The Blueprint of Trust: Our
Global Compliance Framework
Ksolves: Your Trusted LangGraph Consulting Partner
At Ksolves, we build agents that hold up under real production conditions, not just clean demo runs. Our consultants design LangGraph-based systems using StateGraph architecture, durable checkpointing, and conditional routing, so agent state survives crashes, restarts, and long-running tasks without manual intervention. We architect multi-agent workflows through subgraph composition, embed human-in-the-loop interrupts for high-stakes decisions, and wire in LangSmith-based tracing for full execution visibility. Engagements span the entire build: graph and node design, tool integration, persistence layers, deployment on LangGraph Platform, and ongoing monitoring. The result is agentic infrastructure your team can actually trust and maintain.
Our LangGraph
Consulting Services
Whether you're defining your agent strategy or running mission-critical graphs in production, Ksolves supports every phase: architecture, development, observability, and managed operations.
LangGraph Strategy & Architecture
Our consultants assess your current agent workflows and design a future-state LangGraph architecture tailored to your control flow, latency, and reliability requirements. We define state schema, node and edge design, and checkpointing strategy before a single graph is built.
LangGraph Implementation & Agent Development
Our LangGraph consultants build production-ready agents using LangGraph's StateGraph primitives, covering single-agent, multi-agent, and hierarchical control flows. Every implementation is designed for durable execution, so agents can survive restarts and resume from where they left off.
Migration from AgentExecutor or Legacy Agent Frameworks
Still running LangChain's original AgentExecutor, a custom orchestration loop, or another agent framework that can't handle branching or long-running state? We handle the full transition, rewriting orchestration logic into LangGraph's graph-based model with a phased, validated cutover, so you move to production-grade agents with minimal disruption to existing integrations.
Multi-Agent & Subgraph Design
We design orchestrator and subagent patterns, hierarchical graphs, and subgraph composition so complex workloads can be broken into specialized, independently testable agents. Our consultants apply the right coordination pattern for your task rather than defaulting to a single black-box architecture.
Memory, Persistence & Checkpointing
We implement short-term working memory and long-term memory across sessions using LangGraph's built-in persistence layer. Our checkpointing designs ensure agents maintain context reliably, recover cleanly from failures, and support human-in-the-loop review at any point in execution.
Human-in-the-Loop & Guardrail Design
We add moderation, quality controls, and approval gates so agents can pause for human review, accept corrections, and resume execution safely. Our governance approach keeps agent actions auditable and aligned with your compliance requirements.
Observability, Tracing & Managed Services
Running agents in production is an ongoing commitment. Our managed services team monitors execution traces, node failures, latency, and token consumption around the clock using LangSmith and OpenTelemetry-compatible tracing. We deliver proactive tuning, cost monitoring, and 24x7 incident response to keep your agents stable and predictable.
LangGraph Platform Deployment
Our experts handle deployment and scaling of stateful agents using LangGraph Platform or self-hosted infrastructure, covering streaming configuration, environment provisioning, and scaling policy design for long-running workloads.
Tool & Integration Development
We design and build custom tool nodes and integrations connecting your agents to internal APIs, databases, vector stores, and third-party services, using LangChain's integration ecosystem alongside custom-built connectors where standard integrations fall short.
Our LangGraph Consulting Services
Whether you're defining your agent strategy or running mission-critical graphs in production, Ksolves supports every phase: architecture, development, observability, and managed operations.
LangGraph Strategy & Architecture
Our consultants assess your current agent workflows and design a future-state LangGraph architecture tailored to your control flow, latency, and reliability requirements. We define state schema, node and edge design, and checkpointing strategy before a single graph is built.
LangGraph Implementation & Agent Development
Our LangGraph consultants build production-ready agents using LangGraph's StateGraph primitives, covering single-agent, multi-agent, and hierarchical control flows. Every implementation is designed for durable execution, so agents can survive restarts and resume from where they left off.
Migration from AgentExecutor or Legacy Agent Frameworks
Still running LangChain's original AgentExecutor, a custom orchestration loop, or another agent framework that can't handle branching or long-running state? We handle the full transition, rewriting orchestration logic into LangGraph's graph-based model with a phased, validated cutover, so you move to production-grade agents with minimal disruption to existing integrations.
Multi-Agent & Subgraph Design
We design orchestrator and subagent patterns, hierarchical graphs, and subgraph composition so complex workloads can be broken into specialized, independently testable agents. Our consultants apply the right coordination pattern for your task rather than defaulting to a single black-box architecture.
Memory, Persistence & Checkpointing
We implement short-term working memory and long-term memory across sessions using LangGraph's built-in persistence layer. Our checkpointing designs ensure agents maintain context reliably, recover cleanly from failures, and support human-in-the-loop review at any point in execution.
Human-in-the-Loop & Guardrail Design
We add moderation, quality controls, and approval gates so agents can pause for human review, accept corrections, and resume execution safely. Our governance approach keeps agent actions auditable and aligned with your compliance requirements.
