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hermes-agent/website/docs/user-guide/features/fallback-providers.md
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Teknium abf1af5401 feat(session_search): single-shape tool with discovery, scroll, browse — no LLM (#27590)
* feat(session_search): single-shape tool with discovery, scroll, browse — no LLM

Replaces the LLM-summarized session_search with a single-shape tool that
returns actual messages from the DB. Three calling shapes inferred from
args (no mode parameter):

  1. Discovery — pass query. FTS5 + anchored ±5 window + bookends per hit,
     all in one call. ~20ms on a real DB instead of ~90s for the previous
     three aux-LLM calls.
  2. Scroll — pass session_id + around_message_id. Returns a window
     centered on the anchor. To paginate, re-anchor on the first/last id
     of the returned window. Boundary message appears in both windows
     as the orientation marker. ~1ms per scroll call.
  3. Browse — no args. Recent sessions chronologically.

Bookend_start (first 3 user+assistant msgs) and bookend_end (last 3) give
the agent goal + resolution on every discovery hit, so a single tool call
reconstructs a long session's arc without loading the whole transcript.

The aux-LLM summary path is gone: it cost ~$0.30/call, took ~30s, and
laundered FTS5 hits through a model that could confabulate when the right
session wasn't in the hit list. The merged shape returns byte-for-byte
content from SQLite.

History:
- PR #20238 (JabberELF) seeded the fast/summary dual-mode split.
- PR #26419 (yoniebans) expanded to fast/guided/summary with bookends,
  multi-anchor drill-down, default-mode config, and a teaching skill.

This PR collapses that toolkit into one shape with explicit scroll
support, drops the summary path, drops the mode parameter, drops the
config knob, drops the skill. JabberELF's seed work is acknowledged via
the AUTHOR_MAP entry.

Validation:
- 38/38 tool tests pass (tests/tools/test_session_search.py)
- 12/12 get_messages_around tests pass (tests/hermes_state/)
- 11/11 get_anchored_view tests pass (tests/hermes_state/)
- Full tests/tools/ run: 5168 passing, 2 failures pre-exist on main
  (test ordering in test_delegate.py, unrelated)
- E2E against live state DB: discovery 20ms, scroll 1ms, browse 280ms;
  pagination forward+backward works with boundary-message orientation;
  error paths return clean tool_error responses

Co-authored-by: JabberELF <abcdjmm970703@gmail.com>
Co-authored-by: yoniebans <jonny@nousresearch.com>

* chore(session_search): prune dead LLM-summary config and docs

Companion to the single-shape rewrite. The auxiliary.session_search config
block, max_concurrency / extra_body tunables, and matching docs sections
all referenced the removed LLM summarization path. Removing them so users
don't try to tune knobs that nothing reads.

- hermes_cli/config.py: drop dead auxiliary.session_search block from
  DEFAULT_CONFIG. Leftover keys in user config.yaml are harmless and
  ignored.
- hermes_cli/tips.py: drop two tips referencing the removed
  max_concurrency / extra_body knobs.
- website/docs/user-guide/configuration.md: drop 'Session Search Tuning'
  section and the auxiliary.session_search block from the example.
- website/docs/user-guide/features/fallback-providers.md: drop session_search
  rows from the auxiliary-tasks tables and the dedicated tuning subsection.
- website/docs/reference/tools-reference.md: rewrite the session_search
  entry to describe the new three-shape behaviour.
- CONTRIBUTING.md: update the file-tree description.
- tests/tools/test_llm_content_none_guard.py: remove TestSessionSearchContentNone
  class and test_session_search_tool_guarded — both guard against an
  unguarded .content.strip() call site in _summarize_session() that no
  longer exists.

Validation: 97/97 targeted tests still pass (hermes_state + session_search +
llm_content_none_guard). Config tests 55/55.

