tessera-llamaindex 0.1.0

Drop-in Tessera integration for LlamaIndex. One line of config routes your existing OpenAI / Anthropic / Mistral / Groq / Cohere LLM through Tessera's auto-route + auto-cache + auto-compress + auto-batch proxy. Free tier: 60M tokens/mo. Production: 20% of measured savings.

tessera-llamaindex

Drop-in cost optimization for LlamaIndex. One line of config routes your existing OpenAI / Anthropic / MistralAI / Groq / Cohere LLM through the Tessera optimization proxy — auto-route to cheaper-equivalent models, exact + provider-prompt-cache hits, prompt compression with per-stack quality canary, batch arbitrage on async-tolerant calls. Free Dev tier: 60M tokens/month, no card. Production: 20% of measured savings, $0 if we save you nothing.

Companion to tessera-sdk (vanilla provider SDKs), tessera-langchain (LangChain integration), tessera-vercel-ai (Vercel AI SDK integration), tessera-mastra (Mastra Agent framework integration), tessera-pydantic-ai (Pydantic AI integration), tessera-crewai (CrewAI multi-agent integration), and tessera-autogen (AutoGen 0.4+ multi-agent integration). Same proxy, same mechanic stack, LlamaIndex-shaped API.

---

Install

pip install tessera-llamaindex
# Plus whichever LlamaIndex provider package you use:
pip install llama-index-llms-openai          # or llama-index-llms-anthropic / -mistralai / -groq / -cohere

Get a free Tessera API key (60M tokens/mo, no card) — tesseraai.io/dev.

---

Quickstart

from llama_index.llms.openai import OpenAI
from tessera_llamaindex import tessera_openai_config

llm = OpenAI(
    model="gpt-4o",
    api_key="sk-...",                              # your OpenAI key, unchanged
    **tessera_openai_config(api_key="tsr_..."),    # one line, routes through Tessera
)

# Existing LlamaIndex code (queries, RAG pipelines, agents, sub-question
# engines, multi-step reasoning) runs unchanged.
response = llm.complete("Summarize this document in 3 bullets.")

Same pattern for the other 4 providers:

from llama_index.llms.anthropic import Anthropic
from tessera_llamaindex import tessera_anthropic_config

llm = Anthropic(
    model="claude-sonnet-4-5-20250929",
    api_key="sk-ant-...",
    **tessera_anthropic_config(api_key="tsr_..."),
)

---

Provider support — verified constructor signatures

Field names runtime-verified against installed LlamaIndex 0.6+ provider packages:

Provider	Tessera config function	LlamaIndex class	URL param	Headers approach
OpenAI	tessera_openai_config	llama_index.llms.openai.OpenAI	api_base	default_headers
Anthropic	tessera_anthropic_config	llama_index.llms.anthropic.Anthropic	base_url	default_headers
Mistral	tessera_mistral_config	llama_index.llms.mistralai.MistralAI	endpoint	additional_kwargs.http_headers
Groq	tessera_groq_config	llama_index.llms.groq.Groq	api_base	default_headers (via OpenAILike inheritance)
Cohere	tessera_cohere_config	llama_index.llms.cohere.Cohere	base_url	additional_kwargs.headers

Generic dispatcher: tessera_config(provider, api_key=...) returns the right kwargs dict regardless of provider.

Each constructor-shape is locked into CI via tests/test_e2e.py — if a future LlamaIndex release changes the kwargs an __init__ accepts, the regression fails before we ship.

---

What Tessera does on every request

Same mechanic stack as the main tessera-sdk. Each mechanic is opt-in per workload, observable per request, and bypasses when its quality canary drops below the per-stack 0.95 floor.