Observability, Tracing & Managed Services
Running agents in production is an ongoing commitment. Our managed services team monitors execution traces, node failures, latency, and token consumption around the clock using LangSmith and OpenTelemetry-compatible tracing. We deliver proactive tuning, cost monitoring, and 24x7 incident response to keep your agents stable and predictable.
LangGraph Platform Deployment
Our experts handle deployment and scaling of stateful agents using LangGraph Platform or self-hosted infrastructure, covering streaming configuration, environment provisioning, and scaling policy design for long-running workloads.
Tool & Integration Development
We design and build custom tool nodes and integrations connecting your agents to internal APIs, databases, vector stores, and third-party services, using LangChain's integration ecosystem alongside custom-built connectors where standard integrations fall short.
Why Choose Ksolves as Your LangGraph
Consulting Partner?
Whether you're building your first AI agent, modernizing legacy orchestration, or deploying multi-agent systems at scale, Ksolves is your trusted LangGraph consulting provider, delivering end-to-end consulting tailored to your enterprise needs.
90%
Client Retention
Rate
750+
Projects Successfully
Delivered
NSE & BSE
Publicly Listed
Company
600+
Workforce and still
growing
350+
Certifications
200+
Happy Clients
24x7
Support Across All Time Zones
Our Proven LangGraph Implementation Process
A five-phase methodology that reduces risk, accelerates timelines, and delivers measurable outcomes at every stage.
Discovery & Assessment
We start by mapping your current agent workflows, control-flow complexity, and existing tooling, whether that's raw LLM calls, LangChain chains, or a legacy AgentExecutor setup. This phase produces a LangGraph readiness report along with a clear implementation roadmap, so scope and expectations are set before any code is written.
Architecture & Design
State schema, node and edge topology, and checkpointing strategy are designed up front. We also define where human-in-the-loop gates belong in the graph, so approval steps and state persistence are built into the architecture from day one, not bolted on later.
Implementation & Migration
Our team constructs the graph, develops tool nodes, and wires in memory and persistence layers. Every execution path, including error branches and retries, is validated so the agent behaves predictably under real conditions, not just the happy path.
Optimization & Tuning
Once agents are live, we analyze execution traces, profile latency across nodes, and review token consumption to find where cost or performance can improve. Adjustments here are data-driven, based on how the agent actually runs in production, not assumptions.
Managed Support, Ongoing
Round-the-clock monitoring, proactive incident response, and regular performance reviews keep agents stable as usage grows. This phase also covers versioning support as LangGraph evolves, so upgrades don't break existing graphs.
Our Proven LangGraph Implementation Process
A five-phase methodology that reduces risk, accelerates timelines, and delivers measurable outcomes at every stage.
Discovery & Assessment
We start by mapping your current agent workflows, control-flow complexity, and existing tooling, whether that's raw LLM calls, LangChain chains, or a legacy AgentExecutor setup. This phase produces a LangGraph readiness report along with a clear implementation roadmap, so scope and expectations are set before any code is written.
Architecture & Design
State schema, node and edge topology, and checkpointing strategy are designed up front. We also define where human-in-the-loop gates belong in the graph, so approval steps and state persistence are built into the architecture from day one, not bolted on later.
Implementation & Migration
Our team constructs the graph, develops tool nodes, and wires in memory and persistence layers. Every execution path, including error branches and retries, is validated so the agent behaves predictably under real conditions, not just the happy path.
Optimization & Tuning
Once agents are live, we analyze execution traces, profile latency across nodes, and review token consumption to find where cost or performance can improve. Adjustments here are data-driven, based on how the agent actually runs in production, not assumptions.
Managed Support, Ongoing
Round-the-clock monitoring, proactive incident response, and regular performance reviews keep agents stable as usage grows. This phase also covers versioning support as LangGraph evolves, so upgrades don't break existing graphs.
Our Services Across Every Industry
We deliver a sector-aligned agent orchestration advantage, backed by LangGraph consulting expertise and compliance frameworks built for each vertical.
Telecom
Customer support and network operations agents with human-in-the-loop escalation for high-stakes decisions.
Healthcare
HIPAA-aware clinical documentation and triage agents with audit-ready checkpointing and mandatory human review gates.
E-Commerce
Multi-agent shopping assistants and order-management agents with persistent session memory across visits.
Fintech
Fraud investigation and compliance-review agents with PCI-DSS-aligned access controls and full execution traceability.
Entertainment & Media
Content recommendation and moderation agents with subgraph composition for specialized review tasks.
Manufacturing
Maintenance and quality-inspection agents coordinating with IoT data sources and human approval workflows.
Retail
Inventory and customer-service agents with durable execution across long-running, multi-step interactions.
Banking & Financial Services
GDPR and SOC 2-aligned agent workflows with role-based access isolation and disaster-recovery-ready checkpointing.
Logistics & Supply Chain
Route-planning and exception-handling agents that coordinate across multiple specialized subagents.
Technology & SaaS
Internal developer-productivity agents and customer-facing copilots built on hierarchical multi-agent graphs.
