# Community Roundup: February 2026

> Agno hits 400 GitHub contributors, ships v2.5.0 with Team Modes and HITL for Teams, and moves to Apache 2.0 — plus 17 community builds spanning autonomous agent marketplaces, voice RAG, and multi-paper research assistants.

- Published: 2026-02-26
- Author: Yuvaraj Shanmugam
- Category: Community
- Canonical: https://agno-com-nine.vercel.app/articles/community-roundup-february-2026
- Markdown: https://agno-com-nine.vercel.app/articles/community-roundup-february-2026.md

February 2026 was a landmark month for Agno. We hit **400 contributors on GitHub**, released the transformative **v2.5.0** with Team Modes and HITL for Teams, changed our license to **Apache 2.0**, and watched the community ship an incredible wave of production-ready applications.

Whether you're orchestrating multi-agent teams with different execution modes, implementing human-approval workflows, or scheduling autonomous agent runs with cron, February delivered the production infrastructure you need to build real AI systems.

The community responded by shipping everything from autonomous AI marketplaces to voice-enabled RAG systems to multi-paper research assistants.

## New releases & features

### v2.4.4 - v2.5.4

February delivered ten major releases, including our landmark v2.5.0. Here's what we shipped:

### [Workflows HITL](https://docs.agno.com/workflows/hitl/overview) (v2.5.4)

Add support for HITL in workflows at Step level to pause for confirmation and user input:

- **Step-level pausing:** Pause workflows at any step for human approval
- **Confirmation flows:** Require human confirmation before critical workflow steps
- **User input collection:** Gather information from users mid-workflow
- **Seamless resumption:** Continue workflow execution after human interaction

Essential for workflows that require human oversight at specific decision points.

### [Metrics System Redesign](https://github.com/agno-agi/agno/releases/tag/v2.5.4) (v2.5.4)

Redesigned metrics system to provide per-model, per-component granular tracking across the entire agent/team/workflow lifecycle:

- **Granular tracking:** Per-model and per-component metrics
- **Lifecycle coverage:** Track metrics across agents, teams, and workflows
- **RunMetrics replacement:** Replaced heavyweight RunOutput collectors with efficient RunMetrics
- **Team metrics:** Include member response metrics in team session metrics

Better observability and cost tracking for production deployments.

### [TeamMode.tasks Streaming](https://github.com/agno-agi/agno/releases/tag/v2.5.4) (v2.5.4)

Added streaming event support for `TeamMode.tasks`, enabling real-time event emission during autonomous task execution. Watch tasks get assigned and completed in real-time.

### [PgVector Similarity Threshold](https://github.com/agno-agi/agno/releases/tag/v2.5.4) (v2.5.4)

Added `similarity_threshold` parameter for filtering search results by minimum similarity score. Only return results that meet your quality bar.

### [DuckDuckGoTools Enhancements](https://github.com/agno-agi/agno/releases/tag/v2.5.4) (v2.5.4)

Exposed `timelimit`, `region`, and `backend` params in DuckDuckGoTools for more control over web searches.

### [SeltzTools SDK Update](https://github.com/agno-agi/agno/releases/tag/v2.5.4) (v2.5.4)

Updated to use Seltz SDK 0.1.3 for improved search capabilities.

### [Team Execution Modes](https://github.com/agno-agi/agno/releases/tag/v2.5.0) (v2.5.0)

Introduced `TeamMode` enum with four distinct execution patterns:

- **coordinate (default)**: Supervisor pattern where the leader picks members, crafts tasks, and synthesizes responses
- **route**: Router pattern where the leader routes to a specialist and returns their response directly
- **broadcast**: Broadcast pattern where the leader delegates the same task to all members simultaneously
- **tasks**: Autonomous task-based execution where the leader decomposes goals into a shared task list

This transforms how you build multi-agent systems. Different problems need different orchestration patterns—now you can choose the right one.

### [Human-in-the-Loop (HITL) for Teams](https://github.com/agno-agi/agno/releases/tag/v2.5.0) (v2.5.0)

The most-requested feature: comprehensive HITL support for teams with improved `RunRequirement` system:

- **Tool confirmation flows:** Pause for approval before critical operations
- **User input collection:** Gather information with schema validation
- **External tool execution:** Delegate to human-executed tools
- **Session state preservation:** Fixed HITL pause handlers silently dropping session_state changes

Essential for production teams that require human oversight before high-stakes decisions.

