agentloop-py-langchain 0.2.0

LangChain integration for AgentLoop — auto-logs turns to the review queue and provides a memory injection Runnable for retrieval.

agentloop-py-langchain

LangChain integration for AgentLoop — adds memory retrieval before your LLM calls and turn logging after, with a single chain composition step and a single callback.

pip install agentloop-py agentloop-py-langchain

The install name is agentloop-py-langchain but you import from agentloop_langchain (same pip install bs4 / from bs4 import ... convention as the rest of the AgentLoop SDK).

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What this gives you

A LangChain user dropping AgentLoop into an existing chain gets two things:

1. Memory retrieval — before the LLM call, search past corrections for facts relevant to the user's question, and inject them into the prompt automatically. This is the part that makes your agent smarter immediately when reviewers correct it.
2. Turn logging — after every LLM call, post the (question, response) pair to the AgentLoop review queue so reviewers can correct anything the agent got wrong.

Both halves work in sync chains (chain.invoke) and async chains (chain.ainvoke).

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Why two pieces and not one?

Other AgentLoop integrations (OpenAI, Anthropic) wrap the SDK call itself, so a single wrap_openai(client, loop=...) does both halves invisibly. LangChain doesn't allow that pattern. Its callback system is observation-only — by the time on_llm_start fires, the prompt is already finalized and being sent. We can't sneak retrieved facts in.

So we split the work:

• AgentLoopMemoryInjector runs upstream in the chain (before the prompt template), where it can actually shape the LLM input.
• AgentLoopCallbackHandler runs as a callback on the LLM, observing what was sent and what came back, then posting to AgentLoop.

The two-step setup is more code than the OpenAI wrapper, but it's still fewer than 5 lines on top of an existing LangChain chain. And it's honest about what LangChain's design lets us do — no fragile prompt mutation tricks that would break on minor LangChain version bumps.

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The complete pattern

from agentloop import AgentLoop
from agentloop_langchain import (
    AgentLoopMemoryInjector,
    AgentLoopCallbackHandler,
)
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

loop = AgentLoop(api_key="ak_live_...")

# Step 1: a prompt template that has a slot for retrieved facts.
prompt = ChatPromptTemplate.from_messages([
    ("system",
     "You are a helpful assistant.\n\n"
     "Trusted facts from past corrections:\n{agentloop_memories}"),
    ("user", "{question}"),
])

# Step 2: the chain. Three pieces composed with `|`:
chain = (
    # 2a. Retrieve relevant memories — runs first
    AgentLoopMemoryInjector(loop=loop, query_field="question")
    # 2b. Format the prompt with the retrieved facts
    | prompt
    # 2c. Call the LLM, with the callback that logs the turn afterwards
    | ChatOpenAI(model="gpt-4o").with_config(
        callbacks=[AgentLoopCallbackHandler(loop=loop)],
    )
)

# Step 3: use the chain normally.
result = chain.invoke({"question": "What's the Pix limit at night?"})

That's the full pattern. Three changes to a stock LangChain chain:

• One extra step at the start (AgentLoopMemoryInjector | ...)
• One extra placeholder in the prompt ({agentloop_memories})
• One callback on the LLM (callbacks=[AgentLoopCallbackHandler(...)])

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What actually happens, step-by-step

This is the part worth understanding because it's where the value lands.

Before the call: memory retrieval

When you chain.invoke({"question": "What's the Pix limit at night?"}):

1. The injector receives {"question": "What's the Pix limit at night?"}.

2. It calls loop.search(query="What's the Pix limit at night?", limit=3).

3. AgentLoop's backend semantically searches your org's memories (corrections reviewers have approved over time) and returns the most relevant matches. For example:

   • "The Pix nighttime limit is R$1,000 between 8pm and 6am."
   • "Pix limits reset at 6am, not midnight."

4. The injector adds those to the input as a formatted string under the agentloop_memories key:

   {
     "question": "What's the Pix limit at night?",
     "agentloop_memories": "- The Pix nighttime limit is R$1,000 between 8pm and 6am.\n- Pix limits reset at 6am, not midnight.",
   }
   

5. The prompt template renders, using {agentloop_memories} as a slot, producing this final prompt for the LLM:

   system: "You are a helpful assistant.
   
           Trusted facts from past corrections:
           - The Pix nighttime limit is R$1,000 between 8pm and 6am.
           - Pix limits reset at 6am, not midnight."
   
   user: "What's the Pix limit at night?"
   

