what is experia fr?

irzix/experia — explained in plain English

Analysis updated 2026-07-26

★ 2PythonAudience · developerComplexity · 3/5Setup · moderate

vibe map

mindmap
  root((repo))
    What it does
      Captures agent actions
      Evaluates outcomes
      Extracts lessons
      Feeds lessons to agents
    Tech stack
      Python
      LangChain
      LangGraph
      OpenAI
    Use cases
      Avoid repeated agent mistakes
      Improve agent success rate
      Run offline benchmarks
    How it works
      Observation action result cycle
      Shared memory storage
      Semantic or keyword retrieval
      Reflection step for strategies

mindmap root((repo)) What it does Captures agent actions Evaluates outcomes Extracts lessons Feeds lessons to agents Tech stack Python LangChain LangGraph OpenAI Use cases Avoid repeated agent mistakes Improve agent success rate Run offline benchmarks How it works Observation action result cycle Shared memory storage Semantic or keyword retrieval Reflection step for strategies

what do people make with this?

VIBE 1

Stop an AI agent from repeating the same operational mistakes across multiple runs.

VIBE 2

Add a learning memory layer to an existing LangGraph-based agent workflow.

VIBE 3

Benchmark two agents side by side to see if past experience improves success rates.

VIBE 4

Run an offline agent learning loop without needing any external API keys.

what's the stack?

PythonLangChainLangGraphOpenAI

how it stacks up fr

	irzix/experia	0-bingwu-0/live-interpreter	010zx00x1/faresnipe
Stars	2	2	2
Language	Python	Python	Python
Setup difficulty	moderate	moderate	easy
Complexity	3/5	2/5	2/5
Audience	developer	general	general

Figures from each repo's GitHub metadata at analysis time.

how do i run it?

Difficulty · moderate time til it works · 30min

Base install is via pip, but utilizing language model evaluation requires optional extras like OpenAI, and semantic retrieval needs a separate embedder.

in plain english

Experia is an open-source Python library that adds a learning layer to AI agents. Most AI agents start fresh every time they run a task, which means they repeat the same mistakes. Experia tries to fix this by capturing what an agent did, evaluating whether it worked or failed, extracting a lesson, and feeding that lesson back into future decisions. It works alongside existing agent frameworks like LangChain and LangGraph rather than replacing them. The project describes its learning loop as a cycle: observation, action, result, experience, lesson, memory, and then a better future action. Internally, Experia stores actions in a shared memory, uses a language model to evaluate outcomes, generates rules from those evaluations, and can run a reflection step that looks at batches of past actions to produce broader strategies. When an agent is about to act, Experia retrieves relevant context from this memory and injects it so the agent benefits from prior experience. The README includes a benchmark designed to show whether this actually helps. Two identical agents face the same set of operational tasks across four rounds. The baseline agent succeeds zero times out of 24 attempts and repeats the same avoidable mistake 18 times. The Experia agent fails each task at most once, learns from the failure, and reaches a 100% success rate from the second round onward. This benchmark runs fully offline with no API key required. Installation is through pip, with optional extras for language model features and specific integrations such as LangGraph or OpenAI. The README provides code examples for both a native LangGraph integration and a manual core API. In the manual mode, you create a learner, record actions and results, and optionally trigger a reflection step. Retrieval can be semantic if you provide an embedder, otherwise it falls back to keyword search. The full README is longer than what was shown.

prompts (copy fr)

prompt 1

Using the Experia library, write a Python script that creates a learner, records a failed action, extracts a lesson, and retrieves that lesson before the next action.

prompt 2

Show me how to integrate Experia into an existing LangGraph agent so it remembers past mistakes and improves its success rate over multiple rounds.

prompt 3

Set up the Experia offline benchmark that runs two identical agents against operational tasks for four rounds, comparing a baseline agent to an Experia-enhanced agent.

prompt 4

Using Experia's core API, write code that triggers a reflection step over a batch of past actions to generate broader strategy rules.

Frequently asked questions