### [Approval System](https://github.com/agno-agi/agno/releases/tag/v2.5.0) (v2.5.0)

New `@approval` decorator for systematic human approval workflows:

- **@approval(type="required")**: Blocking approval that pauses the run until resolved via approvals API
- **@approval(type="audit")**: Non-blocking audit trail for compliance/logging
- **Persistent approval records** with status tracking (pending, approved, rejected, expired, cancelled)
- **Approvals API** for listing, inspecting, and resolving approvals

Perfect for financial services, healthcare, and other regulated industries that need audit trails.

### [Scheduler](https://github.com/agno-agi/agno/releases/tag/v2.5.0) (v2.5.0)

Cron-based scheduling for agents, teams, and workflows:

- **Cron expressions** for flexible scheduling
- **Retry logic** for failed runs
- **Timeout support** for long-running tasks
- **Timezone handling** for global deployments

Run your agents on a schedule, not just on demand.

### [LearningMachine for Teams](https://github.com/agno-agi/agno/releases/tag/v2.5.0) (v2.5.0)

Extended LearningMachine support to team-based agents for persistent learning across team interactions. Teams can now learn and improve from every conversation, just as individual agents do.

### [Callable Factories for Tools, Knowledge, and Members](https://github.com/agno-agi/agno/releases/tag/v2.5.0) (v2.5.0)

Tools, knowledge, and team members can now be defined as callable factories resolved at runtime with access to `RunContext`:

- **Runtime resolution:** Configure tools/knowledge/members based on session context
- **Caching support** via `cache_callables`
- **Custom cache keys** for session-scoped resolution

Enables dynamic agent configuration based on user permissions, session state, or runtime conditions.

### [Knowledge Isolation](https://github.com/agno-agi/agno/releases/tag/v2.5.0) (v2.5.0)

Added `isolate_vector_search` flag to Knowledge class:

- When enabled, documents are tagged with `linked_to` metadata during insert
- Searches filter by this tag automatically
- Multiple Knowledge instances can share the same vector database while maintaining isolated search results
- Defaults to False for backwards compatibility

Critical for multi-tenant deployments where knowledge bases must be strictly separated.

### [License Change to Apache 2.0](https://github.com/agno-agi/agno/releases/tag/v2.5.2) (v2.5.2)

Changed license from Mozilla Public License 2.0 (MPL 2.0) to **Apache Software License 2.0**. This makes Agno more permissive for commercial use and aligns with industry-standard open source licensing. A major milestone for production adoption.

### [CodingTools](https://github.com/agno-agi/agno/releases/tag/v2.5.1) (v2.5.1)

New toolkit for code-related operations. Build agents that write, analyze, and refactor code.

### [UserFeedbackTools](https://github.com/agno-agi/agno/releases/tag/v2.5.1) (v2.5.1)

New toolkit for collecting user feedback. Essential for building agents that learn from user interactions.

### [SeltzTools](https://github.com/agno-agi/agno/releases/tag/v2.4.5) (v2.4.5)

Integration with Seltz Search for advanced search capabilities.

### [UnsplashTools](https://github.com/agno-agi/agno/releases/tag/v2.4.4) (v2.4.4)

Search and retrieve high-quality, royalty-free images from Unsplash API. Perfect for content generation agents.

### [Workflow Condition Step Else Path](https://github.com/agno-agi/agno/releases/tag/v2.4.7) (v2.4.7)

Added support for an `Else` path in Condition steps. Define alternative paths instead of just skipping execution.

### [CEL Support for Workflow Steps](https://github.com/agno-agi/agno/releases/tag/v2.4.8) (v2.4.8)

Use CEL (Common Expression Language) expressions as evaluators in Condition, Loop, and Router steps. CEL expressions are strings, making workflow steps fully serializable.

### [Workflow Router Step Enhancements](https://github.com/agno-agi/agno/releases/tag/v2.4.8) (v2.4.8)

Added the ability to return the name of the Step instead of the step itself, plus support to return a group of steps as a choice for the router.

### [Neosantara Model Provider](https://github.com/agno-agi/agno/releases/tag/v2.4.8) (v2.4.8)

Added support for Neosantara, an Indonesian LLM gateway that provides an OpenAI-compatible API. Expanding global reach.

