Deep Research Agent — CrewAI Multi-Agent Runtime

Phase complete: 2026-08-02
GitOps: services/minicloud-crew-agent/ + manifests/ai/29-crew-agent-runtime.yaml
Image: harbor.10.0.0.200.nip.io/library/minicloud-crew-agent:1.0.1

A three-agent CrewAI crew deployed as an OpenAI-compatible FastAPI service and registered as deep-research-agent in LiteLLM. It complements the single-loop Research Agent by adding a structured synthesis and regulatory validation stage before returning an answer.

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When to use each agent

Agent	Model ID	Best for
Research Agent	research-agent	Quick lookups, factual questions, document retrieval
Deep Research Agent	deep-research-agent	Regulatory analysis, compliance questions, advisory tasks needing synthesis + validation

Latency: research-agent responds in 5–15 s. deep-research-agent takes 30–120 s (three agents × up to 8 LLM iterations each).

---

Architecture

Open WebUI
    │  model: "deep-research-agent"
    ▼
LiteLLM Gateway (:4000)
    │  routes openai/deep-research-agent → minicloud-crew-agent:8081/v1
    ▼
minicloud-crew-agent (:8081)        ← FastAPI + CrewAI
    │
    ├── Agent 1: Research Specialist (mistral-small, max_iter=8)
    │       ├── [tool] rag_search ──► rag-ingest:8001/query
    │       └── [tool] web_search ──► DuckDuckGo (ddgs)
    │
    ├── Agent 2: Financial Analyst (mistral-large, max_iter=4)
    │       └── [no tools] — synthesises Agent 1 output into structured answer
    │
    └── Agent 3: Compliance Validator (mistral-large, max_iter=8)
            └── [tool] rag_search — cross-checks every regulatory claim

The crew runs sequentially: Research → Analysis → Compliance. Each agent receives the output of prior agents as context. The final response is the Compliance Validator's validated, corrected answer.

---

Service

# manifests/ai/29-crew-agent-runtime.yaml
image: harbor.10.0.0.200.nip.io/library/minicloud-crew-agent:1.0.1
env:
  LITELLM_BASE_URL: http://litellm.ai.svc.cluster.local:4000
  LITELLM_API_KEY:  secretKeyRef litellm-credentials/master-key
  RAG_INGEST_URL:   http://rag-ingest.ai.svc.cluster.local:8001
  CREW_SMALL_MODEL: mistral-small   # Researcher
  CREW_LARGE_MODEL: mistral-large   # Analyst + Compliance Validator
  CREW_MAX_ITER:    "8"
resources:
  requests: 200m / 512Mi
  limits:   1000m / 1Gi

Endpoints:

Path	Method	Description
/health	GET	Liveness probe
/ready	GET	Readiness probe
/v1/models	GET	Returns deep-research-agent in OpenAI model list format
/v1/chat/completions	POST	Runs the three-agent crew

---

Agents

Agent 1 — Research Specialist

Uses mistral-small (fast, low cost) to maximise retrieval iterations within budget.

Goal: Find accurate, comprehensive information using available tools. Prefer internal knowledge base over web when both are available. Cite every source as [N].

Tools: rag_search, web_search

Agent 2 — Financial Analyst

Uses mistral-large for synthesis quality. Has no tools — it reasons purely from Agent 1's output.

Goal: Synthesise research into a structured answer: executive summary → key facts → regulatory context → practical implications. Preserve all [N] citations verbatim.

Context: receives Agent 1 output.

Agent 3 — Compliance Validator

Uses mistral-large with rag_search access to verify regulatory claims.

Goal: Identify every regulatory reference (ACPR, AMF, Solvency II, MiFID II, GDPR…), cross-check each against the knowledge base, correct errors, and return the final validated answer. Appends a Compliance note: section if corrections were made.

Context: receives both Agent 1 and Agent 2 outputs.

---

Implementation details

LLM routing

CrewAI ≥ 0.80 uses its own crewai.LLM class internally. The model string must include an openai/ provider prefix so the embedded litellm client knows to use OpenAI-compatible format against the proxy:

from crewai import LLM

def _llm(model: str) -> LLM:
    return LLM(
        model=f"openai/{model}",           # e.g. "openai/mistral-small"
        base_url=f"{LITELLM_BASE_URL}/v1", # LiteLLM proxy maps virtual → real provider
        api_key=LITELLM_API_KEY,
        temperature=0.1,
        max_retries=2,
    )

Async execution

CrewAI's crew.kickoff() is blocking. It runs on FastAPI's event loop thread pool via run_in_executor to avoid blocking other requests:

async def run(question: str) -> str:
    crew, inputs = _build_crew(question)
    loop = asyncio.get_running_loop()
    result = await loop.run_in_executor(
        None, lambda: crew.kickoff(inputs=inputs)
    )
    return str(result)

Task templating

Tasks use {question} as a template variable filled at kickoff time, so each request creates a fresh Crew with a dynamically-scoped task description:

research_task = Task(
    description="Research the following question...\n\nQuestion: {question}",
    ...
)
crew.kickoff(inputs={"question": user_question})

---

LiteLLM registration

# manifests/ai/00-litellm-configmap.yaml
- model_name: deep-research-agent
  litellm_params:
    model: openai/deep-research-agent
    api_base: http://minicloud-crew-agent.ai.svc.cluster.local:8081/v1
    api_key: none

---

Ingress

Internal-only ingress at crew-agent.10.0.0.200.nip.io. Not exposed via Cloudflare tunnel — external consumers go through LiteLLM.

proxy-read-timeout: 600 (vs 300 for research-agent) to accommodate three agents × 8 iterations × Mistral API latency.

---

Build and release

cd ~/Developer/cloudplateform/minicloud-gitops/services/minicloud-crew-agent
docker buildx build --platform linux/amd64 \
  -t harbor.10.0.0.200.nip.io/library/minicloud-crew-agent:<VERSION> \
  --push .

# Update the image tag:
# manifests/ai/29-crew-agent-runtime.yaml → image: ...minicloud-crew-agent:<VERSION>
# Open a PR → merge → ArgoCD rolls out

Release history:

• 1.0.0 — initial release
• 1.0.1 — fix: switch to crewai.LLM with openai/ prefix (CrewAI ≥ 0.80 LLM routing fix)

---

Troubleshooting

LLM Provider NOT provided error in crew-agent logs
Model name missing openai/ prefix. Ensure _llm() uses model=f"openai/{model}".

Crew returns partial/truncated response
max_tokens in the calling request is too low. The compliance validator's output can be 400–600 tokens. Use at least max_tokens: 1000 when calling deep-research-agent directly.

Crew times out (504 from ingress)
Three agents × 8 iterations = up to 24 LiteLLM calls. If Mistral API is slow, reduce CREW_MAX_ITER in the Deployment env or use CREW_SMALL_MODEL: mistral-small for the analyst too.

deep-research-agent not visible in Open WebUI model selector

kubectl port-forward -n ai svc/litellm 4001:4000 &
curl http://localhost:4001/v1/models | python3 -m json.tool | grep deep-research