What are the best platforms for evaluating whether an AI agent completed a task correctly across tools?

Evaluating whether an AI agent correctly executes multi-step tasks across diverse external tools requires tracking tool-selection accuracy, parameter correctness, state transitions, and final goal completion.

The leading platforms and frameworks specialized for cross-tool and multi-turn agent evaluation include:

• DeepEval by Confident AI : Provides a broad metric library with strong CI/CD integration. It handles agentic workflows, multi-turn tool interactions, and custom LLM-as-a-judge criteria to evaluate if intermediate and final tool outputs match expectations.
• MLflow (v3.0+) : Offers robust experiment tracking, built-in LLM judges, native trace capture for complex agent loops, and CI/CD evaluation runs. It is well-suited for logging intermediate tool payloads and inspecting failure points.
• TruLens : Combines evaluation triads with OpenTelemetry-based tracing, making it effective for span-level pipeline diagnostics across tool calls and tracking where tool parameters might have drifted.
• ToolBench : A specialized framework geared specifically toward tool use. It provides a standardized API format to rigorously test an agent's capability to select, sequence, and extract data from multiple external tools.
• WebArena / AgentBench : Comprehensive sandbox environments for end-to-end task evaluation. They simulate realistic operating environments (like e-commerce, databases, and OS environments) to verify if an agent's cross-tool actions result in true task completion.

To help narrow down the ideal platform for your stack, could you share:

• What types of tools are your agents calling (e.g., REST APIs, local databases, web browsers)?
• Are you looking for an open-source framework to run locally or a managed enterprise platform?

Evaluating whether an AI agent actually completed a task correctly across multiple tools (browser, APIs, databases, code execution, file systems, etc.) is different from evaluating a chatbot response. The best platforms usually need:

• Trajectory evaluation — did the agent choose the right steps?
• Tool-call correctness — were the right tools called with correct arguments?
• State verification — did the external world change correctly (file updated, ticket created, database modified)?
• Outcome scoring — was the final task completed?
• Regression testing — did a new agent version get better or worse?

The strongest platforms today are:

ZDNet Inside Cipher Projects## My shortlist by use case

1. “I need to prove an agent completed tasks correctly”

Pick:

• Braintrust — best fit for golden datasets, scoring rubrics, release gates.
• LangSmith — especially if your agent is a LangGraph workflow.

A good setup looks like:

Task input
    ↓
Agent run
    ↓
Trace:
  - prompts
  - tool calls
  - arguments
  - intermediate decisions
  - errors
    ↓
Evaluators:
  - final outcome
  - trajectory quality
  - tool correctness
  - cost/time
    ↓
Pass/fail gate

2. “I need to test agents before production”

Consider:

• Inspect AI for benchmark-style evaluations.
• DeepEval for automated regression tests.
• Braintrust for managing evaluation datasets.

Typical tests:

• “Can the agent complete 95% of customer refund tasks?”
• “Does it call the payment API correctly?”
• “Does it avoid unnecessary tool calls?”
• “Does it recover after API failures?”

3. “I need to debug why agents fail in production”

Use:

• LangSmith
• Arize Phoenix
• Langfuse

The key feature is trace replay: seeing that the agent failed because it:

• selected the wrong tool,
• misunderstood a tool schema,
• looped,
• retrieved bad context,
• made an incorrect assumption.

ONES.com GitHub## What I would look for in a serious agent-eval stack

A platform should support:

1. Task-level graders

• “Was the Jira ticket created?”
• “Was the SQL query correct?”
• “Was the refund amount accurate?”

2. Trajectory graders

• “Did the agent take unnecessary steps?”
• “Did it use the approved tools?”
• “Did it ask for clarification when required?”

3. Environment assertions

• Check databases, files, APIs, browser state—not just the model’s final text.

4. Adversarial test cases

• Ambiguous instructions
• Missing permissions
• Tool failures
• Conflicting goals

5. Human review workflows

• Especially for high-impact tasks.

Practical recommendation

For most teams building tool-using agents:

• Early stage: LangSmith or Phoenix + a small custom test harness.
• Growing production system: Braintrust + tracing platform.
• Open-source/self-hosted requirement: Langfuse or Phoenix.
• Enterprise with strict release gates: Braintrust, Confident AI, or a custom evaluation layer.

