[SPIKE-005] LLM Provider Abstraction #5

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cardosofelipe · 2025-12-29T03:50:15Z

cardosofelipe commented

2025-12-29 03:50:15 +00:00

Objective

Design an abstraction layer that supports multiple LLM providers with failover capability.

Providers to Support

Anthropic (Claude) - Primary
OpenAI (GPT-4) - Secondary/Failover
Ollama - Self-hosted option

Key Questions

How do we abstract provider-specific APIs?
How do we handle failover between providers?
How do we normalize tool/function calling across providers?
How do we track token usage and costs per provider?
How do we handle streaming responses uniformly?

Research Areas

LiteLLM or similar abstraction libraries
Provider-specific quirks (tool calling, context limits)
Failover patterns and health checks
Cost tracking per model/call

Expected Deliverables

Unified LLM client interface
Provider implementations (Anthropic, OpenAI)
Failover logic
Cost tracking integration
ADR documenting the pattern

Acceptance Criteria

Same code works with multiple providers
Automatic failover on provider error
Tool calling works uniformly
Token/cost tracking functional
Streaming works with all providers

Labels

spike, architecture, llm

## Objective Design an abstraction layer that supports multiple LLM providers with failover capability. ## Providers to Support 1. **Anthropic** (Claude) - Primary 2. **OpenAI** (GPT-4) - Secondary/Failover 3. **Ollama** - Self-hosted option ## Key Questions 1. How do we abstract provider-specific APIs? 2. How do we handle failover between providers? 3. How do we normalize tool/function calling across providers? 4. How do we track token usage and costs per provider? 5. How do we handle streaming responses uniformly? ## Research Areas - [ ] LiteLLM or similar abstraction libraries - [ ] Provider-specific quirks (tool calling, context limits) - [ ] Failover patterns and health checks - [ ] Cost tracking per model/call ## Expected Deliverables - Unified LLM client interface - Provider implementations (Anthropic, OpenAI) - Failover logic - Cost tracking integration - ADR documenting the pattern ## Acceptance Criteria - [ ] Same code works with multiple providers - [ ] Automatic failover on provider error - [ ] Tool calling works uniformly - [ ] Token/cost tracking functional - [ ] Streaming works with all providers ## Labels `spike`, `architecture`, `llm`

cardosofelipe commented

2025-12-29 04:08:56 +00:00

Spike Completed

Research completed and documented in:

Spike Document: docs/spikes/SPIKE-005-llm-provider-abstraction.md
ADR: docs/adrs/ADR-004-llm-provider-abstraction.md

Key Findings:

LiteLLM provides unified API for 100+ LLM providers
Built-in failover and routing with latency-based strategy
Model groups: high-reasoning (Claude 3.5 Sonnet), fast-response (Claude 3 Haiku)
Cost tracking per agent/project with token usage
Redis-backed caching for repeated queries

Decision:

Adopt LiteLLM as the unified LLM abstraction layer with automatic failover, usage-based routing, and Redis-backed caching.

This spike can be closed.

## Spike Completed Research completed and documented in: - **Spike Document:** `docs/spikes/SPIKE-005-llm-provider-abstraction.md` - **ADR:** `docs/adrs/ADR-004-llm-provider-abstraction.md` ### Key Findings: - **LiteLLM** provides unified API for 100+ LLM providers - Built-in **failover and routing** with latency-based strategy - Model groups: `high-reasoning` (Claude 3.5 Sonnet), `fast-response` (Claude 3 Haiku) - **Cost tracking** per agent/project with token usage - **Redis-backed caching** for repeated queries ### Decision: Adopt LiteLLM as the unified LLM abstraction layer with automatic failover, usage-based routing, and Redis-backed caching. This spike can be closed.

cardosofelipe closed this issue

2025-12-29 12:45:55 +00:00

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Reference: cardosofelipe/syndarix#5