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agents-towards-production
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agents-towards-production

Active·★ 21.8k·NOASSERTION·Updated 2026-10-27
★ Most Popular★ Essential★ Trending

Agents Towards Production is an open-source playbook of runnable tutorials for turning GenAI agents into real products. It is organised around the building blocks of an agent stack: orchestration, memory, tool integration, data processing, RAG and knowledge management, observability, security and deployment. Each tutorial sits in its own folder with ready-to-run notebooks or code.

Examples in the README include secure tool calling with OAuth2 and human-in-the-loop approvals (Arcade), web data collection and real-time web search for agents (Bright Data, Tavily), production RAG on a managed platform (Contextual AI), dual short- and long-term memory with semantic search (Redis), self-improving memory (Mem0) and knowledge-graph memory (Cognee), and managed deployment on AWS Bedrock AgentCore. Several tutorials are contributed by the vendors themselves, and the README lists tutorial and general sponsors and a newsletter.

This write-up is based on the project's README and public repository data. We have not hand-tested the tool. Data as of 2026-10-24.

License
Not detected by GitHub
Latest release
No GitHub releases
Last commit
2026-10-22
Contributors
27
Forks
2,881
Open issues and PRs
5
First published
2025-06-16

Source: GitHub, npm and PyPI public APIs.

#AI Agents#GenAI#Production Deployment#LangChain#RAG#Coding
$ Install
$ pip install -r meeting-recorder-agent/requirements.txt
↗ Visit site★ GitHub
01

Features

01Production-ready AI Agent Deployment
02Advanced Memory Systems (Vector, Hybrid, Self-Improving)
03Real-time Web Data Integration & Retrieval Augmented Generation (RAG)
04Secure Tool Calling & Guardrails
05Agent Orchestration & Stateful Workflows
02

Why choose it

+Covers the whole agent stack: orchestration, memory, tools, RAG, observability, security and deployment.
+Each tutorial is a self-contained folder with runnable notebooks or code.
+Includes concrete production topics such as OAuth2 tool calling with approvals, dual memory and managed deployment.
+Free to read and follow, with a large community of followers.
03

Trade-offs

!Many tutorials are written around specific vendors' products, and the README has sponsor sections, so they read partly as vendor showcases.
!It is a tutorial collection, not a framework, so you still assemble and maintain the production stack yourself.
!GitHub shows no detected licence, so check the repository terms before reusing code, and the repository was last updated in September 2026.
04

Maintenance check

ActiveChecked: 2026-10-25
Last commit
2026-10-22
Commits, last 90 days
23
Releases, last 90 days
0
Latest release
—
Open issues
5
Recent issues with at least one comment
13/15
Commits per week, last 12 weeks
Commits per week, last 12 weeks: 1, 0, 6, 2, 2, 5, 2, 0, 0, 2, 2, 0

Oldest week on the left; the latest four weeks are orange. Source: GitHub commit activity.

Status rule: archived on GitHub = Archived; last commit within 90 days = Active; 91 to 365 days = Slowing; older = Stale. All figures come from the GitHub API on the date shown.

05

Compatibility

LangChain
Supported
Verified via docs
Redis
Supported
Verified via docs
Contextual AI
Supported
Verified via docs
Bright Data
Supported
Verified via docs
Tavily
Supported
Verified via docs
Arcade
Supported
Verified via docs
06

Quick start

1
$ pip install -r meeting-recorder-agent/requirements.txt
07

Use cases

↳Developing scalable, production-ready Generative AI agents.
↳Integrating real-time data and advanced memory for intelligent automation.
↳Securing and deploying AI agents in enterprise environments.
08

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On this page
01Features02Why choose it03Trade-offs04Maintenance check05Compatibility06Quick start07Use cases08Alternatives
NirDiamant /
agents-towards-production
↗

Runnable tutorials for taking GenAI agents from prototype to production

21.8k2.9k
Last commit1d ago
StatusActive
CategoryDev Tooling
Contributors27
Links
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