LangGraph

DeepTrade — Lets a user chat with a Telegram bot that researches stocks, drafts Upstox orders, and places them only after manual confirmation.

Sanctorum — Runs teams of LLM-driven agents in a pixel-art office, where each agent's tool permissions come from the desk it sits at.

Maximem Synap — Gives AI agents persistent memory across sessions, storing user preferences and extracted entities so they stop forgetting context.

AgentBridge — Lets an application define agent instructions, tools, and output in one shared spec and run it on different agent frameworks.

Repodify — Turns a set of podcast episodes into one shorter, chronological synthetic episode by transcribing, summarizing, and re-voicing them locally or via API keys.

cveck — Generates job-tailored resume PDFs from a master profile and a job description using an LLM pipeline and Typst.

Cursiva — Generates ATS-friendly single-column LaTeX resumes tailored to job postings, with an AI reviewer that flags invented facts.

employee-onboarding-orchestrator — Generates an IT onboarding and offboarding pack for new or leaving employees, including checklists, PowerShell scripts, and drafted emails, with a human approval step.

SatyaMark — Verifies text claims and detects AI-generated or tampered images inside any app, showing evidence, sources and a confidence score.

FundersAI — Generates sourced comparison reports for mutual fund schemes, citing official AMC documents and abstaining when claims lack backing.

Decree — Routes AI agent actions through policy checks and human approval before execution, then sends a signed webhook with the decision.

Datalk — Turns uploaded documents or website URLs into a private knowledge base that powers a no-code custom chatbot.

I rebuilt my AI agent that… — Generates source-tiered research reports on a topic by classifying the query, searching and ranking sources by credibility, cross-checking claims, and writing a structured report.

Built an AI tool to write… — Generates LinkedIn posts through a multi-step AI pipeline of idea generation, writing, reviewing, and editing to match a user's personal writing style.

AI agent credit score — Computes on-chain credit scores and limits for AI agents from their verified task history, letting them draw and repay credit on testnet.

agent-security-research — Validates agent JSON tool calls against a strict schema and re-syncs stale shared agent state in a LangGraph pipeline using Ollama.

Sulcus — Provides a single dashboard for monitoring AI agent runs, tool calls, LLM activity, errors and resource usage, with planned permissions, approvals and limits.

kafka-agent-bridge — Connects LangGraph or CrewAI agents to Kafka topics, handling consumption, retries, idempotency, dead-letter queues, and result routing.

OpenBox.ai — Enforces external governance controls on AI agents, including action permissions, human approval gates, behavioural rules, and auditable action logs.

SafeAI — Statically analyzes AI agent codebases for prompts, tool permissions, autonomy, memory and governance risks.

TraceSage — Captures LangChain and LangGraph agent runs locally and visualizes their topology, step-by-step replays, and per-call token usage.

Agent Map — Renders AI agent runs as a live, color-coded visual graph of devices, sessions, and tool calls.

sponsio — Enforces declared YAML rules deterministically at the tool-call boundary before agent actions commit.

Built — Manages allow, deny, and audit policies for AI agent MCP tool calls and syncs signed policy bundles to enrolled developer machines.

DriftGuard — Reviews an agent's proposed actions against a semantic memory of past mistakes and warns or blocks before execution.

AgentScan — Scans a public GitHub repo of a Python LLM agent by running adversarial test payloads against a cloned copy and reports confirmed tool-call bypasses.

PyBotchi — Orchestrates LLM intent detection as nested, deterministic Python business logic using Pydantic models and class inheritance.

icm-architect — Generates numbered folder structures with markdown state and routing files so each agent step loads only the context it needs.

TIL that building a 3-agent AI… — Builds multi-step AI agent pipelines from decorated Python functions, deriving tool schemas from type hints and docstrings.