Last updated: 2025-09-22
This repository lists AI Agent Frameworks we’ve battle-tested and/or reviewed while building production-grade systems for 40+ clients. This list is sorted based on our preferability, focuses on real trade-offs, what each framework is REALLY good at, and when to reach for it.
Note: A longer, more detailed version of this list is available as an article here 🙃
- Reliability: state management, retries, determinism, type safety
- Ergonomics: DX, tooling, validation, tracing, testability, etc.
- Deployment: ease of deployment, observability, failure recovery
- Ecosystem fit: language support, community, ease of maintainability
- 🥇 Pydantic AI
- 🥈 OpenAI Agents SDK
- 🏅 CrewAI
- LlamaIndex Agents
- LangGraph
- AutoGen
- Haystack Agents
- Other Frameworks
🥇 1. Pydantic AI
Type-first AI agent framework with tools, validators, provider-agnostic (you can use different LLMs from different providers!), optional graph-style control, and first-class observability via Logfire — built around reliable structured (and unstructured) outputs.
Key Facts
- Open Source: ✅ Yes
- Language: Python
Why we like it? (from our production experience)
- Type safety + validators = dramatically fewer runtime surprises and cleaner contracts between agents and tools
- Logfire integration gives real-time traces, usage, and failure context that feel native—no duct tape dashboards
- Shareable, controllable context primitives cut weeks of ad-hoc “memory/utils” code
- Multi-LLM support that actually ships: OpenAI, Anthropic, Gemini, Groq, Mistral, Cohere, Ollama (we’ve mixed and matched)
- FastAPI-inspired design—minimal, predictable, easy to onboard new engineers
- Tool swapping/mocking is fast, which speeds up testing, troubleshooting, and vendor changes
- Optional graph support when you need explicit plan → act → observe control without giving up the simple core
- Modularity by default: small typed components that other devs can read, test, and fix
Where it shines?
- Production agents that transform/aggregate data or orchestrate many tools with strict output requirements
- Multi-provider setups that need straightforward model swaps/fallbacks without rewriting orchestration
- Teams that care about traceability and cost control (Logfire) for audits, regressions, and SLA debugging
Takeaways
- Treat your Pydantic models as the contract—design them before prompts and tools
- Start with a tight output schema + validators; add tools only after the contract stabilizes
- Keep tools/helper functions small, typed, and mockable; write unit tests against the models and tool I/O
- Turn on Logfire on day one to capture traces/tokens/failures—pays off immediately as you scale (we run 80+ tools in a multi-agent system)
- Use context primitives instead of custom “memory managers”; reach for the graph only when the flow truly needs explicit control
🥈 2. OpenAI Agents SDK
Lightweight, production-minded agent orchestration with a tiny set of primitives (Agents, Tools, Handoffs, Guardrails, Sessions), built-in tracing, and built-in tools (retrieval, web search, computer-use). Provider-agnostic by design.
Key Facts
- Open Source: ✅ Yes
- Languages: Python, JS/TS
Why we like it?
- Small surface area yet expressive — we ship fast and keep orchestration readable as complexity grows
- Handoffs and “agents-as-tools” make multi-agent designs practical without over-engineering
- Built-in traces + usage metrics help us catch regressions early and control cost from day one
- Sessions remove boilerplate memory handling for multi-turn apps
- Works great on OpenAI stack but stays provider-agnostic if we need to mix in other LLMs
Where it shines?
- Production apps that need clear observability/auditing with minimal framework overhead
- Multi-agent workflows (planner → specialist → reviewer) where explicit handoffs improve SLAs
- Assistants that need out-of-the-box web/doc search or computer-use without building custom tools
- Realtime/voice agents that stream audio, tool calls, and state transitions cleanly
Takeaways
- Start with a single agent and 2–3 critical tools; enable tracing and add lightweight guardrails for inputs/outputs
- Use handoffs only when specialization is proven; prefer “agents as tools” for modular skills you can swap in/out
- Persist context with Sessions & enforce token budgets and log usage to keep costs predictable
- If you must be multi-provider, keep model config abstracted — the SDK allows it, but ofc they work better with the OpenAI stack
🏅 3. CrewAI
Role-based “crews” of agents that execute well-defined tasks with tools and memory. Two simple process modes (sequential or hierarchical with a manager) keep orchestration understandable while still enabling multi-agent collaboration.
