Based on the limited social mentions provided, CrewAI appears to be recognized as one of the major AI agent frameworks in the current landscape, frequently mentioned alongside established competitors like LangGraph, AutoGen, and others. The dev.to article suggests there's significant activity and interest in the AI agent framework space, with new frameworks regularly gaining attention and GitHub stars. However, the social mentions also indicate broader concerns about AI agent production deployment, including issues with observability, monitoring, and potential risks like data deletion incidents. Without specific user reviews, it's difficult to assess detailed user sentiment about CrewAI's strengths, weaknesses, or pricing.
Mentions (30d)
1
Reviews
0
Platforms
3
GitHub Stars
47,671
6,464 forks
Based on the limited social mentions provided, CrewAI appears to be recognized as one of the major AI agent frameworks in the current landscape, frequently mentioned alongside established competitors like LangGraph, AutoGen, and others. The dev.to article suggests there's significant activity and interest in the AI agent framework space, with new frameworks regularly gaining attention and GitHub stars. However, the social mentions also indicate broader concerns about AI agent production deployment, including issues with observability, monitoring, and potential risks like data deletion incidents. Without specific user reviews, it's difficult to assess detailed user sentiment about CrewAI's strengths, weaknesses, or pricing.
Features
Industry
information technology & services
Employees
48
Funding Stage
Merger / Acquisition
Total Funding
$12.5M
1,858
GitHub followers
31
GitHub repos
47,671
GitHub stars
3
npm packages
338
npm downloads/wk
6,073,510
PyPI downloads/mo
Ask HN: How are you monitoring AI agents in production?
With the recent incidents (DataTalks database wipe by Claude Code, Replit agent deleting data during code freeze), it's clear that running AI agents in production without observability is risky.<p>Common failure modes I've seen: no visibility into what the agent did step-by-step, surprise LLM bills from untracked token usage, risky outputs going undetected, and no audit trail for post-mortems.<p>I've been building AgentShield (https://useagentshield.com) — an observability SDK for AI agents. It does execution tracing, risk detection on outputs, cost tracking per agent/model, and human-in-the-loop approval for high-risk actions. Plugs into LangChain, CrewAI, and OpenAI Agents SDK with a 2-line integration.<p>Curious what others are using. Rolling your own monitoring? LangSmith? Langfuse? Or just hoping for the best?
View originalPricing found: $0.50/execution, $0.50/execution
EVAL #004: AI Agent Frameworks — LangGraph vs CrewAI vs AutoGen vs Smolagents vs OpenAI Agents SDK
Every week there's a new AI agent framework on Hacker News. The GitHub stars pile up, the demo videos...
View originalAsk HN: How are you monitoring AI agents in production?
With the recent incidents (DataTalks database wipe by Claude Code, Replit agent deleting data during code freeze), it's clear that running AI agents in production without observability is risky.<p>Common failure modes I've seen: no visibility into what the agent did step-by-step, surprise LLM bills from untracked token usage, risky outputs going undetected, and no audit trail for post-mortems.<p>I've been building AgentShield (https://useagentshield.com) — an observability SDK for AI agents. It does execution tracing, risk detection on outputs, cost tracking per agent/model, and human-in-the-loop approval for high-risk actions. Plugs into LangChain, CrewAI, and OpenAI Agents SDK with a 2-line integration.<p>Curious what others are using. Rolling your own monitoring? LangSmith? Langfuse? Or just hoping for the best?