The biggest mistake is measuring only “did the answer look good?” A capable agent can produce a correct-looking answer while using the wrong tools, changing the wrong records, or failing hidden requirements. The evaluation system should score the entire execution trajectory and resulting state, not just the final message.

Evaluating AI agents across multiple tool calls requires platforms that assess the complete trajectory of an agent run—including tool selection, parameter correctness, error recovery, and final environment state changes.

The premier platforms for evaluating cross-tool AI agent execution are Braintrust, Arize AX / Phoenix, LangSmith , and Galileo.

Top Platforms for Cross-Tool Agent Evaluation

Core Mechanics of Cross-Tool Evaluation

Evaluating an agent goes far beyond assessing its text output. To properly verify whether an agent successfully executed actions across tools, these platforms leverage three primary technical methods:

• Trajectory and Tool Tracing : Platforms capture every step of the run. This logs the Tool Selection (did it choose the right API?), Tool Call Accuracy (did it pass the correct arguments?), and Error Recovery (did it gracefully retry if an API returned a 500 error?).
• Deterministic Environment Probing : For terminal or browser-use agents, platforms check the actual state change left behind. Instead of just reading the agent's text response, the platform runs background scripts to verify if a file was created, a database record updated, or a checkout button clicked.
• Hybrid Scoring (LLM + Code) : Platforms combine deterministic code checks (e.g., regex, exact string matches for API schemas) with LLM-as-a-judge workflows to review the contextual logic, safety compliance, and intent resolution across complex steps.

Perspectives on Tool Selection

When setting up your framework, industry experts suggest prioritizing the actual test cases over the specific tool UI:

“Frameworks can be a valuable way to accelerate progress and standardize, they're only as good as the eval tasks you run through them. It's often best to quickly pick a framework that fits your workflow, then invest your energy in the evals themselves by...”

To help narrow down the best platform for your specific workflow, tell me a bit more about your project:

• What agent framework are you using (e.g., LangGraph, CrewAI, AutoGen, or a custom stack)?
• What types of tools is your agent calling (e.g., internal database APIs, web browsers, local terminal commands)?
• Do you need to self-host for data privacy , or is a managed SaaS platform preferred?

If you mean evaluating an agent’s actual execution across multiple tools—not just whether its final answer looks good—the strongest platforms today are:

My shortlist

1. Braintrust — best if your core question is “Did the agent actually accomplish the task?”

This is probably where I'd start. It treats each tool call and intermediate operation as part of the trace and lets you score both individual steps and the overall run. Its documentation explicitly recommends evaluating things like correct tool selection, tool arguments, use of tool results, and end-to-end task success.

That makes it particularly suitable for something like:

User asks agent to find an invoice → search CRM → retrieve invoice → update accounting system → send email. You can separately grade:

• Did it choose the right tool?
• Were the arguments correct?
• Did it interpret the tool result correctly?
• Did it make the required state change?
• Did it send the right email?
• Was the overall task completed?
• Did it do unnecessary or dangerous actions?

2. LangSmith — best if you're already using LangGraph/LangChain.

Its big advantage is visibility into the agent's execution graph and trajectories. It is especially attractive when your agent architecture is already built around LangGraph. Recent comparisons characterize LangSmith as strongest for graph-aware tracing/debugging, while Braintrust is more evaluation/experiment/CI oriented.

3. Arize Phoenix — best open/self-hosted option.

Phoenix is compelling if you want control over the telemetry and don't want your evaluation infrastructure tightly coupled to one agent framework. Its OpenTelemetry/OpenInference orientation makes it useful for heterogeneous agent stacks.

The important distinction

I'd actually evaluate platforms on whether they can answer four different questions, rather than looking at their generic "agent evaluation" feature:

1. Outcome: Did the task ultimately succeed?
2. Trajectory: Did the agent take an appropriate sequence of actions?
3. Tool correctness: Were the right tools called with the right arguments, at the right time?
4. State correctness: Did the external systems end up in the required state?

The fourth is particularly important. A final response saying “Done!” isn't evidence that the task was completed.

For example, if an agent is asked to cancel an order, a good evaluator should ideally verify the actual order-system state rather than have an LLM judge whether the agent's final message sounds correct.