Our Blogs
Discover insightful perspectives, emerging trends, and expert opinions from Kafka thought experts.
Success Stories
Discover real-world case studies showcasing measurable outcomes, faster performance, and successful digital transformation journeys.
Kafka Disaster Recovery Across AWS & Azure
Challenge
No cross-cloud failover existed; a single cloud outage caused immediate data loss and broken SLA commitments.
Solution
Deployed Kafka MirrorMaker 2 for bidirectional replication across AWS and Azure clusters with TLS security, selective topic mirroring, automatic failover, and real failure testing.
Sub-30s
RTO Confirmed
Confluent to Open-Source Kafka Migration
Challenge
High Confluent licensing fees and vendor lock-in required urgent migration with zero downtime across 24x7 critical apps.
Solution
Ran full topology assessment, configured MirrorMaker 2 for live replication, executed phased app cutover, preserved schema compatibility, and safely decommissioned Confluent.
100%
Data Consistency Maintained
Predictive Cable Network Analytics Platform
Challenge
Legacy RDBMS couldn't store or query time-series data from millions of cable modem IoT devices at scale.
Solution
Deployed a 5-node Apache NiFi cluster and 10-node Cassandra cluster with a redesigned data model and elastic zero-downtime scaling capability.
Elastic Scaling
Zero Downtime
Zero-Downtime Kafka to Redpanda Migration
Challenge
Managed Kafka costs and operational complexity became unsustainable; migration needed 100% data integrity and zero downtime.
Solution
Used MirrorMaker 2.0 with source, checkpoint, and heartbeat connectors for live replication; implemented phased validation and auto-failover before full cutover.
38%
Lower Streaming Costs · Zero Downtime
Kafka Disaster Recovery Across AWS & Azure
Challenge
No cross-cloud failover existed; a single cloud outage caused immediate data loss and broken SLA commitments.
Solution
Deployed Kafka MirrorMaker 2 for bidirectional replication across AWS and Azure clusters with TLS security, selective topic mirroring, automatic failover, and real failure testing.
Sub-30s
RTO Confirmed
Confluent to Open-Source Kafka Migration
Challenge
High Confluent licensing fees and vendor lock-in required urgent migration with zero downtime across 24x7 critical apps.
Solution
Ran full topology assessment, configured MirrorMaker 2 for live replication, executed phased app cutover, preserved schema compatibility, and safely decommissioned Confluent.
100%
Data Consistency Maintained
Predictive Cable Network Analytics Platform
Challenge
Legacy RDBMS couldn't store or query time-series data from millions of cable modem IoT devices at scale.
Solution
Deployed a 5-node Apache NiFi cluster and 10-node Cassandra cluster with a redesigned data model and elastic zero-downtime scaling capability.
Elastic Scaling
Zero Downtime
Zero-Downtime Kafka to Redpanda Migration
Challenge
Managed Kafka costs and operational complexity became unsustainable; migration needed 100% data integrity and zero downtime.
Solution
Used MirrorMaker 2.0 with source, checkpoint, and heartbeat connectors for live replication; implemented phased validation and auto-failover before full cutover.
38%
Lower Streaming Costs · Zero Downtime
Frequently Asked Questions
Graph architecture design, state schema design, tool node development, checkpointing and memory setup, tracing configuration, and post-launch handover are all part of our LangGraph consulting engagements. We stay accessible after go-live so your team is never left without support.
Share your role requirements, required skills, and timeline. We match you with an experienced LangGraph consultant within 48 hours, and most engagements start within 5 to 7 business days. Engagement models range from short sprints to full-time embedded consultants.
Cost depends on agent complexity, whether you’re building single-agent or multi-agent systems, and the scope of work, such as a proof of concept versus a full production deployment with managed services. We provide a fixed-scope quote after an initial discovery call, so you know the investment before work begins.
LangGraph consulting covers strategy, architecture, and state and control-flow design planning. Implementation is the hands-on build, including graph construction, tool integration, checkpointing, and validation. At Ksolves, both are integrated into every engagement.
A single-agent workflow with a handful of tools typically takes 2 to 4 weeks from discovery to production go-live. Complex multi-agent systems with hierarchical subgraphs, custom memory design, or migrations from legacy AgentExecutor code can take longer, depending on the number of workflows and integration points involved.
Yes. Knowledge transfer and documentation are built into every engagement. We train your engineers on graph design, tracing, and incident response, so you are not dependent on us long-term unless you choose to continue with our managed services.
Yes. As a LangGraph consulting company, we offer scoped advisory packages, milestone-based delivery, and part-time retainers, so growing teams get experienced agentic AI expertise without enterprise-level spend.
Yes. Our managed services consultants handle day-to-day agent operations, including trace monitoring, failure response, incident management, cost tracking, and quarterly performance reviews. This is the right fit for teams that need continuous LangGraph consulting services without building and retaining an internal team.
Yes. Our migration engagements cover control-flow redesign, state schema mapping, and phased cutover with rollback safeguards. We redesign orchestration logic from linear chains or custom loops into LangGraph’s graph-based, checkpointed model.
