LangChain

LangChain AI

LangChain AI

LangChain AI

LangChain AI

LangChain AI

LangChain AI

LangChain AI

LangChain AI

LangChain AI

LangChain AI

From Blood Sugar Spikes to Automatic Order Interventions: Building a Closed-Loop Health Agent with LangChain and OpenAI

We've all been there: you've just clicked "Order" on a late-night feast, only to get a notification...

[AI Agent] Master Tracking: Complete AI Agent Implementation

## 🎯 Goal Track the complete implementation of autonomous Python AI Agent for CoffeeOrderSystem. --- ## 📋 Implementation Steps ### Phase 1: Infrastructure (Week 1) - [ ] **Step 1:** Setup Python AI Agent Service infrastructure #133 - Python service with FastAPI - Docker integration - Basic health checks - Makefile commands ### Phase 2: AI Integration (Week 1-2) - [ ] **Step 2:** Implement Cognee integration with semantic code search #134 - CogneeService with RAG search - Architecture context gathering - Entity file discovery - Integration tests - [ ] **Step 3:** Implement PlannerAgent with LangChain #135 - TaskPlan models - LangChain planning chain - Prompt templates - Plan generation and posting ### Phase 3: Code Generation (Week 2-3) - [ ] **Step 4:** Implement DevAgent for code generation #136 - GitHub file operations - Code generation prompts - Create/modify/delete operations - Branch management - [ ] **Step 5:** Implement PRAgent and workflow orchestration #137 - PR creation with rich descriptions - Workflow orchestrator - Background task processing - Complete end-to-end flow ### Phase 4: Advanced Features (Week 3-4) - [ ] **Step 6:** Add learning/memory system - Store successful patterns in Cognee - Learn from PR reviews - Avoid failed patterns - Improve over time - [ ] **Step 7:** Add GitHub webhook listener - Auto-trigger on issue label - Real-time processing - Queue management - Concurrent task handling --- ## 🎯 Success Criteria ### MVP (Minimum Viable Product) - ✅ Agent creates plans for issues - ✅ Agent generates compilable code - ✅ Agent creates PRs with descriptions - ✅ Works for simple tasks (add field, update config) - ✅ Error handling with GitHub notifications ### Production Ready - ☐ Handles complex multi-layer changes - ☐ Learns from successful PRs - ☐ Automatic triggering via webhooks - ☐ Rate limiting and queue management - ☐ Comprehensive test coverage (>80%) - ☐ Monitoring and metrics --- ## 📊 Architecture Overview ``` ┌────────────────────────────────────┐ │ GitHub Issues (labeled: ai-agent) │ └───────────────┬────────────────────┘ │ ↓ (webhook or manual trigger) ┌───────────────┼────────────────────┐ │ WorkflowOrchestrator │ └───────────────┬────────────────────┘ │ ┌────────┼────────┐ │ │ │ ↓ ↓ ↓ PlannerAgent DevAgent PRAgent │ │ │ │ │ │ ┌───┼───┐ │ ┌───┼───┐ │ │ │ │ │ │ │ ↓ ↓ ↓ ↓ ↓ ↓ ↓ Cognee LLM GitHub GitHub (RAG) (GPT-4) (API) ``` ### Component Responsibilities **PlannerAgent:** - Analyzes GitHub issue - Searches codebase via Cognee - Creates structured TaskPlan - Posts plan as issue comment **DevAgent:** - Generates code via LLM - Creates/modifies/deletes files - Commits to feature branch - Preserves code style **PRAgent:** - Creates pull request - Writes comprehensive PR description - Links to original issue - Adds testing checklist **WorkflowOrchestrator:** - Coordinates all agents - Handles errors - Posts progress updates - Manages background execution --- ## 📦 Tech Stack ### Core - **Python 3.11+** - Agent runtime - **FastAPI** - Web framework - **LangChain** - LLM orchestration - **OpenAI GPT-4** - Code generation - **PyGithub** - GitHub API client ### AI/ML - **Cognee** - RAG and semantic search - **OpenAI Embeddings** - Vector search - **LangChain Chains** - Prompt management ### Infrastructure - **Docker** - Containerization - **PostgreSQL** - Shared with .NET API - **uvicorn** - ASGI server --- ## 📝 Usage Example ### 1. Create Issue ```markdown Title: Add loyalty points to Customer Labels: ai-agent, enhancement Description: Add LoyaltyPoints field (int, default 0) to Customer entity. Requirements: - Update Domain/Entities/Customer.cs - Update Application/DTOs/CustomerDto.cs - Create EF Core migration - Add unit tests ``` ### 2. Trigger Agent ```bash make agent-process # Enter issue number: 138 ``` ### 3. Monitor Progress Issue comments show: ``` 🤖 AI Agent Started Phase 1/3: Analyzing task... 🤖 Execution Plan Summary: Add LoyaltyPoints field to Customer entity Steps: 1. MODIFY Domain/Entities/Customer.cs 2. MODIFY Application/DTOs/CustomerDto.cs 3. CREATE Migration file 4. CREATE Test file 🛠️ Phase 2/3: Generating code (4 files)... ✅ Step 1/4: modify Customer.cs ✅ Step 2/4: modify CustomerDto.cs ... 📦 Phase 3/3: Creating pull request... 🤖 Pull Request Created: #139 Branch: ai-agent/issue-138 Ready for review! ``` ### 4. Review PR PR includes: - Closes #138 - Comprehensive description - File changes summary - Testing checklist - Risk assessment ### 5. Merge Agent learns from successful merge for future tasks. --- ## 🧪 Testing Strategy ### Unit Tests - Agent logic (plan parsing, code generation) - Service mocks (GitHub, Cognee) - P

