AgentEra/Agently
[GenAI Application Development Framework] 🚀 Build GenAI application quick and easy 💬 Easy to interact with GenAI agent in code using structure data and chained-calls syntax 🧩 Use Event-Driven Flow *TriggerFlow* to manage complex GenAI working logic 🔀 Switch to any model without rewrite application code observed · 2026-08-28
Health v2 · maintenance only
94/100
- Activity 99
- Release rhythm 95
- Longevity 82
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: 10.0
- age_days: 1160
- days_rel: 33
- days_push: 7
- n_releases_24m: 41
Adoption not part of the score
1644 stars · 182 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
Agently is a Python framework for building production-grade GenAI applications with structured outputs, observable actions, MCP/tool integration, and event-driven TriggerFlow workflow orchestration. It abstracts model providers behind protocol-compatible plugins (OpenAI, Anthropic, etc.) so application code can switch models via configuration without rewrites.
Use cases
- build an AI agent backend that returns structured JSON fields
- switch LLM providers without rewriting application code
- orchestrate multi-step GenAI workflows with branching and pause/resume
- stream structured output fields to a frontend as they generate
- add tool calling, MCP servers, and sandboxed code execution to an agent
- build a RAG chatbot over a knowledge base
- expose an agent as an HTTP API service
- route support tickets with structured classification
When to choose
- you need stable, validated structured outputs from LLMs in Python
- you want model-provider portability via config-only switching
- you need persistent, event-driven workflows around agent calls
- you want built-in observability, action logs, and execution evidence for production AI services
When to avoid
- you only need a single one-off LLM call with no structure or tooling
- your stack is not Python
- you want a minimal thin wrapper rather than an opinionated framework with its own orchestration layer
Facets
framework · maturity active
agent-framework llm-inference rag workflow-automation mcp chatbot web-framework sdk artificial-intelligence large-language-models developer-tools backend python cross-platform self-hosted structured-output triggerflow schema-as-prompt model-agnostic event-driven llmops fastapi-integration sandboxed-code-execution streaming ai-agents retrieval-augmented-generation automation docker
10 sources
- readme: https://github.com/AgentEra/Agently · fetched 2026-08-28 · 9947949f67c7
- homepage: http://agently.tech · fetched 2026-08-29 · 8a58db9d34a7
- site_page: https://agently.tech/docs/reference/capability-map.html · fetched 2026-08-29 · b73efbdce9fd
- site_page: https://agently.tech/docs/start/project-framework.html · fetched 2026-08-29 · 9c3cb231e4fa
- site_page: https://agently.tech/docs/playbooks/overview.html · fetched 2026-08-29 · 0d19927f46dd
- site_page: https://agently.tech/docs · fetched 2026-08-29 · 13e6b1fed08e
- site_page: https://agently.tech/docs/case-studies/overview.html · fetched 2026-08-29 · caf5701f3b5c
- site_page: https://agently.tech/docs/start/quickstart.html · fetched 2026-08-29 · 9d0c9065c97b
- site_page: https://agently.tech/docs/requests/output-control.html · fetched 2026-08-29 · 3999b9a701ad
- site_page: https://agently.tech/docs/actions/overview.html · fetched 2026-08-29 · 8d2c47b86eae
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| AgentEra/Agently | main | 94 |
For agents
markdown · JSON · MCP: product_card(name="AgentEra/Agently")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem