axor-langchain 0.2.0

Governance middleware for LangChain 1.0 agents — powered by axor-core compression engines

axor-langchain

Cut token costs 40–80% in LangChain multi-agent pipelines.

One middleware. No graph changes. Works with any create_agent() agent.

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The problem

LangChain agents accumulate messages. By turn 10 you're paying for:

• Tool outputs from 8 turns ago that nobody needs
• Repeated context that hasn't changed
• Intermediate reasoning that's already been acted on

A 10-node research pipeline can balloon from 5k to 80k tokens by the last node — and you're billed for all of it on every API call.

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Installation

pip install axor-langchain

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Quick start

from langchain.agents import create_agent
from axor_langchain import AxorMiddleware

# before: bare agent
agent = create_agent("anthropic:claude-sonnet-4-5", tools=tools)

# after: governed agent — one line change
axor = AxorMiddleware(soft_token_limit=100_000, verbose=True)
agent = create_agent(
    "anthropic:claude-sonnet-4-5",
    tools=tools,
    middleware=[axor],
)

result = await agent.ainvoke({"messages": [("user", "research transformers")]})
print(f"Tokens spent: {axor.total_tokens_spent}")

---

What it does

Context compression

Before each model call, AxorMiddleware compresses the message history based on session length:

Session length	Mode	Window	Tool output cap
≤ 6 messages	broad	all	8,000 chars
7–20 messages	moderate	last 16	2,000 chars
21+ messages	minimal	last 6	800 chars

The longer the session, the more aggressively old context is compressed. Recent messages are always kept. System messages are never dropped.

Typical savings:

Turn  1:  1,200 tokens  (no compression yet)
Turn  5:  1,800 tokens  (moderate: old tools truncated)
Turn 10:  2,100 tokens  (minimal: only recent 6 messages)
Turn 20:  2,300 tokens  (stable — doesn't keep growing)

Without axor:
Turn 20: 45,000 tokens  (full history accumulated)

Tool governance

Filter which tools each agent can call — without changing the graph:

# research agent: read + search only, no write/bash
axor = AxorMiddleware(
    allowed_tools=["search", "read", "web_search"],
)

# audit agent: read only
axor = AxorMiddleware(
    denied_tools=["write", "bash", "delete"],
)

Budget tracking

Hard stop when token limit is reached — no surprise bills:

axor = AxorMiddleware(
    soft_token_limit=80_000,   # log warning
    hard_token_limit=100_000,  # stop agent, return partial result
)

Pinned personality

Personality is always the first system message — survives compression:

axor = AxorMiddleware(
    personality="You are a security-focused code reviewer. "
                "Always check for injection risks and hardcoded secrets.",
)

Cross-session memory (optional)

pip install axor-langchain[memory]

from axor_memory_sqlite import SQLiteMemoryProvider

provider = SQLiteMemoryProvider("~/.axor/memory.db")
axor = AxorMiddleware(
    memory_provider=provider,
    memory_namespace="research-agent",
)
# after each session: last assistant message saved to SQLite
# next session: load with provider.load(MemoryQuery(...))

Small context bypass

By default, contexts under 4,000 tokens skip the compression pipeline entirely. This avoids overhead on small/early turns where compression can't save more than it costs:

# default: auto-bypass for small contexts (recommended)
axor = AxorMiddleware(soft_token_limit=100_000)

# disable bypass — always compress (aggressive savings, may add overhead on small turns)
axor = AxorMiddleware(bypass_token_threshold=0)

# custom threshold
axor = AxorMiddleware(bypass_token_threshold=8000)

Budget tracking and tool governance still apply even when compression is bypassed.

Impact on savings (real data from claude-sonnet-4-6 benchmark):

	Without bypass	With bypass (4000)
Total savings (4t + 8t combined)	+26.4%	+24.8%
Risk of negative savings on small contexts	Yes (-9% at 6t)	No (0% — passed through)
Large context savings (8t+)	+26-48%	+26-48% (unchanged)

Bypass trades ~1.6% total savings for stable, predictable behavior — you never pay more than without axor.