### [Moonshot Model Provider](https://github.com/agno-agi/agno/releases/tag/v2.4.4) (v2.4.4)

Added [moonshot.ai](http://moonshot.ai) model provider for additional model options.

### [AWS Bedrock Reranker](https://github.com/agno-agi/agno/releases/tag/v2.4.7) (v2.4.7)

New `AwsBedrockReranker` class supporting Cohere Rerank 3.5 and Amazon Rerank 1.0.

### [AWS Bedrock Embedder Updates](https://github.com/agno-agi/agno/releases/tag/v2.4.7) (v2.4.7)

Updated AWS Bedrock to support Cohere v4 Embed.

### [Remote Knowledge](https://github.com/agno-agi/agno/releases/tag/v2.5.3) (v2.5.3)

Added endpoints to support the listing of remote contents via API. Enables uploading of content in S3 buckets via AgentOS.

### [PDF Reader Improvements](https://github.com/agno-agi/agno/releases/tag/v2.5.3) (v2.5.3)

Added `sanitize_content` parameter (enabled by default) that normalizes fragmented PDF text extraction—collapses word-per-line artifacts while preserving paragraph breaks. Disable `sanitize_content=False` for PDFs containing structured content, such as code or tables.

### [WebsiteReader Chunking](https://github.com/agno-agi/agno/releases/tag/v2.4.8) (v2.4.8)

Now uses `FixedSizeChunking` instead of Semantic by default, removing the need for an OpenAI API key when not setting a specific chunker.

### [Studio](https://github.com/agno-agi/agno/releases/tag/v2.4.8) (v2.4.8)

Visual editor for building Agents, Teams, and Workflows in AgentOS. Drag-and-drop interface with a Registry for managing tools, models, databases, and schemas.

### [AWS EFS Support](https://github.com/agno-agi/agno/releases/tag/v2.5.0) (v2.5.0)

Adds support for AWS EFS volume and mount point for persistent storage in AWS deployments.

### [MCP Tools HITL](https://github.com/agno-agi/agno/releases/tag/v2.4.7) (v2.4.7)

MCPTools now works with `requires_confirmation_tools`. Human-in-the-loop for external tool execution.

### [External Execution Silent Parameter](https://github.com/agno-agi/agno/releases/tag/v2.4.4) (v2.4.4)

Added a new parameter to make external tool execution silent by not populating the run response content with placeholder strings.

## Community projects & showcases

In February, there was an explosion of production-ready applications. Here are the standout builds:

### 1. paper2saas by Agentra Labs

AI agents that dissect papers, brainstorm killer SaaS ideas, validate markets, crush competitors, and spit out MVP roadmaps—all hallucination-free:

**The workflow:**

- Takes arXiv papers as input
- Analyzes research for commercial potential
- Generates SaaS ideas grounded in the research
- Validates market opportunities
- Produces competitive analysis
- Outputs actionable MVP roadmaps

**Why it matters:** Bridges the gap between academic research and commercial applications. Turns theoretical breakthroughs into business opportunities.

[Twitter announcement](https://x.com/AgentraLabs/status/2022320807377309914)

### 2. OpenClaw Replacement by bitdoze

Built a lightweight AI Assistant alternative to OpenClaw using Agno & Discord:

**The setup:**

- **Host**: $5 VPS (Ubuntu)
- **Interface**: Discord (via Bot API)
- **Tools**: Web search, Shell access, GitHub, File system
- **Automation**: Hourly tech/AI news updates via Cron

**Why it's cool:** OpenClaw is powerful but complex. This proves you can build production-ready AI assistants with Agno on minimal infrastructure—fast, 100% customizable, and budget-friendly.

**Tech stack:** Agno, Discord Bot API, UV for package management

[Full Tutorial](https://www.bitdoze.com/create-your-own-ai-agent/) | [Video Walkthrough](https://youtu.be/9Txk3SpB6fg)

### 3. OpenAgent by Adam (ajshedivy)

Dedicated fork of OpenCode with AgentOS integrations called **openagent**:

**Features:**

- Bring AgentOS to your terminal with `/agno` hub
- Browse agents with keyboard navigation
- Quick-connect with status indicators
- Agent detail views

**Roadmap:** Teams, Workflows, and eventually evals/metrics/knowledge

**Bonus:** Building `agentos-sdk` to improve the dev experience

**Why it matters:** Terminal-native AgentOS experience. Developers can manage their AI agents without leaving the command line.

