Claude CodeOpenAI CodexLangGraphCrewAIAutoGenOpenAI Agents SDKn8nMCP agentsOpenTelemetry
0%
fewer steps per run
0%
lower token cost
0%
faster executions
0 min
to install on an agent
The problem
Every execution starts almost from scratch.
Agents re-derive the same answers run after run — burning tokens, time, and budget on work they've already done.
01
High token costs
02
Long execution times
03
Large context windows
04
Duplicate reasoning
05
Repeated repository exploration
06
No accumulated knowledge between runs
How it works
Every execution becomes a graph. Every graph gets compiled down.
Original Execution
28 steps
189K tokens
14.2s
$2.31
Agent Starts
Planner (Opus 4.8)
Search
LLM Logic
Search
→ extractImports()
Search
LLM Logic
Search
→ findRoutes()
Search
LLM Logic
Search
→ analyzeDeps()
Search
LLM Logic
Search
→ compiles to runTests()
Response
→
Optimized Execution
9 steps
45K tokens
3.1s
$0.42
User Request
Planner (Sonnet 5)
Knowledge Graph Lookup
extractImports()
Tool
findRoutes()
Tool
analyzeDeps()
Tool
runTests()
Tool
Small Model (Sonnet 5)
Response
Effigent Impact
81% fewer steps · 76% faster · 76% cheaper
Pattern Detection
Compares executions to find repeated workflows.
Determinism Engine
Scores every node 0–100 on stability and repeat frequency.
Tool Synthesizer
Turns repeated reasoning into executable, registered tools.
Model Optimizer
Routes each step to the right-sized model.
Context Optimizer
Strips duplicated context and compresses history.
Knowledge Graph Builder
Builds a semantic map of the repository over time.
Cache Generator
Caches deterministic outputs and invalidates on change.
Validation Engine
Replays every optimization against history before activation.
Determinism engine
Every node gets a determinism score, 0 to 100.
Scored on input/output stability, semantic similarity, historical variance, and repeat frequency — then routed automatically.
0
70
90
100
90–100 — Generate deterministic replacement
Reasoning is replaced with a compiled tool or rule.
70–90 — Use a smaller model or cache
Routed to a cheaper model, or served from cache.
Below 70 — Keep using the LLM
Too much variance to safely automate — no change.
The product
See every run. Then watch it get cheaper.
Every session lands as a navigable execution graph with per-model cost — and once an agent has history, the determinism engine turns it into concrete optimization actions.
Universal installation
Install once. Change nothing.
One scoped key per agent, then pick the capture method for your harness — the engine is identical for every one of them.
Claude Code
Codex CLI
Python
Node / TS
CI & containers
npm i -g effigent
# register once, then install the session hook — zero code changes
effigent agent add billing-agent
effigent install claude --agent billing-agent
# ✓ every finished session now uploads automatically
Claude CodeOpenAI CodexLangGraphCrewAIAutoGenOpenAI Agents SDKn8nMCP agents
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