---

LangGraph integration

Works with any LangGraph StateGraph that uses LangChain agents as nodes:

from langgraph.graph import StateGraph, END
from langchain.agents import create_agent
from axor_langchain import AxorMiddleware

# each node gets its own governance config
research_axor = AxorMiddleware(
    allowed_tools=["search", "web_search"],
    soft_token_limit=50_000,
    verbose=True,
)
writer_axor = AxorMiddleware(
    allowed_tools=["read", "write"],
    soft_token_limit=30_000,
)

research_agent = create_agent(
    "anthropic:claude-sonnet-4-5",
    tools=[search_tool, web_search_tool],
    middleware=[research_axor],
)
writer_agent = create_agent(
    "anthropic:claude-sonnet-4-5",
    tools=[read_tool, write_tool],
    middleware=[writer_axor],
)

workflow = StateGraph(State)
workflow.add_node("research", research_agent)
workflow.add_node("write",    writer_agent)
workflow.add_edge("research", "write")
workflow.add_edge("write", END)

app = workflow.compile()
result = await app.ainvoke({"messages": [...]})

print(f"Research tokens: {research_axor.total_tokens_spent}")
print(f"Writer tokens:   {writer_axor.total_tokens_spent}")

Per-node governance: each agent compresses its own context independently.

---

Configuration reference

AxorMiddleware(
    soft_token_limit=None,           # int | None — warning threshold
    hard_token_limit=None,           # int | None — stop threshold (default: soft * 1.5)
    compression_mode="auto",         # "auto" | "minimal" | "moderate" | "broad"
    bypass_token_threshold=4000,     # int — skip compression below this token count
    allowed_tools=None,              # list[str] | None — whitelist
    denied_tools=None,               # list[str] | None — blacklist
    personality=None,                # str | None — pinned system message
    memory_provider=None,            # MemoryProvider | None
    memory_namespace="axor",         # str
    tool_error_handler=None,         # Callable[[str, Exception], str] | None
    tool_max_retries=0,              # int — extra retry attempts
    tool_retry_delay=0.0,            # float — seconds between retries
    track_tool_stats=False,          # bool — per-tool call/latency/error tracking
    verbose=False,                   # bool — log governance decisions
)

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Difference from axor-claude

	axor-claude	axor-langchain
Provider	Anthropic only	any (OpenAI, Anthropic, Google…)
Framework	axor-core GovernedSession	LangChain create_agent()
Governance depth	full (context shaping, IntentLoop)	middleware (message compression, tool filter)
Best for	standalone coding agents	multi-agent LangGraph pipelines

---

Requirements

• Python 3.11+
• langchain >= 1.0.0
• langgraph >= 1.0.0

---

License

MIT

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Benchmarks

Live results (claude-sonnet-4-6, 3-node research pipeline)

Real API calls, real usage_metadata token counts. Pipeline: planner → researcher → writer. Default bypass_token_threshold=4000 — small contexts pass through without compression.

Per-node breakdown (8 turns, auto mode):

Node	Without axor	With axor	Saved
planner	13,678 tok	7,112 tok	48.0%
researcher	27,677 tok	19,750 tok	28.6%
writer	44,963 tok	36,811 tok	18.1%
TOTAL	86,318 tok	63,673 tok	26.2%

Writer sees all accumulated context from planner + researcher — this is where token explosion happens in production.

Across configurations:

Prior turns	Mode	Without axor	With axor	Savings	$/10K runs saved
4 turns	auto	28,366 tok	20,717 tok	27.0%	$274
8 turns	auto	86,318 tok	63,673 tok	26.2%	$733
8 turns	minimal	65,243 tok	52,451 tok	19.6%	$438

Pricing: claude-sonnet-4-6 @ $3/M input, $15/M output tokens. Results vary between runs due to LLM non-determinism. Use --runs 3 for averaged results.

Bypass impact (calculated from real data across 4t + 8t runs):

	Without bypass	With bypass (default)
Total savings	+26.4%	+24.8%
Negative savings risk	Yes	No
Large context savings	+26-48%	+26-48% (same)

~1.6% less total savings, but guaranteed no overhead on small contexts.

Simulated benchmark (no API key needed)

Tests all middleware features: compression, tool governance, budget, tool retry, bypass detection.

python benchmark/run.py                         # all 17 scenarios
python benchmark/run.py --scenario bypass       # test bypass only
python benchmark/run.py --json                  # CI-friendly output

Live benchmark

export ANTHROPIC_API_KEY=sk-ant-...
python benchmark/live_graph.py --provider anthropic --turns 8
python benchmark/live_graph.py --provider anthropic --runs 3  # averaged

# OpenAI
export OPENAI_API_KEY=sk-...
python benchmark/live_graph.py --provider openai

Flag	Default	Description
--provider	anthropic	anthropic or openai
--model	claude-sonnet-4-6 / gpt-4.1-mini	Override model
--task	research topic	Task for the agent
--mode	auto	Compression mode
--turns	6	Prior history turns
--runs	1	Number of runs for averaging
--no-axor	—	Baseline only
--axor-only	—	axor run only