TL;DR
- LangGraph is our default for a new account-research agent with explicit state and a consequential CRM write. Its graph and checkpoint model make the transaction easy to inspect.
- CrewAI is a strong choice when separate research and analysis roles improve the work. Use a Flow to control the outer process and a Crew for the reasoning stage. Current CrewAI also supports checkpointing and resume.
- Retain AutoGen for a working existing application, with a bounded maintenance plan. Microsoft now directs new projects to Microsoft Agent Framework; treating AutoGen as an equally forward-looking new-build option is misleading.
- The open-source framework fee is $0. Models, search, persistence, hosting, monitoring and engineering remain real costs. Extra agents can increase model turns without improving accepted account results.
Compare the same GTM transaction
Use a bounded job: research an existing CRM account, find a recent hiring signal, produce a source-supported qualification note, and update one approved research field. The input carries an account ID and canonical domain. The output carries the source URL, evidence date, proposed note and write receipt.
Account matching, suppression, allowed field names and receipt reconciliation belong in ordinary code. The model decides what a source means and drafts the note. A framework is useful when those stages need branching, saved state, parallel research or a pause; a short linear task may need only application code.
That makes this a comparison of developer orchestration choices. These libraries do not buy a prospect database, a finished CRM connector or a managed sales team. For a broader build pattern, see running GTM from a coding agent.
Framework comparison
| Choice | Organising model | Same account transaction | Persistence and recovery | Buying basis |
|---|---|---|---|---|
| LangGraph | Typed state and graph nodes/edges | Resolve → research → qualify → optional review → write → receipt | Checkpointers and thread IDs; inspect and replay saved state | MIT framework, no software licence fee; self-host or separately metered LangSmith services |
| CrewAI | Agents/tasks in Crews; event-driven Flows | Flow owns identity and writes; research/analyst Crew produces structured evidence | Flow persistence plus configurable Crew/Flow/Agent checkpoints | MIT framework; current hosted Basic is free for 50 executions/month, Enterprise priced by agreement |
| AutoGen | AgentChat teams on event-driven Core | Existing research team calls narrow tools; application owns final write | Agent/team save and load state; application persists it | MIT code; maintenance mode, with model and hosting costs separate |
| Microsoft Agent Framework | Agents and explicit workflows; Python/.NET | Current Microsoft-stack route for the same stages | Session state and workflow checkpointing | Open-source successor; runtime, models and Azure or other hosting charged separately |
Status and commercial details were checked on 30 September 2026. The Microsoft successor is included because it changes the AutoGen buying decision; the existing article route is retained. LangGraph repository, CrewAI repository, CrewAI pricing, AutoGen repository, Agent Framework repository.
LangGraph
LangGraph makes the account job a state machine the team can read. Define fields for canonical domain, CRM ID, evidence, draft note, approval decision and receipt. Put retrieval and synthesis in separate nodes; branch to review when the evidence is ambiguous, rather than asking a group of agents to decide the execution order through conversation.
Checkpointers save thread-scoped graph state; stores hold application data across threads. A consistent thread_id links a resumed account job to its checkpoint. Production needs persistent storage: InMemorySaver loses checkpoints on a process restart. Persistence documentation.
For this job, the benefit is precise diagnosis. If research succeeded and qualification failed, inspect the saved evidence before rerunning the failed work. Checkpoint history also lets developers fork a run to compare a prompt. Replaying after an older checkpoint re-executes downstream nodes, including model and API calls, so the CRM action needs its own receipt logic. Checkpointer documentation.
Deployment: run the library in your own worker with a database and queue, or buy LangSmith's managed services separately. The framework does not require a LangChain application. LangSmith provides a closely integrated debugging path; it is a commercial service rather than the price of using LangGraph. Repository and ecosystem.
Best fit: explicit branches, pauses and recoverable account jobs maintained by developers. Non-fit: a nontechnical sales team expecting a finished automation product, or a simple three-step pipeline where a graph adds more code than it removes.
