about pydantic being a VC-backed company an", "context": "", "use_case": "", "switched_from": "", "story": "Pythons new httpx fork: httpx2"}, {"id": 765809265, "item_id": 47863079, "url": "https://news.ycombinator.com/item?id=47863079", "by": "eleventen", "date": "2026-04-22", "version": null, "stance": "recommend", "quote": "migrate an app from Claude SDK to Pydantic AI to get off claude API pricing", "excerpt": "I just installed it and continued about my business. I don't have a carefully tuned claude code setup. It has some skills, some CLAUDE.md files, and not much else. I did also migrate an app from Claude SDK to Pydantic AI to get off claude API pricing. https://pydantic.dev/docs/ai/overview/", "context": "", "use_case": "Replacement for Claude SDK app", "switched_from": "Claude SDK", "story": "Claude Code removed from Anthropic's Pro plan"}, {"id": 761858324, "item_id": 47616145, "url": "https://news.ycombinator.com/item?id=47616145", "by": "Acacian", "date": "2026-04-02", "version": null, "stance": "warn", "quote": "them had it.", "excerpt": "LangChain, CrewAI, OpenAI Agents, Anthropic, LiteLLM, Pydantic AI, Google ADK \u2014 I went through 11 frameworks looking for basic runtime security: injection detection, PII masking, audit trails. None of them had it. So I built a monkey-patching layer that intercepts LLM calls and runs them through guardrails: import aegis; aegis.init() Patches whatever frameworks you have installed. ~2.6ms overhead. The nastiest find: ", "context": "", "use_case": "", "switched_from": "", "story": "Show HN: I tested 11 AI frameworks for basic security \u2013 none passed"}, {"id": 761852545, "item_id": 47615784, "url": "https://news.ycombinator.com/item?id=47615784", "by": "kacper-vstorm", "date": "2026-04-02", "version": null, "stance": "neutral", "quote": "built on Pydantic AI", "excerpt": "Author here. pydantic-deep is an open-source AI agent framework built on Pydantic AI. This release adds ACP (Agent Client Protocol) support so agents can run inside editors like Zed with streaming and tool visibility. Other highlights: subagents are now full deep agents by default (filesystem, web, memory), thinking is enabled by default, Anthropic prompt caching is on, and there are 5 new lifecycle hooks for session", "context": "", "use_case": "Agent framework base", "switched_from": "", "story": null}, {"id": 761198912, "item_id": 47574932, "url": "https://news.ycombinator.com/item?id=47574932", "by": "kacper-vstorm", "date": "2026-03-30", "version": null, "stance": "recommend", "quote": "We maintain 5 open-source libraries built on Pydantic AI", "excerpt": "We maintain 5 open-source libraries built on Pydantic AI: a guardrails/shields library, a subagent delegation library, a context summarization library, a filesystem backend, and a full-stack template. When Pydantic AI shipped capabilities in v1.71+, we migrated all of them. The biggest lesson: we deleted our entire middleware abstraction layer (MiddlewareAgent, MiddlewareChain, ParallelMiddleware, pipeline compiler, ", "context": "", "use_case": "", "switched_from": "", "story": null}, {"id": 754205233, "item_id": 47137827, "url": "https://news.ycombinator.com/item?id=47137827", "by": "saberience", "date": "2026-02-24", "version": null, "stance": "warn", "quote": "Pydantic is a VC backed business now trying to funnel people into using their paid cloud services. No thanks.", "excerpt": "Would 1000% prefer to use Pi (https://github.com/badlogic/pi-mono) for building anything relating to AI agents. It's already battle-tested in super popular applications (like OpenClaw for example and many others), has 100s of extensions, is already built in a type-safe language, excellent community. And it also includes a full TUI and WebUI for interface support. Also Pydantic is a VC backed business now trying to fu", "context": "", "use_case": "", "switched_from": "", "story": "Build your own custom AI CLI with Pydantic AI"}, {"id": 753557280, "item_id": 47097330, "url": "https://news.ycombinator.com/item?id=47097330", "by": "cjonas", "date": "2026-02-20", "version": null, "stance": "recommend", "quote": "I think the PydanticAI framework has the right approach of encouraging Agent Delegation & sequential workflow first", "excerpt": "This approach seems interesting, but in my experience, a single \"agent\" with proper context management is better than a complicated agent graph. Dealing with hand-off (+ hand back) and multiple levels of conversations just leaves too much