---------

Co-authored-by: JabberELF <abcdjmm970703@gmail.com>
Co-authored-by: yoniebans <jonny@nousresearch.com>
2026-05-17 23:28:45 -07:00

18 KiB
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title, description, sidebar_label, sidebar_position
title description sidebar_label sidebar_position
Fallback Providers Configure automatic failover to backup LLM providers when your primary model is unavailable. Fallback Providers 8

Fallback Providers

Hermes Agent has three layers of resilience that keep your sessions running when providers hit issues:

  1. Credential pools — rotate across multiple API keys for the same provider (tried first)
  2. Primary model fallback — automatically switches to a different provider:model when your main model fails
  3. Auxiliary task fallback — independent provider resolution for side tasks like vision, compression, and web extraction

Credential pools handle same-provider rotation (e.g., multiple OpenRouter keys). This page covers cross-provider fallback. Both are optional and work independently.

Primary Model Fallback

When your main LLM provider encounters errors — rate limits, server overload, auth failures, connection drops — Hermes can automatically switch to a backup provider:model pair mid-session without losing your conversation.

Configuration

The easiest path is the interactive manager:

hermes fallback

hermes fallback reuses the provider picker from hermes model — same provider list, same credential prompts, same validation. Use the subcommands add, list (alias ls), remove (alias rm), and clear to manage the chain. Changes persist under the top-level fallback_providers: list in config.yaml.

If you'd rather edit the YAML directly, add a fallback_model section to ~/.hermes/config.yaml:

fallback_model:
  provider: openrouter
  model: anthropic/claude-sonnet-4

Both provider and model are required. If either is missing, the fallback is disabled.

:::note fallback_model vs fallback_providers fallback_model (singular) is the legacy single-fallback key — Hermes still honors it for back-compat. fallback_providers (plural, list) supports multiple fallbacks tried in order; hermes fallback writes to this key. When both are set, Hermes merges them with fallback_providers taking priority. :::

Supported Providers

Provider Value Requirements
AI Gateway ai-gateway AI_GATEWAY_API_KEY
OpenRouter openrouter OPENROUTER_API_KEY
Nous Portal nous hermes auth (OAuth)
OpenAI Codex openai-codex hermes model (ChatGPT OAuth)
GitHub Copilot copilot COPILOT_GITHUB_TOKEN, GH_TOKEN, or GITHUB_TOKEN
GitHub Copilot ACP copilot-acp External process (editor integration)
Anthropic anthropic ANTHROPIC_API_KEY or Claude Code credentials
z.ai / GLM zai GLM_API_KEY
Kimi / Moonshot kimi-coding KIMI_API_KEY
MiniMax minimax MINIMAX_API_KEY
MiniMax (China) minimax-cn MINIMAX_CN_API_KEY
DeepSeek deepseek DEEPSEEK_API_KEY
NVIDIA NIM nvidia NVIDIA_API_KEY (optional: NVIDIA_BASE_URL)
GMI Cloud gmi GMI_API_KEY (optional: GMI_BASE_URL)
StepFun stepfun STEPFUN_API_KEY (optional: STEPFUN_BASE_URL)
Ollama Cloud ollama-cloud OLLAMA_API_KEY
Google Gemini (OAuth) google-gemini-cli hermes model (Google OAuth; optional: HERMES_GEMINI_PROJECT_ID)
Google AI Studio gemini GOOGLE_API_KEY (alias: GEMINI_API_KEY)
xAI (Grok) xai (alias grok) XAI_API_KEY (optional: XAI_BASE_URL)
xAI Grok OAuth (SuperGrok) xai-oauth (alias grok-oauth) hermes model → xAI Grok OAuth (browser login; SuperGrok subscription)
AWS Bedrock bedrock Standard boto3 auth (AWS_REGION + AWS_PROFILE or AWS_ACCESS_KEY_ID)
Qwen Portal (OAuth) qwen-oauth hermes model (Qwen Portal OAuth; optional: HERMES_QWEN_BASE_URL)
MiniMax (OAuth) minimax-oauth hermes model (MiniMax portal OAuth)
OpenCode Zen opencode-zen OPENCODE_ZEN_API_KEY
OpenCode Go opencode-go OPENCODE_GO_API_KEY
Kilo Code kilocode KILOCODE_API_KEY
Xiaomi MiMo xiaomi XIAOMI_API_KEY
Arcee AI arcee ARCEEAI_API_KEY
GMI Cloud gmi GMI_API_KEY
Alibaba / DashScope alibaba DASHSCOPE_API_KEY
Alibaba Coding Plan alibaba-coding-plan ALIBABA_CODING_PLAN_API_KEY (falls back to DASHSCOPE_API_KEY)
Kimi / Moonshot (China) kimi-coding-cn KIMI_CN_API_KEY
StepFun stepfun STEPFUN_API_KEY
Tencent TokenHub tencent-tokenhub TOKENHUB_API_KEY
Azure AI Foundry azure-foundry AZURE_FOUNDRY_API_KEY + AZURE_FOUNDRY_BASE_URL
LM Studio (local) lmstudio LM_API_KEY (or none for local) + LM_BASE_URL
Hugging Face huggingface HF_TOKEN
Custom endpoint custom base_url + key_env (see below)