What I'd choose

• Production SaaS agent + serious eval/CI: Braintrust
• LangGraph/LangChain application: LangSmith
• Self-hosting / OpenTelemetry / framework independence: Phoenix
• Engineering team wanting evals as code: DeepEval
• Open-source observability-first stack: Langfuse
• Already deeply invested in W&B: Weave

The broader market is moving toward trajectory-level evaluation—scoring tool selection, arguments, intermediate steps, and complete workflows rather than just the final LLM response.

If you're building an agent that operates across arbitrary external tools (browser, APIs, databases, email, SaaS apps, etc.), I'd put Braintrust, LangSmith, and Phoenix at the top of the proof-of-concept list, and test them using the same 20–50 real tasks with explicit expected outcomes and tool/state assertions.

Evaluating whether an AI agent completes multi-step tasks correctly across various external tools requires looking at two distinct layers: trajectory tracking (did it choose the right tools and construct valid parameters?) and outcome validation (did the final result satisfy the user's goal?).

The top platforms for handling these agentic, multi-tool evaluations span open-source frameworks, developer tooling, and enterprise-grade simulation suites.

Top Platforms for Agent Evaluation

• LangSmith : Best when your agent runtime relies on LangChain or LangGraph . It features native execution tracing , detailed visualization of intermediate tool-calling spans, dataset versioning, and both offline and online evaluation workflows.
• Braintrust : Exceptional for evaluation-first scoring and experiment management . It unifies tracing infrastructure with CI/CD integrations to run regression tests on multi-tool decision paths before code hits production.
• DeepEval (by Confident AI): The premier choice if you prefer a pytest -style unit-testing approach . It includes specialized agentic metrics like PlanQualityMetric, PlanAdherenceMetric , and ToolCorrectnessMetric to score reasoning and tool execution independently.
• Maxim AI : Ideal for end-to-end user simulation and scenario validation . It allows teams to define virtual user personas and dynamically simulate hundreds of multi-turn tool orchestration edge cases prior to deployment.
• Galileo : Strong for production observability and data-centric loops . It utilizes small, purpose-built language models (like Luna-2) to compute metrics such as Tool Selection Quality and Action Completion at high speeds with minimal latency overhead.
• Arize Phoenix : Best for OpenTelemetry-native tracing and deep debugging of complex agent loops . It bridges traditional ML observability with advanced multi-step evaluation.

Key Dimensions to Evaluate Across Tools

When building your evaluation suite on these platforms, structure your test rubric around three core inspection tiers:

1. Tool Selection & Parameter Accuracy: Did the agent invoke the correct API/tool out of N possible options , and were the arguments formatted cleanly without hallucinated variables?
2. Trajectory & Path Efficiency: Did the agent fall into redundant retry loops, call unnecessary intermediate tools, or recover gracefully when an external tool failed or returned an error?
3. Outcome & Policy Adherence: Did the final output fulfill the original objective , and did it respect business logic and boundary guardrails across turns?

When multi-agent and tool-calling systems scale, evaluating just the final text response hides critical failures. You have to trace tool-selection accuracy, argument formatting, and execution steps separately from the end output.

If you'd like to narrow this down, tell me:

• What framework are you using to build your agent (e.g., LangGraph, CrewAI, custom Python)?
• Are you evaluating locally in CI/CD or monitoring live production traffic?

I can help you pick the right integration stack.

Evaluating whether an AI agent completed a task correctly across multiple tools (e.g., calling APIs, browsing, updating records, writing files, handing off between agents) is a different problem from evaluating a chatbot answer. The strongest platforms capture the full trajectory:

• What the agent was asked to do
• Which tools it chose
• Whether tool arguments were correct
• Intermediate states
• Final outcome vs. expected outcome
• Cost, latency, failures, and regressions

The best platforms today are:

Cipher Projects GitHub## My shortlist by use case

1. You need to know “did the agent actually accomplish the task?”

Pick Braintrust.

Example:

“The agent had to find a customer record, verify identity, update the CRM, and send a confirmation email.” A good evaluation would score:

• Did it choose the right tools?
• Did it call them in the correct order?
• Were parameters correct?
• Did the database state change correctly?
• Was the final response accurate?

Braintrust is designed around datasets, scorers, experiments, and release gates rather than just looking at traces.

2. You need to debug failures visually

Pick LangSmith.

Useful questions:

• “Why did the agent call the wrong API?”
• “Why did it loop?”
• “Where did it lose context?”
• “Which tool result caused the wrong final answer?”