Mechanic	What it does	Typical savings
Auto-route (m1)	Route to a cheaper-equivalent model gated by a daily promptfoo canary on your eval set	15–35% on routed calls
Auto-cache (m2)	sha256 cache on the canonical request body, 7-day TTL	5–40% depending on prompt repetition
Auto-compress (m3)	Per-role heuristic compression (system + user toggles independent)	5–15% on prompt tokens
Prompt cache (m6)	Inject provider-native cache headers — OpenAI 50% off, Anthropic 90% off cache reads	50–90% on cached prefixes
Context prune (m7)	Conservative trim on long conversations + RAG attachments	5–25% on multi-turn workloads
Output-length ceiling (m9)	Daily compute fits p90 of completion length per workload	5–15% on completion cost
Batch arbitrage (m10)	Route async-tolerant calls to provider Batch APIs (50% off)	50% on batch-eligible traffic
Per-provider circuit breaker	(Reliability primitive.) Rolling 5xx-rate state machine per upstream.	n/a — keeps the savings stack honest

---

Pricing

• Free Dev — 60M tokens/month, 30 requests/minute, all mechanics on, no card. Forever.
• Production — over 60M tokens/month or higher rate limit. 20% of measured savings only. Zero savings, zero fee. Prepaid Stripe balance, $100 minimum top-up.

Existing customers of the other Tessera packages keep their rate_locked_pct (if any) on this package — same tsr_… key, same billing record.

---

FAQ

Q: How is this different from tessera-sdk, tessera-langchain, tessera-vercel-ai?

Same proxy, same mechanics, same billing. The four packages target different code surfaces:

• tessera-sdk — patches provider SDK constructors directly (OpenAI, Anthropic, etc.) via one-line tessera.activate(key). Use when calling provider SDKs without a framework.
• tessera-langchain — wires into LangChain ChatModel constructors.
• tessera-vercel-ai — wires into Vercel AI SDK createX provider factories.
• tessera-llamaindex (this package) — wires into LlamaIndex llama_index.llms.* LLM constructors.

Pick whichever fits your codebase. Side-by-side install is supported.

Q: Does this break my RAG pipeline / query engine / agent?

No. The LlamaIndex LLM object behaves identically — complete, chat, stream_complete, stream_chat all work unchanged. Index queries, retrievers, sub-question engines, OpenAI Agents, multi-step reasoning chains all use the LLM's standard complete/chat interface and route through Tessera transparently.

Q: What happens if Tessera's proxy is down?

Your application gets HTTP errors instead of LLM responses. On the proxy side, a per-provider circuit breaker tracks rolling 5xx rates and skips degraded providers in auto-route decisions. Cross-provider failover (re-routing to a different provider entirely when an upstream is down) is on the roadmap, not shipped yet.

Q: What happens to my OpenAI / Anthropic rate limits?

They pass through. Tessera does not aggregate quotas across customers. Your provider rate limits apply normally; the proxy enforces only the Tessera tier limits (30 rpm Free Dev, 60 rpm Production by default — higher on request).

Q: Are you storing my prompts and completions?

No. We log only token counts, cost deltas, mechanics_stack, and provider response status. Prompts and completions are never persisted. Full data handling on tesseraai.io/security.

Q: Why does Mistral use additional_kwargs.http_headers instead of default_headers?

LlamaIndex's MistralAI wrapper doesn't expose a top-level default_headers argument — it forwards additional_kwargs.http_headers to the underlying mistralai SDK on each request. The Tessera Mistral config function returns the correct shape for this. You don't need to know this; the config function abstracts it. Same story for Cohere (additional_kwargs.headers).

Q: Can I use this with LlamaIndex's Settings.llm = ... global pattern?

Yes — just construct the LLM the same way and assign it:

from llama_index.core import Settings
from llama_index.llms.openai import OpenAI
from tessera_llamaindex import tessera_openai_config

Settings.llm = OpenAI(model="gpt-4o", api_key="sk-...", **tessera_openai_config(api_key="tsr_..."))

---

Architecture

Open-source SDK ↔ closed-source proxy. This package is a thin client that adds one HTTP hop. The actual mechanic decisions run inside the Tessera Cloudflare Worker proxy at api.tesseraai.io. The wire format is open; the mechanic implementations are closed.

License

Apache-2.0. See LICENSE.

Versioning

Semver. Wire format compatibility committed across minor releases; breaking changes only on major bumps.

Security

Coordinated disclosure address: [e-mail].

---

Built by Tessera — Fintechagency OÜ, Tallinn, Estonia (registry 16638667).