Custom Endpoint Fallback

For a custom OpenAI-compatible endpoint, add base_url and optionally key_env:

fallback_model:
  provider: custom
  model: my-local-model
  base_url: http://localhost:8000/v1
  key_env: MY_LOCAL_KEY              # env var name containing the API key

When Fallback Triggers

The fallback activates automatically when the primary model fails with:

  • Rate limits (HTTP 429) — after exhausting retry attempts
  • Server errors (HTTP 500, 502, 503) — after exhausting retry attempts
  • Auth failures (HTTP 401, 403) — immediately (no point retrying)
  • Not found (HTTP 404) — immediately
  • Invalid responses — when the API returns malformed or empty responses repeatedly

When triggered, Hermes:

  1. Resolves credentials for the fallback provider
  2. Builds a new API client
  3. Swaps the model, provider, and client in-place
  4. Resets the retry counter and continues the conversation

The switch is seamless — your conversation history, tool calls, and context are preserved. The agent continues from exactly where it left off, just using a different model.

:::info Per-Turn, Not Per-Session Fallback is turn-scoped: each new user message starts with the primary model restored. If the primary fails mid-turn, fallback activates for that turn only. On the next message, Hermes tries the primary again. Within a single turn, fallback activates at most once — if the fallback also fails, normal error handling takes over (retries, then error message). This prevents cascading failover loops within a turn while giving the primary model a fresh chance every turn. :::

Examples

OpenRouter as fallback for Anthropic native:

model:
  provider: anthropic
  default: claude-sonnet-4-6

fallback_model:
  provider: openrouter
  model: anthropic/claude-sonnet-4

Nous Portal as fallback for OpenRouter:

model:
  provider: openrouter
  default: anthropic/claude-opus-4

fallback_model:
  provider: nous
  model: nous-hermes-3

Local model as fallback for cloud:

fallback_model:
  provider: custom
  model: llama-3.1-70b
  base_url: http://localhost:8000/v1
  key_env: LOCAL_API_KEY

Codex OAuth as fallback:

fallback_model:
  provider: openai-codex
  model: gpt-5.3-codex

Where Fallback Works

Context Fallback Supported
CLI sessions
Messaging gateway (Telegram, Discord, etc.)
Subagent delegation ✘ (subagents do not inherit fallback config)
Cron jobs ✘ (run with a fixed provider)
Auxiliary tasks (vision, compression) ✘ (use their own provider chain — see below)

:::tip There are no environment variables for fallback_model — it is configured exclusively through config.yaml. This is intentional: fallback configuration is a deliberate choice, not something a stale shell export should override. :::


Auxiliary Task Fallback

Hermes uses separate lightweight models for side tasks. Each task has its own provider resolution chain that acts as a built-in fallback system.