[GitHub repo](https://github.com/ajshedivy/openagent) | [agentos-sdk](https://github.com/ajshedivy/agentos-sdk)

### 4. agno-client by rodrigocoliveira

Simplified React SDK for Agent OS — solving the "how do I build a frontend for my agents" problem:

**The problem:** AGUI's CopilotKit integration is complex, and Agent-UI hasn't been updated for full Agent OS capabilities.

**The solution:** Clean React packages for polished interfaces connected to Agno agents:

- Simple React hooks and components
- Clean abstractions over underlying complexity
- Drop-in solution for beautiful agent UI
- Fully customizable components
- Always in sync with AgentOS FastAPI endpoints

**Why it matters:** Frontend development for AI agents shouldn't be harder than building the agents themselves. This makes it trivial.

[GitHub repo](https://github.com/rodrigocoliveira/agno-client)

### 5. Elite Research Agent by Build Fast with AI

Publication-quality research from a single prompt:

**How it works:**

- Searches the web for relevant sources
- Reads full articles (not just snippets)
- Produces NYT-style investigative reports

**Tech stack:** Kimi K2.5 (via OpenRouter), Agno, web search tools

**Why it's impressive:** Demonstrates high-quality reasoning models integrated with Agno for long-form content generation.

[GitHub Code](https://git.new/agno_kimi_k25)

### 6. Production Agent Showcase by Build Fast with AI

Multiple production-ready agents demonstrating different models and use cases:

#### StepFun Step-3.5-Flash Integration

Built and tested with StepFun Step-3.5-Flash (free via OpenRouter). Full notebook showing:

- Tool calling
- Memory management
- Multi-agent teams
- Knowledge bases
- Structured outputs

[GitHub Code](https://git.new/agno_step_2.5)

#### Qwen3-Coder-Next Agents

80B MoE model with only ~3B active params → production-grade coding agents without massive API bills.

[GitHub Code](https://git.new/qwen3_coder_agno)

#### Tic-Tac-Toe AI Arena

Claude Opus 4.5 vs Kimi 2.5 — not just a game, a benchmark for agentic reasoning:

- **Tech stack:** Agno, Streamlit + custom Neon CSS, OpenRouter
- **Purpose:** Test AI reasoning in real-time gameplay

[GitHub Code](https://git.new/tic-tac-toe-ai)

#### HackerNews Digest Agent

Before: 20 mins scrolling HackerNews daily. After: 10-second AI digest.

**The agent:**

- Fetches trending stories
- Summarizes each in 2-3 lines
- Groups by theme (AI, DevTools, etc.)
- Explains why you should care

**Tech stack:** Kimi GLM-5, Agno

[GitHub Code](https://git.new/glm_5_agno)

### 7. AgentBazaar by Harish Kotra

Fully autonomous AI Marketplace running locally—agents hiring each other:

**The workflow:**

1. **Broker Agent**: Takes the request and creates the technical spec
2. **Bidding War**: Multiple Worker Agents (different personalities) generate bids
3. **Negotiation**: Negotiator agent bargains to lower prices
4. **Escrow & Validation**: Validator checks work against the contract before releasing funds

**Why Agno?** Enforcing `output_schema` using Pydantic models meant agents weren't just chatting—they were exchanging valid, executable JSON objects.

**The result:** Self-contained economy with reputation systems, bad-actor filtering, and self-optimizing prices. All running locally on `llama3.2` with Ollama.

**Vision:** Prototype for how future software systems might self-assemble. Instead of monolithic code, imagine micro-services that negotiate their own APIs and SLAs dynamically.

[GitHub Repo](https://github.com/harishkotra/agentbazaar/) | [Blog](https://dev.to/harishkotra/building-an-autonomous-ai-agent-marketplace-with-agno-ollama-4l12)

### 8. Stock AI Agent by Jagdeep Singh

Web-enabled stock analysis agent that fetches real-time data and structured insights:

**What it does:**

- Searches the web for stock data
- Fetches current prices
- Retrieves analyst recommendations
- Returns structured insights in tabular format

**Tech stack:** Agno, DuckDuckGo web search, YFinanceTools, Groq, OpenAI

**Key learning:** Coming from ML model background, this project helped understand how agents work in real-world application workflows, not just notebooks.

