You will get Custom Agent: Tool-Using Automation (LangGraph)
Project details
Automate a real workflow (research → enrich → write/post) with a safe, observable agent.
I design a LangGraph/LangChain flow with tool calling, retries, and short-term memory, wire it to your tools (web/DB/Sheets/CRM/API/email), and deliver an API or lightweight UI with logs and a metrics snapshot.
𝐘𝐨𝐮 𝐠𝐞𝐭:
• A production agent with guardrails (allow/deny tools, rate limits), telemetry dashboard + audit trail of tool calls, optional RAG grounding for accuracy, and a clean handover (repo + runbook + basic tests/fixtures).
• Deploy in a demo environment or privately in your VPC/on-prem (K8s/IaC) as an add-on.
𝐇𝐨𝐰 𝐰𝐞’𝐥𝐥 𝐰𝐨𝐫𝐤:
• You share 1–2 example tasks, tools/keys, and a single success metric (e.g., task success rate or P95 latency).
• I return a flow diagram + plan, then ship the agent, tune for the metric, and walk you through usage and extension.
I design a LangGraph/LangChain flow with tool calling, retries, and short-term memory, wire it to your tools (web/DB/Sheets/CRM/API/email), and deliver an API or lightweight UI with logs and a metrics snapshot.
𝐘𝐨𝐮 𝐠𝐞𝐭:
• A production agent with guardrails (allow/deny tools, rate limits), telemetry dashboard + audit trail of tool calls, optional RAG grounding for accuracy, and a clean handover (repo + runbook + basic tests/fixtures).
• Deploy in a demo environment or privately in your VPC/on-prem (K8s/IaC) as an add-on.
𝐇𝐨𝐰 𝐰𝐞’𝐥𝐥 𝐰𝐨𝐫𝐤:
• You share 1–2 example tasks, tools/keys, and a single success metric (e.g., task success rate or P95 latency).
• I return a flow diagram + plan, then ship the agent, tune for the metric, and walk you through usage and extension.
AI Development Type
Deep Learning, Knowledge RepresentationAI Tools
Amazon SageMaker, Azure Machine Learning, Google AutoML, MLflow, NVIDIA AI Platform, OpenCV, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$1,000
|
Standard
$2,400
|
Advanced
$4,800
|
|---|---|---|---|
| Delivery Time | 5 days | 12 days | 18 days |
Number of Revisions | 2 | 3 | 3 |
AI Model Integration | - | ||
Detailed Code Comments | - | ||
Knowledge Graph | - | - | - |
Model Documentation | - | ||
Ontology | - | - | - |
Source Code | - | ||
Taxonomy | - | - | - |
Optional add-ons
You can add these on the next page.
Additional Revision
+$150Frequently asked questions
2 reviews
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JS
Jay S.
Feb 5, 2026
Edugrow.ai website
Gaurav delivered good work on this development project, and I enjoyed working with them. His communication was top-notch, he met all deadlines, and his skills were reasonably strong. At one point I asked for an additional milestone and he was very forthcoming that the additional work was outside his area of expertise. He helped me find additional freelancers to support the work. I enjoyed working with Gaurav and will likely have additional jobs for him in the future.
TB
Tilok B.
May 12, 2021
Looking for Salesforce hourly project
I am glad I got Gaurav to work on this project. He is brilliant and hard-working. The best thing I like about him he is punctual. He listens to my requirements calmly and once it completed he explained in simple words what he had done. Thank you!
About Gaurav
Senior AI Engineer | GenAI, RAG Systems, AI Agents & LLMOps
Pune, India - 3:37 pm local time
I help startups and enterprises move from LLM experiments to reliable AI in production, across customer-facing and internal workflows.
What I Do:
1. GenAI applications (chatbots, copilots, internal tools)
2. RAG systems with citations and retrieval evaluation
3. AI agents & agentic workflows (LangGraph, tool-use, memory, retries)
4. LLMOps / AgentOps (tracing, evals, prompt versioning, monitoring)
5. Secure AI deployments (Cloud, On-Prem, VPC)
I don’t just integrate APIs; I design systems with clear acceptance criteria, observability, and maintainability.
📊 Outcomes I Care About:
- Fewer hallucinations through grounded retrieval and evals
- Predictable latency and cost at scale
- AI systems teams can operate without vendor lock-in
- Clear success metrics agreed before development starts
Every engagement includes:
1. Source-controlled repository + infrastructure-as-code
2. Evaluation report (quality, latency, cost)
3. Runbook and short Loom walkthrough for handover
🛠️ Tech Stack:
OpenAI · Claude · Mistral
LangGraph · LangChain
Pinecone · FAISS
FastAPI · Docker · Kubernetes
AWS · Azure · GCP
🔐 Security & Reliability:
SSO (SAML / OIDC), RBAC, audit logs
PII scrubbing & secrets management
Tool-call tracing and full auditability
Designed so security and compliance teams don’t block deployment.
If you want serious GenAI, not demos, let’s talk.
Steps for completing your project
After purchasing the project, send requirements so Gaurav can start the project.
Delivery time starts when Gaurav receives requirements from you.
Gaurav works on your project following the steps below.
Revisions may occur after the delivery date.
Step 1: Kickoff & assets (Day 0–1)
Confirm workflow, tools, and KPI; collect sandbox/API keys and example inputs.
Step 2: Tool wiring & auth (Day 1–3)
Connect web/DB/Sheets/CRM/APIs; set secrets, retries, rate limits.