Tasks with Independent Provider Resolution

Task What It Does Config Key
Vision Image analysis, browser screenshots auxiliary.vision
Web Extract Web page summarization auxiliary.web_extract
Compression Context compression summaries auxiliary.compression
Skills Hub Skill search and discovery auxiliary.skills_hub
MCP MCP helper operations auxiliary.mcp
Approval Smart command-approval classification auxiliary.approval
Title Generation Session title summaries auxiliary.title_generation
Triage Specifier hermes kanban specify / dashboard button — fleshes out a one-liner triage task into a real spec auxiliary.triage_specifier

Auto-Detection Chain

When a task's provider is set to "auto" (the default), Hermes tries providers in order until one works:

For text tasks (compression, web extract, etc.):

OpenRouter → Nous Portal → Custom endpoint → Codex OAuth →
API-key providers (z.ai, Kimi, MiniMax, Xiaomi MiMo, Hugging Face, Anthropic) → give up

For vision tasks:

Main provider (if vision-capable) → OpenRouter → Nous Portal →
Codex OAuth → Anthropic → Custom endpoint → give up

If the resolved provider fails at call time, Hermes also has an internal retry: if the provider is not OpenRouter and no explicit base_url is set, it tries OpenRouter as a last-resort fallback.

Configuring Auxiliary Providers

Each task can be configured independently in config.yaml:

auxiliary:
  vision:
    provider: "auto"              # auto | openrouter | nous | codex | main | anthropic
    model: ""                     # e.g. "openai/gpt-4o"
    base_url: ""                  # direct endpoint (takes precedence over provider)
    api_key: ""                   # API key for base_url

  web_extract:
    provider: "auto"
    model: ""

  compression:
    provider: "auto"
    model: ""

  skills_hub:
    provider: "auto"
    model: ""

  mcp:
    provider: "auto"
    model: ""

Every task above follows the same provider / model / base_url pattern. Context compression is configured under auxiliary.compression:

auxiliary:
  compression:
    provider: main                                    # Same provider options as other auxiliary tasks
    model: google/gemini-3-flash-preview
    base_url: null                                    # Custom OpenAI-compatible endpoint

And the fallback model uses:

fallback_model:
  provider: openrouter
  model: anthropic/claude-sonnet-4
  # base_url: http://localhost:8000/v1               # Optional custom endpoint

All three — auxiliary, compression, fallback — work the same way: set provider to pick who handles the request, model to pick which model, and base_url to point at a custom endpoint (overrides provider).

Provider Options for Auxiliary Tasks

These options apply to auxiliary:, compression:, and fallback_model: configs only — "main" is not a valid value for your top-level model.provider. For custom endpoints, use provider: custom in your model: section (see AI Providers).

Provider Description Requirements
"auto" Try providers in order until one works (default) At least one provider configured
"openrouter" Force OpenRouter OPENROUTER_API_KEY
"nous" Force Nous Portal hermes auth
"codex" Force Codex OAuth hermes model → Codex
"main" Use whatever provider the main agent uses (auxiliary tasks only) Active main provider configured
"anthropic" Force Anthropic native ANTHROPIC_API_KEY or Claude Code credentials

Direct Endpoint Override

For any auxiliary task, setting base_url bypasses provider resolution entirely and sends requests directly to that endpoint:

auxiliary:
  vision:
    base_url: "http://localhost:1234/v1"
    api_key: "local-key"
    model: "qwen2.5-vl"

base_url takes precedence over provider. Hermes uses the configured api_key for authentication, falling back to OPENAI_API_KEY if not set. It does not reuse OPENROUTER_API_KEY for custom endpoints.


Auxiliary Capacity-Error Fallback

When you set an explicit auxiliary provider (e.g. auxiliary.vision.provider: glm), Hermes treats that as your preferred choice — but if the provider literally cannot serve the request because of a capacity error (HTTP 402 payment required, HTTP 429 daily-quota exhaustion, connection failure), Hermes falls back through a layered chain instead of failing silently:

  1. Primary aux provider — the one you configured (tried first, always)
  2. auxiliary.<task>.fallback_chain — your per-task override list, if you wrote one
  3. Main agent provider + model — last-resort safety net (always tried, even if you didn't write a chain)
  4. Warn + re-raise — if every layer fails, Hermes logs Auxiliary <task>: ... all fallbacks exhausted at WARNING level and re-raises the original error

Transient HTTP 429 rate limits (Retry-After: ...) are treated as request constraints, not capacity problems — they respect your explicit provider choice and do not trigger the fallback ladder. Only daily/monthly quota exhaustion, payment errors, and connection failures bypass the explicit-provider gate.