CrewAI
CrewAI provides two complementary abstractions. A Crew assigns tasks to agents with roles and tools. A Flow controls state and event-driven execution. For account research, use a Flow to resolve the CRM identity, invoke the research Crew, validate its structured result and call the write service. The researcher and analyst can have different source context without both receiving CRM mutation access. CrewAI framework.
Flows support typed or dictionary state, branching and persistence. The documented @persist decorator saves state with SQLite by default; restoring the same flow ID and forking from a saved state are distinct operations. That matters when an operator retries an account versus an engineer starts an experiment. Flow documentation.
Current checkpointing extends beyond a saved Flow dictionary. Crews, Flows and Agents can save execution snapshots; completed tasks are skipped on restore. The default event is task completion, with JSON and SQLite providers. Automatic checkpoint writes are best-effort, so a logged storage failure does not stop the run. Use a deployment that treats that failure as actionable when recoverability matters. Checkpointing documentation.
Deployment: the open-source library can run in your worker. The commercial platform currently offers a free Basic tier with two automations and a maximum of 50 monthly executions; Enterprise offers customer or CrewAI deployment, identity/governance features and a custom execution allowance. A 1,000-account monthly production budget should use self-hosted operating costs or an Enterprise pricing basis, rather than a retired public paid tier. Current pricing.
Best fit: genuinely different research roles, managed by a Flow, or a team already productive in CrewAI. Non-fit: two agents repeating the same search and passing verbose summaries back and forth. Role names alone do not improve source quality or recovery.
AutoGen and Microsoft Agent Framework
AutoGen remains useful for an existing investment. AgentChat provides team patterns and the lower-level Core supports event-driven agents. Its official repository now says the project is community-managed maintenance software, with no new features or enhancements planned. New users are directed to Microsoft Agent Framework. AutoGen Studio is a prototyping interface rather than a production-ready application. Official project status.
For the existing account-research team, preserve working tools, termination rules and evaluation examples. Agent and team save_state()/load_state() methods let the application serialize state to a file or database. For AssistantAgent, that state is model context; it does not by itself replace a business-job record containing the account ID, allowed changes and final CRM receipt. Managing state.
Our recommendation for an existing deployment: maintain the stable job while moving new features to a supported framework. Do not rewrite a reliable batch merely because the project status changed, but avoid making new long-term product requirements depend on AutoGen enhancements.
For a new Microsoft-stack build: use Agent Framework's current Python/.NET route. It combines agents, session state, middleware and explicit workflows, with multiple provider integrations. The repository documents workflow checkpointing and orchestration patterns. The Python/.NET successor is the current release direction; the separately documented Go implementation remains in preview. Overview, repository.
Migration is an implementation task. Translate the actual tools, messages and workflow boundaries, then run the same account cases through the successor. Microsoft's migration guide is useful for that translation; shared origin does not make stored state or application behaviours automatically interchangeable. Migration guide.
Non-fit: AutoGen for a fresh project expecting active feature development. Agent Framework also adds little value to a tiny Python job if the team already has a clear, supported LangGraph or CrewAI implementation.
Persistence debugging and deployment
The same failure distinguishes the choices. Imagine the CRM writes the note, but the worker times out before receiving the response. A framework checkpoint may still show “write pending.” Resuming the job without checking the remote outcome can repeat the write.
| Framework | What the developer inspects | What the application still owns |
|---|---|---|
| LangGraph | Thread checkpoint, node outputs, graph history and downstream replay | Mutation key, read-back receipt and persistent checkpointer deployment |
| CrewAI | Flow state, task outputs and checkpoint lineage | Reliable checkpoint storage and reconciliation inside the final write stage |
| AutoGen | Agent/team state, messages, tool result and termination reason | Saved business state and durable outcome tracking outside conversation context |
| Agent Framework | Workflow path, session state and checkpointed progress | Tool permissions, record identity and provider-specific receipt handling |
Keep the final mutation as a narrow operation such as write_research_note(account_id, note, evidence_url, job_key). It should return the record ID, committed value and outcome reference. A normal supported note can pass deterministic policy checks; ambiguous account identity can route to a reviewer. This avoids requiring human approval for every harmless research step.