Key Facts
- Open Source: ✅ Yes
- Language: Python
Why we like it?
- Fast mental model: Agents (roles) + Tasks (goals) + simple process = quick shipping without over-engineering
- Multi-agent by default: “manager + specialists” maps cleanly to planner → doer → reviewer patterns
- Solid tool ecosystem: the official tools cover real work (browsering, code exec, RAG-style lookups) so we prototype quickly
- Pragmatic memory options: start with basic memory, opt into longer-term persistence when outcomes justify it
Where it shines?
- Product/research teams shipping practical assistants fast (researcher → analyst → writer)
- Workflows that benefit from a lightweight manager delegating/ordering tasks (hierarchical mode)
- Coding/analysis copilots that need an embedded Python sandbox (Code Interpreter) and web/data tools
Watch-outs
- Less low-level control than graph/state-machine frameworks; complex recovery/checkpointing will be DIY
- Hierarchical setups add indirection—great for complexity, but overkill for linear flows
- Long-term memory persistence requires configuration (paths/providers); plan this early to avoid surprises later
Takeaways
- Start sequential with a 3-agent crew (researcher → analyst → writer) and crisp Task specs (objective, inputs, success criteria, expected output)
- Introduce a manager (hierarchical) only when the task graph truly branches or you need dynamic delegation
- Leverage the official tools first (browser, code interpreter, docs search); add custom tools only where they beat off-the-shelf ones
- Enable basic memory for context; adopt long-term/entity memory once you have a retention plan and storage path/provider locked in
The following list below is sorted by quality, ease of use, and production-readiness based on our experience. However, all the AI agent frameworks mentioned are just as impressive as the Top 3 and have the potential to reach the top as well!
Data-centric agents built on top of strong RAG primitives. You get clean agent abstractions (FunctionAgent / ReAct-style), a robust Tool system, memory, and an ecosystem of connectors via LlamaHub. For heavier workflows, LlamaIndex adds event-driven “Workflows”, plus managed services like LlamaCloud and LlamaParse for high-fidelity document parsing and ingestion
Key Facts
- Open Source: ✅ Yes (MIT)
- Languages: Python, TS
- Building blocks: Agents (FunctionAgent/ReAct), Tools & ToolSpecs, Memory, Query Engines/Retrievers, Workflows, Observability; optional LlamaCloud and LlamaParse
Why we like it?
- Good data connectors + loaders via LlamaHub; Good to wire enterprise sources fast without custom glue
- Clean upgrade path: start with a RAG app (indexes, retrievers), then wrap it in an agent as the use case matures
- Tools are first-class (and typed), so agents can call query engines, functions, or external APIs in a predictable way
- Workflows cover the “agentic but reproducible” gap—great for multi-step plans, routing, and custom orchestration
Where it shines?
- Knowledge assistants over heterogeneous corpora where retrieval quality, routing, and synthesis matter (reports, SOPs, tech docs)
- Multi-document agents that plan, rerank, and call specialized tools for doc-specific questions
- End-to-end “agentic document workflows” that combine parsing, retrieval, structured outputs, and orchestration for knowledge work
Good starting point
- Build your index + retriever and validate retrieval quality in isolation (small gold set + evals)
- Expose 2–3 high-value Tools (query engine, web/doc fetch, domain API). Keep tool I/O narrow and typed
- If docs are messy (tables, figures, scans), introduce LlamaParse to improve chunk fidelity before tuning prompts
- For branching plans or multi-stage jobs, move the core into a Workflow and keep the agent as the front-door
5. LangGraph
Graph/state-machine approach for building stateful, recoverable agent loops with optional checkpointing and persistence. Great when auditability matter more than “auto” magic. Quite heavy!
Key Facts
- Open Source: ✅ Yes
- Languages: Python, JS/TS
Why we like it?
- Precise control over tool-use loops, branching, and backoff/retry strategies
- Checkpoint + resume for long-running tasks and robust failure recovery
- Clean separation of nodes keeps complex flows readable and testable
Where it shines?