[GitHub Repo](https://lnkd.in/gTEsCEWu)

### 9. AI Stock Screener & Interview Agent by MarcelloDOP

Two production applications built with Agno:

#### Stock Screener Agent

**Features:**

- Uses YFinance and Tavily for data
- Suggests the best stocks based on fundamental analysis
- Analyzes analyst ratings and news sentiment

#### Competency-Based Interview Agent

Helps job seekers prepare for interviews:

- Generates personalized questions from the job description and CV
- Creates tailored answers
- Exports to Word document format

**Tech stack:** Agno, OpenAI (gpt-5-mini), ChromaDB, SQLite, Streamlit, python-docx

### 10. LLMOps Architecture by Kameshwara Pavan Kumar Mantha

Production-ready GenAI stack showing what enterprise LLMOps actually looks like:

**Key technologies:**

- **MLflow**: Unified LLMOps backbone (AI Gateway routing, agent serving, prompt registry, evaluation, observability, tracing, telemetry, cost tracking)
- **Agno**: Agent framework
- **Multi-LLM**: OpenAI, Anthropic, Gemini with traffic splitting and fallback
- **Qdrant**: Vector database for RAG

**What it enables:**

- Clear visibility into every response
- Structured evaluation of agent quality
- Controlled multi-model routing
- Fully self-hosted stack (free to run)

**Why it matters:** Practical blueprint for building production GenAI systems with confidence and control.

[Read the blog here](https://blog.devops.dev/designing-an-enterprise-llmops-stack-with-mlflow-and-agno-d173d8aa1137)

### 11. Math Mentor AI by Manoj Kumar Karumanchi

Multimodal RAG + Multi-Agent Math System—not a chatbot, a structured reasoning system:

**Architecture:**

- **Multimodal input**: Text, OCR (EasyOCR), Audio (Whisper)
- **Agent pipeline**: Extract → Guardrail → Parser → Router → Solver → Verifier → Explainer
- **RAG stack**: Hugging Face embeddings + LanceDB
- **Tool execution**: SymPy for deterministic math solving
- **Model abstraction**: Groq + Gemini fallback
- **Memory loop**: SQLite + embedding similarity retrieval
- **HITL checkpoints** for ambiguity and low confidence

**Why it's interesting:** The pattern maps directly to real-world systems like OCR-based document processing, compliance validation, multimodal support automation, and human-in-the-loop AI governance.

**Tech stack:** Agno, Python, Streamlit, Docker, Hugging Face Spaces

[LinkedIn post](https://www.linkedin.com/posts/manoj-kumar-karumanchi_artificialintelligence-generativeai-machinelearning-activity-7427807684613107712-dDH8)

### 12. Voice-RAG by Shivam Singh

Turn any document into a real-time, source-grounded voice assistant:

**What it does:**

- Upload a document
- Auto-index it for retrieval
- Provision a voice-enabled agent
- Open a live speech-to-speech widget
- Have a real-time conversation with your knowledge base

**Key features:**

- Extended Agno plugin with Voice + RAG orchestration
- Integrated ElevenLabs real-time conversational voice (200ms latency)
- Automated ingestion → indexing → agent provisioning
- Embeddable widget
- Multilingual embeddings

**Use cases:**

- Enterprise support (faster resolution, fewer tickets)
- Education (ask instead of search)
- Healthcare (voice access to clinical guidelines)
- Field operations (instant access to SOPs)
- Research (upload papers, ask follow-ups)

**Tech stack:** Agno, ElevenLabs Conversational AI

[GitHub repo](https://github.com/Shivam909058/voice-rag)

### 13. PaperSphere-AI by Saathvik Krishnan

Agentic multi-paper RAG pipeline for research understanding and comparison:

**The problem:** Most paper assistants can answer single questions but struggle to compare multiple papers on results, datasets, and limitations.

**The solution:**

- Upload one or more research PDFs
- Hybrid retrieval using BM25 + vector search
- Reranking and chunk filtering for better context quality
- Single-paper grounded Q&A with citations
- Multi-paper comparative synthesis with metric-aware outputs
- Evaluation signals for retrieval quality and groundedness

**Tech stack:** FastAPI (Render), Streamlit (Community Cloud), Qdrant Cloud, Gemini, Agno

**Key focus:** Reducing hallucinations, improving traceability through citations, and making multi-paper comparison useful for actual research work.