For debugging, attach one run ID to search, model and tool events, along with prompt versions. Compare a normal account, two similarly named businesses, a stale hiring page and the timeout case. The useful result is the correct note on the correct account with one reconciled outcome. These are proposed acceptance cases, not claimed test results from these products.
Worked operating budget
Assume 1,000 monthly account jobs and the same model rates for every framework: $2 per million input tokens and $8 per million output tokens. These are planning assumptions, not a quote for a named model.
A lean design uses two model calls per job. Each call consumes 3,000 input and 500 output tokens: 6 million input tokens cost $12, and 1 million output tokens cost $8. Monthly model cost is $20.
A three-role design using four calls per job costs $40 on those same per-call volumes. Actual conversation context can grow, so this is a controlled comparison of call count rather than a performance claim against CrewAI or AutoGen. All frameworks can implement lean or expansive designs.
| Monthly component | Explicit assumption | Lean design | Four-call design |
|---|---|---|---|
| Framework licence | Open-source code | $0 | $0 |
| Model calls | Same assumed token rates and size | $20 | $40 |
| Search | 3,000 Exa Fast/Auto requests, at $7/1,000 | $21 | $21 |
| Worker and persistence | Illustrative infrastructure allowance | $40 | $40 |
| Monitoring | Langfuse Core base | $29 | $29 |
| Engineer maintenance | Four hours at $75/hour | $300 | $300 |
| Operating subtotal | Before CRM, tax and initial development | $410 | $430 |
The search and monitoring amounts use current public rate bases; infrastructure and labour are editorial assumptions. Exa pricing, Langfuse pricing. Hosted LangSmith or CrewAI Enterprise charges would replace or add to the relevant operating lines; no published Enterprise total is invented here.
If 800 of the 1,000 notes are accepted, the lean design costs $0.5125 per accepted note; the four-call design costs $0.5375 at the same acceptance count. If the extra calls improve acceptance to 850, the second figure falls to about $0.506. The extra reasoning is worth buying when it produces enough usable outcomes, rather than when the diagram looks more sophisticated.
One extra engineer hour at $75 costs more than the $20 call-count difference. A 40-hour initial implementation at that rate adds $3,000. That is why familiarity, debugging and recovery usually outweigh the library's zero-dollar licence price. This budget is a worked decision model, not measured framework speed or engineering effort.
Which framework to choose
Choose LangGraph for a new stateful GTM transaction when the account stages and recovery paths should be explicit. Choose CrewAI when separate agents need materially different tools or context, and put the production transaction inside a Flow. Both can persist state; the decision is the application structure and the team's ability to operate it.
Keep AutoGen for a functioning existing application while planning its maintenance and migration. Choose Microsoft Agent Framework for a new Microsoft-stack project rather than starting a fresh AutoGen dependency.
Before expanding the cast of agents, write the qualification criteria from the signal-based outbound playbook into the job's input and scoring rules. A smaller architecture with clear account identity, evidence and receipts is usually the fastest path to useful production output.
FAQ
Does LangGraph require LangChain?
No. It can be used independently. LangChain and LangSmith are adjacent ecosystem choices.
Can CrewAI recover a failed run?
Yes. Current checkpointing supports restoring Crews, Flows and Agents, while Flow persistence also saves state. The application must still reconcile external side effects.
Should a new project choose AutoGen?
Generally no. Its official project directs new builds to Microsoft Agent Framework. A working existing AutoGen application has a different maintenance decision.
Are the frameworks free?
The open-source code has no subscription fee. Models, tools, hosting, persistence, monitoring and engineering are separate; commercial managed platforms have their own pricing.
How many agents does account research need?
Often one reasoning component with deterministic tools is enough. Add roles when different context or expertise improves accepted results enough to justify their extra calls and operational complexity.
