9.7 KiB
Hermes Multi-Provider Dispatch: Native Anthropic, Gemini, and Multi-Turn History
Hermes is Molecule AI's inference router. Out of the box it proxies every model through an OpenAI-compatible shim. That works for plain text, but the shim does format translation on every round-trip — and it gets the Gemini message format wrong (Gemini expects role: "model" and a parts: [{text}] wrapper; the shim passes role: "assistant" and a flat string). It also flattens multi-turn conversations into a single user blob, losing role attribution across turns.
Phases 2a–2c wire three native dispatch paths keyed on auth_scheme. This tutorial shows you how to unlock them.
Phase 2d scope note: Tool calling, vision content blocks, system instructions, and streaming on the native paths are scoped for Phase 2d and are not yet shipped. This tutorial covers what is merged today: correct native dispatch + multi-turn history continuity.
What you'll need
- A Molecule AI account with API access
ANTHROPIC_API_KEYorGEMINI_API_KEY(or both)curl+jq
The dispatch table
After Phases 2a / 2b / 2c, Hermes picks an inference path based on which provider is configured:
auth_scheme |
Dispatch path | Provider | API |
|---|---|---|---|
openai |
_do_openai_compat |
13 providers (OpenRouter, Groq, Mistral…) | OpenAI-compat shim |
anthropic |
_do_anthropic_native |
Anthropic | Native Messages API |
gemini |
_do_gemini_native |
Native generateContent |
|
| unknown | _do_openai_compat + warning |
any | OpenAI-compat shim (forward-compat) |
Rule of thumb: set ANTHROPIC_API_KEY to get native Anthropic dispatch. Set GEMINI_API_KEY to get native Gemini dispatch. Set NOUS_API_KEY / HERMES_API_KEY / OPENROUTER_API_KEY to stay on the compat shim. Molecule AI reads these in priority order: HERMES_API_KEY → OPENROUTER_API_KEY → ANTHROPIC_API_KEY → GEMINI_API_KEY. The first key found wins, so don't set HERMES_API_KEY if you want native dispatch.
Setup
# 0. Export your platform URL and a workspace to use as orchestrator
export MOLECULE_API=http://localhost:8080
export ORCH_ID=<your-orchestrator-workspace-id>
# 1. Store your Anthropic key as a global secret
curl -s -X PUT $MOLECULE_API/settings/secrets \
-H "Content-Type: application/json" \
-d '{"key":"ANTHROPIC_API_KEY","value":"sk-ant-YOUR-KEY"}' | jq .
# 2. Create a Hermes workspace — Anthropic native dispatch
ANTHROPIC_WS=$(curl -s -X POST $MOLECULE_API/workspaces \
-H "Content-Type: application/json" \
-d '{
"name": "hermes-anthropic",
"role": "Inference worker — native Anthropic path",
"runtime": "hermes",
"model": "anthropic:claude-sonnet-4-5"
}' | jq -r '.id')
echo "Anthropic workspace: $ANTHROPIC_WS"
# 3. Wait for it to be ready (~20–30s)
until curl -s $MOLECULE_API/workspaces/$ANTHROPIC_WS | jq -r '.status' | grep -q ready; do
echo "Waiting..."; sleep 5
done
# 4. Store your Gemini key as a global secret
curl -s -X PUT $MOLECULE_API/settings/secrets \
-H "Content-Type: application/json" \
-d '{"key":"GEMINI_API_KEY","value":"YOUR-GEMINI-KEY"}' | jq .
# 5. Create a Hermes workspace — Gemini native dispatch
GEMINI_WS=$(curl -s -X POST $MOLECULE_API/workspaces \
-H "Content-Type: application/json" \
-d '{
"name": "hermes-gemini",
"role": "Inference worker — native Gemini path",
"runtime": "hermes",
"model": "gemini:gemini-2.0-flash"
}' | jq -r '.id')
echo "Gemini workspace: $GEMINI_WS"
# 6. Pin the Gemini workspace to Gemini-only keys (no ANTHROPIC_API_KEY override)
curl -s -X PUT $MOLECULE_API/workspaces/$GEMINI_WS/secrets \
-H "Content-Type: application/json" \
-d '{"key":"ANTHROPIC_API_KEY","value":""}' | jq .