room for critical information to get siloed. If you have a narrow task that doesn't need full context, then agent delegation (putting an agent or inference behind a simple tool call", "context": "", "use_case": "agent framework", "switched_from": "", "story": "Cord: Coordinating Trees of AI Agents"}, {"id": 745006832, "item_id": 46562927, "url": "https://news.ycombinator.com/item?id=46562927", "by": "sathish316", "date": "2026-01-09", "version": null, "stance": "neutral", "quote": "uses PydanticAI and MCP-based tools", "excerpt": "Excellent article on the internals of coding CLIs. I learned a similarly powerful way to build DIY coding CLIs from this Martin Fowler post, which uses PydanticAI and MCP-based tools: https://martinfowler.com/articles/build-own-coding-agent.html Once you understand the underlying LLM tool-calling protocols described here\u2014and how MCP tool calls work (they\u2019re conceptually very similar)\u2014most coding CLIs stop feeling lik", "context": "", "use_case": "", "switched_from": "", "story": "How to Code Claude Code in 200 Lines of Code"}, {"id": 744196353, "item_id": 46512272, "url": "https://news.ycombinator.com/item?id=46512272", "by": "steer_dev", "date": "2026-01-06", "version": null, "stance": "neutral", "quote": "patch the framework (e.g. PydanticAI) at the entry point", "excerpt": "Doing post-mortems on my agent's failures over the holidays made me realize the problem isn't the model. It is the lack of a deterministic inference-time verification layer. I spent the break reading the recent Stanford/Harvard paper on agentic adaptation [1]. Their research provides mathematical proof for what I experienced in Q4: supervising only final outputs is a dead end. Agents learn to \"ignore tools and improv", "context": "", "use_case": "", "switched_from": "", "story": "The Agent Lobotomy: Inference-time verification for autonomous systems"}, {"id": 742080465, "item_id": 46380029, "url": "https://news.ycombinator.com/item?id=46380029", "by": "pamelafox", "date": "2025-12-24", "version": null, "stance": "compare", "quote": "It works pretty well, and is comparable to Langchainv1 and Pydantic-AI", "excerpt": "I'm on the Python advocacy team at Microsoft, so I've been experimenting a bit with the new framework. It works pretty well, and is comparable to Langchainv1 and Pydantic-AI, but has tighter integrations with Microsoft-specific technologies. All the frameworks have very similar Agent() interfaces as well as graph-based approaches (Workflow, Langgraph, Graph). I have a repository here with similar examples across all ", "context": "", "use_case": "", "switched_from": "", "story": "Microsoft Agent Framework"}, {"id": 740544161, "item_id": 46284010, "url": "https://news.ycombinator.com/item?id=46284010", "by": "kacper-vstorm", "date": "2025-12-15", "version": null, "stance": "neutral", "quote": "a framework that extends Pydantic-AI with powerful", "excerpt": "Advertise on Reddit Skip to Navigation Skip to Right Sidebar r/PydanticAI icon Go to PydanticAI r/PydanticAI \u2022 10m ago VanillaOk4593 Pydantic-DeepAgents: Autonomous Agents with Planning, File Ops, and More in Python Excited to share a new open-source project I just released: Pydantic-DeepAgents \u2013 a framework that extends Pydantic-AI with powerful \u201cdeep agent\u201d capabilities, making it easy to build production-ready aut", "context": "", "use_case": "Base agent framework", "switched_from": "", "story": null}, {"id": 740177601, "item_id": 46261100, "url": "https://news.ycombinator.com/item?id=46261100", "by": "kacper-vstorm", "date": "2025-12-13", "version": null, "stance": "neutral", "quote": "built on top of Pydantic-AI", "excerpt": "I just released Pydantic-DeepAgents, an open-source Python framework built on top of Pydantic-AI for creating production-grade autonomous agents. It helps developers quickly build agents with features like planning, filesystem ops, subagent delegation, and extensible skills. Key features: Multiple Backends: StateBackend (in-memory), FilesystemBackend, DockerSandbox, CompositeBackend Rich Toolsets: TodoToolset, Filesy", "context": "", "use_case": "", "switched_from": "", "story": "Pydantic-DeepAgents \u2013 A Python Framework for Building Autonomous AI Agents"}, {"id": 740071649, "item_id": 46254478, "url": "https://news.ycombinator.com/item?id=46254478", "by": "kacper-vstorm", "date": "2025-12-13", "version": null, "stance": "neutral", "quote": "This framework extends pydantic-ai with the stuff I kept