It is especially strong if your agent uses LangGraph/LangChain because the execution trace maps naturally onto the framework.

3. You need flexibility and control over your data

Pick Langfuse or Arize Phoenix.

Good fit when:

• You run multiple agent frameworks
• You need self-hosting
• You want OpenTelemetry-compatible instrumentation
• You do not want your evaluation data locked into one vendor

Web3AIBlog App Stack Builder## What I would evaluate (regardless of platform)

A serious agent eval suite should include:

Task completion

• Did the requested end state happen?
• Was the user goal achieved?

Tool selection

• Did it use the correct tool?
• Did it avoid unnecessary tools?

Tool arguments

• Were API parameters correct?
• Did it pass the right IDs, dates, filters, permissions?

Trajectory quality

• Did it recover from failures?
• Did it retry appropriately?
• Did it avoid loops?

Safety and policy

• Did it avoid unauthorized actions?
• Did it respect constraints?

Efficiency

• Number of tool calls
• Token usage
• Latency
• Cost

A common production setup

Many mature teams combine:

1. Tracing layer

• LangSmith / Langfuse / Phoenix
• Captures every run

2. Evaluation layer

• Braintrust / DeepEval / custom evaluators
• Scores correctness

3. CI gate

• Runs a regression suite before deployment

4. Human review loop

• Converts real failures into new test cases

The biggest mistake is evaluating only the final answer. A tool-using agent can produce a plausible response while having taken the wrong actions internally. Trajectory-level evaluation is the key.

For a new production agent, a practical default would be:

• LangSmith if you are already on LangGraph/LangChain
• Braintrust + a tracing tool if quality gates are the priority
• Langfuse or Phoenix if portability and self-hosting matter most

If you mean evaluating an agent’s entire trajectory—not just whether its final answer looks good, but whether it chose the right tools, used them correctly, recovered from errors, respected constraints, and ultimately accomplished the task—the strongest platforms right now are:

Recent 2026 comparisons consistently put Braintrust, LangSmith, and Arize Phoenix among the leading choices for trajectory-level agent evaluation.

My shortlist

1. Braintrust — probably the best pure choice for “did the agent actually do the task?”

Braintrust treats every tool call and reasoning step as part of a trace, allowing you to score the entire run as well as individual spans. It supports metrics such as task completion and tool-use accuracy, experiment comparisons, and turning failed production traces into future evaluation cases.

I'd choose it if your core workflow is:

task → agent trajectory → evaluator → score → regression test → CI gate

2. LangSmith — best if you're building sophisticated agents

LangSmith is particularly strong when you need to inspect complex multi-step executions and turn production traces into evaluation datasets. It also covers observability and deployment, rather than being purely an evaluation system.

It's especially compelling for LangGraph, but isn't limited to that ecosystem.

3. Arize Phoenix — best open/self-hosted option

Phoenix is attractive if you want OpenTelemetry/OpenInference-based tracing and control over your infrastructure. It provides trajectory evaluation primitives while remaining relatively framework-neutral.

4. DeepEval — best developer-centric testing framework

This is a good choice when you want agent evaluations to look more like ordinary software tests: define cases, run them in CI, and write/customize metrics.

5. Promptfoo — particularly good for regression/security testing

Promptfoo is compelling when evaluation needs to live in the repository and run as part of CI, particularly when adversarial/security testing is important.

The important distinction

For tool-using agents, I'd avoid platforms that primarily evaluate the final response.

A useful evaluation should separately measure things like:

• Task success: Did the desired state actually occur?
• Tool selection: Did it call the appropriate tools?
• Tool-call correctness: Were arguments, parameters, and ordering correct?
• Trajectory: Was the sequence of actions valid?
• State transitions: Did it leave external systems in the right state?
• Constraint adherence: Did it obey permissions, budgets, approval gates, etc.?
• Recovery: Did it appropriately handle tool failures?
• Efficiency: Did it accomplish the task without unnecessary calls/loops?
• Final answer: Did it accurately report what happened?

That's important because an agent can produce a convincing final answer while having made an incorrect API call or failing to actually modify the underlying system. Agent evaluation specifically exists to catch those multi-step failures.