# 7. Confirm dispatch — send a single-turn probe to the Anthropic workspace
curl -s -X POST $MOLECULE_API/workspaces/$ANTHROPIC_WS/a2a \
-H "Content-Type: application/json" \
-d '{
"jsonrpc":"2.0","id":"probe-1","method":"message/send",
"params":{"message":{"role":"user","parts":[{"kind":"text","text":"Which API are you using to generate this response?"}]}}
}' | jq '.result.parts[0].text'
# 8. Same probe to the Gemini workspace
curl -s -X POST $MOLECULE_API/workspaces/$GEMINI_WS/a2a \
-H "Content-Type: application/json" \
-d '{
"jsonrpc":"2.0","id":"probe-2","method":"message/send",
"params":{"message":{"role":"user","parts":[{"kind":"text","text":"Which API are you using to generate this response?"}]}}
}' | jq '.result.parts[0].text'
# 9. Multi-turn history — Phase 2c keeps turns as turns (not flattened)
# Send turn 1
curl -s -X POST $MOLECULE_API/workspaces/$ANTHROPIC_WS/a2a \
-H "Content-Type: application/json" \
-d '{
"jsonrpc":"2.0","id":"turn-1","method":"message/send",
"params":{"message":{"role":"user","parts":[{"kind":"text","text":"My name is Alice. Remember that."}]}}
}' | jq '.result.parts[0].text'
# 10. Send turn 2 — history is automatically threaded by Hermes Phase 2c
curl -s -X POST $MOLECULE_API/workspaces/$ANTHROPIC_WS/a2a \
-H "Content-Type: application/json" \
-d '{
"jsonrpc":"2.0","id":"turn-2","method":"message/send",
"params":{"message":{"role":"user","parts":[{"kind":"text","text":"What is my name?"}]}}
}' | jq '.result.parts[0].text'
# Expected: "Alice" — not "I don't know", which the old flattened path could produce
Expected output
Step 7 (Anthropic workspace): The agent confirms it is calling the Anthropic Messages API natively. Hermes executed _do_anthropic_native — no OpenAI-compat translation layer.
Step 8 (Gemini workspace): The agent confirms Google generateContent. Hermes called _do_gemini_native, which passes role: "model" (not "assistant") and the parts: [{text: ...}] wrapper the native SDK requires. The compat-shim translation that produced incorrect message format is bypassed.
Step 10 (multi-turn, Phase 2c): Returns "Alice". Before Phase 2c, history was flattened into a single user blob — the model could recover the gist but lost clean role attribution. Phase 2c passes turns as turns: OpenAI uses {role, content}, Anthropic uses the same wire shape for text-only, Gemini uses {role: "model", parts: [{text}]}.
How dispatch works under the hood
HermesA2AExecutor._do_inference(user_message, history) reads self.provider_cfg.auth_scheme:
if self.provider_cfg.auth_scheme == "anthropic":
return await self._do_anthropic_native(user_message, history)
elif self.provider_cfg.auth_scheme == "gemini":
return await self._do_gemini_native(user_message, history)
else: # "openai" + unknown (forward-compat fallback)
return await self._do_openai_compat(user_message, history)
Fail-loud semantics: if the anthropic package isn't installed, _do_anthropic_native raises a clear RuntimeError before any inference attempt. Same for google-genai. Silent fallback to the compat shim would mask format errors — Molecule AI chooses loud failure.
Building a multi-provider team
The real win surfaces in a mixed-provider agent team. Your orchestrator can fan tasks to an Anthropic worker and a Gemini worker simultaneously, each receiving properly formatted messages through their native API paths:
# Fan out from the orchestrator — both fire in parallel
curl -s -X POST $MOLECULE_API/workspaces/$ORCH_ID/a2a \
-H "Content-Type: application/json" \
-d "{
\"jsonrpc\":\"2.0\",\"id\":\"fan-1\",\"method\":\"message/send\",
\"params\":{\"message\":{\"role\":\"user\",\"parts\":[{\"kind\":\"text\",
\"text\":\"delegate_task_async $ANTHROPIC_WS 'Draft release notes for v2.1' AND delegate_task_async $GEMINI_WS 'Summarise the last 30 days of support tickets'\"}]}}
}" | jq .
Both workers use their native inference paths. No LiteLLM proxy layer. No format translation on every request. The orchestrator gets results back through the same A2A protocol regardless of which underlying model powered each task.
Capability comparison: Hermes native vs the compat shim
What is shipping today (Phases 2a + 2b + 2c — all merged to main):
| Capability | OpenAI-compat shim | Anthropic native | Gemini native |
|---|---|---|---|
| Plain text (single-turn) | ✅ | ✅ | ✅ |
| Multi-turn history | ⚠️ flattened into one user blob | ✅ role-attributed turns | ✅ role: "model" + parts wrapper |
| Correct Gemini message format | ❌ wrong role + missing parts wrapper | — | ✅ |
| No compat-shim translation overhead | ❌ every request translated | ✅ | ✅ |
What is on the roadmap for Phase 2d (not yet shipped):
| Capability | Anthropic native | Gemini native |
|---|---|---|
tool_use / tool_result blocks |
📋 Phase 2d | 📋 Phase 2d |
| Vision content blocks | 📋 Phase 2d | 📋 Phase 2d |
System instructions (system=) |
📋 Phase 2d | 📋 Phase 2d (system_instruction=) |
| Extended thinking | 📋 Phase 2d | — |
| Streaming | 📋 Phase 2d | 📋 Phase 2d |
Why Molecule AI vs Letta / AG2 / n8n: Those frameworks handle multi-LLM at the application layer — you write different agent classes per provider. Molecule AI handles it at the infrastructure layer. Your workspace configs change; your orchestration code doesn't. Swap a Gemini worker for an Anthropic worker by changing one secret. No code redeploy.