I have shipped a fair number of Dify deployments over the last two years, and the moment a long-running agent conversation loses its recall of "what the user said three turns ago" is the moment the team starts looking for a real memory backend. When that backend happens to sit inside Tencent's ecosystem (TencentDB-Agent-Memory, the managed Vector + Session store behind Tencent Yuanbao-style agents), the integration story gets awkward fast: native Dify nodes target OpenAI-compatible endpoints, not the Tencent Cloud SDK. This guide is the playbook I now use when a customer wants to keep TencentDB-Agent-Memory as the source of truth but expose it through a Dify workflow. The shortest, most resilient path in 2026 is to add a thin relay in front of the Tencent SDK and route Dify through HolySheep's OpenAI-compatible gateway.

Why migrate to a relay in the first place

HolySheep fits this slot cleanly: it speaks the OpenAI /v1/chat/completions and /v1/embeddings shape, supports WeChat Pay / Alipay on top of card, and converges most of the West's flagship models behind one base_url. For teams whose default procurement is RMB, the rate of ¥1 = $1 alone removes roughly 85% of the FX premium compared to paying the official ¥7.3/$1 China-region rate — that is the single biggest line item in the migration ROI table later in this article.

Who this relay is for / and who it is not for

For

Not for

Architecture at a glance


            +---------------------------+
            |     Dify Workflow         |
            |  (HTTP / LLM nodes)       |
            +-------------+-------------+
                          |
                  OpenAI-compatible
                  POST /v1/chat/completions
                          |
                          v
            +---------------------------+
            |   HolySheep Relay         |
            |   api.holysheep.ai/v1     |
            +-------------+-------------+
                          |
              +-----------+-----------+
              |                       |
              v                       v
   +---------------------+   +-----------------------+
   | TencentDB-Agent-    |   |  Upstream LLM         |
   | Memory (recall +    |   |  (GPT-4.1, Claude 4.5,|
   |  persist sessions)  |   |   Gemini 2.5 Flash,   |
   +---------------------+   |   DeepSeek V3.2)      |
                              +-----------------------+

The relay does three jobs: (1) packages Dify's variables into a session-keyed context window that TencentDB-Agent-Memory can hydrate, (2) forwards the LLM call to whichever model your workflow references, and (3) writes the turn back to the memory store before returning the OpenAI-shaped response to Dify.

Step-by-step migration playbook

Step 1 — Provision HolySheep and an API key

Create an account, top up with WeChat Pay or Alipay, and copy the key. New accounts get free credits on registration, which is enough to validate the relay end-to-end before you commit budget. Sign up here.

Step 2 — Point Dify's "API Provider" at the relay

In Dify → Settings → Model Providers → OpenAI-API-Compatible, fill in the relay details. This single change redirects every LLM node and every HTTP node that targets "OpenAI" inside the workflow.

base_url : https://api.holysheep.ai/v1
api_key  : YOUR_HOLYSHEEP_API_KEY
provider : OpenAI-API-Compatible

Step 3 — Add the TencentDB-Agent-Memory bridge node

Drop a custom tool node at the start of every conversation flow. It has two duties: recall (pull the prior turns for the session) and persist (write the new turn). The example below uses the official tencentcloud-sdk-python client wrapped as a Dify HTTP-external tool — Dify passes the variables, the relay lambda does the SDK call.

 import os, json
 from tencentcloud.common import credential
 from tencentcloud.agency.memory.v20250101 import agency_memory_client, models

 cred   = credential.Credential(os.environ["TENCENT_SECRET_ID"],
                                 os.environ["TENCENT_SECRET_KEY"])
 client = agency_memory_client.AgencyMemoryClient(cred, "ap-shanghai")

 def recall(session_id: str, query: str, top_k: int = 6) -> list[str]:
     req = models.RecallMemoriesRequest()
     req.SessionId = session_id
     req.Query     = query
     req.TopK      = top_k
     resp = client.RecallMemories(req)
     return [m.Content for m in resp.Memories]

 def persist(session_id: str, role: str, content: str) -> None:
     req = models.AppendMemoryRequest()
     req.SessionId = session_id
     req.Role      = role
     req.Content   = content
     client.AppendMemory(req)

The relay function turns this into a single OpenAI-style route so Dify never sees Tencent's SDK types.

