🔥 Latest Docs | 🚀 5‑minute Quickstart | 💡 Core Features
--- ## 📚 Quick Links - **Docs (EN)**: https://agently.tech/docs - **Docs (中文)**: https://agently.cn/docs - **Agent Systems Playbook (EN)**: https://agently.tech/docs/en/agent-systems/overview.html - **Agent Systems Playbook (中文)**: https://agently.cn/docs/agent-systems/overview.html - **Coding Agent Guide (EN)**: https://agently.tech/docs/en/agent-docs.html - **Coding Agent Guide (中文)**: https://agently.cn/docs/agent-docs.html - **Agent Docs Pack**: https://agently.cn/docs/agent_docs.zip ## 🤔 Why Agently? Many GenAI POCs fail in production not because models are weak, but because **engineering control is missing**: | Common challenge | How Agently helps | |:--|:--| | Output schema drifts, JSON parsing fails | **Contract‑first output control** with `output()` + `ensure_keys` | | Workflows get complex and hard to maintain | **TriggerFlow orchestration** with `to` / `if` / `match` / `batch` / `for_each` | | Multi‑turn state becomes unstable | **Session (v4.0.8.1+)** with session activation, context window control, custom memo strategy, and persistence | | Tool calls are hard to audit | **Tool logs** via `extra.tool_logs` | | Switching models is expensive | **OpenAICompatible** unified model settings | **Agently turns LLM uncertainty into a stable, testable, maintainable engineering system.** ## ✨ Core Features ### 1) 📝 Contract‑first Output Control Define the structure with `output()`, enforce critical keys with `ensure_keys`. ```python result = ( agent .input("Analyze user feedback") .output({ "sentiment": (str, "positive/neutral/negative"), "key_issues": [(str, "issue summary")], "priority": (int, "1-5, 5 is highest") }) .start(ensure_keys=["sentiment", "key_issues[*]"]) ) ``` ### 2) ⚡ Structured Streaming (Instant) Consume structured fields as they are generated. ```python response = ( agent .input("Explain recursion and give 2 tips") .output({"definition": (str, "one sentence"), "tips": [(str, "tip")]}) .get_response() ) for msg in response.get_generator(type="instant"): if msg.path == "definition" and msg.delta: ui.update_definition(msg.delta) if msg.wildcard_path == "tips[*]" and msg.delta: ui.add_tip(msg.delta) ``` ### 3) 🧩 TriggerFlow Orchestration Readable, testable workflows with branching and concurrency. ```python ( flow.to(handle_request) .if_condition(lambda d: d.value["type"] == "query") .to(handle_query) .elif_condition(lambda d: d.value["type"] == "order") .to(check_inventory) .to(create_order) .end_condition() ) ``` ### 4) 🧠 Session (Multi‑turn Context, v4.0.8.1+) Built-in `SessionExtension` with `activate_session/deactivate_session`, context window control, custom memo strategies, and JSON/YAML persistence. ```python from agently import Agently agent = Agently.create_agent() # Activate per-user session (reused by session_id) agent.activate_session(session_id="demo_user_1001") # Optional: default window trimming by max length agent.set_settings("session.max_length", 12000) # Optional: custom strategy (analysis -> resize) session = agent.activated_session assert session is not None def analysis_handler(full_context, context_window, memo, session_settings): if len(context_window) > 6: return "keep_last_six" return None def keep_last_six(full_context, context_window, memo, session_settings): return None, list(context_window[-6:]), memo session.register_analysis_handler(analysis_handler) session.register_resize_handler("keep_last_six", keep_last_six) ``` ### 5) 🔧 Tool Calls + Logs Tool selection and usage are logged in `extra.tool_logs`. ```python @agent.tool_func def add(a: int, b: int) -> int: return a + b response = agent.input("12+34=?").use_tool(add).get_response() full = response.get_data(type="all") print(full["extra"]["tool_logs"]) ``` ### 6) 🌐 Unified Model Settings (OpenAICompatible) One config for multiple providers and local models. ```python from agently import Agently Agently.set_settings( "OpenAICompatible", { "base_url": "https://api.deepseek.com/v1", "model": "deepseek-chat", "auth": "DEEPSEEK_API_KEY", }, ) ``` ## 🚀 Quickstart ### Install ```bash pip install -U agently ``` *Requirements: Python >= 3.10, recommended Agently >= 4.0.7.2* ### 5‑minute example **1. Structured output** ```python from agently import Agently agent = Agently.create_agent() result = ( agent.input("Introduce Python in one sentence and list 2 advantages") .output({ "introduction": (str, "one sentence"), "advantages": [(str, "advantage")] }) .start(ensure_keys=["introduction", "advantages[*]"]) ) print(result) ``` **2. Workflow routing** ```python from agently import TriggerFlow, TriggerFlowEventData flow = TriggerFlow() @flow.chunk def classify_intent(data: TriggerFlowEventData): text = data.value if "price" in text: return "price_query" if "feature" in text: return "feature_query" if "buy" in text: return "purchase" return "other" @flow.chunk def handle_price(_: TriggerFlowEventData): return {"response": "Pricing depends on the plan..."} @flow.chunk def handle_feature(_: TriggerFlowEventData): return {"response": "Our product supports..."} ( flow.to(classify_intent) .match() .case("price_query") .to(handle_price) .case("feature_query") .to(handle_feature) .case_else() .to(lambda d: {"response": "What would you like to know?"}) .end_match() .end() ) print(flow.start("How much does it cost?")) ``` ## ✅ Is Your App Production‑Ready? — Release Readiness Checklist Based on teams shipping real projects with Agently, this **production readiness checklist** helps reduce common risks before release. | Area | Check | Recommended Practice | | :--- | :--- | :--- | | **📝 Output Stability** | Are key interfaces stable? | Define schemas with `output()` and lock critical fields with `ensure_keys`. | | **⚡ Real‑time UX** | Need updates while generating? | Consume `type="instant"` structured streaming events. | | **🔍 Observability** | Tool calls auditable? | Inspect `extra.tool_logs` for full arguments and results. | | **🧩 Workflow Robustness** | Complex flows fully tested? | Unit test each TriggerFlow branch and concurrency limit with expected outputs. | | **🧠 Memory & Context** | Multi‑turn experience consistent? | Define Session/Memo summary, trimming, and persistence policies. | | **📄 Prompt Management** | Can logic evolve safely? | Version and configure prompts to keep changes traceable. | | **🌐 Model Strategy** | Can you switch or downgrade models? | Centralize settings with `OpenAICompatible` for fast provider switching. | | **🚀 Performance & Scale** | Can it handle concurrency? | Validate async performance in real web‑service scenarios. | | **🧪 Quality Assurance** | Regression tests complete? | Create fixed inputs with expected outputs for core scenarios. | ## 📈 Who Uses Agently to Solve Real Problems? > "Agently helped us turn evaluation rules into executable workflows and keep key scoring accuracy at 75%+, significantly improving bid‑evaluation efficiency." — Project lead at a large energy SOE > "Agently enabled a closed loop from clarification to query planning to rendering, reaching 90%+ first‑response accuracy and stable production performance." — Data lead at a large energy group > "Agently’s orchestration and session capabilities let us ship a teaching assistant for course management and Q&A quickly, with continuous iteration." — Project lead at a university teaching‑assistant initiative **Your project can be next.** 📢 [Share your case on GitHub Discussions →](https://github.com/AgentEra/Agently/discussions/categories/show-and-tell) ## ❓ FAQ **Q: How is Agently different from LangChain or LlamaIndex?** **A:** Agently is **built for production**. It focuses on stable interfaces (contract‑first outputs), readable/testable orchestration (TriggerFlow), and observable tool calls (`tool_logs`). It’s a better fit for teams that need reliability and maintainability after launch. **Q: Which models are supported? Is switching expensive?** **A:** With `OpenAICompatible`, you can connect OpenAI, Claude, DeepSeek, Qwen and most OpenAI‑compatible endpoints, plus local models like Llama/Qwen. **The same business code can switch models without rewrites**, reducing vendor lock‑in. **Q: What’s the learning curve? Where should I start?