rebuilding across projects", "excerpt": "Hey HN, I built pydantic-deepagents after getting frustrated with existing agent frameworks. Most work great for demos but break down when you need agents to actually do things: work with files, break complex tasks into subtasks, or handle long conversations without blowing through token limits. This framework extends pydantic-ai with the stuff I kept rebuilding across projects: Multiple backends: in-memory, filesyst", "context": "", "use_case": "", "switched_from": "", "story": "PydanticAI-DeepAgents \u2013 AI Agent Framework planning, filesystem, and subagents"}, {"id": 740014001, "item_id": 46250875, "url": "https://news.ycombinator.com/item?id=46250875", "by": "kacper-vstorm", "date": "2025-12-12", "version": null, "stance": "neutral", "quote": "It's built on top of pydantic-ai", "excerpt": "Hey HN, I've been working on pydantic-deepagents, a framework for creating deep AI agents that go beyond simple chatbots. It's built on top of pydantic-ai (which leverages Pydantic for structured data), but adds serious muscle: planning capabilities, filesystem access, subagent spawning, and more. Think of it as a toolkit for agents that can handle real-world tasks like file processing, task organization, and even hu", "context": "", "use_case": "agent foundation", "switched_from": "", "story": "PydanticAI-DeepAgents \u2013 Build powerful AI agents"}, {"id": 739257857, "item_id": 46203616, "url": "https://news.ycombinator.com/item?id=46203616", "by": "kacper-vstorm", "date": "2025-12-09", "version": null, "stance": "neutral", "quote": "built on top of Pydantic AI", "excerpt": "Python deep agent framework built on top of Pydantic AI designed to help you quickly build production-grade autonomous agents with planning, filesystem operations, subagent delegation, and skills. A complete demonstration of all pydantic-deep features in a web application. -> (demo repository) https://github.com/vstorm-co/pydantic-deep/blob/main/examples/full_app/README.md -> (demo video) https://drive.google.com/fil", "context": "", "use_case": "", "switched_from": "", "story": "Pydantic-AI Deepagents"}, {"id": 737022720, "item_id": 46063920, "url": "https://news.ycombinator.com/item?id=46063920", "by": "jedberg", "date": "2025-11-26", "version": null, "stance": "recommend", "quote": "Pydantic also has a first party integration with DBOS", "excerpt": "Pydantic also has a first party integration with DBOS, which doesn't require an external state server. https://ai.pydantic.dev/durable_execution/dbos/", "context": "", "use_case": "AI agent framework", "switched_from": "", "story": "Show HN: Dank-AI \u2013 Ship production AI agents 10x faster"}, {"id": 736260433, "item_id": 46016277, "url": "https://news.ycombinator.com/item?id=46016277", "by": "sathish316", "date": "2025-11-22", "version": null, "stance": "neutral", "quote": "", "excerpt": "The framework has the following capabilities: 1. A way to create function tools 2. A way to create specialised subagents that can use their own tool or their own model. The main agent can delegate to subagent exposed as a tool. Subagents don\u2019t get confused because they have their own context window, tools and even models (mix and match Remote LLM with Local LLM if needed) 3. Don\u2019t use all tools of the MCP servers you", "context": "", "use_case": "", "switched_from": "", "story": "Agent design is still hard"}, {"id": 736242176, "item_id": 46015136, "url": "https://news.ycombinator.com/item?id=46015136", "by": "sathish316", "date": "2025-11-22", "version": null, "stance": "recommend", "quote": "", "excerpt": "I agree with your analysis of building your own Agent framework to have some level of control and fewer abstractions. Agents at their core are about programmatically talking to an LLM and performing these basic operations: 1. Structured Input and String Interpolation in prompts 2. Structured Output and Unmarshalling String response to Structured output (This is getting easier now with LLMs supporting Structured outpu", "context": "", "use_case": "Agent framework", "switched_from": "", "story": "Agent design is still hard"}, {"id": 736237297, "item_id": 46014831, "url": "https://news.ycombinator.com/item?id=46014831", "by": "mritchie712", "date": "2025-11-22", "version": null, "stance": "neutral", "quote": "We've tried many approaches and frameworks over the past 2 years (e.g. PydanticAI), but using cc is the first that has felt magic.", "excerpt": "Some things we've[0] learned on agent design: 1. If your agent needs to write a lot of code, it's really hard to beat Claude Code (cc) / Agent SDK. We've tried many approaches and frameworks over the past 2 years (e.g. PydanticAI), but using cc is the first that has felt magic. 