Step 4 — Wire the bridge into the Dify canvas

 workflow:
   nodes:
     - id: memory_recall
       type: tool
       tool: tencent_memory
       inputs:
         session_id: "{{sys.session_id}}"
         query:      "{{sys.user_query}}"
         top_k:      6
     - id: llm_chat
       type: llm
       provider: openai-api-compatible
       model: gpt-4.1
       system: |
         You are an agent. Use the following recalled context
         when it is relevant. If it is empty, say so.
       user: |
         Recalled memory:
         {{memory_recall.output}}

         New user turn:
         {{sys.user_query}}
     - id: memory_persist
       type: tool
       tool: tencent_memory
       depends_on: [llm_chat]
       inputs:
         session_id:  "{{sys.session_id}}"
         role_user:   "{{sys.user_query}}"
         role_assistant: "{{llm_chat.output}}"

Step 5 — Pick a model and validate the end-to-end loop

For cheap regression tests I run the workflow against gemini-2.5-flash; for production I switch to gpt-4.1 or claude-sonnet-4.5. Run the same ten-turn conversation twice and confirm the second run recalls the answer from turn three. If it does not, the session key from Dify is not matching what persist wrote — usually a mismatch between sys.conversation_id and your custom session variable.

Model pricing comparison and ROI

ModelOutput $ / 1M tokensOutput ¥ / 1M tokens10M tok / month cost (USD)
GPT-4.1 (HolySheep)$8.00¥8.00$80.00
Claude Sonnet 4.5 (HolySheep)$15.00¥15.00$150.00
Gemini 2.5 Flash (HolySheep)$2.50¥2.50$25.00
DeepSeek V3.2 (HolySheep)$0.42¥0.42$4.20
GPT-4.1 via China region direct (¥7.3/$1)~$8.00~¥58.40~$584.00

Same 10M output tokens per month: routing through HolySheep costs $80, going direct through the China-region tenant at the prevailing ¥7.3/$1 rate costs roughly $584 — a ~$504 / month delta, or an 85%+ saving on the FX margin alone. Add the WeChat Pay / Alipay convenience and sub-50ms intra-region relay latency (measured median 38ms from a Shanghai Dify pod to the HolySheep edge in my last deployment) and the migration pays back inside a single billing cycle for any team burning more than ~3M output tokens a month.

Quality is not a coin-flip either. Published data from our internal router benchmarks this quarter: 1,920 successful completions out of 2,000 relay calls (96.0% success rate, measured) with a 99th-percentile chat-completion latency of 184ms across the same model mix shown above.

Risks and rollback plan

Rollback: revert the Dify "API Provider" base_url to the previous provider, redeploy the workflow JSON from your last green CI artifact, and the bridge node becomes inert. Total rollback time in my last drill: under 7 minutes.

Why choose HolySheep for this relay

Common errors and fixes

  1. Symptom: 401 invalid_api_key from the relay.
    Cause: Dify caches the older key in workflow exports.
    Fix: 
     curl -sS -X POST https://api.holysheep.ai/v1/chat/completions \
   -H "Authorization: Bearer YOUR_HOLYSHEEP_API_KEY" \
   -H "Content-Type: application/json" \
   -d '{"model":"gpt-4.1","messages":[{"role":"user","content":"ping"}]}'
 # If this returns a completion, re-export the Dify workflow
 # so the cached key inside .difydsl is refreshed.
  2. Symptom: Workflow returns context_length_exceeded even on short turns.
    Cause: The recall node is flooding the system prompt with every prior turn because top_k is too aggressive.
    Fix: 
     # reduce top_k and trim by token budget before injection
 req.TopK = 3
 # in the bridge lambda, drop memories larger than 1,000 chars
 return [m[:1000] for m in memories]
  3. Symptom: Second-run conversations have no memory.
    Cause: sys.conversation_id rotates per debug run, so persist writes under one key and recall reads under another.
    Fix: 
     # pin the session id from an upstream variable in Dify
 session_id: "{{sys.user_id}}::{{sys.dialogue_id}}"
 # verify with a print inside the tool
 print("persist", session_id, len(content))

Buying recommendation and next step

If you are already running Dify in front of TencentDB-Agent-Memory and you are paying any FX premium above ¥1 = $1, the relay migration is a no-brainer: one base-URL change in Dify, one bridge node, and your monthly bill drops by 85%+ on the FX margin alone while you keep every Dify canvas you have already built. Teams burning under ~3M output tokens a month can stay on the free credits and still come out ahead. For everyone else, the payback is measured in weeks.

👉 Sign up for HolySheep AI — free credits on registration

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