[GitHub repo](https://github.com/Saathvik-Krishnan/Research-Paper-Copilot) | [LinkedIn post](https://www.linkedin.com/posts/saathvik-krishnan_rag-llm-genai-activity-7429252696479604736-xo80)

### 14. Full-Stack AI Chat App by Shubham Vedi

Production-ready AI agent stack with Agno Skills:

**Features:**

- Agno Agent Skills (modular, hot-reloadable)
- Claude Sonnet 4.6 via OpenRouter
- Tavily for real-time web search
- Streaming responses (SSE)
- Neubrutalism UI design
- One-command startup
- Markdown rendering

**Tech stack:** Agno Skills, Claude Sonnet 4.6, OpenRouter, Tavily, React, FastAPI

[GitHub repo](https://git.new/agno-skills-agent)

### 15. FrankenAgent Lab by Ilkan Yıldırım

Modular AI agent builder where you assemble agents like high-tech creations:

**The metaphor:**

- 🧠 Head: LLM brain
- 🦾 Arms: Tools & integrations
- 🦿 Legs: Workflows & execution
- ❤️ Heart: Memory
- 🦴 Spine: Guardrails & safety

**What you can do:**

- Build agents visually using YAML blueprints
- Run them via CLI or API
- Trace every tool call
- Share blueprints in the marketplace
- Deploy to production with Cloud Run + Cloud SQL + Redis

**Tech stack:** FastAPI, Agno, Web UI, execution tracing, user auth, API keys

**Why it's cool:** Structured way to experiment with agent architectures with production deployment built in.

### 16. Elite Financial Intelligence Coach by Mihir Kapile

An AI-native platform that transforms raw transaction ledgers into a governed financial roadmap:

**Key features:**

- **Agentic Orchestration**: Powered by Agno and Llama 3.1
- **Sub-Second Inference**: Leveraging Groq's LPU architecture
- **Fiscal Stress Test**: Simulates capital depletion events
- **Zero-Trust Approach**: In-memory processing, fuzzy header mapping, encrypted-ready PDFs

**Security focus:**

- In-memory processing (no database persistence)
- Anonymizes banking formats locally
- User maintains total control over financial data

**Tech stack:** Groq (Llama-3.1-8B-Instant), Agno, Streamlit, Pandas

**Submission for:** Palo Alto Networks Case Study

### 17. OCR Translation System by Qubrid AI

Production-grade OCR-powered language translation system:

**What it does:**

- Extract text from images using Hunyuan OCR 1B
- Automatically detect source language
- Translate into 48+ languages
- All without GPU setup or infrastructure management

**Architecture:**

- 4-layer production structure (UI → Pipeline → Agents → Models)
- Multimodal OCR + language models
- Streaming responses for real-time UX

**Tech stack:** Qubrid AI serverless APIs, Agno, Hunyuan OCR 1B, GPT-OSS-20B

[YouTube tutorial](https://www.youtube.com/watch?v=5-ZmAgQbCJ0)

## Contributor shoutouts

February saw continued growth with contributions from our expanding community. We hit **400 contributors on GitHub** — a massive milestone!

### Top community code contributors

- **@rodrigocoliveira** (GitHub) — agno-client React SDK, HITL session state fix, silent external execution, Firestore session fixes
- **@ajshedivy** (GitHub) — MCP HITL functionality, OpenAgent terminal interface
- **@theagenticguy** (GitHub) — AWS Bedrock Reranker and Embeddings expansion
- **@WilliamEspegren** (GitHub) — Seltz tools integration
- **@dvy246** (GitHub) — Unsplash image search tools
- **@Tuyohai** (GitHub) — Moonshot model provider
- **@Byunk** (GitHub) — Firestore session storage fixes
- **@salimhamed** (GitHub) — Workflow run_input JSON serialization
- **@tanmaydarmorha** (GitHub) — AzureOpenAI authentication enhancements
- **@Samyak2** (GitHub) — WebsiteReader OpenAI requirement removal

### Feature implementers

- **@mklrlnd424** (GitHub) — TavilyTools deprecated parameter removal
- **@themavik** (GitHub) — Knowledge filename pattern matching, reasoning content deduplication
- **@ArivunidhiA** (GitHub) — Anthropic streaming metrics fix
- **@guguoyi** (GitHub) — Cookbook typo refinements
- **@jss367** (GitHub) — Documentation link fixes
- **@kepler** (GitHub) — Session state extraction from run_context, LearningMachine Claude/Gemini compatibility
- **@Programocles (dsarlo)** (GitHub) — AWS Bedrock Guardrail support