- Regulated or high-SLA workflows that need traceability and resumability
- Multi-step retrieval/decision pipelines (plan → act → observe) with human approval gates
Watch-outs
- Memory semantics are nontrivial
- Often version shifts introduce notable API changes (plan migrations intentionally!)
- Heavy multi-agent abstractions (very difficult to keep up)
6. AutoGen
Event-driven multi-agent framework where agents (human, assistant, tool, evaluator) converse, collaborate, and call tools. Includes patterns like GroupChat and a visual Studio for orchestration and inspection.
Key Facts
- Open Source: ✅ Yes
- Primary Language: Python (with a .NET variant available)
- Core concepts: AgentChat APIs (
ConversableAgent,AssistantAgent,UserProxyAgent), GroupChat with aGroupChatManager, tool/function calling, optional code execution via the user proxy agent
Why we like it?
- Rich multi-agent patterns out of the box (planner/solver/reviewer, debate, supervisor) without re-implementing orchestration
- Studio trims debugging time by making agent interactions observable and tweakable
- Easy to add human-in-the-loop checkpoints exactly where decisions matter (e.g., before execution or external calls)
- “Agents as chat participants” is a mental model that scales from two roles to larger teams cleanly
Where it shines?
- Research & prototyping of collaborative behaviors and role specialization
- Complex tasks that benefit from peer-review or committee-style decision making
- Teams that want a GUI (Studio) to wire skills/tools and iterate quickly with stakeholders
Watch-outs
- Heavier footprint vs. minimal SDKs; expect more moving parts and some learning curve
- Often version shifts introduce notable API changes (plan migrations intentionally!)
Key Facts
- Open Source: ✅ Yes
- Language: Python
Why we like it?
- Mature retrieval/search stack and clean integration between RAG components and the agent loop
- “Exit conditions” make agent behavior predictable—great for SLAs and cost control
- Strong production story: tracing, logging, and cloud-native deployment paths are documented
Where it shines?
- Enterprise RAG with observability and on-prem/cloud portability (vector stores, retrievers, rankers, tools)
Watch-outs:
- Can be verbose - start with a small set of components and add conditional routing only when needed (agentic pipelines)
- Quite difficult to maintain
- Very easy to over-engineer and hard to read if you add too many nodes early
The frameworks below were NEVER tested by us, but they've been on our radar and timeline quite often, so maybe you can check them out first :)
- AutoGPT — Classic autonomous agent project; great for experiments and understanding long-running loops. Ideal for self-contained tasks like market research, but watch for potential API cost overruns.
- MetaGPT — Multi-agent “company-as-code” pattern with research-friendly team roles (PM/Eng/QA). Excels in simulating collaborative workflows for prototyping complex systems.
- Camel-AI — Role-playing agents with conversation curricula for cooperative task-solving. Strong for research-driven scenarios requiring structured multi-agent interactions.
- OpenHands — Computer-use/desktop-control agents for browser and OS actions. Perfect for grounded tasks like automating desktop workflows or testing UI interactions.
- AgentScope — Alibaba’s framework for building and evaluating scalable multi-agent systems. Suited for large-scale, enterprise-grade applications with robust evaluation needs.
- LangChain — Modular framework for building LLM applications with chains, tools, and agents. Shines in rapid prototyping and integrations; our go-to for 50+ client projects needing flexible data pipelines.
- AgentFlow — Low-code framework using Markdown and natural language for AI-powered workflows. Simplifies agent creation for quick automation; ideal for non-technical teams.
- Atomic Agents — Lightweight, modular framework for building agentic AI pipelines with atomic components. Great for developers prioritizing simplicity and composability in smaller projects.
- PromptFlow — Microsoft’s tool for developing and evaluating LLM-based flows. Excels in structured prompt engineering and testing for enterprise-grade applications.
- n8n — Workflow automation platform with AI capabilities for integrating agent-like tasks. Perfect for connecting APIs and automating business processes with minimal coding.
- OpenAgents — Open platform for hosting and using language agents in real-world scenarios. Strong for community-driven, extensible agent deployments.
- Lindy — AI agent platform for creating and managing business agents with simple prompts. Ideal for startups needing quick, user-friendly automation solutions.