For users on provider: auto (no explicit aux provider), the existing auto-detection chain runs in place of steps 23. Its first step is already the main agent model, so auto users get the same outcome with zero config.

Optional: per-task fallback chain

If you want a different fallback ordering than "main agent model first", configure fallback_chain explicitly. Each entry needs at least provider; model, base_url, and api_key are optional.

auxiliary:
  vision:
    provider: glm
    model: glm-4v-flash
    fallback_chain:
      - provider: openrouter
        model: google/gemini-3-flash-preview
      - provider: nous
        model: anthropic/claude-sonnet-4

  compression:
    provider: openrouter
    fallback_chain:
      - provider: openai
        model: gpt-4o-mini

You do not need to configure fallback_chain to get fallback — the main-agent safety net runs regardless. Use it only when you specifically want a different order than the default.

Provider quota errors that trigger fallback

Hermes recognizes these as capacity-equivalent to 402 credit exhaustion (not transient rate limits):

  • Bedrock / LiteLLM: Too many tokens per day, daily limit, tokens per day
  • Vertex AI / GCP: quota exceeded, resource exhausted, RESOURCE_EXHAUSTED
  • Generic: daily quota, quota_exceeded

If your provider returns a different phrase for daily-quota exhaustion and Hermes doesn't trigger fallback, that's a bug — open an issue with the exact error string.


Context Compression Fallback

Context compression uses the auxiliary.compression config block to control which model and provider handles summarization:

auxiliary:
  compression:
    provider: "auto"                              # auto | openrouter | nous | main
    model: "google/gemini-3-flash-preview"

:::info Legacy migration Older configs with compression.summary_model / compression.summary_provider / compression.summary_base_url are automatically migrated to auxiliary.compression.* on first load (config version 17). :::

If no provider is available for compression, Hermes drops middle conversation turns without generating a summary rather than failing the session.


Delegation Provider Override

Subagents spawned by delegate_task do not use the primary fallback model. However, they can be routed to a different provider:model pair for cost optimization:

delegation:
  provider: "openrouter"                      # override provider for all subagents
  model: "google/gemini-3-flash-preview"      # override model
  # base_url: "http://localhost:1234/v1"      # or use a direct endpoint
  # api_key: "local-key"

See Subagent Delegation for full configuration details.


Cron Job Providers

Cron jobs run with whatever provider is configured at execution time. They do not support a fallback model. To use a different provider for cron jobs, configure provider and model overrides on the cron job itself:

cronjob(
    action="create",
    schedule="every 2h",
    prompt="Check server status",
    provider="openrouter",
    model="google/gemini-3-flash-preview"
)

See Scheduled Tasks (Cron) for full configuration details.


Summary

Feature Fallback Mechanism Config Location
Main agent model fallback_model in config.yaml — per-turn failover on errors (primary restored each turn) fallback_model: (top-level)
Auxiliary tasks (any) — auto users Full auto-detection chain (main agent model first, then provider chain) on capacity errors auxiliary.<task>.provider: auto
Auxiliary tasks (any) — explicit provider fallback_chain (if set) → main agent model → warn + raise, on capacity errors only auxiliary.<task>.fallback_chain
Vision Layered (see above) + internal OpenRouter retry auxiliary.vision
Web extraction Layered (see above) + internal OpenRouter retry auxiliary.web_extract
Context compression Layered (see above); degrades to no-summary if all layers unavailable auxiliary.compression
Skills hub Layered (see above) auxiliary.skills_hub
MCP helpers Layered (see above) auxiliary.mcp
Approval classification Layered (see above) auxiliary.approval
Title generation Layered (see above) auxiliary.title_generation
Triage specifier Layered (see above) auxiliary.triage_specifier
Delegation Provider override only (no automatic fallback) delegation.provider / delegation.model
Cron jobs Per-job provider override only (no automatic fallback) Per-job provider / model