### First-time contributors

February welcomed 15+ first-time contributors, including **@rodrigocoliveira** (agno-client and multiple fixes), **@ajshedivy** (MCP HITL), **@theagenticguy** (AWS Bedrock features), **@WilliamEspegren** (Seltz tools), **@dvy246** (Unsplash tools), **@Tuyohai** (Moonshot provider), **@Byunk** (Firestore fixes), **@salimhamed** (workflow serialization), **@tanmaydarmorha** (Azure auth), **@Samyak2** (WebsiteReader fix), **@mklrlnd424** (Tavily fix), **@themavik** (Knowledge and reasoning fixes), **@ArivunidhiA** (metrics fix), **@guguoyi** (docs), and **@jss367** (link fixes). Every contribution strengthens the framework.

### Community MVPs

- **@aberk** — Continued exceptional advocacy for production patterns: caching backend abstractions, error logging with stack traces, multi-worker setup support, and detailed feedback on v2.5 release. Always pushing for production-ready features with concrete technical proposals.
- **@annedefined** — Active community helper explaining framework comparisons (Agno vs LangChain/Langgraph/Pydantic AI), sharing production deployment experience, and helping newcomers with setup issues
- **@kepler** — Critical contributions including session state fixes and LearningMachine compatibility improvements, plus thoughtful questions about Learning Machines in Teams
- **@rocasagrande** — Raised critical production issue about media storage in DB (base64 overload), built comprehensive media_storage abstraction with S3 support, and actively contributed dependency injection fixes
- **@Griflet** — Continued advocacy for memory manager usage tracking and identified max tool call limit issues
- **@Omar** — Raised important production questions about image serialization and learning machine knowledge base behavior
- **@LaGernouille** — Detailed reporting on Gemini tool calling issues with thought signatures
- **@josef** — Proposed PR for cancelled runs being saved in DB
- **@Ilya I. Lubenets** — Asked important questions about dynamic prompt updates with Langfuse integration
- **@chico** — HITL for subagents in teams discussion

## What's next

### On the roadmap

- **Studio enhancements:** Visual builder improvements for complex workflows
- **HITL for Workflows** (When it's set on Agent/Team, which are used as a part of a step)
- **Improved caching abstractions:** Better support for multi-worker setups with pluggable cache backends
- **Media storage optimization:**  Addressing base64 storage in DB for production deployments
- **Enhanced observability:**  Better error logging with stack traces and debugging tools
- **Workflow-level culture:**  Global context across workflow agents
- **MCP Resources and Prompts:**  Expanded MCP protocol support

### Get involved

We're always looking for contributors! Areas where you can help:

- **Production patterns:**  Share your deployment experiences and best practices
- **Documentation:**  Help new users get started faster
- **Toolkits:**  Build integrations for your favorite services
- **Bug reports:**  Production feedback makes Agno better (thank you to everyone filing detailed issues!)
- **Frontend tooling:**  Help build better UI/UX for AI agents (check out agno-client!)
- **Testing:**  Help test new releases and report issues

Check out [open issues](https://github.com/agno-agi/agno/issues) or join the [Discord](https://agno.link/discord) to connect with the team.

## Join the movement

February 2026 proved that Agno is the production framework for building AI agents that ship. From Team Modes to HITL for Teams, from the Approval System to the Apache license change, every feature this month answered real production needs.

The community is thriving. **400 contributors** strong. The projects are shipping to real users. The framework is maturing at an incredible pace.

Whether you're building autonomous AI marketplaces, voice-enabled RAG systems, or multi-paper research assistants, Agno gives you the production infrastructure to build AI systems that work reliably at scale.

**Stats that matter:**

- 400 contributors on GitHub 🎉
- 10 major releases in one month
- License changed to Apache 2.0
- HITL for Teams (finally!) shipped
- Hundreds of production deployments
- One incredible community
- ⭐ [Star Agno on GitHub](https://github.com/agno-agi/agno)
- 💬 [Join our Discord](https://agno.link/discord)
- 📖 [Read the docs](https://docs.agno.com/)
- 🚀 Share what you're building — tag us and we'll feature YOUR project in March!

Keep building, keep shipping, keep pushing the boundaries of what agents can do.

**— Team Agno**