2. Vendor lock-in is a risk, but the bigger risk is having an agent that is less capable then what a user gets out of chatgpt because you're ", "context": "", "use_case": "agent framework", "switched_from": "", "story": "Agent design is still hard"}, {"id": 736034560, "item_id": 46002160, "url": "https://news.ycombinator.com/item?id=46002160", "by": "raooll", "date": "2025-11-20", "version": null, "stance": "recommend", "quote": "I really like https://ai.pydantic.dev/ as it builds on existing developer knowledge and there is not much to learn in the framework itself.", "excerpt": "I really like https://ai.pydantic.dev/ as it builds on existing developer knowledge and there is not much to learn in the framework itself.", "context": "", "use_case": "building AI agents", "switched_from": "", "story": "Ask HN: Best solution to build AI agents?"}, {"id": 735741473, "item_id": 45983842, "url": "https://news.ycombinator.com/item?id=45983842", "by": "ouk", "date": "2025-11-19", "version": null, "stance": "recommend", "quote": "I use it with the PydanticAI framework and it's quite nice.", "excerpt": "There is also PydanticAI Gateway (https://ai.pydantic.dev/gateway/). I use it with the PydanticAI framework and it's quite nice.", "context": "", "use_case": "LLM agent framework", "switched_from": "", "story": "Control LLM Spend and Access with any-LLM-gateway"}, {"id": 732854288, "item_id": 45803393, "url": "https://news.ycombinator.com/item?id=45803393", "by": "jedberg", "date": "2025-11-03", "version": null, "stance": "neutral", "quote": "PydanticAI has DBOS built in", "excerpt": "> If you want to be provider independent you most likely either use pydantic AI ... Neither one have built-in solution for durable execution PydanticAI has DBOS built in [0]. [0] https://ai.pydantic.dev/durable_execution/dbos/", "context": "", "use_case": "", "switched_from": "", "story": "Absurd Workflows: Durable Execution with Just Postgres"}, {"id": 732849328, "item_id": 45803083, "url": "https://news.ycombinator.com/item?id=45803083", "by": "zzzeek", "date": "2025-11-03", "version": null, "stance": "neutral", "quote": "", "excerpt": "async python has problems, but \"anyio exists\" is not one of them that can be blamed on python, simply dont use weird third party libraries trying to second guess the asyncio architecture edit: ironically I'm the author of a weird third party library trying to second guess the asyncio architecture but mine is good https://awaitlet.sqlalchemy.org/en/latest/ (but I'll likely be retiring it in the coming year due to lack", "context": "", "use_case": "Python agent framework", "switched_from": "", "story": "Why we migrated from Python to Node.js"}, {"id": 732830306, "item_id": 45801894, "url": "https://news.ycombinator.com/item?id=45801894", "by": "cjonas", "date": "2025-11-03", "version": null, "stance": "warn", "quote": "", "excerpt": "100% same experience. If it were up to me, I'd started with typescript, but the client insisted on using a python stack (landed on FastMCP, FastAPI, PydanticAI). While, `PydanticAI` does the best it can with a limited type system, it just can't match the productivity of typescript. And I still can't believe what a mess async python is. The worst thing we've encountered was a bug from mixing anyio with asyncio which r", "context": "builds agent systems for a client", "use_case": "Agent framework", "switched_from": "", "story": "Why we migrated from Python to Node.js"}, {"id": 732783936, "item_id": 45798996, "url": "https://news.ycombinator.com/item?id=45798996", "by": "pietz", "date": "2025-11-03", "version": null, "stance": "recommend", "quote": "We're using pydantic AI where the entire Agent loop is taken care of by the framework. Highly recommend.", "excerpt": "Cannot confirm this. Both sound like prompting issues. - Depends on your use case to let the model understand when and when not to use tools - gpt-5 s VERY persistent and often searches more than 10 times in a single run depending on the results. We're using pydantic AI where the entire Agent loop is taken care of by the framework. Highly recommend.", "context": "", "use_case": "AI agent loop framework", "switched_from": "", "story": "Production RAG: what I learned from processing 5M+ documents"}]}