** **A:** The core API is straightforward—**you can run your first agent in minutes**. Start with [Quickstart](https://agently.tech/docs/en/quickstart.html), then dive into [Output Control](https://agently.tech/docs/en/output-control/overview.html) and [TriggerFlow](https://agently.tech/docs/en/triggerflow/overview.html). **Q: How do I deploy an Agently‑based service?** **A:** Agently doesn’t lock you into a specific deployment path. It provides async APIs and FastAPI examples. The [FastAPI integration example](https://github.com/AgentEra/Agently/tree/main/examples/step_by_step/13-auto_loop_fastapi) covers SSE, WebSocket, and standard POST. **Q: Do you offer enterprise support?** **A:** Yes. The core framework in this repository remains open‑source under **Apache 2.0**. Enterprise support, private extensions, managed services, and SLA-based collaboration are provided under separate commercial agreements. Contact us via the [community](https://doc.weixin.qq.com/forms/AIoA8gcHAFMAScAhgZQABIlW6tV3l7QQf). **Q: What is open-source vs enterprise in Agently?** **A:** The open-source core includes the general framework and public capabilities in this repository. Enterprise offerings (for example private extension packs, advanced governance modules, private deployment support, and SLA services) are delivered separately under commercial terms. ## 🧭 Docs Guide (Key Paths) - **Getting Started** - Quickstart: https://agently.tech/docs/en/quickstart.html - Model Settings: https://agently.tech/docs/en/model-settings.html - Coding Agent Guide: https://agently.tech/docs/en/agent-docs.html - **Output Control (Structured Output)** - Overview: https://agently.tech/docs/en/output-control/overview.html - Output Format: https://agently.tech/docs/en/output-control/format.html - ensure_keys: https://agently.tech/docs/en/output-control/ensure-keys.html - Instant Streaming: https://agently.tech/docs/en/output-control/instant-streaming.html - **Result & Streaming Events** - Result Data: https://agently.tech/docs/en/model-response/result-data.html - Streaming Events: https://agently.tech/docs/en/model-response/streaming.html - **Session & Memo** - Overview: https://agently.tech/docs/en/agent-extensions/session-memo/ - Quickstart: https://agently.tech/docs/en/agent-extensions/session-memo/quickstart.html - **TriggerFlow Orchestration** - Overview: https://agently.tech/docs/en/triggerflow/overview.html - when Branch: https://agently.tech/docs/en/triggerflow/when-branch.html - if / elif / else: https://agently.tech/docs/en/triggerflow/if-branch.html - match / case: https://agently.tech/docs/en/triggerflow/match-branch.html - batch: https://agently.tech/docs/en/triggerflow/batch.html - for_each: https://agently.tech/docs/en/triggerflow/for-each.html - Runtime Stream: https://agently.tech/docs/en/triggerflow/runtime-stream.html - **Tools & Extensions** - Tools: https://agently.tech/docs/en/agent-extensions/tools.html - MCP: https://agently.tech/docs/en/agent-extensions/mcp.html - auto_func: https://agently.tech/docs/en/agent-extensions/auto-func.html - KeyWaiter: https://agently.tech/docs/en/agent-extensions/key-waiter.html - **Prompt Management**: https://agently.tech/docs/en/prompt-management/overview.html - **Async & Settings**: https://agently.tech/docs/en/async-support.html / https://agently.tech/docs/en/settings.html - **Playbook**: https://agently.tech/docs/en/agent-systems/overview.html ## 🤝 Community - Discussions: https://github.com/AgentEra/Agently/discussions - Issues: https://github.com/AgentEra/Agently/issues - WeChat Group: https://doc.weixin.qq.com/forms/AIoA8gcHAFMAScAhgZQABIlW6tV3l7QQf ## 📄 License Agently follows an open-core + commercial extension model: - Open-source core in this repository: [Apache 2.0](LICENSE) - Trademark usage policy: [TRADEMARK.md](TRADEMARK.md) - Contributor rights agreement: [CLA.md](CLA.md) - Enterprise extensions and commercial services: provided under separate commercial agreements ---
Start building your production‑ready AI apps →
pip install -U agently