What I'd pick

If you're evaluating real production agents that operate across multiple tools/APIs, my ranking would be:

Braintrust → LangSmith → Arize Phoenix → DeepEval → Promptfoo

But the choice changes substantially depending on your architecture:

• Need CI/CD quality gates: Braintrust
• LangGraph/LangChain: LangSmith
• Self-hosting / OpenTelemetry: Phoenix
• Python/pytest engineering workflow: DeepEval
• Security + regression testing: Promptfoo
• Production monitoring + agent quality: Galileo

One important architectural point: the evaluator itself should usually inspect the complete trace and, where possible, verify the resulting external state—not merely have an LLM judge the transcript. That's the difference between evaluating “did the agent sound like it succeeded?” and “did it actually succeed?”

Evaluating whether an AI agent completed a task correctly across multi-tool workflows requires trajectory tracing, environment state inspection, and specific tool-call metrics . Standard "final-answer" evaluation misses intermediate execution failures, rule breaches, or loop inefficiencies.

The best specialized evaluation and observability platforms for multi-tool AI agents vary by workflow needs:

1. Dedicated Agent-Specific Frameworks

• Confident AI (DeepEval) : Widely considered a top choice for granular agent testing. It provides span-level evaluations and unique, pre-built agent metrics like PlanQualityMetric, PlanAdherenceMetric , and ToolCorrectnessMetric . It is heavily integrated with Pytest for development.
• Maxim AI : Highly effective for multi-step agent simulation and scenario validation. It tracks tool correctness, trajectory execution paths, and system efficiency (completion time and token cost per tool call).

2. Enterprise Observability & Trace Logging

• Arize AI / Phoenix : Exceptional for open-source production tracking. Its Arize AX layer judges entire agent trajectories rather than just the final string output, catching errors when an agent hallucinates a tool argument or fails to recover from an API error.
• LangSmith (by LangChain) : The industry standard for full-trace logging across multiple steps. It breaks down complex multi-turn loops into structured trees, allowing you to manually or algorithmically score individual node/tool outputs.
• Galileo AI : Features purpose-built agentic metrics alongside graph, trace, and message-view visualizers. It uses its proprietary "Luna-2" small models to perform fast, highly cost-effective hallucination and parameter detection across production streams.

3. Open-Source & CI/CD Validation

• Braintrust : The optimal platform for setting up automated regression tests inside your CI/CD pipelines . It excels at offline staging experiments, allowing you to run thousands of parallel tool-use test cases before deploying code updates.
• MLflow : A widely deployed open-source suite designed explicitly for the agent development loop. Its scorer framework looks holistically at tool execution logs and reasoning chains rather than grading text responses in isolation.

Key Evaluation Patterns to Implement

No matter which platform you select, you should structure your evaluation using these core patterns:

### Community Perspectives

Production teams heavily emphasize moving past static testing datasets.

> LangSmith and Arize are solid for evaluation. There are also newer purpose-built tools that focus on ai agents specifically, testing multiple configurations in parallel, replaying failures, and monitoring tool calls live.

> Tools like Arize Phoenix and Arize AX, LangSmith, DeepEval, Ragas, and Opik are built for multi-turn, multi-tool agents. They can trace an entire agent trajectory and compute metrics on full sessions.

To narrow down the best platform for your pipeline, tell me:

• What agent framework are you using (e.g., LangGraph, CrewAI, AutoGen, or custom)?
• Do you primarily need offline testing during pull requests, or live monitoring of user sessions?
• What types of tools is your agent calling (e.g., database execution, internal APIs, web scraping)?

For evaluating whether an AI agent completed a task correctly across multiple tools (e.g., browsing, APIs, databases, code execution, file operations), the best platforms are those that evaluate the full trajectory: tool selection, arguments, intermediate states, recovery behavior, and final outcome—not just the final text response.

Here are the strongest options by use case:

My shortlist for "did the agent actually accomplish the task?"

If your core question is task completion across tools, I would prioritize:

1. Braintrust — best fit when you want:

   • "Given this goal, did the agent succeed?"
   • Compare agent versions
   • Score tool-call trajectories
   • Run evals in CI/CD
     

2. LangSmith — best if you use LangGraph/LangChain:

   • Inspect every step
   • Replay failures
   • Build regression suites from real traces
     

3. Langfuse + custom evaluators — best if you need:

   • Open source
   • Self-hosting
   • Control over data and scoring

Metrics you should evaluate (beyond final answer quality)

A good agent evaluator should score:

• Goal completion

  • Did it actually achieve the user's objective?
  • Example: "Created the calendar event" vs "said it created it"

• Tool correctness

  • Did it choose the right tool?
  • Were parameters correct?
  • Did it verify tool outputs?