6. The LLM sees this and answers correctly, even if its training data would have made it answer wrongly without those facts.

This is the magic. Every correction your reviewers make in the AgentLoop dashboard becomes a future-tense fact that gets injected into the next relevant prompt automatically. No fine-tuning, no retraining, no manual prompt engineering — it just works the next time someone asks a similar question.

After the call: turn logging

After the LLM responds:

1. The callback handler (registered via with_config(callbacks=[...])) receives the response in its on_llm_end hook.
2. It pulls the question (captured at on_llm_start) and the response text out of the LLMResult.
3. It calls loop.log_turn(question=..., agent_response=...) which posts the turn to the AgentLoop review queue.
4. Reviewers see the turn in the dashboard. If the LLM got it wrong, they correct it. The correction becomes a memory. The next time a similar question comes through, that memory gets retrieved in step 3 above. The loop closes.

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Per-call options

Pass user-specific or call-specific data via LangChain's metadata:

chain.invoke(
    {"question": "What's the Pix limit at night?"},
    config={"metadata": {"agentloop": {
        "user_id": "u_42",
        "session_id": "sess_xyz",
        "signals": {"thumbs_down": True},
        "tags": ["pix", "limits"],
    }}},
)

Recognized keys: user_id, session_id, signals (dict), tags (list), metadata (dict, free-form), skip (bool — skip logging this turn entirely).

You can also set defaults at construction:

AgentLoopCallbackHandler(loop=loop, user_id="u_42", tags=["beta"])

Per-call values override construction-time defaults.

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Async chains

Use AsyncAgentLoopCallbackHandler and pass AsyncAgentLoop (from agentloop.aio) to the injector:

from agentloop.aio import AsyncAgentLoop
from agentloop_langchain import (
    AgentLoopMemoryInjector,
    AsyncAgentLoopCallbackHandler,
)

async with AsyncAgentLoop(api_key="ak_...") as loop:
    chain = (
        AgentLoopMemoryInjector(loop=loop, query_field="question")
        | prompt
        | ChatOpenAI(model="gpt-4o").with_config(
            callbacks=[AsyncAgentLoopCallbackHandler(loop=loop)],
        )
    )
    result = await chain.ainvoke({"question": "..."})

If you accidentally pass a sync AgentLoop to either async component, they'll detect that and dispatch the network calls to a thread-pool executor so the event loop isn't blocked. Works either way; passing an AsyncAgentLoop is just slightly more efficient.

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Customization

Where memories appear in the prompt

The injector writes to input["agentloop_memories"] by default. Use that key in your prompt template wherever you want the retrieved facts to appear. To use a different key:

AgentLoopMemoryInjector(loop=loop, query_field="user_question",
                        output_field="known_facts")

How memories are formatted

The default formatter renders memories as a bulleted list of .fact strings, or (none yet) when the search returns nothing. Override:

def my_formatter(memories):
    if not memories:
        return ""
    return "Reference material:\n" + "\n".join(
        f"  • {m.fact}" for m in memories
    )

AgentLoopMemoryInjector(loop=loop, query_field="q",
                        format_memories=my_formatter)

Filtering search by tags / user

AgentLoopMemoryInjector(
    loop=loop,
    query_field="question",
    tags=["pix"],              # only memories tagged "pix"
    user_id_field="user_id",   # search per-user when input has user_id
    limit=5,                   # retrieve top 5 instead of default 3
)

Imperative usage (no LCEL)

If your code doesn't compose with |:

from agentloop_langchain import inject_memories

enriched = inject_memories({"question": q}, loop=loop)
# enriched["agentloop_memories"] is the formatted string
prompt_text = template.format(**enriched)
response = llm.invoke(prompt_text)
loop.log_turn(question=q, agent_response=response.content)

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Failure mode

If AgentLoop is unreachable or returns an error:

• The injector returns the input unchanged with agentloop_memories set to (none yet). The chain continues; the LLM just doesn't get retrieved facts for that call.
• The callback handler swallows the error and logs a warning. The chain's response goes back to the user normally; that turn just doesn't end up in the review queue.

Both behaviors are deliberate. AgentLoop is a value-add layer; if it's having a bad day, your agent should still respond.

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Compatibility

• Python 3.9+
• langchain-core 0.1.0+
• Works with any LLM that has a LangChain integration (langchain-openai, langchain-anthropic, langchain-google-genai, langchain-cohere, Ollama, vLLM, etc.) — we depend on the abstract callback interface, not any specific provider.

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License

MIT