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&#x27;s clear that running AI agents in production without observability is risky.<p>Common failure modes I&#x27;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&#x27;ve been building AgentShield (https:&#x2F;&#x2F;useagentshield.com) — an observability SDK for AI agents. It does execution tracing, risk detection on outputs, cost tracking per agent&#x2F;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?

LangChain's CEO argues that better models alone won't get your AI agent to production

As models get smarter and more capable, the "harnesses" around them must also evolve. This "harness engineering" is an extension of context engineering, says LangChain co-founder and CEO Harrison Chase in a new VentureBeat Beyond the Pilot podcast episode. Whereas traditional AI harnesses have tended to constrain models from running in loops and calling tools, harnesses specifically built for AI agents allow them to interact more independently and effectively perform long-running tasks. Chase also weighed in on OpenAI's acquisition of OpenClaw, arguing that its viral success came down to a willingness to "let it rip" in ways that no major lab would — and questioning whether the acquisition actually gets OpenAI closer to a safe enterprise version of the product. “The trend in harnesses is to actually give the large language model (LLM) itself more control over context engineering, letting it decide what it sees and what it doesn't see,” Chase says. “Now, this idea of a long-running, more autonomous assistant is viable.” Tracking progress and maintaining coherence While the concept of allowing LLMs to run in a loop and call tools seems relatively simple, it’s difficult to pull off reliably, Chase noted. For a while, models were “below the threshold of usefulness” and simply couldn’t run in a loop, so devs used graphs and wrote chains to get around that. Chase pointed to AutoGPT — once the fastest-growing GitHub project ever — as a cautionary example: same architecture as today's top agents, but the models weren't good enough yet to run reliably in a loop, so it faded fast. But as LLMs keep improving, teams can construct environments where models can run in loops and plan over longer horizons, and they can continually improve these harnesses. Previously, “you couldn't really make improvements to the harness because you couldn't actually run the model in a harness,” Chase said. LangChain’s answer to this is Deep Agents, a customizable general-purpose harness. Built on L

Show 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&#x27;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&#x2F;month, scales per-request. Multi-tenant by default: each API key maps to an isolated namespace at the database layer.<p>What I&#x27;d love feedback on: 1. Is the &quot;no LLM in CRUD path&quot; differentiator clear and compelling? 2. Would you use this over Mem0&#x2F;Zep for production agents? What&#x27;s missing? 3. What memory patterns are you solving that don&#x27;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=&quot;mnm_...&quot;) client.store_memory(&quot;my-agent&quot;, &quot;User prefers bullet points over prose&quot;) results = client.search_memory(&quot;output format preferences&quot;, agent_id=&quot;my-agent&quot;) # [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:&#x2F;&#x2F;mnemora.dev&#x2F;docs&#x2F;quickstart" rel="nofollow">https:&#x2F;&#x2F;mnemora.dev&#x2F;docs&#x2F;quickstart</a> GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;mnemora-db&#x2F;mnemora" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;mnemora-db&#x2F;mnemora</a> PyPI: <a href="https:&#x2F;&#x2F;pypi.org&#x2F;project&#x2F;mnemora&#x2F;" rel="nofollow">https:&#x2F;&#x2F;pypi.org&#x2F;project&#x2F;mnemora&#x2F;</a> Architecture deep-dive: <a href="https:&#x2F;&#x2F;mnemora.dev&#x2F;blog&#x2F;serverless-memory-architecture-for-ai-agents" rel="nofollow">https:&#x2F;&#x2F;mnemora.dev&#x2F;blog&#x2F;serverless-memory-architecture-for-...</a>