• Trajectory quality

  • Number of unnecessary steps
  • Recovery after failures
  • Whether it got stuck in loops

• State correctness

  • Did it update databases/files correctly?
  • Did it preserve constraints?

• Safety and policy compliance

  • Did it avoid prohibited actions?
  • Did it request confirmation when needed?

• Cost and efficiency

  • Token usage
  • API calls
  • Latency

Agent benchmarks such as AgentBench also emphasize that evaluating agents requires interactive environments and multi-step behavior rather than simple answer scoring.

A practical production setup

Many teams end up with a stack like:

• Tracing: LangSmith / Langfuse / Phoenix
• Offline evals: Braintrust or custom test harness
• Scorers: rule-based checks + LLM judges + human review
• Golden tasks: a dataset of real workflows with expected outcomes

The key design choice is: make the evaluator inspect the world after the agent runs. For example, don't score "Did the agent claim it sent an email?" Score "Was the email actually sent with the correct recipient, content, and attachments?" This is the difference between evaluating a chatbot and evaluating an agent.

Evaluating whether an AI agent completes multi-step tasks correctly across various tools requires tracing the entire execution trajectory (planning, tool selection, argument construction, and state updates) rather than just looking at a final text response.

The top platforms and frameworks designed for tracking and scoring multi-tool agent behavior balance developer flexibility, trajectory visualization, and automated scoring:

1. Braintrust

• Best for CI/CD-integrated evaluation workflows and enterprise tracking.
• Why it fits multi-tool agents: Consolidates tracing infrastructure with scoring mechanics, making it seamless to run programmatic evaluations on tool choice, argument correctness, and task convergence inside automated development pipelines.
• Key feature: Excellent data structure for logging nested tool spans and turning production logs directly into regression test sets.

2. DeepEval (by Confident AI)

• Best for Python-first, pytest-driven agent testing.
• Why it fits multi-tool agents: It includes specialized metrics specifically built for the reasoning and action layers—such as PlanQualityMetric, PlanAdherenceMetric , and ToolCorrectnessMetric.
• Key feature: Brings a familiar unit-testing paradigm (pytest ) to agent evaluation, allowing you to assert that an agent picks the correct tool and parameters at every step.

3. Galileo AI

• Best for cost-efficient trace evaluation and failure pattern detection.
• Why it fits multi-tool agents: Uses custom small language models (like Luna-2) to process dozens of evaluation metrics simultaneously (including tool selection quality and step efficiency) at a fraction of the cost and latency of traditional LLM judges.
• Key feature: Advanced clustering and insights engine that automatically surfaces root-cause failure patterns across complex tool chains.

4. Arize Phoenix (and Arize AX)

• Best for open-source, OpenTelemetry-native trace observability.
• Why it fits multi-tool agents: Built to capture full agent trajectories and session-level behaviors across multiple turns and tool integrations, with strong support for both Python and TypeScript.
• Key feature: Easily self-hostable with flexible, composable building blocks for writing custom code-based or LLM-as-a-judge evaluators.

5. MLflow 3.0

• Best for broad metric coverage and lifecycle experiment tracking.
• Why it fits multi-tool agents: Integrates natively with plugins from other evaluation libraries while providing robust tooling to capture trace data, run rule-based or custom LLM metrics, and incorporate human reviewer feedback to refine automated judges over time.

Key Evaluation Dimensions to Track Across Tools

When configuring any of these platforms for multi-tool workflows, ensure your custom metrics cover:

• Tool Selection Accuracy: Did the agent choose the correct tool for the specific sub-task, especially when presented with overlapping tool definitions (e.g., in Model Context Protocol setups)?
• Argument Correctness: Did it populate the required parameters and payload fields properly for the invoked API or function?
• Trajectory Efficiency: Did the agent reach the goal smoothly, or did it get trapped in redundant execution loops repeating the same failed tool call?

If you'd like to narrow this down, tell me:

• What framework are you building the agent with (e.g., LangGraph, CrewAI, LlamaIndex, or custom code)?
• Are you looking for an open-source library or a managed enterprise platform?

I can tailor a specific integration approach for your stack.