View originalShow HN: Mnemora – Serverless memory DB for AI agents (no LLM in your CRUD path)
Hi HN,<p>I built Mnemora because every AI agent memory solution I evaluated (Mem0, Zep, Letta) routes data through an LLM on every read and write. At scale, that means 200-500ms latency per operation, token costs on your memory layer, and a runtime dependency you don't control.<p>Mnemora takes the opposite approach: direct database CRUD. State reads hit DynamoDB at sub-10ms. Semantic search uses pgvector with Bedrock Titan embeddings — the LLM only runs at write time to generate the embedding vector. All reads are pure database queries.<p>Four memory types, one API: 1. Working memory: key-value state in DynamoDB (sub-10ms reads) 2. Semantic memory: vector-searchable facts in Aurora pgvector 3. Episodic memory: time-stamped event logs in S3 + DynamoDB 4. Procedural memory: rules and tool definitions (coming v0.2)<p>Architecture: fully serverless on AWS — Aurora Serverless v2, DynamoDB on-demand, Lambda, S3. Idles at ~$1/month, scales per-request. Multi-tenant by default: each API key maps to an isolated namespace at the database layer.<p>What I'd love feedback on: 1. Is the "no LLM in CRUD path" differentiator clear and compelling? 2. Would you use this over Mem0/Zep for production agents? What's missing? 3. What memory patterns are you solving that don't fit these 4 types?<p>Happy to answer architecture questions.<p>SDK: pythonpip install mnemora<p>from mnemora import MnemoraSync<p>client = MnemoraSync(api_key="mnm_...") client.store_memory("my-agent", "User prefers bullet points over prose") results = client.search_memory("output format preferences", agent_id="my-agent") # [0.54] User prefers bullet points over prose Drop-in LangGraph CheckpointSaver, plus LangChain and CrewAI integrations.<p>Links: 5-min quickstart: <a href="https://mnemora.dev/docs/quickstart" rel="nofollow">https://mnemora.dev/docs/quickstart</a> GitHub: <a href="https://github.com/mnemora-db/mnemora" rel="nofollow">https://github.com/mnemora-db/mnemora</a> PyPI: <a href="https://pypi.org/project/mnemora/" rel="nofollow">https://pypi.org/project/mnemora/</a> Architecture deep-dive: <a href="https://mnemora.dev/blog/serverless-memory-architecture-for-ai-agents" rel="nofollow">https://mnemora.dev/blog/serverless-memory-architecture-for-...</a>
View originalShow HN: AgentLens – Open-source observability for AI agents
Hi HN,<p>I built AgentLens because debugging multi-agent systems is painful. LangSmith is cloud-only and paid. Langfuse tracks LLM calls but doesn't understand agent topology — tool calls, handoffs, decision trees.<p>AgentLens is a self-hosted observability platform built specifically for AI agents:<p>- *Topology graph* — see your agent's tool calls, LLM calls, and sub-agent spawns as an interactive DAG - *Time-travel replay* — step through an agent run frame-by-frame with a scrubber timeline - *Trace comparison* — side-by-side diff of two runs with color-coded span matching - *Cost tracking* — 27 models priced (GPT-4.1, Claude 4, Gemini 2.0, etc.) - *Live streaming* — watch spans appear in real-time via SSE - *Alerting* — anomaly detection for cost spikes, error rates, latency - *OTel ingestion* — accepts OTLP HTTP JSON, so any OTel-instrumented app works<p>Works with LangChain, CrewAI, AutoGen, LlamaIndex, and Google ADK.<p>Tech: React 19 + FastAPI + SQLite/PostgreSQL. MIT licensed. 231 tests, 100% coverage.<p><pre><code> docker run -p 3000:3000 tranhoangtu/agentlens-observe:0.6.0 pip install agentlens-observe </code></pre> Demo GIF and screenshots in the README.<p>GitHub: <a href="https://github.com/tranhoangtu-it/agentlens-observe" rel="nofollow">https://github.com/tranhoangtu-it/agentlens-observe</a> Docs: <a href="https://agentlens-observe.pages.dev" rel="nofollow">https://agentlens-observe.pages.dev</a><p>I'd love feedback on the trace visualization approach and what features matter most for your agent debugging workflow.
View originalRepository Audit Available
Deep analysis of crewAIInc/crewAI — architecture, costs, security, dependencies & more
Yes, CrewAI offers a free tier. Pricing found: $0.50/execution, $0.50/execution
Key features include: Trusted, Scalable, Loved by AI builders, Trusted by AI leaders.
CrewAI has a public GitHub repository with 47,671 stars.
Based on user reviews and social mentions, the most common pain points are: cost tracking, token usage, token cost.