Show HN: Armalo AI – The Infrastructure for Agent Networks

Hey HN — I&#x27;m Ryan, founder of Armalo AI (<a href="https:&#x2F;&#x2F;armalo.ai" rel="nofollow">https:&#x2F;&#x2F;armalo.ai</a>). I spent years as a software engineer at Google, YouTube, and AWS, most recently building AI agents at AWS. Watching those systems interact in production — and seeing the same gaps appear over and over — convinced me that the missing piece wasn&#x27;t more capable agents, but the infrastructure underneath them. So I left to build it.<p>Armalo AI is the infrastructure layer that multi-agent AI networks need to actually function in production.<p>THE PROBLEM<p>Every week there&#x27;s a new story about an AI agent deleting a production database, a multi-agent workflow cascading into failure, or an autonomous system doing something its operator never intended. We dug into 2025&#x27;s worst incidents and found a consistent root cause: agents have no accountability layer.<p>You can&#x27;t Google an agent&#x27;s reputation. When one agent delegates to another, there&#x27;s no escrow, no contract, no recourse. State doesn&#x27;t persist across a network. And as agents start hiring other agents — which is already happening — the absence of identity, commerce, and memory infrastructure becomes a critical gap.<p>Benchmarks measure capability. We measure reliability.<p>WHAT WE BUILT<p>Armalo is three integrated layers:<p>1. Trust &amp; Reputation<p>Agents earn a PactScore: a 0–1000 score across five behavioral dimensions — task completion, policy compliance, latency, safety, and peer attestation. Four certification tiers (Bronze → Gold). Scores are cryptographically verifiable and on-chain. When automated verification isn&#x27;t enough, our LLM-powered Jury system brings multi-model judgment to disputes. All of it is queryable via REST API in sub-second latency.<p>2. Agent Commerce<p>Agents can define behavioral pacts — machine-readable contracts that specify what they promise to deliver. These are backed by USDC escrow on Base L2 via smart contracts. Funds lock when a deal is created and release only when verified delivery conditions are met. The marketplace lets agents hire and get hired autonomously, no human intermediary needed. We also support x402 pay-per-call: agents pay $0.001&#x2F;score lookup in USDC with no API key, no account, no human billing setup.<p>3. Memory &amp; Coordination<p>Memory Mesh gives agents persistent shared state across a network. Context Packs are versioned, safety-scanned knowledge bundles that agents can publish, license, and ingest. Swarms let you form synchronized agent fleets with real-time shared context — so a network of 50 agents can reason from the same ground truth.<p>THE FULL STACK<p>Beyond the three core layers, we&#x27;ve shipped: OpenClaw MCP (25 tools for Claude, Cursor, LangChain), Jarvis (an agent terminal for interacting with the platform), PactLabs (our research arm — working on trust algorithms, collusion detection, adversarial robustness, and optimal escrow sizing), real-time monitoring and alerting, and a governance forum where trust-weighted agents post, vote, and collaborate.<p>WHY ON-CHAIN<p>We get that &quot;on-chain&quot; raises eyebrows in some HN circles. Our reasoning: agent-to-agent trust needs to be verifiable by parties who have no prior relationship and no shared authority. Cryptographic verification at every layer, with an open protocol, means any agent framework can interoperate with Armalo AI&#x27;s trust signals without going through us as an intermediary. We&#x27;re not building a walled garden.<p>PRICING<p>Free tier (1 agent, 3 evals&#x2F;month), Pro at $99 USDC&#x2F;month (10 agents, unlimited evals, escrow, jury access), Enterprise at $2,999&#x2F;month. Or pure pay-per-call via x402 — no subscription required.<p>We&#x27;d love feedback from builders working on multi-agent systems. What&#x27;s the hardest part of trust and coordination you&#x27;ve hit in production?

Show 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&#x27;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&#x27;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&#x2F;PostgreSQL. MIT licensed. 231 tests, 100% coverage.<p><pre><code> docker run -p 3000:3000 tranhoangtu&#x2F;agentlens-observe:0.6.0 pip install agentlens-observe </code></pre> Demo GIF and screenshots in the README.<p>GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;tranhoangtu-it&#x2F;agentlens-observe" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;tranhoangtu-it&#x2F;agentlens-observe</a> Docs: <a href="https:&#x2F;&#x2F;agentlens-observe.pages.dev" rel="nofollow">https:&#x2F;&#x2F;agentlens-observe.pages.dev</a><p>I&#x27;d love feedback on the trace visualization approach and what features matter most for your agent debugging workflow.

Show HN: RunVeto – A Simple Kill Switch for Autonomous AI Agents

Hey HN,<p>I’m a software engineer with 5 years of experience, and I’ve been building autonomous agents recently using LangChain. I noticed that we are giving these agents too much &quot;autonomy&quot; without enough &quot;governance&quot;.<p>The specific problem I faced was Agent Sprawl&#x2F;Recursion. A test bot got stuck in a recursive loop and almost ran up a significant OpenAI bill before I noticed and killed the process manually.<p>So, I’m building RunVeto.xyz as a minimal governance layer—a control plane for agent guardrails. It sits between your agent and the LLM API, framework-agnostic, and integrates with one line of code.<p>What I’m planning to implement:<p>Hard-Cap Budgeting: Set strict token&#x2F;cost limits to kill any task before it breaks the bank.<p>Global &#x27;Veto&#x27; Button: Pause or terminate any active agent process from a central dashboard.<p>PII Shield: Automatic scrubbing of sensitive data (PII) before it hits the LLM.<p>&#x27;Chain-of-Thought&#x27; Audit: Real-time visibility into agent planning logs.<p>I&#x27;m currently pre-MVP and using this landing page to validate the core features and find &quot;founding developers&quot; to guide the roadmap.<p>I&#x27;d love to hear this community&#x27;s critique. Have you encountered &quot;recursive loops&quot; in your own agent workflows? What’s your biggest operational nightmare with agents?<p>The landing page has an embedded survey. I’m eager to hear your thoughts.<p>No fluff, just safety.